A Comparison of String Metrics for Matching Names and Records

We describe an open-source Java toolkit of methods for matching names and records. We summarize results obtained from using various string distance metrics on the task of matching entity names: these metrics include distance functions proposed by several different communities, including edit-distance metrics, fast heuristic string comparators, token-based distance metrics, and hybrid methods. We then describe an extension to the toolkit which allows records to be compared. We discuss some issues involved in performing a similar comparison for record-matching techniques, and finally present results for some baseline record-matching algorithms which are based on string comparisons between fields.


Development and validation of blue ray, an optical modem for the MEDUSA class AUVs

Wireless optical communications are emerging as a viable solution for high-speed data transmission over short ranges in the ocean, complementing mainstream acoustic communication systems that operate over much longer ranges, but at lower data rates. The current drive to develop cooperative autonomous vehicular systems to carry out surveying and other complex missions in the ocean critically depends on the existence of such communication links to share sensory and coordination information. This paper presents a compact, low power consumption, cost-efficient, and lightweight optical modem designed for fast data transmission between MEDUSA underwater vehicles at ranges on the order of 10 m. The transmitter uses LEDs for ON-OFF keying, while the receiver front-end adopts a transimpedance architecture with a photodiode detector. This simplistic transmission and reception technique reduces the overall design and hardware complexities compared to, e.g., laser diodes and photomultiplier tubes. The LEDs are arranged in a circular array, with a photodiode at the centre to enable half-duplex operation. These modems are designed to achieve data rates of 20-200 kb/s over short-range, line-of-sight, configurations. A transparent casing is customised to fit the Medusa vehicles, with proper alignment for inter-vehicle communication when they move in formation.


Agnostic Estimation of Mean and Covariance

We consider the problem of estimating the mean and covariance of a distribution from i.i.d. samples in the presence of a fraction of malicious noise. This is in contrast to much recent work where the noise itself is assumed to be from a distribution of known type. The agnostic problem includes many interesting special cases, e.g., learning the parameters of a single Gaussian (or finding the best-fit Gaussian) when a fraction of data is adversarially corrupted, agnostically learning mixtures, agnostic ICA, etc. We present polynomial-time algorithms to estimate the mean and covariance with error guarantees in terms of information-theoretic lower bounds. As a corollary, we also obtain an agnostic algorithm for Singular Value Decomposition.


Robotics-logistics: Challenges for automation of logistic processes

Robotics-logistics can be understood as the field of activities in which applications of industrial robot-technologies are offered and demanded in order to ensure the optimization of internal material flows. According to a survey conducted by BIBA in 2007, the area of robotics-logistics has great need for modernization. Robotics-Logistics activities can be classified in several scenarios as unloading / loading of goods and palletizing / depalletizing of goods. Possible scenarios for research and development activities within theses fields are given.


Clothing identification via deep learning: forensic applications

Attribute-based identification systems are essential for forensic investigations because they help in identifying individuals. An item such as clothing is a visual attribute because it can usually be used to describe people. The method proposed in this article aims to identify people based on the visual information derived from their attire. Deep learning is used to train the computer to classify images based on clothing content. We first demonstrate clothing classification using a large scale dataset, where the proposed model performs relatively poorly. Then, we use clothing classification on a dataset containing popular logos and famous brand images. The results show that the model correctly classifies most of the test images with a success rate that is higher than 70%. Finally, we evaluate clothing classification using footage from surveillance cameras. The system performs well on this dataset, labelling 70% of the test images correctly.


Human Postures Recognition Based on D-S Evidence Theory and Multi-sensor Data Fusion

Body Sensor Networks (BSNs) are conveying notable attention due to their capabilities in supporting humans in their daily life. In particular, real-time and noninvasive monitoring of assisted livings is having great potential in many application domains, such as health care, sport/fitness, e-entertainment, social interaction and e-factory. And the basic as well as crucial feature characterizing such systems is the ability of detecting human actions and behaviors. In this paper, a novel approach for human posture recognition is proposed. Our BSN system relies on an information fusion method based on the D-S Evidence Theory, which is applied on the accelerometer data coming from multiple wearable sensors. Experimental results demonstrate that the developed prototype system is able to achieve a recognition accuracy between 98.5% and 100% for basic postures (standing, sitting, lying, squatting).


Weight-averaged consistency targets improve semi-supervised deep learning results

The recently proposed Temporal Ensembling has achieved state-of-the-art results in several semi-supervised learning benchmarks. It maintains an exponential moving average of label predictions on each training example, and penalizes predictions that are inconsistent with this target. However, because the targets change only once per epoch, Temporal Ensembling becomes unwieldy when learning large datasets. To overcome this problem, we propose Mean Teacher, a method that averages model weights instead of label predictions. As an additional benefit, Mean Teacher improves test accuracy and enables training with fewer labels than Temporal Ensembling. Without changing the network architecture, Mean Teacher achieves an error rate of 4.35% on SVHN with 250 labels, outperforming Temporal Ensembling trained with 1000 labels. We also show that a good network architecture is crucial to performance. Combining Mean Teacher and Residual Networks, we improve the state of the art on CIFAR-10 with 4000 labels from 10.55% to 6.28%, and on ImageNet 2012 with 10% of the labels from 35.24% to 9.11%.


Ensembles of nested dichotomies for multi-class problems

Nested dichotomies are a standard statistical technique for tackling certain polytomous classification problems with logistic regression. They can be represented as binary trees that recursively split a multi-class classification task into a system of dichotomies and provide a statistically sound way of applying two-class learning algorithms to multi-class problems (assuming these algorithms generate class probability estimates). However, there are usually many candidate trees for a given problem and in the standard approach the choice of a particular tree is based on domain knowledge that may not be available in practice. An alternative is to treat every system of nested dichotomies as equally likely and to form an ensemble classifier based on this assumption. We show that this approach produces more accurate classifications than applying C4.5 and logistic regression directly to multi-class problems. Our results also show that ensembles of nested dichotomies produce more accurate classifiers than pairwise classification if both techniques are used with C4.5, and comparable results for logistic regression. Compared to error-correcting output codes, they are preferable if logistic regression is used, and comparable in the case of C4.5. An additional benefit is that they generate class probability estimates. Consequently they appear to be a good general-purpose method for applying binary classifiers to multi-class problems.


A Case Study on Korean Wave: Focused on K-POP Concert by Korean Idol Group in Paris, June 2011

The study dealt with Korean Wave focusing on K-POP and analyzed its success factors, the changes in Korean Wave and the future directions for development. Also the study has compared the results of the idol groups’ performance, held by SM Entertainment in June 2011 in Paris, and the perspective of Korean and French media. Key reasons were examined to analyze what led K-POP to play a crucial part in spreading the Korean Wave: The expansion of the age of its takers ranging from teens to females in their 20s; the fusion of a variety of cultural elements including oriental dance and occidental pop; the systematic system of idol training; marketing activities based on social media, etc. For the expansion of Korean Wave including K-POP and its successful positioning in the world market, there are several suggestions to make which are inventing the differentiated contents and highly appealing stories, approaching the local customers while considering local features, operating co-marketing with other cultural products.


An acute bout of self-myofascial release increases range of motion without a subsequent decrease in muscle activation or force.

Foam rolling is thought to improve muscular function, performance, overuse, and joint range of motion (ROM); however, there is no empirical evidence demonstrating this. Thus, the objective of the study was to determine the effect of self-myofascial release (SMR) via foam roller application on knee extensor force and activation and knee joint ROM. Eleven healthy male (height 178.9 ± 3.5 cm, mass 86.3 ± 7.4 kg, age 22.3 ± 3.8 years) subjects who were physically active participated. Subjects' quadriceps maximum voluntary contraction force, evoked force and activation, and knee joint ROM were measured before, 2 minutes, and 10 minutes after 2 conditions: (a) 2, 1-minute trials of SMR of the quadriceps via a foam roller and (b) no SMR (Control). A 2-way analysis of variance (condition × time) with repeated measures was performed on all dependent variables recorded in the precondition and postcondition tests. There were no significant differences between conditions for any of the neuromuscular dependent variables. However, after foam rolling, subjects' ROM significantly (p < 0.001) increased by 10° and 8° at 2 and 10 minutes, respectively. There was a significant (p < 0.01) negative correlation between subjects' force and ROM before foam rolling, which no longer existed after foam rolling. In conclusion, an acute bout of SMR of the quadriceps was an effective treatment to acutely enhance knee joint ROM without a concomitant deficit in muscle performance.


SATIRE: a software architecture for smart AtTIRE

Personal instrumentation and monitoring services that collect and archive the physical activities of a user have recently been introduced for various medical, personal, safety, and entertainment purposes. A general software architecture is needed to support different categories of such monitoring services. This paper presents a software architecture, implementation, and preliminary evaluation of SATIRE, a wearable personal monitoring service transparently embedded in user garments. SATIRE records the owner's activity and location for subsequent automated uploading and archiving. The personal archive can later be searched for particular events to answer questions regarding past and present user activity, location, and behavior patterns. A short feasibility and usage study of a prototype based on MicaZ motes provides a proof of concept for the SATIRE architecture.


CDN brokering

Content distribution networks (CDNs) increase the capacity of individual Web sites and attempt to deliver content from caches that are located “closer” to end-users than the origin servers that provide the content. CDN brokering provides CDNs a way to interoperate by allowing one CDN to intelligently redirect clients dynamically to other CDNs. This paper describes the goals, architecture, and performance of a CDN brokerage system. Our system has been deployed on the Internet on a provisional basis, and our architectural ideas have helped advance the evolution of Internet standards for interoperating CDNs.


Editing Description Logic Ontologies with the Protégé OWL Plugin

The growing interest in the Semantic Web and the Web Ontology Language (OWL) will reveal the potential of Description Logics in industrial projects. The rich semantics of OWL provide powerful reasoning capabilities that help build, maintain and query domain models for many purposes. However, before OWL can unfold its full potential, user-friendly tools with a scalable architecture are required. We present the OWL Plugin, an extension of the Protégé ontology development environment, which can be used to define classes and properties, to edit logical class expressions, to invoke reasoners, and to link ontologies into the Semantic Web. We analyze some of the challenges for developers of Description Logic editors, and discuss some of our user interface design decisions.


Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models

We present a new, fast and flexible pipeline for indoor scene synthesis that is based on deep convolutional generative models. Our method operates on a top-down image-based representation, and inserts objects iteratively into the scene by predicting their category, location, orientation and size with separate neural network modules. Our pipeline naturally supports automatic completion of partial scenes, as well as synthesis of complete scenes. Our method is significantly faster than the previous image-based method and generates result that outperforms state-of-the-art generative scene models in terms of faithfulness to training data and perceived visual quality.


When Is It Too Early for Single Sport Specialization?

Over the past 15 years, there has been an increase in youth sports participation with a concomitant increase in early year-round training in a single sport. Many factors contribute to the desire of parents and coaches to encourage early single sport specialization, including the desire to give the young athlete an edge in competition, pursuit of scholarships, and potential professional status, and the ability to label a young athlete as elite at an early age. Despite these perceived advantages, some data suggest that early sport specialization does not lead to a competitive advantage over athletes who participate in multiple sports. Although the data are limited, there is some evidence that early sport specialization may put the young athlete at risk for overuse injuries. The focus of this review is to highlight the evidence regarding early sport specialization and risk for injury; discuss the risk factors for overuse injury in high-risk sports including ice hockey, swimming, gymnastics, and baseball; and discuss future potential research that would help define the risk of injury for young athletes who participate in early sport specialization.


Coordinated Power Control of Variable-Speed Diesel Generators and Lithium-Battery on a Hybrid Electric Boat

This paper presents coordinated power management strategies for a hybrid electric boat (HEB) based on dynamic load's power distribution approach using diesel generators and batteries. Two variable-speed diesel generator sets connecting to dc-bus via fully controlled rectifiers serve as the main power supply source; one lithium-battery pack linked to dc-bus through a bidirectional DC/DC converter serves as an auxiliary power supply source. The power requirement of the thrusters and other onboard equipment is represented by a load power profile. The main contribution of this paper is focused on the load power sharing of the HEB between the two variable-speed diesel generators and the lithium-battery pack, through the fluctuations extracted from load power and their assignation to the batteries. Another important issue is that when the total load demand is low, one of the diesel generator set will be switched off to improve the diesel-fuel efficiency. The output powers of the diesel generators and the batteries are controlled based on the power distribution strategy via the converters controlling. The performances of the proposed hybrid power system control are evaluated through simulations in MATALB/Simulink, and through reduced-scale experimental tests carried out for a specific driving cycle of the HEB.


Multiple description-DASH: Pragmatic video streaming maximizing end-users' quality of experience

Over the past few years, adaptive bitrate streaming protocols, such as DASH, have risen to enhance the End-Users' experience towards video consumption over the Internet. Current methods perform a smoother playback (i.e., fewer re-buffering states) trading off with quality fluctuations due to the client-server link state. The proposed work goes a step further and introduces an innovative lightweight streaming solution by taking advantage of bandwidth aggregation over multiple paths using multiple content sources at the same time. This pragmatic evolving approach outperforms the QoE delivered by current DASH-based or P2P-based solutions and leverages the scope of action made available to the media delivery chain actors. The proposed solution has been implemented and is compared to a DASH-based approach used in most existing VoD use-cases through a first set of experiments. Results demonstrate the strong advantages in terms of quality delivered at the End-User's side and buffer occupancy. Discussion on the necessary trade-offs is also tackled.


Optimizing power-accuracy trade-off in approximate adders

Approximate circuit design has gained significance in recent years targeting applications like media processing where full accuracy is not required. In this paper, we propose an approximate adder in which the approximate part of the sum is obtained by finding a single optimal level that minimises the mean error distance. Therefore hardware needed for the approximate part computation can be removed, which effectively results in very low power consumption. We compare the proposed adder with various approximate adders in the literature in terms of power and accuracy metrics. The power savings of our adder is shown to be 17% to 55% more than power savings of the existing approximate adders over a significant range of accuracy values. Further, in an image addition application, this adder is shown to provide the best trade-off between PSNR and power.


Substrate Integrated Waveguide (SIW) Rotman Lens and Its Ka-Band Multibeam Array Antenna Applications

A new type of substrate integrated waveguide (SIW) multibeam antenna is proposed for mobile satellite communications using beam switching and diversity techniques. It employs an SIW Rotman lens as the beamforming network. The prototype of a single multibeam antenna is implemented at 28.5 GHz with seven input ports and an antenna array constructed by nine SIW linear slot arrays, which can generate a corresponding number of beams along one dimension. Several such antennas are grouped in two different ways to cover a 2-D solid angle with multiple beams. Experiment results show that the 2-D solid angle around (-40deg, 40deg) X (-35deg, 35deg) or (-25deg, 25deg) x (-35deg, 35deg) are covered with 20 or 25 beams with 5 -dB beamwidth, respectively. It is demonstrated that this type of printed multibeam antenna is a good choice for communication applications where mobility and high gain are simultaneously required.


From Trust in Automation to Decision Neuroscience: Applying Cognitive Neuroscience Methods to Understand and Improve Interaction Decisions Involved in Human Automation Interaction

Human automation interaction (HAI) systems have thus far failed to live up to expectations mainly because human users do not always interact with the automation appropriately. Trust in automation (TiA) has been considered a central influence on the way a human user interacts with an automation; if TiA is too high there will be overuse, if TiA is too low there will be disuse. However, even though extensive research into TiA has identified specific HAI behaviors, or trust outcomes, a unique mapping between trust states and trust outcomes has yet to be clearly identified. Interaction behaviors have been intensely studied in the domain of HAI and TiA and this has led to a reframing of the issues of problems with HAI in terms of reliance and compliance. We find the behaviorally defined terms reliance and compliance to be useful in their functionality for application in real-world situations. However, we note that once an inappropriate interaction behavior has occurred it is too late to mitigate it. We therefore take a step back and look at the interaction decision that precedes the behavior. We note that the decision neuroscience community has revealed that decisions are fairly stereotyped processes accompanied by measurable psychophysiological correlates. Two literatures were therefore reviewed. TiA literature was extensively reviewed in order to understand the relationship between TiA and trust outcomes, as well as to identify gaps in current knowledge. We note that an interaction decision precedes an interaction behavior and believe that we can leverage knowledge of the psychophysiological correlates of decisions to improve joint system performance. As we believe that understanding the interaction decision will be critical to the eventual mitigation of inappropriate interaction behavior, we reviewed the decision making literature and provide a synopsis of the state of the art understanding of the decision process from a decision neuroscience perspective. We forward hypotheses based on this understanding that could shape a research path toward the ability to mitigate interaction behavior in the real world.


Predicting Students’ Performance using Modified ID3 Algorithm

The ability to predict performance of students is very crucial in our present education system. We can use data mining concepts for this purpose. ID3 algorithm is one of the famous algorithms present today to generate decision trees. But this algorithm has a shortcoming that it is inclined to attributes with many values. So , this research aims to overcome this shortcoming of the algorithm by using gain ratio(instead of information gain) as well as by giving weights to each attribute at every decision making point. Several other algorithms like J48 and Naive Bayes classification algorithm are also applied on the dataset. The WEKA tool was used for the analysis of J48 and Naive Bayes algorithms. The results are compared and presented. The dataset used in our study is taken from the School of Computing Sciences and Engineering (SCSE), VIT University. Keyworddata mining, educational data mining (EDM), decision tree, gain ratio, weighted ID3


Improving read performance of STT-MRAM based main memories through Smash Read and Flexible Read

Spin Transfer Torque Magnetoresistive RAM (STT-MRAM) has been recently deemed as one promising main memory alternative for high-end mobile processors. With process technology scaling, the amplitude of write current approaches that of read current in deep sub-micrometer STT-MRAM arrays. As a result, read disturbance errors (RDEs) emerge. Both high current restore required (HCRR) reads and low current long latency (LCLL) reads can guarantee read reliability and utterly remove RDEs. However, both of them degrade system performance, because of extra restores or a longer read latency. And neither of them always achieves the better performance when running a wide variety of applications. In this paper, we present two architectural techniques to boost read performance for STT-MRAM based main memories in the presence of RDEs. We first propose Smash Read (S-RD) to shorten the latency of HCRR reads by injecting a larger read current. We further introduce Flexible Read (F-RD) to dynamically adopt different types of read schemes, S-RD and LCLL, to maximize main memory system performance. On average, our techniques improve system performance by 9~13% and reduces total energy by 4~8% over all existing read schemes including HCRR and LCLL.


Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights

This paper presents incremental network quantization (INQ), a novel method, targeting to efficiently convert any pre-trained full-precision convolutional neural network (CNN) model into a low-precision version whose weights are constrained to be either powers of two or zero. Unlike existing methods which are struggled in noticeable accuracy loss, our INQ has the potential to resolve this issue, as benefiting from two innovations. On one hand, we introduce three interdependent operations, namely weight partition, group-wise quantization and re-training. A wellproven measure is employed to divide the weights in each layer of a pre-trained CNN model into two disjoint groups. The weights in the first group are responsible to form a low-precision base, thus they are quantized by a variable-length encoding method. The weights in the other group are responsible to compensate for the accuracy loss from the quantization, thus they are the ones to be re-trained. On the other hand, these three operations are repeated on the latest re-trained group in an iterative manner until all the weights are converted into low-precision ones, acting as an incremental network quantization and accuracy enhancement procedure. Extensive experiments on the ImageNet classification task using almost all known deep CNN architectures including AlexNet, VGG-16, GoogleNet and ResNets well testify the efficacy of the proposed method. Specifically, at 5-bit quantization (a variable-length encoding: 1 bit for representing zero value, and the remaining 4 bits represent at most 16 different values for the powers of two) , our models have improved accuracy than the 32-bit floating-point references. Taking ResNet-18 as an example, we further show that our quantized models with 4-bit, 3-bit and 2-bit ternary weights have improved or very similar accuracy against its 32-bit floating-point baseline. Besides, impressive results with the combination of network pruning and INQ are also reported. We believe that our method sheds new insights on how to make deep CNNs to be applicable on mobile or embedded devices. The code is available at https://github.com/Zhouaojun/IncrementalNetwork-Quantization.


MIMO-Net: A multi-input multi-output convolutional neural network for cell segmentation in fluorescence microscopy images

We propose a novel multiple-input multiple-output convolution neural network (MIMO-Net) for cell segmentation in fluorescence microscopy images. The proposed network trains the network parameters using multiple resolutions of the input image, connects the intermediate layers for better localization and context and generates the output using multi-resolution deconvolution filters. The MIMO-Net allows us to deal with variable intensity cell boundaries and highly variable cell size in the mouse pancreatic tissue by adding extra convolutional layers which bypass the max-pooling operation. The results show that our method outperforms state-of-the-art deep learning based approaches for segmentation.


Machine Learning for Performance Prediction in Mobile Cellular Networks

In this paper, we discuss the application of machine learning techniques for performance prediction problems in wireless networks. These problems often involve using existing measurement data to predict network performance where direct measurements are not available. We explore the performance of existing machine learning algorithms for these problems and propose a simple taxonomy of main problem categories. As an example, we use an extensive real-world drive test data set to show that classical machine learning methods such as Gaussian process regression, exponential smoothing of time series, and random forests can yield excellent prediction results. Applying these methods to the management of wireless mobile networks has the potential to significantly reduce operational costs while simultaneously improving user experience. We also discuss key challenges for future work, especially with the focus on practical deployment of machine learning techniques for performance prediction in mobile wireless networks.


Algorithmic Authority: the Ethics, Politics, and Economics of Algorithms that Interpret, Decide, and Manage

This panel will explore algorithmic authority as it manifests and plays out across multiple domains. Algorithmic authority refers to the power of algorithms to manage human action and influence what information is accessible to users. Algorithms increasingly have the ability to affect everyday life, work practices, and economic systems through automated decision-making and interpretation of "big data". Cases of algorithmic authority include algorithmically curating news and social media feeds, evaluating job performance, matching dates, and hiring and firing employees. This panel will bring together researchers of quantified self, healthcare, digital labor, social media, and the sharing economy to deepen the emerging discourses on the ethics, politics, and economics of algorithmic authority in multiple domains.


Joint pose estimation and action recognition in image graphs

Human analysis in images and video is a hard problem due to the large variation in human pose, clothing, camera view-points, lighting and other factors. While the explicit modeling of this variability is difficult, the huge amount of available person images motivates for the implicit, data-driven approach to human analysis. In this work we aim to explore this approach using the large amount of images spanning a subspace of human appearance. We model this subspace by connecting images into a graph and propagating information through such a graph using a discriminatively-trained graphical model. We particularly address the problems of human pose estimation and action recognition and demonstrate how image graphs help solving these problems jointly. We report results on still images with human actions from the KTH dataset.


Function points as a universal software metric

Function point metrics are the most accurate and effective metrics yet developed for software sizing and also for studying software productivity, quality, costs, risks, and economic value. Unlike the older "lines of code" metric function points can be used to study requirements, design, and in fact all software activities from development through maintenance. In the future function point metrics can easily become a universal metric used for all software applications and for all software contracts in all countries. The government of Brazil already requires function points for all software contracts, and South Korea and Italy may soon follow. However, there are some logistical problems with function point metrics that need to be understood and overcome in order for function point metrics to become the primary metric for software economic analysis. Manual function point counting is too slow and costly to be used on large software projects above 10,000 function points in size. Also, application size is not constant but grows at about 2% per calendar month during development and 8% or more per calendar year for as long as software is in active use. This paper discusses a method of high-speed function point counting that can size any application in less than two minutes, and which can predict application growth during development and for five years after release. This new method is based on pattern matching and is covered by U.S. utility patent application and hence is patent pending.


Security and Privacy Challenges in Smart Cities

The construction of smart cities will bring about a higher quality of life to the masses through digital interconnectivity, leading to increased efficiency and accessibility in cities. Smart cities must ensure individual privacy and security in order to ensure that its citizens will participate. If citizens are reluctant to participate, the core advantages of a smart city will dissolve. This article will identify and offer possible solutions to five smart city challenges, in hopes of anticipating destabilizing and costly disruptions. The challenges include privacy preservation with high dimensional data, securing a network with a large attack surface, establishing trustworthy data sharing practices, properly utilizing artificial intelligence, and mitigating failures cascading through the smart network. Finally, further research directions are provided to encourage further exploration of smart city challenges before their construction.


SRT 除算に基づくスケーラブル剰余乗算回路 A Scalable Modular Multiplication Hardware Based on High-radix SRT Division Algorithm

べき剰余演算は RSA暗号 [1]を始めとする公開鍵暗 号の基本演算である.このべき剰余演算は剰余乗算に分 解できる.演算のビット数が非常に大きく,処理時間が 長いことから,高速化のために多くの回路構成法が研究 されている.剰余乗算アルゴリズム [2]の中で,回路化 に有効なものとして,乗/除算をインタリーブする SRT 除算に基づく方法 [3]と,モンゴメリ乗算に基づく方法 [4]がある.後者は幾つかの前処理と後処理が必要にな るが,法の倍数の選択が容易になる.そのため,基本的 な基数 2の構成同士の比較では前者に比べて 2倍程度高 速であるとされる [5].このことから,近年は,モンゴ メリ乗算に基づく回路構成の研究が多い. また,近年は,モンゴメリ乗算に基づくスケーラブル な回路構成に関する研究が多い [6, 7, 8].これらは,ワー ド単位に処理することで,以前のものに比べてバス幅を 小さくして,より小さな回路量で実現できる.スマート カードなどの小型の装置では,従来の構成法では回路が 比較的大きいため,このような回路規模の小さい構成が 多少速度は劣るが有効となる. 本論文では,モンゴメリ乗算ではなく,高基数 SRT除 算 [9]に基づくスケーラブルな回路構成法を提案し,こ の構成の面積/速度の性能がモンゴメリ乗算に基づく構 成に匹敵することを示す.また,法の倍数選択には,高


Depth-Dependent Halos: Illustrative Rendering of Dense Line Data

We present a technique for the illustrative rendering of 3D line data at interactive frame rates. We create depth-dependent halos around lines to emphasize tight line bundles while less structured lines are de-emphasized. Moreover, the depth-dependent halos combined with depth cueing via line width attenuation increase depth perception, extending techniques from sparse line rendering to the illustrative visualization of dense line data. We demonstrate how the technique can be used, in particular, for illustrating DTI fiber tracts but also show examples from gas and fluid flow simulations and mathematics as well as describe how the technique extends to point data. We report on an informal evaluation of the illustrative DTI fiber tract visualizations with domain experts in neurosurgery and tractography who commented positively about the results and suggested a number of directions for future work.


OpenSky: A swiss army knife for air traffic security research

The Automatic Dependent Surveillance - Broadcast (ADS-B) protocol is one of the key components of the next generation air transportation system. Since ADS-B will become mandatory by 2017 in the European airspace, it is crucial that aspects such as its security and privacy are promptly investigated by the research community. However, as expensive specialized equipment was previously necessary to collect real-world data on a large scale, such data has not been freely accessible until now. To enable researchers around the world to conduct experimental studies based on real air traffic data, we have created OpenSky, a participatory sensor network for air traffic research. In this paper, we describe the setup and capabilities of OpenSky, and detail some of the research into air traffic security that we have conducted using OpenSky.


Introduction to the Special Issue on Machine Learning for Multiple Modalities in Interactive Systems and Robots

This special issue highlights research articles that apply machine learning to robots and other systems that interact with users through more than one modality, such as speech, gestures, and vision. For example, a robot may coordinate its speech with its actions, taking into account (audio-)visual feedback during their execution. Machine learning provides interactive systems with opportunities to improve performance not only of individual components but also of the system as a whole. However, machine learning methods that encompass multiple modalities of an interactive system are still relatively hard to find. The articles in this special issue represent examples that contribute to filling this gap.


Antibacterial drug discovery in the resistance era

The looming antibiotic-resistance crisis has penetrated the consciousness of clinicians, researchers, policymakers, politicians and the public at large. The evolution and widespread distribution of antibiotic-resistance elements in bacterial pathogens has made diseases that were once easily treatable deadly again. Unfortunately, accompanying the rise in global resistance is a failure in antibacterial drug discovery. Lessons from the history of antibiotic discovery and fresh understanding of antibiotic action and the cell biology of microorganisms have the potential to deliver twenty-first century medicines that are able to control infection in the resistance era.


Hunting ELFs : An investigation into Android malware detection

Hunting ELFs: An Investigation Into Android Malware Detection by Thomas Sheridan Atkinson Master of Science in Information Security Royal Holloway, University of London 2015/2016 Android has risen to become the de facto operating system for mobile technologies. Its huge success and ubiquity has made it a target for malware writers. It has been found that recently sampled Android malware apps make use of repackaged malicious ELF binaries. The research carried out by this project has found a previously unknown technique used by malicious ELF binaries to hide calls to external packed binaries. An original and robust method for detecting ELF binaries that make calls to a packed binary has been developed and implemented into a state of the art framework with an aim to improving how the framework detects malicious APKs. When trained and tested on 84,979 benign samples and 16,132 malware samples, an accuracy of 96.50% was maintained using XGBoost as the classifier algorithm. The accuracy achieved using the Extra Trees algorithm was improved by 0.01% to 95.79% and the accuracy achieved when using the Random Forest algorithm was increased by 0.05% to 95.34%.


A unified framework for generalized Linear Discriminant Analysis

Linear discriminant analysis (LDA) is one of the well- known methods for supervised dimensionality reduction. Over the years, many LDA-based algorithms have been developed to cope with the curse of dimensionality. In essence, most of these algorithms employ various techniques to deal with the singularity problem, which occurs when the data dimensionality is larger than the sample size. They have been applied successfully in various applications. However, there is a lack of a systematic study of the commonalities and differences of these algorithms, as well as their intrinsic relationships. In this paper, a unified framework for generalized LDA is proposed via a transfer function. The proposed framework elucidates the properties of various algorithms and their relationships. Based on the presented analysis, we propose an efficient model selection algorithm for LDA. We conduct extensive experiments using a collection of high-dimensional data, including text documents, face images, gene expression data, and gene expression pattern images, to evaluate the proposed theories and algorithms.


You can't always get what you want: educational attainment, agency, and choice.

Using educational attainment to indicate socioeconomic status, the authors examined models of agency and effects of choice among European American adults of different educational backgrounds in 3 studies. Whereas college-educated (BA) participants and their preferred cultural products (i.e., rock music lyrics) emphasized expressing uniqueness, controlling environments, and influencing others, less educated (HS) participants and their preferred cultural products (i.e., country music lyrics) emphasized maintaining integrity, adjusting selves, and resisting influence. Reflecting these models of agency, HS and BA participants differently responded to choice in dissonance and reactance paradigms: BA participants liked chosen objects more than unchosen objects, but choice did not affect HS participants' preferences. Results suggest that HS and BA models of agency qualitatively differ, despite overlap between HS and BA worlds.


Implicit Discourse Relation Classification via Multi-Task Neural Networks

Without discourse connectives, classifying implicit discourse relations is a challenging task and a bottleneck for building a practical discourse parser. Previous research usually makes use of one kind of discourse framework such as PDTB or RST to improve the classification performance on discourse relations. Actually, under different discourse annotation frameworks, there exist multiple corpora which have internal connections. To exploit the combination of different discourse corpora, we design related discourse classification tasks specific to a corpus, and propose a novel Convolutional Neural Network embedded multi-task learning system to synthesize these tasks by learning both unique and shared representations for each task. The experimental results on the PDTB implicit discourse relation classification task demonstrate that our model achieves significant gains over baseline systems.


Multi-level bootstrap analysis of stable clusters in resting-state fMRI

A variety of methods have been developed to identify brain networks with spontaneous, coherent activity in resting-state functional magnetic resonance imaging (fMRI). We propose here a generic statistical framework to quantify the stability of such resting-state networks (RSNs), which was implemented with k-means clustering. The core of the method consists in bootstrapping the available datasets to replicate the clustering process a large number of times and quantify the stable features across all replications. This bootstrap analysis of stable clusters (BASC) has several benefits: (1) it can be implemented in a multi-level fashion to investigate stable RSNs at the level of individual subjects and at the level of a group; (2) it provides a principled measure of RSN stability; and (3) the maximization of the stability measure can be used as a natural criterion to select the number of RSNs. A simulation study validated the good performance of the multi-level BASC on purely synthetic data. Stable networks were also derived from a real resting-state study for 43 subjects. At the group level, seven RSNs were identified which exhibited a good agreement with the previous findings from the literature. The comparison between the individual and group-level stability maps demonstrated the capacity of BASC to establish successful correspondences between these two levels of analysis and at the same time retain some interesting subject-specific characteristics, e.g. the specific involvement of subcortical regions in the visual and fronto-parietal networks for some subjects.


Haptic identification of objects using a modular soft robotic gripper

This work presents a soft hand capable of robustly grasping and identifying objects based on internal state measurements. A highly compliant hand allows for intrinsic robustness to grasping uncertainty, but the specific configuration of the hand and object is not known, leaving undetermined if a grasp was successful in picking up the right object. A soft finger was adapted and combined to form a three finger gripper that can easily be attached to existing robots, for example, to the wrist of the Baxter robot. Resistive bend sensors were added within each finger to provide a configuration estimate sufficient for distinguishing between a set of objects. With one data point from each finger, the object grasped by the gripper can be identified. A clustering algorithm to find the correspondence for each grasped object is presented for both enveloping grasps and pinch grasps. This hand is a first step towards robust proprioceptive soft grasping.


Onboard Reconfigurable Battery Charger for Electric Vehicles With Traction-to-Auxiliary Mode

This paper proposes a single-phase reconfigurable battery charger for an electric vehicle (EV) that operates in three different modes: grid-to-vehicle (G2V) mode, in which the traction batteries are charged from the power grid; vehicle-to-grid (V2G) mode, in which the traction batteries deliver part of the stored energy back to the power grid; and traction-to-auxiliary (T2A) mode, in which the auxiliary battery is charged from the traction batteries. When connected to the power grid, the battery charger works with a sinusoidal current in the ac side, for both G2V and V2G modes, and regulates the reactive power. When the EV is disconnected from the power grid, the control algorithms are modified, and the full-bridge ac-dc bidirectional converter works as a full-bridge isolated dc-dc converter that is used to charge the auxiliary battery of the EV, avoiding the use of an additional charger to accomplish this task. To assess the behavior of the proposed reconfigurable battery charger under different operation scenarios, a 3.6-kW laboratory prototype has been developed, and experimental results are presented.


AutoISES: Automatically Inferring Security Specification and Detecting Violations

The importance of software security cannot be overstated. In the past, researchers have applied program analysis techniques to automatically detect security vulnerabilities and verify security properties. However, such techniques have limited success in reality because they require manually provided code-level security specifications. Manually writing and generating these code-level security specifications are tedious and error-prone. Additionally, they seldom exist in production software. In this paper, we propose a novel method and tool, called AutoISES, which Automatically Infers Security Specifications by statically analyzing source code, and then directly use these specifications to automatically detect security violations. Our experiments with the Linux kernel and Xen demonstrated the effectiveness of this approach – AutoISES automatically generated 84 security specifications and detected 8 vulnerabilities in the Linux kernel and Xen, 7 of which have already been confirmed by the corresponding developers.


Cultural capital or relative risk aversion? Two mechanisms for educational inequality compared.

In this paper we empirically examined two explanatory mechanisms for educational inequality: cultural reproduction and relative risk aversion, using survey data taken from secondary school pupils in Amsterdam. Cultural reproduction theory seeks to explain class variations in schooling by cultural differences between social classes. Relative risk aversion theory argues that educational inequalities can be understood by between-class variation in the necessity of pursuing education at branching points in order to avoid downward mobility. We showed that class variations in early demonstrated ability are for a substantial part cultural: cultural capital - measured by parental involvement in highbrow culture - affected school performance at the primary and secondary level. However, relative risk aversion - operationalized by being concerned with downward mobility - strongly affects schooling ambitions, whereas cultural capital had no effect. Thus, we conclude that 'primary effects' of social origin on schooling outcomes are manifested through cultural capital and not through relative risk aversion (in addition to other potential sources of class variations such as genetics). Relative risk aversion, and not cultural capital, affects schooling ambitions, which is relevant for our understanding of secondary effects.


Entropy-Based Subword Mining with an Application to Word Embeddings

Recent literature has shown a wide variety of benefits to mapping traditional onehot representations of words and phrases to lower-dimensional real-valued vectors known as word embeddings. Traditionally, most word embedding algorithms treat each word as the finest meaningful semantic granularity and perform embedding by learning distinct embedding vectors for each word. Contrary to this line of thought, technical domains such as scientific and medical literature compose words from subword structures such as prefixes, suffixes, and root-words as well as compound words. Treating individual words as the finest-granularity unit discards meaningful shared semantic structure between words sharing substructures. This not only leads to poor embeddings for text corpora that have longtail distributions, but also heuristic methods for handling out-of-vocabulary words. In this paper we propose SubwordMine, an entropybased subword mining algorithm that is fast, unsupervised, and fully data-driven. We show that this allows for great cross-domain performance in identifying semantically meaningful subwords. We then investigate utilizing the mined subwords within the FastText embedding model and compare performance of the learned representations in a downstream language modeling task.


High throughput ultralong (20 cm) nanowire fabrication using a wafer-scale nanograting template.

Nanowires are being actively explored as promising nanostructured materials for high performance flexible electronics, biochemical sensors, photonic applications, solar cells, and secondary batteries. In particular, ultralong (centimeter-long) nanowires are highly attractive from the perspective of electronic performance, device throughput (or productivity), and the possibility of novel applications. However, most previous works on ultralong nanowires have issues related to limited length, productivity, difficult alignment, and deploying onto the planar substrate complying with well-matured device fabrication technologies. Here, we demonstrate a highly ordered ultralong (up to 20 cm) nanowire array, with a diameter of 50 nm (aspect ratio of up to 4,000,000:1), in an unprecedented large (8 in.) scale (2,000,000 strands on a wafer). We first devised a perfectly connected ultralong nanograting master template on the whole area of an 8 in. substrate using a top-down approach, with a density equivalent to that achieved with e-beam lithography (100 nm). Using this large-area, ultralong, high-density nanograting template, we developed a fast and effective method for fabricating up to 20 cm long nanowire arrays on a plastic substrate, composed of metal, dielectric, oxide, and ferroelectric materials. As a suggestion of practical application, a prototype of a large-area aluminum wire grid polarizer was demonstrated.


pubmed.mineR: An R package with text-mining algorithms to analyse PubMed abstracts

The PubMed literature database is a valuable source of information for scientific research. It is rich in biomedical literature with more than 24 million citations. Data-mining of voluminous literature is a challenging task. Although several text-mining algorithms have been developed in recent years with focus on data visualization, they have limitations such as speed, are rigid and are not available in the open source. We have developed an R package, pubmed.mineR, wherein we have combined the advantages of existing algorithms, overcome their limitations, and offer user flexibility and link with other packages in Bioconductor and the Comprehensive R Network (CRAN) in order to expand the user capabilities for executing multifaceted approaches. Three case studies are presented, namely, ‘Evolving role of diabetes educators’, ‘Cancer risk assessment’ and ‘Dynamic concepts on disease and comorbidity’ to illustrate the use of pubmed.mineR. The package generally runs fast with small elapsed times in regular workstations even on large corpus sizes and with compute intensive functions. The pubmed.mineR is available at http://cran.r-project.org/web/packages/pubmed.mineR .


Visualization Criticism - The Missing Link Between Information Visualization and Art

Classifications of visualization are often based on technical criteria, and leave out artistic ways of visualizing information. Understanding the differences between information visualization and other forms of visual communication provides important insights into the way the field works, though, and also shows the path to new approaches. We propose a classification of several types of information visualization based on aesthetic criteria. The notions of artistic and pragmatic visualization are introduced, and their properties discussed. Finally, the idea of visualization criticism is proposed, and its rules are laid out. Visualization criticism bridges the gap between design, art, and technical/pragmatic information visualization. It guides the view away from implementation details and single mouse clicks to the meaning of a visualization.


Automating the Design of Graphical Presentations of Relational Information

The goal of the research described in this paper is to develop an application-independent presentation tool that automatically designs effective graphical presentations (such as bar charts, scatter plots, and connected graphs) of relational information. Two problems are raised by this goal: The codification of graphic design criteria in a form that can be used by the presentation tool, and the generation of a wide variety of designs so that the presentation tool can accommodate a wide variety of information. The approach described in this paper is based on the view that graphical presentations are sentences of graphical languages. The graphic design issues are codified as expressiveness and effectiveness criteria for graphical languages. Expressiveness criteria determine whether a graphical language can express the desired information. Effectiveness criteria determine whether a graphical language exploits the capabilities of the output medium and the human visual system. A wide variety of designs can be systematically generated by using a composition algebra that composes a small set of primitive graphical languages. Artificial intelligence techniques are used to implement a prototype presentation tool called APT (A Presentation Tool), which is based on the composition algebra and the graphic design criteria.


Parallel Coordinates: A Tool for Visualizing Multi-dimensional Geometry

A methodology for visualizing analytic and synthetic geometry in RN is presented. It is based on a system of parallel coordinates which induces a non-projective mapping between N-Dimensional and 2-Dimensional sets. Hypersurfaces are represented by their planar images which have some geometrical properties analogous to the properties of the hypersurface that they represent. A point ← → line duality when N = 2 generalizes to lines and hyperplanes enabling the representation of polyhedra in RN. The representation of a class of convex and non-convex hypersurfaces is discussed together with an algorithm for constructing and displaying any interior point. The display shows some local properties of the hypersurface and provides information on the point's proximity to the boundary. Applications to Air Traffic Control, Robotics, Computer Vision, Computational Geometry, Statistics, Instrumentation and other areas are discussed.


Visualizing the non-visual: spatial analysis and interaction with information from text documents

The paper describes an approach to IV that involves spatializing text content for enhanced visual browsing and analysis. The application arena is large text document corpora such as digital libraries, regulations and procedures, archived reports, etc. The basic idea is that text content from these sources may be transformed to a spatial representation that preserves informational characteristics from the documents. The spatial representation may then be visually browsed and analyzed in ways that avoid language processing and that reduce the analysts mental workload. The result is an interaction with text that more nearly resembles perception and action with the natural world than with the abstractions of written language.


Accelerating fish detection and recognition by sharing CNNs with objectness learning

Daily increasing underwater visual data makes automatic object detection and recognition a great demand and challenging task. In this paper, we adopt a region proposal network to accelerate underwater object detection and recognition from Faster R-CNN. This process implement detection acceleration by using convolutional networks to generate high-quality object candidates, and by sharing these networks with original detection networks. Applied to a real-world fish dataset, which includes 24,277 ImageCLEF fish images belonging to 12 classes, this automatic detection and recognition system has a nearly real-time frame rate of 9.8 ftps, while yielding 15.1% higher Mean Average Precision (mAP) than the Deformable Parts Model (DPM) baseline.


Image annotation via deep neural network

Multilabel image annotation is one of the most important open problems in computer vision field. Unlike existing works that usually use conventional visual features to annotate images, features based on deep learning have shown potential to achieve outstanding performance. In this work, we propose a multimodal deep learning framework, which aims to optimally integrate multiple deep neural networks pretrained with convolutional neural networks. In particular, the proposed framework explores a unified two-stage learning scheme that consists of (i) learning to fune-tune the parameters of deep neural network with respect to each individual modality, and (ii) learning to find the optimal combination of diverse modalities simultaneously in a coherent process. Experiments conducted on the NUS-WIDE dataset evaluate the performance of the proposed framework for multilabel image annotation, in which the encouraging results validate the effectiveness of the proposed algorithms.


Procrastination and cramming: how adept students ace the system.

Clear power differentials between teacher and pupil and the assignment of well-delineated tasks within specified time constraints characterize the academic system. Most university students are supremely adept at working within that structure. Many students who outwardly adapt to the system, however, engage in an intense and private ritual that comprises five aspects: calculated procrastination, preparatory anxiety, climactic cramming, nick-of-time deadline-making, and a secret, if often uncelebrated, victory. These adept students often find it difficult to admit others into their efficient program of academic survival. Although such behaviors are adaptive for school rhythms and expectations, these students may also try to impose them onto personal relationships, including those that are psychotherapeutic. The students' tendency to transfer their longstanding habits of procrastination and cramming to the workplace after graduation is less problematic. The argument is made that school rhythms and expectations shape the workplace, not vice versa. Methods of addressing the troublesome aspects of these work styles are suggested. Rather than attempting to change existing work patterns, the therapist can identify the underlying psychodynamic drama and its attendant manifestations that are becoming problematic.


An efficient FPGA overlay for portable custom instruction set extensions

Custom instruction set extensions can substantially boost performance of reconfigurable softcore CPUs. While this approach is commonly tailored to one specific FPGA system, we are presenting a fine-grained FPGA-like overlay architecture which can be implemented in the user logic of various FPGA families from different vendors. This allows the execution of a portable application consisting of a program binary and an overlay configuration in a completely heterogeneous environment. Furthermore, we are presenting different optimizations for dramatically reducing the implementation cost of the proposed overlay architecture. In particular, this includes the mapping of the overlay interconnection network directly into the switch fabric of the hosting FPGA. Our case study demonstrates an overhead reduction of an order of magnitude as compared to related approaches.


What a Nasty Day: Exploring Mood-Weather Relationship from Twitter

While it has long been believed in psychology that weather somehow influences human's mood, the debates have been going on for decades about how they are correlated. In this paper, we try to study this long-lasting topic by harnessing a new source of data compared from traditional psychological researches: Twitter. We analyze 2 years' twitter data collected by twitter API which amounts to 10% of all postings and try to reveal the correlations between multiple dimensional structure of human mood with meteorological effects. Some of our findings confirm existing hypotheses, while others contradict them. We are hopeful that our approach, along with the new data source, can shed on the long-going debates on weather-mood correlation.


Object detection by spatio-temporal analysis and tracking of the detected objects in a video with variable background

In this paper we propose a novel approach for detecting and tracking objects in videos with variable background i.e. videos captured by moving cameras without any additional sensor. The performance of tracking in videos with variable background depends on the successful detection of an object in variable background. The most attractive feature of detecting an object in variable background is that it does not depend on any a priori information of the scene. In a video captured by a moving camera, both the background and foreground are changing in each frame of the image sequence. So for these videos, modeling a single background with traditional background modeling methods is infeasible and thus the detection of actual moving object in a variable background is a challenging task. To detect actual moving object in this work, spatio-temporal blobs have been generated in each frame by spatio-temporal analysis of the image sequence using a three-dimensional Gabor filter. Then individual blobs, which are parts of one object are merged using Minimum Spanning Tree to form the moving object in the variable background. The height, width and four-bin grayvalue histogram of the object are calculated as its features and an object is tracked in each frame using these features to generate the trajectories of the object through the video sequence. In this work, problem of data association during tracking is solved by Linear Assignment Problem and occlusion is handled by the application of kalman filter. The major advantage of our method over most of the existing tracking algorithms is that, the proposed method does not require initialization in the first frame or training on sample data to perform. Performance of the algorithm has been tested on benchmark videos and very satisfactory result has been achieved. The performance of the algorithm is also comparable and superior with respect to some benchmark algorithms.


The Influence of Information Overload on the Development of Trust and Purchase Intention Based on Online Product Reviews in a Mobile vs . Web Environment : A Research Proposal

Information overload has been studied extensively by decision science researchers, particularly in the context of task-based optimization decisions. Media selection research has similarly investigated the extent to which task characteristics influence media choice and use. This paper outlines a proposed study, which would compare the effectiveness of web-based online product review systems in facilitation trust and purchase intention to those of mobile product review systems. We propose that since web-based systems are more effective in fostering focus and are less prone to navigation frustration, information overload is less likely influence the extent to which a consumer trusts an online product review.


Real-time detection of planar regions in unorganized point clouds

Automatic detection of planar regions in point clouds is an important step for many graphics, image processing, and computer vision applications. While laser scanners and digital photography have allowed us to capture increasingly larger datasets, previous techniques are computationally expensive, being unable to achieve real-time performance for datasets containing tens of thousands of points, even when detection is performed in a non-deterministic way. We present a deterministic technique for plane detection in unorganized point clouds whose cost is O(n log n) in the number of input samples. It is based on an efficient Hough-transform voting scheme and works by clustering approximately co-planar points and by casting votes for these clusters on a spherical accumulator using a trivariate Gaussian kernel. A comparison with competing techniques shows that our approach is considerably faster and scales significantly better than previous ones, being the first practical solution for deterministic plane detection in large unorganized point clouds.


AVATAR: A Framework to Support Dynamic Security Analysis of Embedded Systems' Firmwares

To address the growing concerns about the security of embedded systems, it is important to perform accurate analysis of firmware binaries, even when the source code or the hardware documentation are not available. However, research in this field is hindered by the lack of dedicated tools. For example, dynamic analysis is one of the main foundations of security analysis, e.g., through dynamic taint tracing or symbolic execution. Unlike static analysis, dynamic analysis relies on the ability to execute software in a controlled environment, often an instrumented emulator. However, emulating firmwares of embedded devices requires accurate models of all hardware components used by the system under analysis. Unfortunately, the lack of documentation and the large variety of hardware on the market make this approach infeasible in practice. In this paper we present Avatar, a framework that enables complex dynamic analysis of embedded devices by orchestrating the execution of an emulator together with the real hardware. We first introduce the basic mechanism to forward I/O accesses from the emulator to the embedded device, and then describe several techniques to improve the system’s performance by dynamically optimizing the distribution of code and data between the two environments. Finally, we evaluate our tool by applying it to three different security scenarios, including reverse engineering, vulnerability discovery and hardcoded backdoor detection. To show the flexibility of Avatar, we perform this analysis on three completely different devices: a GSM feature phone, a hard disk bootloader, and a wireless sensor node.


Optimization of Helical Antennas

Helical antennas have been known for a long time, but the literature is overwhelmed with controversial information about their performance. We have systematically investigated helical antennas located above an infinite ground plane and obtained design curves. We have also observed that the shape and size of the ground conductor have influence on the helical antenna performance. By optimizing the dimensions of ground conductors that have the form of a cup and a cone, we have significantly increased the antenna gain. Simultaneous optimization of the helix and the ground conductor is under way to improve the antenna performance.


Correlated discrete data generation using adversarial training

Generative Adversarial Networks (GAN) have shown great promise in tasks like synthetic image generation, image inpainting, style transfer, and anomaly detection. However, generating discrete data is a challenge. This work presents an adversarial training based correlated discrete data (CDD) generation model. It also details an approach for conditional CDD generation. The results of our approach are presented over two datasets; job-seeking candidates skill set (private dataset) and MNIST (public dataset). From quantitative and qualitative analysis of these results, we show that our model performs better as it leverages inherent correlation in the data, than an existing model that overlooks correlation.


HotFlip: White-Box Adversarial Examples for NLP

We propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier. We find that only a few manipulations are needed to greatly decrease the accuracy. Our method relies on an atomic flip operation, which swaps one token for another, based on the gradients of the onehot input vectors. Due to efficiency of our method, we can perform adversarial training which makes the model more robust to attacks at test time. With the use of a few semantics-preserving constraints, we demonstrate that HotFlip can be adapted to attack a word-level classifier as well.


Experiences Using the RISC-V Ecosystem to Design an Accelerator-Centric SoC in TSMC 16 nm

The recent trend towards accelerator-centric architectures has renewed the need for demonstrating new research ideas in prototype systems with custom chips. Unfortunately, building such research prototypes is tremendously challenging, but the emerging RISC-V open-source software and hardware ecosystem can partly address this challenge by reducing design, implementation, and verification effort. This paper briefly describes the Celerity system-on-chip (SoC), a 5× 5mm 385M-transistor chip in TSMC 16 nm, which uses a tiered parallel accelerator fabric to improve both the performance and energy efficiency of embedded applications. The Celerity SoC includes five RV64G cores, a 496-core RV32IM tiled manycore processor, and a complex BNN (binarized neural network) accelerator implemented as a Rocket custom co-processor (RoCC). We describe our experiences using the RISC-V ecosystem to build Celerity, and highlight both key benefits and challenges in leveraging the RISCV instruction set, RISC-V software stack, RISC-V processor and memory system generators, RISC-V on-chip network interfaces, RISC-V verification suite, and RISC-V system-level hardware infrastructure. The RISC-V ecosystem played an important role in enabling a team of junior graduate students to design and tapeout the highest-performance RISC-V SoC to date in just nine months.


Relation Networks for Object Detection

Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era. All state-of-the-art object detection systems still rely on recognizing object instances individually, without exploiting their relations during learning. This work proposes an object relation module. It processes a set of objects simultaneously through interaction between their appearance feature and geometry, thus allowing modeling of their relations. It is lightweight and in-place. It does not require additional supervision and is easy to embed in existing networks. It is shown effective on improving object recognition and duplicate removal steps in the modern object detection pipeline. It verifies the efficacy of modeling object relations in CNN based detection. It gives rise to the first fully end-to-end object detector.


Social Media Security Culture The Human Dimension in Social Media Management

Social media provides both opportunities and risks for any organization. Secure integration of social media platforms in organizational ICT infrastructures tends to be focused mainly on technical aspects. Social media security management usually ignores the human dimension, but protection can only be achieved through a holistic approach. Social media security culture must be part of the overall organizational culture. From a survey conducted to determine social media guidelines, a management model was developed for creating, monitoring and controlling social media security culture. The management model will be mapped to an assessment and reporting tool.


Retrograde amnesia and memory consolidation: a neurobiological perspective

The fact that information acquired before the onset of amnesia can be lost (retrograde amnesia) has fascinated psychologists, biologists, and clinicians for over 100 years. Studies of retrograde amnesia have led to the concept of memory consolidation, whereby medial temporal lobe structures direct the gradual establishment of memory representations in neocortex. Recent theoretical accounts have inspired a simple neural network model that produces behavior consistent with experimental data and makes these ideas about memory consolidation more concrete. Recent physiological and anatomical findings provide important information about how memory consolidation might actually occur.


The International Society for Biomechanics ) 5 July 1993 THE USE OF SURFACE ELECTROMYOGRAPHY IN BIOMECHANICS

................................................................................................................... 2 INTRODUCTION ............................................................................................................. 3 FACTORS AFFECTING THE EMG SIGNAL AND FORCE PRODUCED BY A MUSCLE.............. 5 DETECTION AND PROCESSING OF THE EMG SIGNAL.................................................... 10 THE ACTIVATION TIMING OF MUSCLES......................................................................... 12 THE FORCE / EMG SIGNAL RELATIONSHIP .................................................................. 17 THE EMG SIGNAL AS A FATIGUE INDEX ...................................................................... 25 SUMMARY OF RECOMMENDATIONS.............................................................................. 32 SUMMARY OF PROBLEMS FOR RESOLUTION................................................................. 34 ISSUES FOR INTERNATIONAL AGREEMENT.................................................................... 36 ACKNOWLEDGEMENTS ................................................................................................ 37 © DelSys Incorporated


Enhancing recurrent neural network-based language models by word tokenization

Introduction Statistical language models estimate the probability for a given sequence of words. Given a sentence s with n words such as s = (w1,w2 . . .wn), the language model assigns P(s). Statistical language models assess good word sequence estimations based on the sequence probability estimation. Building robust, fast and accurate language models is one of the main factors in the success of building systems such as machine translation systems and automatic speech recognition systems. Statistical language models can be estimated based on various approaches. Classical language models are estimated based on n-gram word sequences such as P(s) = P(w1,w2 . . .wn) = ∏n i=1 P(wi|wi−1) and can be approximated based on the Markov concept for shorter contexts (for example, bigram if n = 2 or trigram if n = 3 and so on). Recent researchers have applied neural networks different architectures to build and estimate language models. The classical feed-forward neural network-based language models have been continuously reporting good results among the traditional n-gram language modeling techniques [1]. It Abstract


The relationship between trauma and beliefs about hearing voices: a study of psychiatric and non-psychiatric voice hearers.

BACKGROUND
Cognitive models suggest that distress associated with auditory hallucinations is best understood in terms of beliefs about voices. What is less clear is what factors govern such beliefs. This study aimed to explore the way in which traumatic life events contribute towards beliefs about voices and any associated distress.

METHOD
The difference in the nature and prevalence of traumatic life events and associated psychological sequelae was compared in two groups of voice hearers: psychiatric voice hearers with predominantly negative beliefs about voices (PVH) and non-psychiatric voice hearers with predominantly positive beliefs about voices (NPVH). The data from the two groups were then combined in order to examine which factors could significantly account for the variance in beliefs about voices and therefore levels of distress.

RESULTS
Both groups reported a high prevalence of traumatic life events although significantly more PVH reported trauma symptoms sufficient for a diagnosis of post-traumatic stress disorder (PTSD). Furthermore, significantly more PVH reported experiencing childhood sexual abuse. Current trauma symptoms (re-experiencing, avoidance and hyperarousal) were found to be a significant predictor of beliefs about voices. Trauma variables accounted for a significant proportion of the variance in anxiety and depression.

CONCLUSIONS
The results suggest that beliefs about voices may be at least partially understood in the context of traumatic life events.


Universal Planning Networks

A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization. To this end, we introduce universal planning networks (UPN). UPNs embed differentiable planning within a goal-directed policy. This planning computation unrolls a forward model in a latent space and infers an optimal action plan through gradient descent trajectory optimization. The plan-by-gradient-descent process and its underlying representations are learned end-to-end to directly optimize a supervised imitation learning objective. We find that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images. The learned representations can be leveraged to specify distance-based rewards to reach new target states for model-free reinforcement learning, resulting in substantially more effective learning when solving new tasks described via imagebased goals. Visit https://sites.google. com/view/upn-public/home for video highlights.


ADE: A Framework for Robust Complex Robotic Architectures

Robots that can interact naturally with humans require the integration and coordination of many different components with heavy computational demands. We argue that an architecture framework with facilities for dynamic, reliable, fault-recovering, remotely accessible, distributed computing is needed for the development and operation of applications that support and enhance human activities and capabilities. We describe a robotic architecture development system, called ADE, that is built on top of a multi-agent system in order to provide all of the above features. Specifically, we discuss support for autonomic computing in ADE, briefly comparing it to related features of other commonly used robotic systems. We also report our experiences with ADE in the development of an architecture for an intelligent robot assistant and provide experimental results demonstrating the system's utility


Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos

There has recently been a great deal of interest in the development of algorithms that objectively measure the integrity of video signals. Since video signals are being delivered to human end users in an increasingly wide array of applications and products, it is important that automatic methods of video quality assessment (VQA) be available that can assist in controlling the quality of video being delivered to this critical audience. Naturally, the quality of motion representation in videos plays an important role in the perception of video quality, yet existing VQA algorithms make little direct use of motion information, thus limiting their effectiveness. We seek to ameliorate this by developing a general, spatio-spectrally localized multiscale framework for evaluating dynamic video fidelity that integrates both spatial and temporal (and spatio-temporal) aspects of distortion assessment. Video quality is evaluated not only in space and time, but also in space-time, by evaluating motion quality along computed motion trajectories. Using this framework, we develop a full reference VQA algorithm for which we coin the term the MOtion-based Video Integrity Evaluation index, or MOVIE index. It is found that the MOVIE index delivers VQA scores that correlate quite closely with human subjective judgment, using the Video Quality Expert Group (VQEG) FRTV Phase 1 database as a test bed. Indeed, the MOVIE index is found to be quite competitive with, and even outperform, algorithms developed and submitted to the VQEG FRTV Phase 1 study, as well as more recent VQA algorithms tested on this database.


Visibility of wavelet quantization noise

The discrete wavelet transform (DWT) decomposes an image into bands that vary in spatial frequency and orientation. It is widely used for image compression, measures of the visibility of DWT quantization errors are required to achieve optimal compression. Uniform quantization of a single band of coefficients results in an artifact that we call DWT uniform quantization noise; it is the sum of a lattice of random amplitude basis functions of the corresponding DWT synthesis filter. We measured visual detection thresholds for samples of DWT uniform quantization noise in Y, Cb, and Cr color channels. The spatial frequency of a wavelet is r2/sup -/spl lambda//, where r is the display visual resolution in pixels/degree, and /spl lambda/ is the wavelet level. Thresholds increase rapidly with wavelet spatial frequency. Thresholds also increase from Y to Cr to Cb, and with orientation from lowpass to horizontal/vertical to diagonal. We construct a mathematical model for DWT noise detection thresholds that is a function of level, orientation, and display visual resolution. This allows calculation of a "perceptually lossless" quantization matrix for which all errors are in theory below the visual threshold. The model may also be used as the basis for adaptive quantization schemes.


Image features that draw fixations

The ability to automatically detect ‘visually interesting’ regions in an image has many practical applications especially in the design of active machine vision systems. This paper describes a data-driven approach that uses eye tracking in tandem with principal component analysis to extract lowlevel image features that attract human gaze. Data analysis on an ensemble of image patches extracted at the observer’s point of gaze revealed features that resemble derivatives of the 2D Gaussian operator. Dissimilarities between human and random fixations are investigated by comparing the features extracted at the point of gaze to the general image structure obtained by random sampling in monte-carlo simulations. Finally, a simple application where these features are used to predict fixations is illustrated.


Image dissimilarity

In this paper we compare the performance of a number of representative instrumental models for image dissimilarity with respect to their ability to predict both image dissimilarity and image quality, as perceived by human subjects. Two sets of experimental data, one for images degraded by noise and blur, and one for JPEG-coded images, are used in the comparison. ( 1998 Elsevier Science B.V. All rights reserved.


ObliviStore: High Performance Oblivious Cloud Storage

We design and build ObliviStore, a high performance, distributed ORAM-based cloud data store secure in the malicious model. To the best of our knowledge, ObliviStore is the fastest ORAM implementation known to date, and is faster by 10X or more in comparison with the best known ORAM implementation. ObliviStore achieves high throughput by making I/O operations asynchronous. Asynchrony introduces security challenges, i.e., we must prevent information leakage not only through access patterns, but also through timing of I/O events. We propose various practical optimizations which are key to achieving high performance, as well as techniques for a data center to dynamically scale up a distributed ORAM. We show that with 11 trusted machines (each with a modern CPU), and 20 Solid State Drives, ObliviStore achieves a throughput of 31.5MB/s with a block size of 4KB.


Neural Network Implementation Using CUDA and OpenMP

Many algorithms for image processing and pattern recognition have recently been implemented on GPU (graphic processing unit) for faster computational times. However, the implementation using GPU encounters two problems. First, the programmer should master the fundamentals of the graphics shading languages that require the prior knowledge on computer graphics. Second, in a job which needs much cooperation between CPU and GPU, which is usual in image processings and pattern recognitions contrary to the graphics area, CPU should generate raw feature data for GPU processing as much as possible to effectively utilize GPU performance. This paper proposes more quick and efficient implementation of neural networks on both GPU and multi-core CPU. We use CUDA (compute unified device architecture) that can be easily programmed due to its simple C language-like style instead of GPU to solve the first problem. Moreover, OpenMP (Open Multi-Processing) is used to concurrently process multiple data with single instruction on multi-core CPU, which results ineffectively utilizing the memories of GPU. In the experiments, we implemented neural networks-based text detection system using the proposed architecture, and the computational times showed about 15 times faster than implementation using CPU and about 4 times faster than implementation on only GPU without OpenMP.


An empirical evaluation of internet latency expansion

The Internet's latency expansion determines the asymptotic performance of large-scale distributed systems (such as Peer-to-Peer systems), but previous studies on the Internet have defined expansion in terms of router-level hops. In this paper, we empirically determine the Internet's latency expansion characteristics using measurements from two different Internet topology datasets. Our results show that the Internet router-level topology exhibits a power-law latency expansion, in contrast to its exponential expansion rate in terms of hops.


From Cloud to Fog Computing: A Review and a Conceptual Live VM Migration Framework

Fog computing, an extension of cloud computing services to the edge of the network to decrease latency and network congestion, is a relatively recent research trend. Although both cloud and fog offer similar resources and services, the latter is characterized by low latency with a wider spread and geographically distributed nodes to support mobility and real-time interaction. In this paper, we describe the fog computing architecture and review its different services and applications. We then discuss security and privacy issues in fog computing, focusing on service and resource availability. Virtualization is a vital technology in both fog and cloud computing that enables virtual machines (VMs) to coexist in a physical server (host) to share resources. These VMs could be subject to malicious attacks or the physical server hosting it could experience system failure, both of which result in unavailability of services and resources. Therefore, a conceptual smart pre-copy live migration approach is presented for VM migration. Using this approach, we can estimate the downtime after each iteration to determine whether to proceed to the stop-and-copy stage during a system failure or an attack on a fog computing node. This will minimize both the downtime and the migration time to guarantee resource and service availability to the end users of fog computing. Last, future research directions are outlined.


On image classification: city images vs. landscapes

Grouping images into semantically meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Based on these groupings, eeective indices can be built for an image database. In this paper, we show how a speciic high-level classiication problem (city images vs. landscapes) can be solved from relatively simple low-level features geared for the particular classes. We have developed a procedure to qualitatively measure the saliency of a feature towards a classiication problem based on the plot of the intra-class and inter-class distance distributions. We use this approach to determine the discriminative power of the following features: color histogram, color coherence vector, DCT coeecient, edge direction histogram, and edge direction coherence vector. We determine that the edge direction-based features have the most discriminative power for the classiication problem of interest here. A weighted k-NN classiier is used for the classiication which results in an accuracy of 93:9% when evaluated on an image database of 2; 716 images using the leave-one-out method. This approach has been extended to further classify 528 landscape images into forests, mountains, and sunset/sunrise classes. First, the input images are classiied as sunset/sunrise images vs. forest & mountain images (94:5% accuracy) and then the forest & mountain images are classiied as forest images or mountain images (91:7% accuracy). We are currently identifying further semantic classes to assign to images as well as extracting low level features which are salient for these classes. Our nal goal is to combine multiple 2-class classiiers into a single hierarchical classiier.


Programming by choice: urban youth learning programming with scratch

This paper describes Scratch, a visual, block-based programming language designed to facilitate media manipulation for novice programmers. We report on the Scratch programming experiences of urban youth ages 8-18 at a Computer Clubhouse 'an after school center' over an 18-month period. Our analyses of 536 Scratch projects collected during this time documents the learning of key programming concepts even in the absence of instructional interventions or experienced mentors. We discuss the motivations of urban youth who choose to program in Scratch rather than using one of the many other software packages available to them and the implications for introducing programming at after school settings in underserved communities.


HyperFlex: An SDN virtualization architecture with flexible hypervisor function allocation

Network Virtualization (NV) and Software-Defined Networking (SDN) are both expected to increase the flexibility and programmability of today's communication networks. Combining both approaches may even be a further step towards increasing the efficiency of network resource utilization. Multiple solutions for virtualizing SDN networks have already been proposed, however, they are either implemented in software or they require special network hardware. We propose HyperFlex, an SDN hypervisor architecture that relies on the decomposition of the hypervisor into functions that are essential for virtualizing SDN networks. The hypervisor functions can be flexibly executed in software or hosted on SDN network elements. Furthermore, existing hypervisor solutions focus on data-plane virtualization mechanisms and neglect the virtualization of the control-plane of SDN networks. HyperFlex provides control-plane virtualization by adding a control-plane isolation function, either in software or on network elements. The isolation function ensures that the resources of the control-plane are shared correctly between each virtual SDN network while it also protects the hypervisor resources from resource exhaustion.


MapReduce-based fuzzy c-means clustering algorithm: implementation and scalability

The management and analysis of big data has been identified as one of the most important emerging needs in recent years. This is because of the sheer volume and increasing complexity of data being created or collected. Current clustering algorithms can not handle big data, and therefore, scalable solutions are necessary. Since fuzzy clustering algorithms have shown to outperform hard clustering approaches in terms of accuracy, this paper investigates the parallelization and scalability of a common and effective fuzzy clustering algorithm named Fuzzy C-Means (FCM) algorithm. The algorithm is parallelized using the MapReduce paradigm outlining how the Map and Reduce primitives are implemented. A validity analysis is conducted in order to show that the implementation works correctly achieving competitive purity results compared to state-of-the art clustering algorithms. Furthermore, a scalability analysis is conducted to demonstrate the performance of the parallel FCM implementation with increasing number of computing nodes used.


Design and implementation of FROST: Digital forensic tools for the OpenStack cloud computing platform

We describe the design, implementation, and evaluation of FROSTdthree new forensic tools for the OpenStack cloud platform. Our implementation for the OpenStack cloud platform supports an Infrastructure-as-a-Service (IaaS) cloud and provides trustworthy forensic acquisition of virtual disks, API logs, and guest firewall logs. Unlike traditional acquisition tools, FROST works at the cloud management plane rather than interacting with the operating system inside the guest virtual machines, thereby requiring no trust in the guest machine. We assume trust in the cloud provider, but FROST overcomes nontrivial challenges of remote evidence integrity by storing log data in hash trees and returning evidence with cryptographic hashes. Our tools are user-driven, allowing customers, forensic examiners, and law enforcement to conduct investigations without necessitating interaction with the cloud provider. We demonstrate how FROST’s new features enable forensic investigators to obtain forensically-sound data from OpenStack clouds independent of provider interaction. Our preliminary evaluation indicates the ability of our approach to scale in a dynamic cloud environment. The design supports an extensible set of forensic objectives, including the future addition of other data preservation, discovery, real-time monitoring, metrics, auditing, and acquisition capabilities. a 2013 Josiah Dykstra and Alan T. Sherman. Published by Elsevier Ltd. All rights reserved.


Geographical Routing With Location Service in Intermittently Connected MANETs

Combining mobile platforms such as manned or unmanned vehicles and peer-assisted wireless communication is an enabler for a vast number of applications. A key enabler for the applications is the routing protocol that directs the packets in the network. Routing packets in fully connected mobile ad hoc networks (MANETs) has been studied to a great extent, but the assumption on full connectivity is generally not valid in a real system. This case means that a practical routing protocol must handle intermittent connectivity and the absence of end-to-end connections. In this paper, we propose a geographical routing algorithm called location-aware routing for delay-tolerant networks (LAROD), enhanced with a location service, location dissemination service (LoDiS), which together are shown to suit an intermittently connected MANET (IC-MANET). Because location dissemination takes time in IC-MANETs, LAROD is designed to route packets with only partial knowledge of geographic position. To achieve low overhead, LAROD uses a beaconless strategy combined with a position-based resolution of bids when forwarding packets. LoDiS maintains a local database of node locations, which is updated using broadcast gossip combined with routing overhearing. The algorithms are evaluated under a realistic application, i.e., unmanned aerial vehicles deployed in a reconnaissance scenario, using the low-level packet simulator ns-2. The novelty of this paper is the illustration of sound design choices in a realistic application, with holistic choices in routing, location management, and the mobility model. This holistic approach justifies that the choice of maintaining a local database of node locations is both essential and feasible. The LAROD-LoDiS scheme is compared with a leading delay-tolerant routing algorithm (spray and wait) and is shown to have a competitive edge, both in terms of delivery ratio and overhead. For spray and wait, this case involved a new packet-level implementation in ns-2 as opposed to the original connection-level custom simulator.


Low cost 60 GHz millimeter-wave microstrip patch antenna array using low-loss planar feeding scheme

A novel low cost feeding mechanism based upon new synthesized planar waveguide substrate integrated waveguide (SIW), is presented for microstrip antenna arrays (MPAs) at millimeter-wave (mmW) band to avoid feeding loss, specially the conduction loss. As an example, SIW-fed two dimensional (2D) 2×2 MPA array @ 60 GHz is introduced. The presented antenna is simulated by HFSS to validate its performance for mmW applications. The simulated results show a broadside gain of 11.71 dB within an operating impedance bandwidth of 500 MHz around a center frequency of 59.95 GHz. Also, the simulated radiation efficiency of the developed antenna is better than 85%, which is higher than that of conventional mmW microstrip antenna array.


Design of a wideband dual-polarized stacked patch antenna with high isolation and low cross polarization for X-band applications

The design and realization of a low-profile wideband dual-polarized patch antenna with high isolation and low cross polarization has been presented. The aperture-coupled antenna element has four layers of substrates. Two microstrip lines terminated with circular patches are printed on the backside of the bottom substrate for feeding the radiating patches, while on the other side is a ground plane with two offset H-shaped slots. One port is excited for horizontally polarized wave and the other one is for vertical polarization. Two slots are arranged in a `T' configuration to achieve a high isolation of over 35 dB. A square radiating patch is printed on the top layer of a Duroid substrate which is placed above the ground plane. A piece of Rohacell foam with a thickness of 3mm and a permittivity of 1.06 is located above the Duroid substrate. A top radiating patch is printed on the bottom side of a top Duroid substrate. Thus, a wideband performance can be obtained due to the multilayer structure and the impedance matching can be improved by adjusting the dimensions of the circular patches and the slots. Further to reduce the cross polarization, two identical elements with one rotated by 180° are combined and each pair of single linearly polarized elements are fed out of phase. In addition, a four-element array with two mirrored two-element arrays has been investigated. The obtained results can confirm that, the proposed dual-polarized patch array can provide a wideband performance across 8.5-11 GHz with VSWR <; 2. The isolation between the two ports is over 35 dB and the cross polarization level is less than -35 dB over the operating frequency band. Therefore, the proposed design technique is promising for X-band SAR applications.