title: The impact of technology scaling on lifetime reliability

text: The relentless scaling of CMOS technology has provided a steady increase in processor performance for the past three decades. However, increased power densities (hence temperatures) and other scaling effects have an adverse impact on long-term processor lifetime reliability. This paper represents a first attempt at quantifying the impact of scaling on lifetime reliability due to intrinsic hard errors, taking workload characteristics into consideration. For our quantitative evaluation, we use RAMP (Srinivasan et al., 2004), a previously proposed industrial-strength model that provides reliability estimates for a workload, but for a given technology. We extend RAMP by adding scaling specific parameters to enable workload-dependent lifetime reliability evaluation at different technologies. We show that (1) scaling has a significant impact on processor hard failure rates - on average, with SPEC benchmarks, we find the failure rate of a scaled 65nm processor to be 316% higher than a similarly pipelined 180nm processor; (2) time-dependent dielectric breakdown and electromigration have the largest increases; and (3) with scaling, the difference in reliability from running at worst-case vs. typical workload operating conditions increases significantly, as does the difference from running different workloads. Our results imply that leveraging a single microarchitecture design for multiple remaps across a few technology generations will become increasingly difficult, and motivate a need for workload specific, microarchitectural lifetime reliability awareness at an early design stage.


title: Temperature-Aware Microarchitecture

text: With power density and hence cooling costs rising exponentially, processor packaging can no longer be designed for the worst case, and there is an urgent need for runtime processor-level techniques that can regulate operating temperature when the package's capacity is exceeded. Evaluating such techniques, however, requires a thermal model that is practical for architectural studies.This paper describes HotSpot, an accurate yet fast model based on an equivalent circuit of thermal resistances and capacitances that correspond to microarchitecture blocks and essential aspects of the thermal package. Validation was performed using finite-element simulation. The paper also introduces several effective methods for dynamic thermal management (DTM): "temperature-tracking" frequency scaling, localized toggling, and migrating computation to spare hardware units. Modeling temperature at the microarchitecture level also shows that power metrics are poor predictors of temperature, and that sensor imprecision has a substantial impact on the performance of DTM.


title: CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules

text: Previous studies propose that associative classification has high classification accuracy and strong flexibility at handling unstructured data. However, it still suffers from the huge set of mined rules and sometimes biased classification or overfitting since the classification is based on only single high-confidence rule. In this study, we propose a new associative classification method, CMAR, i.e., Classification based on Multiple Association Rules. The method extends an efficient frequent pattern mining method, FP-growth, constructs a class distribution-associated FP-tree, and mines large database efficiently. Moreover, it applies a CR-tree structure to store and retrieve mined association rules efficiently, and prunes rules effectively based on confidence, correlation and database coverage. The classification is performed based on a weighted analysis using multiple strong association rules. Our extensive experiments on databases from UCI machine learning database repository show that CMAR is consistent, highly effective at classification of various kinds of databases and has better average classification accuracy in comparison with CBA and C4.5. Moreover, our performance study shows that the method is highly efficient and scalable in comparison with other reported associative classification methods.


title: ApLeaf: An efficient android-based plant leaf identification system

text: To automatically identify plant species is very useful for ecologists, amateur botanists, educators, and so on. The Leafsnap is the first successful mobile application system which tackles this problem. However, the Leafsnap is based on the IOS platform. And to the best of our knowledge, as the mobile operation system, the Android is more popular than the IOS. In this paper, an Android-based mobile application designed to automatically identify plant species according to the photographs of tree leaves is described. In this application, one leaf image can be either a digital image from one existing leaf image database or a picture collected by a camera. The picture should be a single leaf placed on a light and untextured background without other clutter. The identification process consists of three steps: leaf image segmentation, feature extraction, and species identification. The demo system is evaluated on the ImageCLEF2012 Plant Identification database which contains 126 tree species from the French Mediterranean area. The outputs of the system to users are the top several species which match the query leaf image the best, as well as the textual descriptions and additional images about plant leaves, flowers, etc. Our system works well with state-of-the-art identification performance.


title: Usability evaluation considered harmful (some of the time)

text: Current practice in Human Computer Interaction as encouraged by educational institutes, academic review processes, and institutions with usability groups advocate usability evaluation as a critical part of every design process. This is for good reason: usability evaluation has a significant role to play when conditions warrant it. Yet evaluation can be ineffective and even harmful if naively done 'by rule' rather than 'by thought'. If done during early stage design, it can mute creative ideas that do not conform to current interface norms. If done to test radical innovations, the many interface issues that would likely arise from an immature technology can quash what could have been an inspired vision. If done to validate an academic prototype, it may incorrectly suggest a design's scientific worthiness rather than offer a meaningful critique of how it would be adopted and used in everyday practice. If done without regard to how cultures adopt technology over time, then today's reluctant reactions by users will forestall tomorrow's eager acceptance. The choice of evaluation methodology - if any - must arise from and be appropriate for the actual problem or research question under consideration.


title: Competing for Attention in Social Media under Information Overload Conditions

text: Modern social media are becoming overloaded with information because of the rapidly-expanding number of information feeds. We analyze the user-generated content in Sina Weibo, and find evidence that the spread of popular messages often follow a mechanism that differs from the spread of disease, in contrast to common belief. In this mechanism, an individual with more friends needs more repeated exposures to spread further the information. Moreover, our data suggest that for certain messages the chance of an individual to share the message is proportional to the fraction of its neighbours who shared it with him/her, which is a result of competition for attention. We model this process using a fractional susceptible infected recovered (FSIR) model, where the infection probability of a node is proportional to its fraction of infected neighbors. Our findings have dramatic implications for information contagion. For example, using the FSIR model we find that real-world social networks have a finite epidemic threshold in contrast to the zero threshold in disease epidemic models. This means that when individuals are overloaded with excess information feeds, the information either reaches out the population if it is above the critical epidemic threshold, or it would never be well received.


title: An adaptive FEC algorithm using hidden Markov chains

text: Anumber of performance issues must be addressed in order to transmit continuous media stream over the Internet with acceptable quality [9, 6] . These include reducing jitter and recovering from packet losses mainly due to congestion in the routers . Several methods can be used to deal with the loss of a packet such as : retransmission schemes, error concealment, error resilience, interleaving, and FEC (forward error correction) . Each has its advantages/disadvantages depending on the network scenario and conditions (RTT, propagation delay, topology, channel error rate, congestion in the sender/receiver path, etc.) . In this work we focus on FEC techniques. FEC works by adding redundant information to the stream being transmitted in order to reconstruct the original stream at the receiver when packet losses occur. Among its advantages are the small delays introduced to recover information compared to retransmission, for instance, and simplicity of implementation . A clear disadvantage is the increase in the transmission rate due to the redundancy added. One of the simplest approaches among FEC techniques that have been proposed in the literature [11] is to add a packet to each group of N 1 packets with a payload equal to the result of an XOR operation performed in the group. Clearly, if one packet out of the N is lost, the stream is completely recovered . This is accomplished at the expense ofextra bandwidth, in this example an increase of 1/(N 1) . In [4] a clever FEC scheme was proposed in which each packet carries a sample of the previous transmitted packet but compression is used to reduce the overhead . In [7] another technique was developed aimed at maintaining a good compromise between recovery efficiency and bandwidth usage. Since packet losses are mostly due to congestion, increasing the amount of protection using FEC may be unfair to flow controlled streams and also may even have an adverse effect on the results [1] . Therefore, the FEC algorithm should be carefully chosen according to the error characteristics ofthe path between the sender and receiver. Another issue is to develop accurate models ofthe loss process . Many studies exist to characterize the loss process [2, 14, 8] . Simple models such as the Bernoulli process, the Gilbert model and sophisticated Markov models have been proposed. Using either the Bernoulli or the Gilbert process, the work of[3] propose an adaptive algorithm based on the FECscheme of[4] . Recently, the work of Salamatian and Vaton [13] has shown that Hidden


title: Brain–machine interface via real-time fMRI: Preliminary study on thought-controlled robotic arm

text: Real-time functional MRI (rtfMRI) has been used as a basis for brain-computer interface (BCI) due to its ability to characterize region-specific brain activity in real-time. As an extension of BCI, we present an rtfMRI-based brain-machine interface (BMI) whereby 2-dimensional movement of a robotic arm was controlled by the regulation (and concurrent detection) of regional cortical activations in the primary motor areas. To do so, the subjects were engaged in the right- and/or left-hand motor imagery tasks. The blood oxygenation level dependent (BOLD) signal originating from the corresponding hand motor areas was then translated into horizontal or vertical robotic arm movement. The movement was broadcasted visually back to the subject as a feedback. We demonstrated that real-time control of the robotic arm only through the subjects' thought processes was possible using the rtfMRI-based BMI trials.


title: Similarity-Based Approach for Positive and Unlabeled Learning

text: Positive and unlabelled learning (PU learning) has been investigated to deal with the situation where only the positive examples and the unlabelled examples are available. Most of the previous works focus on identifying some negative examples from the unlabelled data, so that the supervised learning methods can be applied to build a classifier. However, for the remaining unlabelled data, which can not be explicitly identified as positive or negative (we call them ambiguous examples), they either exclude them from the training phase or simply enforce them to either class. Consequently, their performance may be constrained. This paper proposes a novel approach, called similarity-based PU learning (SPUL) method, by associating the ambiguous examples with two similarity weights, which indicate the similarity of an ambiguous example towards the positive class and the negative class, respectively. The local similarity-based and global similarity-based mechanisms are proposed to generate the similarity weights. The ambiguous examples and their similarity-weights are thereafter incorporated into an SVM-based learning phase to build a more accurate classifier. Extensive experiments on real-world datasets have shown that SPUL outperforms state-of-the-art PU learning methods.


title: Systematic Variation of Prosthetic Foot Spring Affects Center-of-Mass Mechanics and Metabolic Cost During Walking

text: Lower-limb amputees expend more energy to walk than non-amputees and have an elevated risk of secondary disabilities. Insufficient push-off by the prosthetic foot may be a contributing factor. We aimed to systematically study the effect of prosthetic foot mechanics on gait, to gain insight into fundamental prosthetic design principles. We varied a single parameter in isolation, the energy-storing spring in a prototype prosthetic foot, the controlled energy storage and return (CESR) foot, and observed the effect on gait. Subjects walked on the CESR foot with three different springs. We performed parallel studies on amputees and on non-amputees wearing prosthetic simulators. In both groups, spring characteristics similarly affected ankle and body center-of-mass (COM) mechanics and metabolic cost. Softer springs led to greater energy storage, energy return, and prosthetic limb COM push-off work. But metabolic energy expenditure was lowest with a spring of intermediate stiffness, suggesting biomechanical disadvantages to the softest spring despite its greater push-off. Disadvantages of the softest spring may include excessive heel displacements and COM collision losses. We also observed some differences in joint kinetics between amputees and non-amputees walking on the prototype foot. During prosthetic push-off, amputees exhibited reduced energy transfer from the prosthesis to the COM along with increased hip work, perhaps due to greater energy dissipation at the knee. Nevertheless, the results indicate that spring compliance can contribute to push-off, but with biomechanical trade-offs that limit the degree to which greater push-off might improve walking economy.


title: Meta-Learning in Distributed Data Mining Systems: Issues and Approaches

text: Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. A widely adopted approach to this objective is to apply various machine learning algorithms to compute descriptive models of the available data. Here, we explore one of the main challenges in this research area, the development of techniques that scale up to large and possibly physically distributed databases. Meta-learning is a technique that seeks to compute higher-level classifiers (or classification models), called meta-classifiers, that integrate in some p rincipled fashion multiple classifiers computed separately over different databas es. This study, describes meta-learning and presents the JAM system (Java Ag ents for Meta-learning), an agent-based meta-learning system for large-scale data mining applications. Specifically, it identifies and addresses several impor tant desiderata for distributed data mining systems that stem from thei r additional complexity compared to centralized or host-based systems. Distributed systems may need to deal with heterogenous platforms, with multiple databases and (possibly) different schemas, with the design and imp lementation of scalable and effective protocols for communicating among the data sites, and the selective and efficient use of the information that is gat hered from other peer data sites. Other important problems, intrinsic wi thin ∗Supported in part by an IBM fellowship. data mining systems that must not be ignored, include, first, the abil ity to take advantage of newly acquired information that was not previously av ailable when models were computed and combine it with existing models, and second, the flexibility to incorporate new machine learning methods and d ta mining technologies. We explore these issues within the context of JAM and evaluate various proposed solutions through extensive empirical st udie .


title: Observations on periorbital and midface aging.

text:
BACKGROUND
Many of the anatomical changes of facial aging are still poorly understood. This study looked at the aging process in individuals linearly over time, focusing on aspects of periorbital aging and the upper midface.

METHODS
The author compared photographs of patients' friends and relatives taken 10 to 50 years before with closely matched recent follow-up pictures. The best-matching old and recent pictures were equally sized and superimposed in the computer. The images were then assembled into GIF animations, which automate the fading of one image into the other and back again indefinitely.

RESULTS
The following findings were new to the author: (1) the border of the pigmented lid skin and thicker cheek skin (the lid-cheek junction) is remarkably stable in position over time, becoming more visible by contrast, not by vertical descent as is commonly assumed. (2) Orbicularis wrinkles on the cheek and moles and other markers on the upper midface were also stable over decades. (3) With aging, there can be a distinct change in the shape of the upper eyelid. The young upper lid frequently has a medially biased peak. The upper lid peak becomes more central in the older lid. This article addresses these three issues. No evidence was seen here for descent of the globe in the orbit.

CONCLUSIONS
There seems to be very little ptosis (inferior descent) of the lid-cheek junction or of the upper midface. These findings suggest that vertical descent of skin, and by association, subcutaneous tissue, is not necessarily a major component of aging in those areas. In addition, the arc of the upper lid changes shape in a characteristic way in some patients. Other known changes of the periorbital area are visualized.


title: Performance of elasticsearch in cloud environment with nGram and non-nGram indexing

text: The fact that technology have changed the lives of human beings cannot be denied. It has drastically reduced the effort needed to perform a particular task and has increased the productivity and efficiency. Computers especially have been playing a very important role in almost all fields in today's world. They are used to store large amount of data in almost all sectors, be it business and industrial sectors, personal lives or any other. The research areas of science and technology uses computers to solve complex and critical problems. Information is the most important requirement of each individual. In this era of quick-growing and huge data, it has become increasingly illogical to analyse it with the help of traditional techniques or relational databases. New big data instruments, architectures and designs have come into existence to give better support to the requirements of organizations/institutions in analysing large data. Specifically, Elasticsearch, a full-text java based search engine, designed keeping cloud environment in mind solves issues of scalability, search in real time, and efficiency that relational databases were not able to address. In this paper, we present our own experience with Elasticsearch an open source, Apache Lucene based, full-text search engine that provides near real-time search ability, as well as a RESTful API for the ease of user in the field of research.


title: Development of the radiographic union score for tibial fractures for the assessment of tibial fracture healing after intramedullary fixation.

text:
BACKGROUND
: The objective was to evaluate the newly developed Radiographic Union Score for Tibial fractures (RUST). Because there is no "gold standard," it was hypothesized that the RUST score would provide substantial improvements compared with previous scores presented in the literature.

METHODS
: Forty-five sets of X-rays of tibial shaft fractures treated with intramedullary fixation were selected. Seven orthopedic reviewers independently scored bony union using RUST. Radiographs were reassessed at 9 weeks. Intraclass correlation coefficients (ICC) with 95% confidence intervals (CI) measured agreement.

RESULTS
: Overall agreement was substantial (ICC, 0.86; 95% CI, 0.79-0.91). There was improved reliability among traumatologists compared with others (ICC = 0.86, 0.81, and 0.83, respectively). Overall intraobserver reliability was also substantial (ICC, 0.88; 95% CI, 0.80-0.96).

CONCLUSIONS
: The RUST score exhibits substantial improvements in reliability from previously published scores and produces equally reproducible results among a variety of orthopedic specialties and experience levels. Because no "gold standards" currently exist against which RUST can be compared, this study provides only the initial step in the score's full validation for use in a clinical context.


title: Comparison of Measurement-Based Call Admission Control Algorithms for Controlled-Load Service

text: We compare the performance of four admission control algorithms—one parameter-based and three measurementbased—for controlled-load service. The parameter-based admission control ensures that the sum of reserved resources is bounded by capacity. The three measurementbased algorithms are based on measured bandwidth, acceptance region [9], and equivalent bandwidth [7]. We use simulationon several network scenarios to evaluate the link utilization and adherence to service commitment achieved by these four algorithms.


title: ProteusTM: Abstraction Meets Performance in Transactional Memory

text:
The Transactional Memory (TM) paradigm promises to greatly simplify the development of concurrent applications. This led, over the years, to the creation of a plethora of TM implementations delivering wide ranges of performance across workloads. Yet, no universal implementation fits each and every workload. In fact, the best TM in a given workload can reveal to be disastrous for another one. This forces developers to face the complex task of tuning TM implementations, which significantly hampers their wide adoption. In this paper, we address the challenge of automatically identifying the best TM implementation for a given workload. Our proposed system, ProteusTM, hides behind the TM interface a large library of implementations. Underneath, it leverages a novel multi-dimensional online optimization scheme, combining two popular learning techniques: Collaborative Filtering and Bayesian Optimization.
 We integrated ProteusTM in GCC and demonstrate its ability to switch between TMs and adapt several configuration parameters (e.g., number of threads). We extensively evaluated ProteusTM, obtaining average performance <3% from optimal, and gains up to 100x over static alternatives.


title: Upper limb exoskeleton control based on sliding mode control and feedback linearization

text: Exoskeletons have potential to improve human quality of life by relieving loads on the human musculoskeletal system or by helping motor rehabilitation. Controllers that were initially developed for industrial applications are also applied to control an exoskeleton, despite of major differences in requirements. Nevertheless, which controller has better performance for this specific application remains an open question. This paper presents a comparison between sliding mode control and feedback linearization control. The implementation of the sliding mode controller assumes a complete measurement of the biological system's dynamics. On the other hand, the feedback linearization method does not need any information from the biological system. Our results indicate that a model combining biological control and dynamics could improve several characteristics of robotic exoskeleton, such as energy consumption.


title: Part 5: Machine Translation Evaluation Chapter 5.1 Introduction

text: The evaluation of machine translation (MT) systems is a vital field of research, both for determining the effectiveness of existing MT systems and for optimizing the performance of MT systems. This part describes a range of different evaluation approaches used in the GALE community and introduces evaluation protocols and methodologies used in the program. We discuss the development and use of automatic, human, task-based and semi-automatic (human-in-the-loop) methods of evaluating machine translation, focusing on the use of a human-mediated translation error rate HTER as the evaluation standard used in GALE. We discuss the workflow associated with the use of this measure, including post editing, quality control, and scoring. We document the evaluation tasks, data, protocols, and results of recent GALE MT Evaluations. In addition, we present a range of different approaches for optimizing MT systems on the basis of different measures. We outline the requirements and specific problems when using different optimization approaches and describe how the characteristics of different MT metrics affect the optimization. Finally, we describe novel recent and ongoing work on the development of fully automatic MT evaluation metrics that can have the potential to substantially improve the effectiveness of evaluation and optimization of MT systems. Progress in the field of machine translation relies on assessing the quality of a new system through systematic evaluation, such that the new system can be shown to perform better than pre-existing systems. The difficulty arises in the definition of a better system. When assessing the quality of a translation, there is no single correct answer; rather, there may be any number of possible correct translations. In addition, when two translations are only partially correct but in different ways it is difficult to distinguish quality. Moreover, quality assessments may be dependent on the intended use for the translation, e.g., the tone of a translation may be crucial in some applications, but irrelevant in other applications. Traditionally, there are two paradigms of machine translation evaluation: (1) Glass Box evaluation, which measures the quality of a system based upon internal system properties, and (2) Black Box evaluation, which measures the quality of a system based solely upon its output, without respect to the internal mechanisms of the translation system. Glass Box evaluation focuses upon an examination of the system's linguistic


title: A Model-Based Method for Computer-Aided Medical Decision-Making

text: While MYCIN and PIP were under development at Stanford and Tufts! M.I. T., a group of computer scientists at Rutgers University was developing a system to aid in the evaluation and treatment of patients with glaucoma. The group was led by Professor Casimir K ulikou1ski, a researcher with extensive background in mathematical and pattern-recognition approaches to computer-based medical decision making (Nordyke et al., 1971), working within the Rutgers Research Resource on Computers in Biomedicine headed by Professor Saul Amarel. Working collaboratively with Dr. Arin Safir, Professor of Ophthalmology, "who was then based at the Mt. Sinai School of Medicine in New York City, Kulikowski and Sholom Weiss (a graduate student at Rutgers who "went on to become a research scientist there) developed a method of computer-assisted medical decision making that was based on causal-associational network (CASNET) models of disease. Although the work was inspired by the glaucoma domain, the approach had general features that were later refined in the development of the EXPERT system-building tool (see Chapters 18 and 20). A CASNET model consists of three rnain components: observations of a patient, pathophysiological states, and disease classifications. As observations are recorded, they are associated with the appropriate intennediate states. These states, in turn, are typically causally related, thereby forming a network that summarizes the mechanisms of disease. It is these patterns of states in the network that are linked to individual disease classes. Strat-


title: Storage and Querying of E-Commerce Data

text: New generation of e-commerce applications require data sch emas that are constantly evolving and sparsely populated. The co nventional horizontal row representation fails to meet these re quir ments. We represent objects in a vertical format storing an o bject as a set of tuples. Each tuple consists of an object identifier and attribute name-value pair. Schema evolution is now easy. Ho wever, writing queries against this format becomes cumberso me. We create a logical horizontal view of the vertical representa tion and transform queries on this view to the vertical table. We pres ent alternative implementations and performance results that s ow the effectiveness of the vertical representation for sparse da ta. We also identify additional facilities needed in database systems to support these applications well.


title: Jitter and Phase Noise in Ring Oscillators

text: A companion analysis of clock jitter and phase noise of single-ended and differential ring oscillators is presented. The impulse sensitivity functions are used to derive expressions for the jitter and phase noise of ring oscillators. The effect of the number of stages, power dissipation, frequency of oscillation, and shortchannel effects on the jitter and phase noise of ring oscillators is analyzed. Jitter and phase noise due to substrate and supply noise is discussed, and the effect of symmetry on the upconversion of 1/f noise is demonstrated. Several new design insights are given for low jitter/phase-noise design. Good agreement between theory and measurements is observed.


title: Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction

text: A hierarchical gamma process infinite edge partition model is proposed to factorize the binary adjacency matrix of an unweighted undirected relational network under a Bernoulli-Poisson link. The model describes both homophily and stochastic equivalence, and is scalable to big sparse networks by focusing its computation on pairs of linked nodes. It can not only discover overlapping communities and inter-community interactions, but also predict missing edges. A simplified version omitting inter-community interactions is also provided and we reveal its interesting connections to existing models. The number of communities is automatically inferred in a nonparametric Bayesian manner, and efficient inference via Gibbs sampling is derived using novel data augmentation techniques. Experimental results on four real networks demonstrate the models’ scalability and state-of-the-art performance.


title: GCap: Graph-based Automatic Image Captioning

text: Given an image, how do we automatically assign keywords to it? In this paper, we propose a novel, graph-based approach (GCap) which outperforms previously reported methods for automatic image captioning. Moreover, it is fast and scales well, with its training and testing time linear to the data set size. We report auto-captioning experiments on the "standard" Corel image database of 680 MBytes, where GCap outperforms recent, successful auto-captioning methods by up to 10 percentage points in captioning accuracy (50% relative improvement).


title: A cortical motor nucleus drives the basal ganglia-recipient thalamus in singing birds

text: The pallido-recipient thalamus transmits information from the basal ganglia to the cortex and is critical for motor initiation and learning. Thalamic activity is strongly inhibited by pallidal inputs from the basal ganglia, but the role of nonpallidal inputs, such as excitatory inputs from cortex, remains unclear. We simultaneously recorded from presynaptic pallidal axon terminals and postsynaptic thalamocortical neurons in a basal ganglia–recipient thalamic nucleus that is necessary for vocal variability and learning in zebra finches. We found that song-locked rate modulations in the thalamus could not be explained by pallidal inputs alone and persisted following pallidal lesion. Instead, thalamic activity was likely driven by inputs from a motor cortical nucleus that is also necessary for singing. These findings suggest a role for cortical inputs to the pallido-recipient thalamus in driving premotor signals that are important for exploratory behavior and learning.


title: A Graph Traversal Based Approach to Answer Non-Aggregation Questions Over DBpedia

text: We present a question answering system over DBpedia, filling the gap between user information needs expressed in natural language and a structured query interface expressed in SPARQL over the underlying knowledge base (KB). Given the KB, our goal is to comprehend a natural language query and provide corresponding accurate answers. Focusing on solving the non-aggregation questions, in this paper, we construct a subgraph of the knowledge base from the detected entities and propose a graph traversal method to solve both the semantic item mapping problem and the disambiguation problem in a joint way. Compared with existing work, we simplify the process of query intention understanding and pay more attention to the answer path ranking. We evaluate our method on a non-aggregation question dataset and further on a complete dataset. Experimental results show that our method achieves best performance compared with several state-of-the-art systems.


title: Optimal Cell Load and Throughput in Green Small Cell Networks With Generalized Cell Association

text: This paper thoroughly explores the fundamental interactions between cell association, cell load, and throughput in a green (energy-efficient) small cell network in which all base stations form a homogeneous Poisson point process (PPP) of intensity λB and all users form another independent PPP of intensity λ∪. Cell voidness, usually disregarded due to rarity in cellular network modeling, is first theoretically analyzed under generalized (channel-aware) cell association (GCA). We show that the void cell probability cannot be neglected any more since it is bounded above by exp(-λ∪/λB) that is typically not small in a small cell network. The accurate expression of the void cell probability for GCA is characterized and it is used to derive the average cell and user throughputs. We learn that cell association and cell load λ∪/λB significantly affect these two throughputs. According to the average cell and user throughputs, the green cell and user throughputs are defined respectively to reflect whether the energy of a base station is efficiently used to transmit information or not. In order to achieve satisfactory throughput with certain level of greenness, cell load should be properly determined. We present the theoretical solutions of the optimal cell loads that maximize the green cell and user throughputs, respectively, and verify their correctness by simulation.


title: Control Flow Integrity for COTS Binaries

text: Control-Flow Integrity (CFI) has been recognized as an important low-level security property. Its enforcement can defeat most injected and existing code attacks, including those based on Return-Oriented Programming (ROP). Previous implementations of CFI have required compiler support or the presence of relocation or debug information in the binary. In contrast, we present a technique for applying CFI to stripped binaries on x86/Linux. Ours is the first work to apply CFI to complex shared libraries such as glibc. Through experimental evaluation, we demonstrate that our CFI implementation is effective against control-flow hijack attacks, and eliminates the vast majority of ROP gadgets. To achieve this result, we have developed robust techniques for disassembly, static analysis, and transformation of large binaries. Our techniques have been tested on over 300MB of binaries (executables and shared libraries).


title: SOK: (State of) The Art of War: Offensive Techniques in Binary Analysis

text: Finding and exploiting vulnerabilities in binary code is a challenging task. The lack of high-level, semantically rich information about data structures and control constructs makes the analysis of program properties harder to scale. However, the importance of binary analysis is on the rise. In many situations binary analysis is the only possible way to prove (or disprove) properties about the code that is actually executed. In this paper, we present a binary analysis framework that implements a number of analysis techniques that have been proposed in the past. We present a systematized implementation of these techniques, which allows other researchers to compose them and develop new approaches. In addition, the implementation of these techniques in a unifying framework allows for the direct comparison of these apporaches and the identification of their advantages and disadvantages. The evaluation included in this paper is performed using a recent dataset created by DARPA for evaluating the effectiveness of binary vulnerability analysis techniques. Our framework has been open-sourced and is available to the security community.


title: Binary code is not easy

text: Binary code analysis is an enabling technique for many applications. Modern compilers and run-time libraries have introduced significant complexities to binary code, which negatively affect the capabilities of binary analysis tool kits to analyze binary code, and may cause tools to report inaccurate information about binary code. Analysts may hence be confused and applications based on these tool kits may have degrading quality. We examine the problem of constructing control flow graphs from binary code and labeling the graphs with accurate function boundary annotations. We identified several challenging code constructs that represent hard-to-analyze aspects of binary code, and show code examples for each code construct. As part of this discussion, we present new code parsing algorithms in our open source Dyninst tool kit that support these constructs, including a new model for describing jump tables that improves our ability to precisely determine the control flow targets, a new interprocedural analysis to determine when a function is non-returning, and techniques for handling tail calls. We evaluated how various tool kits fare when handling these code constructs with real software as well as test binaries patterned after each challenging code construct we found in real software.


title: Firmalice - Automatic Detection of Authentication Bypass Vulnerabilities in Binary Firmware

text: Embedded devices have become ubiquitous, and they are used in a range of privacy-sensitive and security-critical applications. Most of these devices run proprietary software, and little documentation is available about the software’s inner workings. In some cases, the cost of the hardware and protection mechanisms might make access to the devices themselves infeasible. Analyzing the software that is present in such environments is challenging, but necessary, if the risks associated with software bugs and vulnerabilities must be avoided. As a matter of fact, recent studies revealed the presence of backdoors in a number of embedded devices available on the market. In this paper, we present Firmalice, a binary analysis framework to support the analysis of firmware running on embedded devices. Firmalice builds on top of a symbolic execution engine, and techniques, such as program slicing, to increase its scalability. Furthermore, Firmalice utilizes a novel model of authentication bypass flaws, based on the attacker’s ability to determine the required inputs to perform privileged operations. We evaluated Firmalice on the firmware of three commercially-available devices, and were able to detect authentication bypass backdoors in two of them. Additionally, Firmalice was able to determine that the backdoor in the third firmware sample was not exploitable by an attacker without knowledge of a set of unprivileged credentials.


title: Who Wrote This Code? Identifying the Authors of Program Binaries

text: Program authorship attribution—identifying a programmer based on stylistic characteristics of code—has practical implications for detecting software theft, digital forensics, and malware analysis. Authorship attribution is challenging in these domains where usually only binary code is available; existing source code-based approaches to attribution have left unclear whether and to what extent programmer style survives the compilation process. Casting authorship attribution as a machine learning problem, we present a novel program representation and techniques that automatically detect the stylistic features of binary code. We apply these techniques to two attribution problems: identifying the precise author of a program, and finding stylistic similarities between programs by unknown authors. Our experiments provide strong evidence that programmer style is preserved in program binaries.


title: Importance filtering for image retargeting

text: Content-aware image retargeting has attracted a lot of interests recently. The key and most challenging issue for this task is how to balance the tradeoff between preserving the important contents and minimizing the visual distortions on the consistency of the image structure. In this paper we present a novel filtering-based technique to tackle this issue, called ”importance filtering”. Specifically, we first filter the image saliency guided by the image itself to achieve a structure-consistent importance map. We then use the pixel importance as the key constraint to compute the gradient map of pixel shifts from the original resolution to the target. Finally, we integrate the shift gradient across the image using a weighted filter to construct a smooth shift map and render the target image. The weight is again controlled by the pixel importance. The two filtering processes enforce to maintain the structural consistency and yet preserve the important contents in the target image. Furthermore, the simple nature of filter operations allows highly efficient implementation for real-time applications and easy extension to video retargeting, as the structural constraints from the original image naturally convey the temporal coherence between frames. The effectiveness and efficiency of our importance filtering algorithm are confirmed in extensive experiments.


title: PUBLIC-KEY STEGANOGRAPHY BASED ON MODIFIED LSB METHOD

text: Steganography is the art and science of invisible communication. It is a technique which keeps the existence of the message secret. This paper proposed a technique to implement steganogaraphy and cryptography together to hide the data into an image. This technique describes as: Find the shared stego-key between the two communication parties by applying Diffie-Hellman Key exchange protocol, then encrypt the data using secret stego-key and then select the pixels by encryption process with the help of same secret stegokey to hide the data. Each selected pixel will be used to hide 8 bits of data by using LSB method.


title: Bayesian Pot-Assembly from Fragments as Problems in Perceptual-Grouping and Geometric-Learning

text: A heretofore unsolved problem of great archaeological importance is the automatic assembly of pots made on a wheel from the hundreds (or thousands) of sherds found at an excavation site. An approach is presented to the automatic estimation of mathematical models of such pots from 3D measurements of sherds. A Bayesian approach is formulated beginning with a description of the complete set of geometric parameters that determine the distribution of the sherd measurement data. Matching of fragments and aligning them geometrically into configurations is based on matching break-curves (curves on a pot surface separating fragments), estimated axis and profile curve pairs for individual fragments and configurations of fragments, and a number of features of groups of break-curves. Pot assembly is a bottom-up maximum likelihood performance-based search. Experiments are illustrated on pots which were broken for the purpose, and on sherds from an archaeological dig located in Petra, Jordan. The performance measure can also be aposteriori probability, and many other types of information can be included, e.g., pot wall thickness, surface color, patterns on the surface, etc. This can also be viewed as the problem of learning a geometric object from an unorganized set of free-form fragments of the object and of clutter, or as a problem of perceptual grouping.


title: Drawing lithography for microneedles: a review of fundamentals and biomedical applications.

text: A microneedle is a three-dimensional (3D) micromechanical structure and has been in the spotlight recently as a drug delivery system (DDS). Because a microneedle delivers the target drug after penetrating the skin barrier, the therapeutic effects of microneedles proceed from its 3D structural geometry. Various types of microneedles have been fabricated using subtractive micromanufacturing methods which are based on the inherently planar two-dimensional (2D) geometries. However, traditional subtractive processes are limited for flexible structural microneedles and makes functional biomedical applications for efficient drug delivery difficult. The authors of the present study propose drawing lithography as a unique additive process for the fabrication of a microneedle directly from 2D planar substrates, thus overcoming a subtractive process shortcoming. The present article provides the first overview of the principal drawing lithography technology: fundamentals and biomedical applications. The continuous drawing technique for an ultrahigh-aspect ratio (UHAR) hollow microneedle, stepwise controlled drawing technique for a dissolving microneedle, and drawing technique with antidromic isolation for a hybrid electro-microneedle (HEM) are reviewed, and efficient biomedical applications by drawing lithography-mediated microneedles as an innovative drug and gene delivery system are described. Drawing lithography herein can provide a great breakthrough in the development of materials science and biotechnology.


title: Optimization of Cooperative Sensing in Cognitive Radio Networks: A Sensing-Throughput Tradeoff View

text: In cognitive radio networks, the performance of the spectrum sensing depends on the sensing time and the fusion scheme that are used when cooperative sensing is applied. In this paper, we consider the case where the secondary users cooperatively sense a channel using the k -out-of-N fusion rule to determine the presence of the primary user. A sensing-throughput tradeoff problem under a cooperative sensing scenario is formulated to find a pair of sensing time and k value that maximize the secondary users' throughput subject to sufficient protection that is provided to the primary user. An iterative algorithm is proposed to obtain the optimal values for these two parameters. Computer simulations show that significant improvement in the throughput of the secondary users is achieved when the parameters for the fusion scheme and the sensing time are jointly optimized.


title: Primary gonadal failure and precocious adrenarche in a boy with Prader-Labhart-Willi syndrome

text: A 7-year-old boy with Prader-Labhart-Willi syndrome who had precocious adrenarche was found to have primary gonadal failure, as evidenced by appropriate laboratory investigations: elevated basal levels of plasma FSH and LH with exaggerated responses to LH-RH stimulation and unresponsiveness of plasma testosterone to repeated hCG stimulations. The elevated values of plasma DHEA which were found indicate an early activation of the adrenal gland. This patient demonstrates the varibility of pubertal development in the Prader-Labhart-Willi syndrome, with the unusual association of primary gonadal failure and precocious adrenarche.


title: Improving Image Classification Using Semantic Attributes

text: The Bag-of-Words (BoW) model—commonly used for image classification—has two strong limitations: on one hand, visual words are lacking of explicit meanings, on the other hand, they are usually polysemous. This paper proposes to address these two limitations by introducing an intermediate representation based on the use of semantic attributes. Specifically, two different approaches are proposed. Both approaches consist in predicting a set of semantic attributes for the entire images as well as for local image regions, and in using these predictions to build the intermediate level features. Experiments on four challenging image databases (PASCAL VOC 2007, Scene-15, MSRCv2 and SUN-397) show that both approaches improve performance of the BoW model significantly. Moreover, their combination achieves the state-of-the-art results on several of these image databases.


title: Deceptive Reviews : The Influential Tail

text: Research in the psycholinguistics literature has identified linguistic cues indicating when a message is more likely to be deceptive and we find that the textual comments in the reviews without prior transactions exhibit many of these characteristics. In contrast, these reviews are less likely to contain expressions describing the fit or feel of the items, which can generally only be assessed by physical inspection of the items.


title: Process Mining and Fraud Detection

text: A case study on the theoretical and practical value of using process mining for the detection of fraudulent behavior in the procurement process Abstract This thesis presents the results of a six month research period on process mining and fraud detection. This thesis aimed to answer the research question as to how process mining can be utilized in fraud detection and what the benefits of using process mining for fraud detection are. Based on a literature study it provides a discussion of the theory and application of process mining and its various aspects and techniques. Using both a literature study and an interview with a domain expert, the concepts of fraud and fraud detection are discussed. These results are combined with an analysis of existing case studies on the application of process mining and fraud detection to construct an initial setup of two case studies, in which process mining is applied to detect possible fraudulent behavior in the procurement process. Based on the experiences and results of these case studies, the 1+5+1 methodology is presented as a first step towards operationalizing principles with advice on how process mining techniques can be used in practice when trying to detect fraud. This thesis presents three conclusions: (1) process mining is a valuable addition to fraud detection, (2) using the 1+5+1 concept it was possible to detect indicators of possibly fraudulent behavior (3) the practical use of process mining for fraud detection is diminished by the poor performance of the current tools. The techniques and tools that do not suffer from performance issues are an addition, rather than a replacement, to regular data analysis techniques by providing either new, quicker, or more easily obtainable insights into the process and possible fraudulent behavior. iii Occam's Razor: " One should not increase, beyond what is necessary, the number of entities required to explain anything " iv Contents


title: Question Answering on Freebase via Relation Extraction and Textual Evidence

text: Existing knowledge-based question answering systems often rely on small annotated training data. While shallow methods like relation extraction are robust to data scarcity, they are less expressive than the deep meaning representation methods like semantic parsing, thereby failing at answering questions involving multiple constraints. Here we alleviate this problem by empowering a relation extraction method with additional evidence from Wikipedia. We first present a neural network based relation extractor to retrieve the candidate answers from Freebase, and then infer over Wikipedia to validate these answers. Experiments on the WebQuestions question answering dataset show that our method achieves an F1 of 53.3%, a substantial improvement over the state-of-the-art.


title: Beyond Big and Little : The Four C Model of Creativity

text: Most investigations of creativity tend to take one of two directions: everyday creativity (also called “little-c”), which can be found in nearly all people, and eminent creativity (also called “Big-C”), which is reserved for the great. In this paper, the authors propose a Four C model of creativity that expands this dichotomy. Specifically, the authors add the idea of “mini-c,” creativity inherent in the learning process, and Pro-c, the developmental and effortful progression beyond little-c that represents professional-level expertise in any creative area. The authors include different transitions and gradations of these four dimensions of creativity, and then discuss advantages and examples of the Four C Model.


title: Comparing a computer agent with a humanoid robot

text: HRI researchers interested in social robots have made large investments in humanoid robots. There is still sparse evidence that peoples' responses to robots differ from their responses to computer agents, suggesting that agent studies might serve to test HRI hypotheses. To help us understand the difference between people's social interactions with an agent and a robot, we experimentally compared people's responses in a health interview with (a) a computer agent projected either on a computer monitor or life-size on a screen, (b) a remote robot projected life-size on a screen, or (c) a collocated robot in the same room. We found a few behavioral and large attitude differences across these conditions. Participants forgot more and disclosed least with the collocated robot, next with the projected remote robot, and then with the agent. They spent more time with the collocated robot and their attitudes were most positive toward that robot. We discuss tradeoffs for HRI research of using collocated robots, remote robots, and computer agents as proxies of robots.


title: Why is image quality assessment so difficult?

text: Image quality assessment plays an important role in various image processing applications. A great deal of effort has been made in recent years to develop objective image quality metrics that correlate with perceived quality measurement. Unfortunately, only limited success has been achieved. In this paper, we provide some insights on why image quality assessment is so difficult by pointing out the weaknesses of the error sensitivity based framework, which has been used by most image quality assessment approaches in the literature. Furthermore, we propose a new philosophy in designing image quality metrics: The main function of the human eyes is to extract structural information from the viewing field, and the human visual system is highly adapted for this purpose. Therefore, a measurement of structural distortion should be a good approximation of perceived image distortion. Based on the new philosophy, we implemented a simple but effective image quality indexing algorithm, which is very promising as shown by our current results.


title: Performance indicators for an objective measure of public transport service quality

text: The measurement of transit performance represents a very useful tool for ensuring continuous increase of the quality of the delivered transit services, and for allocating resources among competing transit agencies. Transit service quality can be evaluated by subjective measures based on passengers’ perceptions, and objective measures represented by disaggregate performance measures expressed as numerical values, which must be compared with fixed standards or past performances. The proposed research work deals with service quality evaluation based on objective measures; specifically, an extensive overview and an interpretative review of the objective indicators until investigated by researchers are proposed. The final aim of the work is to give a review as comprehensive as possible of the objective indicators, and to provide some suggestions for the selection of the most appropriate indicators for evaluating a transit service aspect.


title: Novel paradigms for advanced distribution grid energy management

text: The electricity distribution grid was not designed to cope with load dynamics imposed by high penetration of electric vehicles, neither to deal with the increasing deployment of distributed Renewable Energy Sources. Distribution System Operators (DSO) will increasingly rely on flexible Distributed Energy Resources (flexible loads, controllable generation and storage) to keep the grid stable and to ensure quality of supply. In order to properly integrate demand-side flexibility, DSOs need new energy management architectures, capable of fostering collaboration with wholesale market actors and prosumers. We propose the creation of Virtual Distribution Grids (VDG) over a common physical infrastructure, to cope with heterogeneity of resources and actors, and with the increasing complexity of distribution grid management and related resources allocation problems. Focusing on residential VDG, we propose an agent-based hierarchical architecture for providing Demand-Side Management services through a market-based approach, where households transact their surplus/lack of energy and their flexibility with neighbours, aggregators, utilities and DSOs. For implementing the overall solution, we consider fine-grained control of smart homes based on Internet of Things technology. Homes seamlessly transact self-enforcing smart contracts over a blockchainbased generic platform. Finally, we extend the architecture to solve existing problems on smart home control, beyond energy management.


title: Planar Differential Elliptical UWB Antenna Optimization

text: A recently proposed optimization procedure, based on the time domain characteristics of an antenna, is exploited to design a planar differential elliptical antenna for ultrawideband (UWB) applications. The optimization procedure aims at finding an antenna not only with low VSWR but also one exhibiting low-dispersion characteristics over the relevant frequency band. Furthermore, since in pulse communications systems the input signal is often of a given form, suited to a particular purpose, the optimization procedure also aims at finding the best antenna for the given input signal form. Specifically, the optimized antenna is designed for high temporal correlation between its electric field intensity signal and the given input signal. The optimization technique followed in this work makes use of genetic algorithm (GA) search concepts. The electromagnetic analysis of the antenna is done by means of a finite-difference time-domain method using the commercially available CST Microwave Studio software


title: Tracking-based deer vehicle collision detection using thermal imaging

text: Deer vehicle collision (DVC) is constantly a major safety issue for the driving on rural road. It is estimated that there are over 35,000 DVCs yearly in the US resulting in about 200 deaths and close to 4,000 reported property damages of one thousand dollars or more. This justifies many attempts trying to detect deer on road. However, very little success has been achieved. In order to reduce the number of DVCs, this work focused on the study of using an infrared thermal camera with tracking system to detect the presence of deer to avoid DVCs. The prototype consists of an infrared thermal temperature image grabbing and processing system, which includes an infrared thermal camera, a frame grabber, an image processing system and a motion tracking system, which includes two motors with their motion control system. By analyzing the infrared thermal images which are independent of visible light, the presence of an animal can be determined in either night or day time through pattern recognition and matching.


title: Beyond One-hot Encoding: lower dimensional target embedding

text: Target encoding plays a central role when learning Convolutional Neural Networks. In this realm, One-hot encoding is the most prevalent strategy due to its simplicity. However, this so widespread encoding schema assumes a flat label space, thus ignoring rich relationships existing among labels that can be exploited during training. In large-scale datasets, data does not span the full label space, but instead lies in a low-dimensional output manifold. Following this observation, we embed the targets into a lowdimensional space, drastically improving convergence speed while preserving accuracy. Our contribution is two fold: (i) We show that random projections of the label space are a valid tool to find such lower dimensional embeddings, boosting dramatically convergence rates at zero computational cost; and (ii) we propose a normalized eigenrepresentation of the class manifold that encodes the targets with minimal information loss, improving the accuracy of random projections encoding while enjoying the same convergence rates. Experiments on CIFAR-100, CUB200-2011, Imagenet, and MIT Places demonstrate that the proposed approach drastically improves convergence speed while reaching very competitive accuracy rates.


title: Simba: Efficient In-Memory Spatial Analytics

text: Large spatial data becomes ubiquitous. As a result, it is critical to provide fast, scalable, and high-throughput spatial queries and analytics for numerous applications in location-based services (LBS). Traditional spatial databases and spatial analytics systems are disk-based and optimized for IO efficiency. But increasingly, data are stored and processed in memory to achieve low latency, and CPU time becomes the new bottleneck. We present the Simba (Spatial In-Memory Big data Analytics) system that offers scalable and efficient in-memory spatial query processing and analytics for big spatial data. Simba is based on Spark and runs over a cluster of commodity machines. In particular, Simba extends the Spark SQL engine to support rich spatial queries and analytics through both SQL and the DataFrame API. It introduces indexes over RDDs in order to work with big spatial data and complex spatial operations. Lastly, Simba implements an effective query optimizer, which leverages its indexes and novel spatial-aware optimizations, to achieve both low latency and high throughput. Extensive experiments over large data sets demonstrate Simba's superior performance compared against other spatial analytics system.


title: Smart fabrics and interactive textile enabling wearable personal applications: R&D state of the art and future challenges

text: Smart fabrics and interactive textiles (SFIT) are fibrous structures that are capable of sensing, actuating, generating/storing power and/or communicating. Research and development towards wearable textile-based personal systems allowing e.g. health monitoring, protection & safety, and healthy lifestyle gained strong interest during the last 10 years. Under the Information and Communication Programme of the European Commission, a cluster of R&D projects dealing with smart fabrics and interactive textile wearable systems regroup activities along two different and complementary approaches i.e. “application pull” and “technology push”. This includes projects aiming at personal health management through integration, validation, and use of smart clothing and other networked mobile devices as well as projects targeting the full integration of sensors/actuators, energy sources, processing and communication within the clothes to enable personal applications such as protection/safety, emergency and healthcare. The integration part of the technologies into a real SFIT product is at present stage on the threshold of prototyping and testing. Several issues, technical as well user-centred, societal and business, remain to be solved. The paper presents on going major R&D activities, identifies gaps and discuss key challenges for the future.


title: Multilingual speech-to-speech translation system for mobile consumer devices

text: Along with the advancement of speech recognition technology and machine translation technology in addition to the fast distribution of mobile devices, speech-to-speech translation technology no longer remains as a subject of research as it has become popularized throughout many users. In order to develop a speech-to-speech translation system that can be widely used by many users, however, the system needs to reflect various characteristics of utterances by the users who are actually to use the speech-to-speech translation system other than improving the basic functions under the experimental environment. This study has established a massive language and speech database closest to the environment where speech-to- speech translation device actually is being used after mobilizing plenty of people based on the survey on users' demands. Through this study, it was made possible to secure excellent basic performance under the environment similar to speech-to-speech translation environment, rather than just under the experimental environment. Moreover, with the speech-to-speech translation UI, a user-friendly UI has been designed; and at the same time, errors were reduced during the process of translation as many measures to enhance user satisfaction were employed. After implementing the actual services, the massive database collected through the service was additionally applied to the system following a filtering process in order to procure the best-possible robustness toward both the details and the environment of the users' utterances. By applying these measures, this study is to unveil the procedures where multi-language speech-to-speech translation system has been successfully developed for mobile devices.


title: Missing value imputation for gene expression data: computational techniques to recover missing data from available information

text: Microarray gene expression data generally suffers from missing value problem due to a variety of experimental reasons. Since the missing data points can adversely affect downstream analysis, many algorithms have been proposed to impute missing values. In this survey, we provide a comprehensive review of existing missing value imputation algorithms, focusing on their underlying algorithmic techniques and how they utilize local or global information from within the data, or their use of domain knowledge during imputation. In addition, we describe how the imputation results can be validated and the different ways to assess the performance of different imputation algorithms, as well as a discussion on some possible future research directions. It is hoped that this review will give the readers a good understanding of the current development in this field and inspire them to come up with the next generation of imputation algorithms.


title: FML-based feature similarity assessment agent for Japanese/Taiwanese language learning

text: In this paper, we propose a fuzzy markup language (FML)-based feature similarity assessment agent with machine-learning ability to evaluate easy-to-learn degree of the Japanese and Taiwanese words. The involved domain experts define knowledge base (KB) and rule base (RB) of the proposed agent. The KB and RB are stored in the constructed ontology, including features of pronunciation similarity, writing similarity, and culture similarity. Next, we calculate feature similarity in pronunciation, writing, and culture for each word pair between Japanese and Taiwanese. Finally, we infer the easy-to-learn degree for one Japanese word and its corresponding Taiwanese one. Additionally, a genetic learning is also adopted to tune the KB and RB of the intelligent agent. The experimental results show that after-learning results perform better than before-learning ones.


title: foo . castr : visualising the future AI workforce

text: *Correspondence: mmolinas@ic.ac.uk 1Data Science Institute, Imperial College London, London, UK Full list of author information is available at the end of the article Abstract Organization of companies and their HR departments are becoming hugely affected by recent advancements in computational power and Artificial Intelligence, with this trend likely to dramatically rise in the next few years. This work presents foo.castr, a tool we are developing to visualise, communicate and facilitate the understanding of the impact of these advancements in the future of workforce. It builds upon the idea that particular tasks within job descriptions will be progressively taken by computers, forcing the shaping of human jobs. In its current version, foo.castr presents three different scenarios to help HR departments planning potential changes and disruptions brought by the adoption of Artificial Intelligence.


title: On happiness and human potentials: a review of research on hedonic and eudaimonic well-being.

text: Well-being is a complex construct that concerns optimal experience and functioning. Current research on well-being has been derived from two general perspectives: the hedonic approach, which focuses on happiness and defines well-being in terms of pleasure attainment and pain avoidance; and the eudaimonic approach, which focuses on meaning and self-realization and defines well-being in terms of the degree to which a person is fully functioning. These two views have given rise to different research foci and a body of knowledge that is in some areas divergent and in others complementary. New methodological developments concerning multilevel modeling and construct comparisons are also allowing researchers to formulate new questions for the field. This review considers research from both perspectives concerning the nature of well-being, its antecedents, and its stability across time and culture.


title: Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being.

text: Human beings can be proactive and engaged or, alternatively, passive and alienated, largely as a function of the social conditions in which they develop and function. Accordingly, research guided by self-determination theory has focused on the social-contextual conditions that facilitate versus forestall the natural processes of self-motivation and healthy psychological development. Specifically, factors have been examined that enhance versus undermine intrinsic motivation, self-regulation, and well-being. The findings have led to the postulate of three innate psychological needs--competence, autonomy, and relatedness--which when satisfied yield enhanced self-motivation and mental health and when thwarted lead to diminished motivation and well-being. Also considered is the significance of these psychological needs and processes within domains such as health care, education, work, sport, religion, and psychotherapy.


title: Mean and Covariance Structures (MACS) Analyses of Cross-Cultural Data: Practical and Theoretical Issues.

text: Practical and theoretical issues are discussed for testing (a) the comparability, or measurement equivalence, of psychological constructs and (b) detecting possible sociocultural difference on the constructs in cross-cultural research designs. Specifically, strong factorial invariance (Meredith, 1993) of each variable's loading and intercept (mean-level) parameters implies that constructs are fundamentally the same in each sociocultural group, and thus comparable. Under this condition, hypotheses about the nature of sociocultural differences and similarities can be confidently and meaningfully tested among the constructs' moments in each sociocultural sample. Some of the issues involved in making such tests are reviewed and explicated within the framework of multiple-group mean and covariance structures analyses.


title: Daily Well-Being : The Role of Autonomy , Competence , and Relatedness

text: Emotional well-being is most typically studied in trait or traitlike terms, yet a growing literature indicates that daily (withinperson) fluctuations in emotional well-being may be equally important. The present research explored the hypothesis that daily variations may be understood in terms of the degree to which three basic needs—autonomy, competence, and relatedness—are satisfied in daily activity. Hierarchical linear models were used to examine this hypothesis across 2 weeks of daily activity and well-being reports controlling for trait-level individual differences. Results strongly supported the hypothesis. The authors also examined the social activities that contribute to satisfaction of relatedness needs. The best predictors were meaningful talk and feeling understood and appreciated by interaction partners. Finally, the authors found systematic day-of-the-week variations in emotional well-being and need satisfaction. These results are discussed in terms of the importance of daily activities and the need to consider both trait and day-level determinants of well-being.


title: Personal projects, happiness, and meaning: on doing well and being yourself.

text: Personal Projects Analysis (B. R. Little, 1983) was adapted to examine relations between participants' appraisals of their goal characteristics and orthogonal happiness and meaning factors that emerged from factor analyses of diverse well-being measures. In two studies with 146 and 179 university students, goal efficacy was associated with happiness and goal integrity was associated with meaning. A new technique for classifying participants according to emergent identity themes is introduced. In both studies, identity-compensatory predictors of happiness were apparent. Agentic participants were happiest if their goals were supported by others, communal participants were happiest if their goals were fun, and hedonistic participants were happiest if their goals were being accomplished. The distinction between happiness and meaning is emphasized, and the tension between efficacy and integrity is discussed. Developmental implications are discussed with reference to results from archival data from a sample of senior managers.


title: Quasi-Dense Reconstruction from Image Sequence

text: This paper proposes a quasi-dense reconstruction from uncalibrated sequence. The main innovation is that all geometry is computed based on re-sampled quasi-dense correspondences rather than the standard sparse points of interest. It not only produces more accurate and robust reconstruction due to highly redundant and well spread input data, but also fills the gap of insufficiency of sparse reconstruction for visualization application. The computational engine is the quasi-dense 2-view and the quasi-dense 3-view algorithms developed in this paper. Experiments on real sequences demonstrate the superior performance of quasi-dense w.r.t. sparse reconstruction both in accuracy and robustness.


title: Novel Dataset for Fine-Grained Image Categorization : Stanford Dogs

text: We introduce a 120 class Stanford Dogs dataset, a challenging and large-scale dataset aimed at fine-grained image categorization. Stanford Dogs includes over 22,000 annotated images of dogs belonging to 120 species. Each image is annotated with a bounding box and object class label. Fig. 1 shows examples of images from Stanford Dogs. This dataset is extremely challenging due to a variety of reasons. First, being a fine-grained categorization problem, there is little inter-class variation. For example the basset hound and bloodhound share very similar facial characteristics but differ significantly in their color, while the Japanese spaniel and papillion share very similar color but greatly differ in their facial characteristics. Second, there is very large intra-class variation. The images show that dogs within a class could have different ages (e.g. beagle), poses (e.g. blenheim spaniel), occlusion/self-occlusion and even color (e.g. Shih-tzu). Furthermore, compared to other animal datasets that tend to exist in natural scenes, a large proportion of the images contain humans and are taken in manmade environments leading to greater background variation. The aforementioned reasons make this an extremely challenging dataset.


title: The importance of being flexible: the ability to both enhance and suppress emotional expression predicts long-term adjustment.

text: Researchers have documented the consequences of both expressing and suppressing emotion using between-subjects designs. It may be argued, however, that successful adaptation depends not so much on any one regulatory process, but on the ability to flexibly enhance or suppress emotional expression in accord with situational demands. We tested this hypothesis among New York City college students in the aftermath of the September 11th terrorist attacks. Subjects' performance in a laboratory task in which they enhanced emotional expression, suppressed emotional expression, and behaved normally on different trials was examined as a prospective predictor of their adjustment across the first two years of college. Results supported the flexibility hypothesis. A regression analysis controlling for initial distress and motivation and cognitive resources found that subjects who were better able to enhance and suppress the expression of emotion evidenced less distress by the end of the second year. Memory deficits were also observed for both the enhancement and the suppression tasks, suggesting that both processes require cognitive resources.


title: OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles

text: We present a new major release of the OpenSubtitles collection of parallel corpora. The release is compiled from a large database of movie and TV subtitles and includes a total of 1689 bitexts spanning 2.6 billion sentences across 60 languages. The release also incorporates a number of enhancements in the preprocessing and alignment of the subtitles, such as the automatic correction of OCR errors and the use of meta-data to estimate the quality of each subtitle and score subtitle pairs.


title: Training RNN simulated vehicle controllers using the SVD and evolutionary algorithms

text: We describe an approach to creating a controller for The Open Car Racing Simulator (TORCS), based on The Simulated Car Racing Championship (SCRC) client, using unsupervised evolutionary learning for recurrent neural networks. Our method of training the recurrent neural network controllers relies on combining the components of the singular value decomposition of two different neural network connection matrices.


title: Label Embedding Trees for Large Multi-Class Tasks

text: Multi-class classification becomes challenging at test time when the number of classes is very large and testing against every possible class can become computationally infeasible. This problem can be alleviated by imposing (or learning) a structure over the set of classes. We propose an algorithm for learning a treestructure of classifiers which, by optimizing the overall tree loss, provides superior accuracy to existing tree labeling methods. We also propose a method that learns to embed labels in a low dimensional space that is faster than non-embedding approaches and has superior accuracy to existing embedding approaches. Finally we combine the two ideas resulting in the label embedding tree that outperforms alternative methods including One-vs-Rest while being orders of magnitude faster.


title: Dependency Grammar and Dependency Parsing

text: Despite a long and venerable tradition in descriptive lingu stics, dependency grammar has until recently played a fairly marginal role both in t heoretical linguistics and in natural language processing. The increasing interes t in dependency-based representations in natural language parsing in recent year s appears to be motivated both by the potential usefulness of bilexical relations in d isambiguation and by the gains in efficiency that result from the more constrained par sing problem for these representations. In this paper, we will review the state of the art in dependenc y-based parsing, starting with the theoretical foundations of dependency gr ammar and moving on to consider both grammar-driven and data-driven methods fo r dependency parsing. We will limit our attention to systems for dependency parsin g i a narrow sense, i.e. systems where the analysis assigned to an input sentenc akes the form of a dependency structure. This means that we will not discuss sy tems that exploit dependency relations for the construction of another type o f r presentation, such as the head-driven parsing models of Collins (1997, 1999). M oreover, we will restrict ourselves to systems for full parsing, which means that we will not deal with systems that produce a partial or underspecified repres ntation of dependency structure, such as Constraint Grammar parsers (Karlsson, 1 990; Karlsson et al., 1995).


title: Realized Volatility and Variance : Options via Swaps

text: Let St denote the value of a stock or stock index at time t. Given a variance/volatility option to be priced and hedged, let us designate as time 0 the start of its averaging period, and time T the end. For t ∈ [0, T ], and τ ≤ t, let R2 τ,t denote the realized variance of returns over the time interval [τ, t]. The mathematical results about the synthesis of volatility and variance swaps will hold exactly if R2 refers to the continuously-monitored variance. This means the quadratic variation of logS, ∗Bloomberg L.P. and NYU Courant Institute pcarr@nyc.rr.com. †University of Chicago. RL@math.uchicago.edu. We thank the International Securities Exchange, Kris Monaco,


title: Single-Stage Single-Switch PFC Flyback Converter Using a Synchronous Rectifier

text: A single-stage single-switch power factor correction (PFC) flyback converter with a synchronous rectifier (SR) is proposed for improving power factor and efficiency. Using a variable switching-frequency controller, this converter is continuously operated with a reduced turn-on switching loss at the boundary of the continuous conduction mode and discontinuous conduction mode (DCM). The proposed PFC circuit provides relatively low dc-link voltage in the universal line voltage, and also complies with Standard IEC 61000-3-2 Class D limits. In addition, a new driving circuit as the voltage driven-synchronous rectifier is proposed to achieve high efficiency. In particular, since a driving signal is generated according to the voltage polarity, the SR driving circuit can easily be used in DCM applications. The proposed PFC circuit and SR driving circuit in the flyback converter with the reduced switching loss are analyzed in detail and optimized for high performance. Experimental results for a 19 V/90 W adapter at the variable switching-frequency of 30~70 kHz were obtained to show the performance of the proposed converter.


title: Current and Emerging Topics in Sports Video Processing

text: Sports video processing is an interesting topic for research, since the clearly defined game rules in sports provide the rich domain knowledge for analysis. Moreover, it is interesting because many specialized applications for sports video processing are emerging. This paper gives an overview of sports video research, where we describe both basic algorithmic techniques and applications.


title: Improving Business Process Quality through Exception Understanding, Prediction, and Prevention

text: Business process automation technologies are being increasingly used by many companies to improve the efficiency of both internal processes as well as of e-services offered to customers. In order to satisfy customers and employees, business processes need to be executed with a high and predictable quality. In particular, it is crucial for organizations to meet the Service Level Agreements (SLAs) stipulated with the customers and to foresee as early as possible the risk of missing SLAs, in order to set the right expectations and to allow for corrective actions. In this paper we focus on a critical issue in business process quality: that of analyzing, predicting and preventing the occurrence of exceptions, i.e., of deviations from the desired or acceptable behavior. We characterize the problem and propose a solution, based on data warehousing and mining techniques. We then describe the architecture and implementation of a tool suite that enables exception analysis, prediction, and prevention. Finally, we show experimental results obtained by using the tool suite to analyze internal HP processes.


title: A Service-Oriented Architecture for Virtualizing Robots in Robot-as-a-Service Clouds

text: Exposing software and hardware computing resources as services through a cloud is increasingly emerging in the recent years. This comes as a result of extending the service-oriented architecture (SOA) paradigm to virtualize computing resources. In this paper, we extend the paradigm of the SOA approach to virtualize robotic hardware and software resources to expose them as services through the Web. This allows non-technical users to access, interact and manipulate robots simply through a Web browser. The proposed RoboWeb system is based on a SOAP-based Web service middleware that binds robots computing resources as services and publish them to the end-users. We consider robots that operates with the Robotic Operating System (ROS), as it provides hardware abstraction that makes easier applications development. We describe the implementation of RoboWeb and demonstrate how researchers can use it to interact remotely with the robots. We believe that this work consistently contributes to enabling remote robotic labs using the cloud paradigm.


title: TumorNet: Lung nodule characterization using multi-view Convolutional Neural Network with Gaussian Process

text: Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning. While the variation in the appearance of the nodules remains large, there is a need for a fast and robust computer aided system. In this work, we propose an end-to-end trainable multi-view deep Convolutional Neural Network (CNN) for nodule characterization. First, we use median intensity projection to obtain a 2D patch corresponding to each dimension. The three images are then concatenated to form a tensor, where the images serve as different channels of the input image. In order to increase the number of training samples, we perform data augmentation by scaling, rotating and adding noise to the input image. The trained network is used to extract features from the input image followed by a Gaussian Process (GP) regression to obtain the malignancy score. We also empirically establish the significance of different high level nodule attributes such as calcification, sphericity and others for malignancy determination. These attributes are found to be complementary to the deep multi-view CNN features and a significant improvement over other methods is obtained.


title: Behavior Analysis Using a Multilevel Motion Pattern Learning Framework

text: The increasing availability of video data, through existing traffic cameras or dedicated field data collection, paves the way for the collection of massive datasets about the microscopic behaviour of road users using computer vision techniques. Analysis of such datasets helps to understand the normal road user behaviour, and it can be used for realistic prediction of future motion and computing surrogate safety indicators. A multi-level motion pattern learning framework is developed that enables automated scene interpretation, anomalous behaviour detection and surrogate safety analysis. Firstly, Points of interest (POI) are learnt based on Gaussian Mixture Model and the Expectation Maximization algorithm and then used to form activity paths (APs). Secondly, motion patterns, represented by trajectory prototypes, are learnt from road users’ trajectories in each AP using a two-stage trajectory clustering method based on spatial then temporal (speed) information. Finally, motion prediction relies on matching at each instant partial trajectories to the learnt prototypes to evaluate the potential for collision by computing indicators. An intersection case study demonstrates the framework ability in many ways: it helps to reduce the computation cost up to 90 %, clean trajectories dataset from tracking outliers, use actual trajectories as prototypes without any preand post-processing and predict future motion realistically to compute surrogate safety indicators.


title: A High-Power Broadband Passive Terahertz Frequency Doubler in CMOS

text: To realize a high-efficiency terahertz signal source, a varactor-based frequency-doubler topology is proposed. The structure is based on a compact partially coupled ring that simultaneously achieves isolation, matching, and harmonic filtering for both input and output signals at f0 and 2f0. The optimum varactor pumping/loading conditions for the highest conversion efficiency are also presented analytically along with intuitive circuit representations. Using the proposed circuit, a passive 480-GHz frequency doubler with a measured minimum conversion loss of 14.3 dB and an unsaturated output power of 0.23 mW is reported. Within 20-GHz range, the fluctuation of the measured output power is less than 1.5 dB, and the simulated 3-dB output bandwidth is 70 GHz (14.6%). The doubler is fabricated using 65-nm low-power bulk CMOS technology and consumes near zero dc power.


title: Anonymous CoinJoin Transactions with Arbitrary Values

text: Bitcoin, the arguably most popular cryptocurrency to date, allows users to perform transactions using freely chosen pseudonymous addresses. Previous research, however, suggests that these pseudonyms can easily be linked, implying a lower level of privacy than originally expected. To obfuscate the links between pseudonyms, different mixing methods have been proposed. One of the first approaches is the CoinJoin concept, where multiple users merge their transactions into one larger transaction. In theory, CoinJoin can be used to mix and transact bitcoins simultaneously, in one step. Yet, it is expected that differing bitcoin amounts would allow an attacker to derive the original single transactions. Solutions based on CoinJoin therefore prescribe the use of fixed bitcoin amounts and cannot be used to perform arbitrary transactions.In this paper, we define a model for CoinJoin transactions and metrics that allow conclusions about the provided anonymity. We generate and analyze CoinJoin transactions and show that with differing, representative amounts they generally do not provide any significant anonymity gains. As a solution to this problem, we present an output splitting approach that introduces sufficient ambiguity to effectively prevent linking in CoinJoin transactions. Furthermore, we discuss how this approach could be used in Bitcoin today.


title: Blockchain — Literature survey

text: In the modern world that we live in everything is gradually shifting towards a more digitized outlook. From teaching methods to online transactions, every small aspect of our life is given a more technological touch. In such a case “money” is not left behind either. An approach towards digitizing money led to the creation of “bitcoin”. Bitcoin is the most efficient crypto-currency so far in the history of digital currency. Ruling out the interference of any third party which monitors the cash flow, Bitcoin is a decentralized form of online currency and is widely accepted for internet transactions all over the world. The need for digital money would be extensive in the near future.


title: IMPLICIT SLICING METHOD FOR ADDITIVE MANUFACTURING PROCESSES

text: All additive manufacturing processes involve a distinct preprocessing stage in which a set of instructions, or GCode, that control the process specific manufacturing tool are generated, otherwise known as slicing. In regards to fused deposition modeling, the GCode defines many crucial parameters which are needed to produce a part, including tool path, infill density, layer height, feed rate, tool head and plate temperature. The majority of current commercial slicing programs generate tool paths explicitly, and do not consider particular geometric properties of parts, such as thin walls, small corners and round profiles that can result in critical voids leading to part failure. This work replicates an implicit slicing algorithm in which functionally derived infill patterns are overlaid onto each layer of a part, reducing the possibility of undesired voids and flaws. This work also further investigates the effects that varying implicitly derived infill patterns have on a part’s mechanical properties through tensile testing dog bone specimens with three different infill patterns and comparing ultimate stress and elastic modulus properties.


title: Adversarial Cross-Modal Retrieval

text: Cross-modal retrieval aims to enable flexible retrieval experience across different modalities (e.g., texts vs. images). The core of cross-modal retrieval research is to learn a common subspace where the items of different modalities can be directly compared to each other. In this paper, we present a novel Adversarial Cross-Modal Retrieval (ACMR) method, which seeks an effective common subspace based on adversarial learning. Adversarial learning is implemented as an interplay between two processes. The first process, a feature projector, tries to generate a modality-invariant representation in the common subspace and to confuse the other process, modality classifier, which tries to discriminate between different modalities based on the generated representation. We further impose triplet constraints on the feature projector in order to minimize the gap among the representations of all items from different modalities with same semantic labels, while maximizing the distances among semantically different images and texts. Through the joint exploitation of the above, the underlying cross-modal semantic structure of multimedia data is better preserved when this data is projected into the common subspace. Comprehensive experimental results on four widely used benchmark datasets show that the proposed ACMR method is superior in learning effective subspace representation and that it significantly outperforms the state-of-the-art cross-modal retrieval methods.


title: Key drivers of internet banking services use

text: Purpose – The purpose of this paper is to analyse the determinants of internet banking use, paying special attention to the role of product involvement, perceived risk and trust. Design/methodology/approach – The impact of trust, perceived risks, product involvement and TAM beliefs (ease of use and usefulness) on internet banking adoption is tested through structural equation modelling techniques. The sample consists of 511 Spanish internet banking services users and the data are collected through an internet survey. Risk is measured as a formative construct. Findings – Data analysis shows that TAM beliefs and perceived risks (security, privacy, performance and social) have a direct influence on e-banking adoption. Trust appears as a key variable that reduces perceived risk. Involvement plays an important role in increasing perceived ease of use. Practical implications – This research provides banks with knowledge of what aspects to highlight in their communications strategies to increase their internet banking services adoption rates. The research findings show managers that web contents and design are key tools to increase internet banking services adoption. Practical recommendations to increase web usefulness and trust, and guidelines to reduce perceived risk dimensions are also provided. Originality/value – Despite the importance of trust issues and risk perceptions for internet banking adoption, only limited work has been done to identify trust and risk dimensions in an online banking context. We have evaluated the impact of each risk dimension instead of treating risk as a whole. Furthermore, risk has been measured as a formative construct because there is no reason to expect that risk dimensions in online financial services are correlated.


title: Information technology and organizational learning: a review and assessment of research

text: This paper reviews and assesses the emerging research literature on information technology and organizational learning. After discussing issues of meaning and measurement, we identify and assess two main streams of research: studies that apply organizational learning concepts to the process of implementing and using information technology in organizations; and studies concerned with the design of information technology applications to support organizational learning. From the former stream of research, we conclude that experience plays an important, yet indeterminate role in implementation success; learning is accomplished through both formal training and participation in practice; organizational knowledge barriers may be overcome by learning from other organizations; and that learning new technologies is a dynamic process characterized by relatively narrow windows of opportunity. From the latter stream, we conclude that conceptual designs for organizational memory information systems are a valuable contribution to artifact development; learning is enhanced through systems that support communication and discourse; and that information technologies have the potential to both enable and disable organizational learning. Currently, these two streams flow independently of each other, despite their close conceptual and practical links. We advise that future research on information technology and organizational learning proceeds in a more integrated fashion, recognizes the situated nature of organizational learning, focuses on distributed organizational memory, demonstrates the effectiveness of artifacts in practice, and looks for relevant research findings in related fields.