Near-lighting Photometric Stereo for unknown scene distance and medium attenuation

Photometric Stereo in murky water is subject to light attenuation and near-field illumination, and the resulting image formation model is complex. Apart from the scene normals and albedo, the incident illumination varies per-pixel and it depends on the scene depth and the attenuation coefficient of the medium. When these are unknown, e.g. in a realistic scenario where a robotic platform explores an underwater scene (unknown shape and distance) within the dynamic subsea environment (unknown scattering level), Photometric Stereo becomes ambiguous. Previous approaches have tackled the problem by assuming distant-lighting and resorting to external hardware for estimating the unknown model variables. In our work, we show that the Photometric Stereo problem can be determined as soon as some additional constraints regarding the scene albedo and the presence of pixels with local intensity maxima within the image are incorporated into the optimization framework. Our proposed solution leads to effective Photometric Stereo and yields detailed 3D reconstruction of objects in murky water when the scene distance and the medium attenuation are unknown. We evaluate our work using both numerical simulations and real experiments in the controlled environment of a water tank and real port water using a remotely operated vehicle. © 2016 Elsevier B.V. All rights reserved.


LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection

Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine. However, there are often external factors or variables which are not captured by sensors leading to time-series which are inherently unpredictable. For instance, manual controls and/or unmonitored environmental conditions or load may lead to inherently unpredictable time-series. Detecting anomalies in such scenarios becomes challenging using standard approaches based on mathematical models that rely on stationarity, or prediction models that utilize prediction errors to detect anomalies. We propose a Long Short Term Memory Networks based Encoder-Decoder scheme for Anomaly Detection (EncDec-AD) that learns to reconstruct ‘normal’ time-series behavior, and thereafter uses reconstruction error to detect anomalies. We experiment with three publicly available quasi predictable time-series datasets: power demand, space shuttle, and ECG, and two realworld engine datasets with both predictive and unpredictable behavior. We show that EncDecAD is robust and can detect anomalies from predictable, unpredictable, periodic, aperiodic, and quasi-periodic time-series. Further, we show that EncDec-AD is able to detect anomalies from short time-series (length as small as 30) as well as long time-series (length as large as 500).


Neural Graph Machines: Learning Neural Networks Using Graphs

Label propagation is a powerful and flexible semi-supervised learning technique on graphs. Neural networks, on the other hand, have proven track records in many supervised learning tasks. In this work, we propose a training framework with a graph-regularised objective, namely Neural Graph Machines, that can combine the power of neural networks and label propagation. This work generalises previous literature on graphaugmented training of neural networks, enabling it to be applied to multiple neural architectures (Feed-forward NNs, CNNs and LSTM RNNs) and a wide range of graphs. The new objective allows the neural networks to harness both labeled and unlabeled data by: (a) allowing the network to train using labeled data as in the supervised setting, (b) biasing the network to learn similar hidden representations for neighboring nodes on a graph, in the same vein as label propagation. Such architectures with the proposed objective can be trained efficiently using stochastic gradient descent and scaled to large graphs, with a runtime that is linear in the number of edges. The proposed joint training approach convincingly outperforms many existing methods on a wide range of tasks (multi-label classification on social graphs, news categorization, document classification and semantic intent classification), with multiple forms of graph inputs (including graphs with and without node-level features) and using different types of neural networks. Work done during an internship at Google.


A Variational Principle for Graphical Models

Graphical models bring together graph theory and probability theory in a powerful formalism for multivariate statistical modeling. In statistical signal processing— as well as in related fields such as communication theory, control theory and bioinformatics—statistical models have long been formulated in terms of graphs, and algorithms for computing basic statistical quantities such as likelihoods and marginal probabilities have often been expressed in terms of recursions operating on these graphs. Examples include hidden Markov models, Markov random fields, the forward-backward algorithm and Kalman filtering [ Rabiner and Juang (1993); Pearl (1988); Kailath et al. (2000)]. These ideas can be understood, unified and generalized within the formalism of graphical models. Indeed, graphical models provide a natural framework for formulating variations on these classical architectures, and for exploring entirely new families of statistical models. The recursive algorithms cited above are all instances of a general recursive algorithm known as the junction tree algorithm [ Lauritzen and Spiegelhalter, 1988]. The junction tree algorithm takes advantage of factorization properties of the joint probability distribution that are encoded by the pattern of missing edges in a graphical model. For suitably sparse graphs, the junction tree algorithm provides a systematic and practical solution to the general problem of computing likelihoods and other statistical quantities associated with a graphical model. Unfortunately, many graphical models of practical interest are not " suitably sparse, " so that the junction tree algorithm no longer provides a viable computational solution to the problem of computing marginal probabilities and other expectations. One popular source of methods for attempting to cope with such cases is the Markov chain Monte Carlo (MCMC) framework, and indeed there is a significant literature on


A distributed representation of temporal context

The author acknowledge support from National Institute of Health Research Grants MH55687 and AG15685. Marc Howard is now at the Department of Psychology, Boston University, 64 Cunningham Street, Boston, MA 02215. Address correspondence and reprint requests to Michael J. Kahana, Volen Center for Complex Systems, Brandeis University, MS 013, Waltham, MA 02254. E-mail: kahana@brandeis.edu. Marc W. Howard and Michael J. Kahana


AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling

In vision-enabled autonomous systems such as robots and autonomous cars, video object detection plays a crucial role, and both its speed and accuracy are important factors to provide reliable operation. The key insight we show in this paper is that speed and accuracy are not necessarily a trade-off when it comes to image scaling. Our results show that re-scaling the image to a lower resolution will sometimes produce better accuracy. Based on this observation, we propose a novel approach, dubbed AdaScale, which adaptively selects the input image scale that improves both accuracy and speed for video object detection. To this end, our results on ImageNet VID and mini YouTube-BoundingBoxes datasets demonstrate 1.3 points and 2.7 points mAP improvement with 1.6× and 1.8× speedup, respectively. Additionally, we improve state-of-the-art video acceleration work by an extra 1.25× speedup with slightly better mAP on ImageNet VID dataset.


Designing of Robust Image Steganography Technique Based on LSB Insertion and Encryption

This paper discusses the design of a Robust image steganography technique based on LSB (Least Significant Bit) insertion and RSA encryption technique. Steganography is the term used to describe the hiding of data in images to avoid detection by attackers. Steganalysis is the method used by attackers to determine if images have hidden data and to recover that data. The application discussed in this paper ranks images in a users library based on their suitability as cover objects for some data. By matching data to an image, there is less chance of an attacker being able to use steganalysis to recover the data. Before hiding the data in an image the application first encrypts it. The steganography method proposed in this paper and illustrated by the application is superior to that used by current steganography tools.


Imaging without lenses: achievements and remaining challenges of wide-field on-chip microscopy

We discuss unique features of lens-free computational imaging tools and report some of their emerging results for wide-field on-chip microscopy, such as the achievement of a numerical aperture (NA) of ∼0.8–0.9 across a field of view (FOV) of more than 20 mm2 or an NA of ∼0.1 across a FOV of ∼18 cm2, which corresponds to an image with more than 1.5 gigapixels. We also discuss the current challenges that these computational on-chip microscopes face, shedding light on their future directions and applications.


Task offloading decision in fog computing system

Fog computing is an emerging paradigm of cloud computing which to meet the growing computation demand of mobile application. It can help mobile devices to overcome resource constraints by offloading the computationally intensive tasks to cloud servers. The challenge of the cloud is to minimize the time of data transfer and task execution to the user, whose location changes owing to mobility, and the energy consumption for the mobile device. To provide satisfactory computation performance is particularly challenging in the fog computing environment. In this paper, we propose a novel fog computing model and offloading policy which can effectively bring the fog computing power closer to the mobile user. The fog computing model consist of remote cloud nodes and local cloud nodes, which is attached to wireless access infrastructure. And we give task offloading policy taking into account executi+on, energy consumption and other expenses. We finally evaluate the performance of our method through experimental simulations. The experimental results show that this method has a significant effect on reducing the execution time of tasks and energy consumption of mobile devices.


A distributed attenuator for K-band using standard SMD thin-film chip resistors

This paper presents a distributed attenuator with standard SMD chip components. For low frequencies simple Pi and T-networks with chip resistors offer a good flexibility to design low cost attenuators in a microwave circuit. Nevertheless, with increasing frequency the parasitics of the chip resistors as well as the short additional line sections in the Pi and T network cause the design to fail. Here, a model of an 0201 chip resistor has been setup to show the limited operating range of the Pi and T-networks. Using a distributed attenuator design, two resistors separated by a quarter wavelength transmission line, can overcome these problems and extent the frequency range to K-band.


LSHDB: a parallel and distributed engine for record linkage and similarity search

In this paper, we present LSHDB, the first parallel and distributed engine for record linkage and similarity search. LSHDB materializes an abstraction layer to hide the mechanics of the Locality-Sensitive Hashing (a popular method for detecting similar items in high dimensions) which is used as the underlying similarity search engine. LSHDB creates the appropriate data structures from the input data and persists these structures on disk using a noSQL engine. It inherently supports the parallel processing of distributed queries, is highly extensible, and is easy to use.We will demonstrate LSHDB both as the underlying system for detecting similar records in the context of Record Linkage (and of Privacy-Preserving Record Linkage) tasks, as well as a search engine for identifying string values that are similar to submitted queries.


Peer-to-Peer Botnets: Overview and Case Study

Botnets have recently been identified as one of the most important threats to the security of the Internet. Traditionally, botnets organize themselves in an hierarchical manner with a central command and control location. This location can be statically defined in the bot, or it can be dynamically defined based on a directory server. Presently, the centralized characteristic of botnets is us eful to security professionals because it offers a central point of failure for the botnet. In the near future, we believe attackers will move to more resilient architectures. In particular, one class of botnet structure that has entered initial stages of development is peer-to-peer based architectures. In this paper, we present an overview of peer-to-peer botnets. We also present a case study of a Kademlia-based Trojan.Peacomm bot.


Gatekeeper: Monitoring Auto-Start Extensibility Points (ASEPs) for Spyware Management

Spyware is a rapidly spreading problem for PC users causing significant impact on system stability and privacy concerns. It attaches to extensibility points in the system to ensure the spyware will be instantiated when the system starts. Users may willingly install free versions of software containing spyware as an alternative to paying for it. Traditional anti-virus techniques are less effective in this scenario because they lack the context to decide if the spyware should be removed. In this paper, we introduce Auto-Start Extensibility Points (ASEPs) as the key concept for modeling the spyware problem. By monitoring and grouping ‘‘hooking’’ operations made to the ASEPs, our Gatekeeper solution complements the traditional signature-based approach and provides a comprehensive framework for spyware management. We present ASEP hooking statistics for 120 real-world spyware programs. We also describe several techniques for discovering new ASEPs to further enhance the effectiveness of our solution.


Detecting stealth software with Strider GhostBuster

Stealth malware programs that silently infect enterprise and consumer machines are becoming a major threat to the future of the Internet. Resource hiding is a powerful stealth technique commonly used by malware to evade detection by computer users and anti-malware scanners. In this paper, we focus on a subclass of malware, termed "ghostware", which hide files, configuration settings, processes, and loaded modules from the operating system's query and enumeration application programming interfaces (APIs). Instead of targeting individual stealth implementations, we describe a systematic framework for detecting multiple types of hidden resources by leveraging the hiding behavior as a detection mechanism. Specifically, we adopt a cross-view diff-based approach to ghostware detection by comparing a high-level infected scan with a low-level clean scan and alternatively comparing an inside-the-box infected scan with an outside-the-box clean scan. We describe the design and implementation of the Strider GhostBuster tool and demonstrate its efficiency and effectiveness in detecting resources hidden by real-world malware such as rootkits, Trojans, and key-loggers.


A multifaceted approach to understanding the botnet phenomenon

The academic community has long acknowledged the existence of malicious botnets, however to date, very little is known about the behavior of these distributed computing platforms. To the best of our knowledge, botnet behavior has never been methodically studied, botnet prevalence on the Internet is mostly a mystery, and the botnet life cycle has yet to be modeled. Uncertainty abounds. In this paper, we attempt to clear the fog surrounding botnets by constructing a multifaceted and distributed measurement infrastructure. Throughout a period of more than three months, we used this infrastructure to track 192 unique IRC botnets of size ranging from a few hundred to several thousand infected end-hosts. Our results show that botnets represent a major contributor to unwanted Internet traffic - 27% of all malicious connection attempts observed from our distributed darknet can be directly attributed to botnet-related spreading activity. Furthermore, we discovered evidence of botnet infections in 11% of the 800,000 DNS domains we examined, indicating a high diversity among botnet victims. Taken as a whole, these results not only highlight the prominence of botnets, but also provide deep insights that may facilitate further research to curtail this phenomenon.


Modeling Botnet Propagation Using Time Zones

Time zones play an important and unexplored role in malware epidemics. To understand how time and location affect malware spread dynamics, we studied botnets, or large coordinated collections of victim machines (zombies) controlled by attackers. Over a six month period we observed dozens of botnets representing millions of victims. We noted diurnal properties in botnet activity, which we suspect occurs because victims turn their computers off at night. Through binary analysis, we also confirmed that some botnets demonstrated a bias in infecting regional populations. Clearly, computers that are offline are not infectious, and any regional bias in infections will affect the overall growth of the botnet. We therefore created a diurnal propagation model. The model uses diurnal shaping functions to capture regional variations in online vulnerable populations. The diurnal model also lets one compare propagation rates for different botnets, and prioritize response. Because of variations in release times and diurnal shaping functions particular to an infection, botnets released later in time may actually surpass other botnets that have an advanced start. Since response times for malware outbreaks is now measured in hours, being able to predict short-term propagation dynamics lets us allocate resources more intelligently. We used empirical data from botnets to evaluate the analytical model.


Structural description and classification of signature images

-This, paper introduces a new method for describing signature images. The description involves features of the signatures and relations among its parts organized in a hierarchical structure. The description is used to classify the signatures and globally analyse their structure. Description Segmentation Feature extraction Classification Signature analysis I. I N T R O D U C T I O N Although a lot of research on signatures has been reported in the past 20 years (t) they are mainly confined to verification in which a set of goal oriented features is used to give the decision about the authenticity of the claimed signature. However, it is interesting to be able to access a signature database using a compact and meaningful description derived from its pictorial content. Constructing such a signature database, requires an automatic description of signature images. Researches on signature description are very limited and appeared implicitly in verification researches. In on-line systems, Lamarche and Plamondon (z) reconsidered the theoretical model of the handwriting process introduced by Verdenberg and Koster <3) to describe the pen tip speed and orientation and used this description for feature extraction. In off-line systems, Nagel and Rosenfeld (̀ ~ modified the model introduced by Eden (~) for describing upright handwriting by adding the slant parameter and used the parameters of the modified model as a basis for choosing their features for verification, and Brocklehurst (6) used data reduction techniques to encode the signatures into a list of 7 features (called encoded description) and used them for verification. In all cases, neither a structural description of the signature as a 2-D image nor a conceptualization of the features was proposed. In this paper, we introduce a method for signature description and a reliable approach that can describe digitized signature images by this method. The description uses a hierarchical approach, i.e. the signature is at the top level and each lower level gives more detail about the higher one. Since the range of the natural variation of signature writing will change with time as found by Kapoor <~ and Evett, (s) and the ratios of the writing parameters for different samples of the same individual's writing remain essentially constant over a wide range of the writing surface and space available, ('~ features and relations of o u r description are extracted in a relative way (e.g. the length is measured by its ratio to the width, and so on) to attempt to make it insensitive to the natural variation in the genuine signatures. The signature is described using a global description suitable for purposes like classifying the signature data and studying its nature, and a local description convenient for applications which require more detailed information about the signature like signature analysis or similarity oriented retrieval. The global description is represented by a character string, and the local description is represented by a tree structure. In Section 2, the general approach for describing signature images is introduced, and Section 3 introduces the practical method of the description. Section 4 presents the application of the global description to classifying and analysing the signature data, and finally, Section 5 contains the closing remarks. 2. SIGNATURE DESCRIPTION Signatures are a special case of handwriting in which special characters and flourishes are available, and the ratio between the middle zone width and the letter heights is sometimes abnormal. In many cases, the signature is not readable even by a human. Therefore, we deal with the signature as an image that has the principal zones (upper, middle and lower zones) of the handwriting, not as strokes, letters and words. As indicated by Rosenfeld, (9) description of a picture involves properties of the picture or its parts and relationships among them. Our description involves features like slant, and relations among signature properties like the comparison between the widths of the zones. The description is not reversible, that is, the signature cannot be reconstructed from its description. However, the description involves sufficient information to characterize the signature and its parts. Features and relations are extracted quantitatively, and then mapped into a conceptual


Analysis of the wrench-closure workspace of planar parallel cable-driven mechanisms

The mobile platform of a parallel cable-driven mechanism is connected in parallel to a base by lightweight links, such as cables. Since the cables can only work in tension, the set of poses of the mobile platform for which the cables can balance any external wrench, i.e., for which the platform of the mechanism is fully constrained, is often limited or even nonexistent. Thus, the study and determination of this set of poses, called the wrench-closure workspace (WCW), is an important issue for parallel cable-driven mechanisms. In this paper, the case of planar parallel cable-driven mechanisms is addressed. Theorems that characterize the poses of the WCW are proposed. Then, these theorems are used to disclose the parts of the reachable workspace which belong to the WCW. Finally, an efficient algorithm that determines the constant-orientation cross-sections of these parts is introduced.


Additive lattice kirigami

Kirigami uses bending, folding, cutting, and pasting to create complex three-dimensional (3D) structures from a flat sheet. In the case of lattice kirigami, this cutting and rejoining introduces defects into an underlying 2D lattice in the form of points of nonzero Gaussian curvature. A set of simple rules was previously used to generate a wide variety of stepped structures; we now pare back these rules to their minimum. This allows us to describe a set of techniques that unify a wide variety of cut-and-paste actions under the rubric of lattice kirigami, including adding new material and rejoining material across arbitrary cuts in the sheet. We also explore the use of more complex lattices and the different structures that consequently arise. Regardless of the choice of lattice, creating complex structures may require multiple overlapping kirigami cuts, where subsequent cuts are not performed on a locally flat lattice. Our additive kirigami method describes such cuts, providing a simple methodology and a set of techniques to build a huge variety of complex 3D shapes.


Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson's method

Within-subject ANOVAs are a powerful tool to analyze data because the variance associated to differences between the participants is removed from the analysis. Hence, small differences, when present for most of the participants, can be significant even when the participants are very different from one another. Yet, graphs showing standard error or confidence interval bars are misleading since these bars include the between-subject variability. Loftus and Masson (1994) noticed this fact and proposed an alternate method to compute the error bars. However, i) their approach requires that the ANOVA be performed first, which is paradoxical since a graph is an aid to decide whether to perform analyses or not; ii) their method provides a single error bar for all the conditions, masking information such as the heterogeneity of variances across conditions; iii) the method proposed is difficult to implement in commonly-used graphing software. Here we propose a simple alternative and sow how it can be implemented in SPSS.


Decision Support Systems (DSS) in Construction Tendering Processes

The successful execution of a construction project is heavily impacted by making the right decision during tendering processes. Managing tender procedures is very complex and uncertain involving coordination of many tasks and individuals with different priorities and objectives. Bias and inconsistent decision are inevitable if the decision-making process is totally depends on intuition, subjective judgement or emotion. In making transparent decision and healthy competition tendering, there exists a need for flexible guidance tool for decision support. Aim of this paper is to give a review on current practices of Decision Support Systems (DSS) technology in construction tendering processes. Current practices of general tendering processes as applied to the most countries in different regions such as United States, Europe, Middle East and Asia are comprehensively discussed. Applications of Web-based tendering processes is also summarised in terms of its properties. Besides that, a summary of Decision Support System (DSS) components is included in the next section. Furthermore, prior researches on implementation of DSS approaches in tendering processes are discussed in details. Current issues arise from both of paper-based and Web-based tendering processes are outlined. Finally, conclusion is included at the end of this paper.


An empirical resource for discovering cognitive principles of discourse organisation: the ANNODIS corpus

This paper describes the ANNODIS resource, a discourse-level annotated corpus for French. The corpus combines two perspectives on discourse: a bottom-up approach and a top-down approach. The bottom-up view incrementally builds a structure from elementary discourse units, while the top-down view focuses on the selective annotation of multi-level discourse structures. The corpus is composed of texts that are diversified with respect to genre, length and type of discursive organisation. The methodology followed here involves an iterative design of annotation guidelines in order to reach satisfactory inter-annotator agreement levels. This allows us to raise a few issues relevant for the comparison of such complex objects as discourse structures. The corpus also serves as a source of empirical evidence for discourse theories. We present here two first analyses taking advantage of this new annotated corpus, one that tested hypotheses on constraints governing discourse structure, and another that studied the variations in composition and signalling of multi-level discourse structures.


The slot-coupled hemispherical dielectric resonator antenna with a parasitic patch: applications to the circularly polarized antenna and wide-band antenna

The aperture-coupled hemispherical dielectric resonator antenna (DRA) with a parasitic patch is studied rigorously. Using the Green's function approach, integral equations for the unknown patch and slot currents are formulated and solved using the method of moments. The theory is utilized to design a circularly polarized (CP) DRA and a wide-band linearly polarized (LP) DRA. In the former, the CP frequency and axial ratio (AR) can easily be controlled by the patch location and patch size, respectively, with the impedance matched by varying the slot length and microstrip stub length. It is important that the AR will not be affected when the input impedance is tuned, and the CP design is therefore greatly facilitated. For the wide-band LP antenna, a maximum bandwidth of 22% can be obtained, which is much wider than the previous bandwidth of 7.5% with no parasitic patches. Finally, the frequency-tuning characteristics of the proposed antenna are discussed. Since the parasitic patch can be applied to any DRAs, the method will find applications in practical DRA designs.


Measuring Urban Social Diversity Using Interconnected Geo-Social Networks

Large metropolitan cities bring together diverse individuals, creating opportunities for cultural and intellectual exchanges, which can ultimately lead to social and economic enrichment. In this work, we present a novel network perspective on the interconnected nature of people and places, allowing us to capture the social diversity of urban locations through the social network and mobility patterns of their visitors. We use a dataset of approximately 37K users and 42K venues in London to build a network of Foursquare places and the parallel Twitter social network of visitors through check-ins. We define four metrics of the social diversity of places which relate to their social brokerage role, their entropy, the homogeneity of their visitors and the amount of serendipitous encounters they are able to induce. This allows us to distinguish between places that bring together strangers versus those which tend to bring together friends, as well as places that attract diverse individuals as opposed to those which attract regulars. We correlate these properties with wellbeing indicators for London neighbourhoods and discover signals of gentrification in deprived areas with high entropy and brokerage, where an influx of more affluent and diverse visitors points to an overall improvement of their rank according to the UK Index of Multiple Deprivation for the area over the five-year census period. Our analysis sheds light on the relationship between the prosperity of people and places, distinguishing between different categories and urban geographies of consequence to the development of urban policy and the next generation of socially-aware location-based applications.


SLING: A framework for frame semantic parsing

We describe SLING, a framework for parsing natural language into semantic frames. SLING supports general transition-based, neural-network parsing with bidirectional LSTM input encoding and a Transition Based Recurrent Unit (TBRU) for output decoding. The parsing model is trained end-to-end using only the text tokens as input. The transition system has been designed to output frame graphs directly without any intervening symbolic representation. The SLING framework includes an efficient and scalable frame store implementation as well as a neural network JIT compiler for fast inference during parsing. SLING is implemented in C++ and it is available for download on GitHub.


Context-Sensitive Flow Graph and Projective Single Assignment Form for Resolving Context-Dependency of Binary Code

Program analysis on binary code is considered as difficult because one has to resolve destinations of indirect jumps. However, there is another difficulty of context-dependency that matters when one processes binary programs that are not compiler generated. In this paper, we propose a novel approach for tackling these difficulties and describe a way to reconstruct a control flow from a binary program with no extra assumptions than the operational meaning of machine instructions.


Human-in-the-Loop Challenges for Entity Matching: A Midterm Report

Entity matching (EM) has been a long-standing challenge in data management. In the past few years we have started two major projects on EM (Magellan and Corleone/Falcon). These projects have raised many human-in-the-loop (HIL) challenges. In this paper we discuss these challenges. In particular, we show how these challenges forced us to revise our solution architecture, from a typical RDBMS-style architecture to a very human-centric one, in which human users are first-class objects driving the EM process, using tools at pain-point places. We discuss how such solution architectures can be viewed as combining "tools in the loop" with "human in the loop". Finally, we discuss lessons learned which can potentially be applied to other problem settings. We also hope that more researchers will investigate EM, as it can be a rich "playground" for HIL research.


ReticularSpaces: activity-based computing support for physically distributed and collaborative smart spaces

Smart spaces research focuses on technology for multiple displays and devices for collocated participants. In most approaches, however, users have to cope with heterogeneous interfaces and information organization, as well as a lack of support for collaboration with mobile and remote users outside the smart space. In this paper, we present ReticularSpaces; a multi-display smart space system built on the principles of activity-based computing. The focus of ReticularSpaces is to support: (i) unified interaction with applications and documents through ReticularUI, a novel distributed user interfaces design; (ii) management of the complexity of tasks between users and displays; (iii) mobile users in a local, remote or 'nomadic' settings; and (iv) collaboration among local and remote users. We describe the motivation, design, and architecture of ReticularSpaces, and report from a preliminary feasibility study. The study shows that participants found ReticularSpaces useful and effective, but at the same time reveals new areas for research on smart environments.


Advanced control strategies for balancing LED usage of AC LED driver

This paper presents two enhanced methods to balance LEDs energy usage in the AC LED Driver. While conventional AC LED driver has energy consumption imbalance, the proposed control methods provide equally distributed energy usage. First method is to apply the appropriate firing voltage that is the threshold voltage for equalizing energy consumption of LEDs. This method improves balancing energy consumption within a rectified line cycle. The other method is to change the order of LEDs turning on sequence by every two rectified line cycle. These two approaches are compared with mathematical analysis in terms of the energy consumption of each LED. A prototype driver with three 12 W-LED strings is built. The proposed method 1 and method 2 achieve 0.0030 J and 0.0033 J standard deviation of energy consumption, respectively, compared to the 0.0070 J deviation for the conventional approach.


Deep convolutional neural network based large-scale oil palm tree detection for high-resolution remote sensing images

This paper proposed a deep convolutional neural network (DCNN) based framework for large-scale oil palm tree detection using high-resolution remote sensing images in Malaysia. Different from the previous palm tree or tree crown detection studies, the palm trees in our study area are very crowded and their crowns often overlap. Moreover, there are various land cover types in our study area, e.g. impervious, bare land, and other vegetation, etc. The main steps of our proposed method include large-scale and multi-class sample collection, AlexNet-based DCNN training and optimization, sliding window-based label prediction, and post-processing. Compared with the manually interpreted ground truth, our proposed method achieves detection accuracies of 92%–97% in our study area, which are greatly higher than the accuracies obtained from another two detection methods used in this paper.


Multiple asynchronous stimulus- and task-dependent hierarchies (STDH) within the visual brain's parallel processing systems.

Results from a variety of sources, some many years old, lead ineluctably to a re-appraisal of the twin strategies of hierarchical and parallel processing used by the brain to construct an image of the visual world. Contrary to common supposition, there are at least three 'feed-forward' anatomical hierarchies that reach the primary visual cortex (V1) and the specialized visual areas outside it, in parallel. These anatomical hierarchies do not conform to the temporal order with which visual signals reach the specialized visual areas through V1. Furthermore, neither the anatomical hierarchies nor the temporal order of activation through V1 predict the perceptual hierarchies. The latter shows that we see (and become aware of) different visual attributes at different times, with colour leading form (orientation) and directional visual motion, even though signals from fast-moving, high-contrast stimuli are among the earliest to reach the visual cortex (of area V5). Parallel processing, on the other hand, is much more ubiquitous than commonly supposed but is subject to a barely noticed but fundamental aspect of brain operations, namely that different parallel systems operate asynchronously with respect to each other and reach perceptual endpoints at different times. This re-assessment leads to the conclusion that the visual brain is constituted of multiple, parallel and asynchronously operating task- and stimulus-dependent hierarchies (STDH); which of these parallel anatomical hierarchies have temporal and perceptual precedence at any given moment is stimulus and task related, and dependent on the visual brain's ability to undertake multiple operations asynchronously.


From Theory to Experimental Evaluation: Resource Management in Software-Defined Vehicular Networks

Managing resources in dynamic vehicular environments is a tough task, which is becoming more challenging with the increased number of access technologies today available in connected cars (e.g., IEEE 802.11, LTE), in the variety of applications provided on the road (e.g., safety, traffic efficiency, and infotainment), in the amount of driving awareness/coordination required (e.g., local, context, and cooperative awareness), and in the level of automation toward zero-accident driving (e.g., platooning and autonomous driving). The open programmability and logically centralized control features of the software–defined networking (SDN) paradigm offer an attractive means to manage communication and networking resources in the vehicular environment and promise improved performance. In this paper, we enumerate the potentials of software-defined vehicular networks, analyze the need to rethink the traditional SDN approach from theoretical and practical standpoints when applied in this application context, and present an emulation approach based on the proposed node car architecture in Mininet-WiFi to showcase the applicability and some expected benefits of SDN in a selected use case scenario.


Violent and nonviolent video games differentially affect physical aggression for individuals high vs. low in dispositional anger.

Although numerous experiments have shown that exposure to violent video games (VVG) causes increases in aggression, relatively few studies have investigated the extent to which this effect differs as a function of theoretically relevant individual difference factors. This study investigated whether video game content differentially influences aggression as a function of individual differences in trait anger. Participants were randomly assigned to play a violent or nonviolent video game before completing a task in which they could behave aggressively. Results showed that participants high in trait anger were the most aggressive, but only if they first played a VVG. This relationship held while statistically controlling for dimensions other than violent content on which game conditions differed (e.g. frustration, arousal). Implications of these findings for models explaining the effects of video games on behavior are discussed.


Avoiding, finding and fixing spreadsheet errors - A survey of automated approaches for spreadsheet QA

Spreadsheet programs can be found everywhere in organizations and they are used for a variety of purposes, including financial calculations, planning, data aggregation and decision making tasks. A number of research surveys have however shown that such programs are particularly prone to errors. Some reasons for the error-proneness of spreadsheets are that spreadsheets are developed by end users and that standard software quality assurance processes are mostly not applied. Correspondingly, during the last two decades, researchers have proposed a number of techniques and automated tools aimed at supporting the end user in the development of error-free spreadsheets. In this paper, we provide a review of the research literature and develop a classification of automated spreadsheet quality assurance (QA) approaches, which range from spreadsheet visualization, static analysis and quality reports, over testing and support to model-based spreadsheet development. Based on this review, we outline possible opportunities for future work in the area of automated spreadsheet QA.


Applying High-Resolution Visible Imagery to Satellite Melt Pond Fraction Retrieval: A Neural Network Approach

During summer, melt ponds have a significant influence on Arctic sea-ice albedo. The melt pond fraction (MPF) also has the ability to forecast the Arctic sea-ice in a certain period. It is important to retrieve accurate melt pond fraction (MPF) from satellite data for Arctic research. This paper proposes a satellite MPF retrieval model based on the multi-layer neural network, named MPF-NN. Our model uses multi-spectral satellite data as model input and MPF information from multi-site and multiperiod visible imagery as prior knowledge for modeling. It can effectively model melt ponds evolution of different regions and periods over the Arctic. Evaluation results show that the MPF retrieved from MODIS data using the proposed model has an RMSE of 3.91% and a correlation coefficient of 0.73. The seasonal distribution of MPF is also consistent with previous results.


Multimaterial 3D Printing of Graphene-Based Electrodes for Electrochemical Energy Storage Using Thermoresponsive Inks.

The current lifestyles, increasing population, and limited resources result in energy research being at the forefront of worldwide grand challenges, increasing the demand for sustainable and more efficient energy devices. In this context, additive manufacturing brings the possibility of making electrodes and electrical energy storage devices in any desired three-dimensional (3D) shape and dimensions, while preserving the multifunctional properties of the active materials in terms of surface area and conductivity. This paves the way to optimized and more efficient designs for energy devices. Here, we describe how three-dimensional (3D) printing will allow the fabrication of bespoke devices, with complex geometries, tailored to fit specific requirements and applications, by designing water-based thermoresponsive inks to 3D-print different materials in one step, for example, printing the active material precursor (reduced chemically modified graphene (rCMG)) and the current collector (copper) for supercapacitors or anodes for lithium-ion batteries. The formulation of thermoresponsive inks using Pluronic F127 provides an aqueous-based, robust, flexible, and easily upscalable approach. The devices are designed to provide low resistance interface, enhanced electrical properties, mechanical performance, packing of rCMG, and low active material density while facilitating the postprocessing of the multicomponent 3D-printed structures. The electrode materials are selected to match postprocessing conditions. The reduction of the active material (rCMG) and sintering of the current collector (Cu) take place simultaneously. The electrochemical performance of the rCMG-based self-standing binder-free electrode and the two materials coupled rCMG/Cu printed electrode prove the potential of multimaterial printing in energy applications.


Knowledge management and organizational culture: a theoretical integrative framework

Purpose – Organizational culture is a critical factor in building and reinforcing knowledge management in organizations. However, there is no theoretical framework that comprehensively explains the effect of organizational culture on knowledge management in organizations. This paper endeavors to develop a theoretical integrative framework for organizational knowledge management and organizational culture. Design/methodology/approach – This is a conceptual paper. It modifies the ‘‘competing value framework’’ by adding a new dimension representing ethical and trusting culture, and then integrates it with the SECI model of knowledge creation and conversion by identifying the conceptual parallels between the two frameworks and then analyzing the interaction effects among the dimensions. Findings – Based on the congruity between the modified competing values framework and the knowledge creation and conversion framework, the paper formulates six propositions about the propensity of organizations of different dominant cultural styles to engage in the four processes of knowledge creation and conversion. Research limitations/implications – The dynamic nature of the framework presented in the paper points to the importance of longitudinal and comparative research in understanding the effects of organizational culture on organizational knowledge management systems in organizations. Practical implications – The proposed integrative framework would facilitate organizational learning and lead to the improvement of knowledge management practices in organizations as it helps managers to understand the linkages between culture and knowledge management. Originality/value – This paper presents a new framework linking organizational culture to knowledge management. It moves away from analyzing culture only in terms of its positive and negative influences on knowledge management. Instead, it suggests a typology of the kind of knowledge management processes that organizations are likely to focus on depending on the culture that prevails in an organization.


Improving lip-reading performance for robust audiovisual speech recognition using DNNs

This paper presents preliminary experiments using the Kaldi toolkit [1] to investigate audiovisual speech recognition (AVSR) in noisy environments using deep neural networks (DNNs). In particular we use a single-speaker large vocabulary, continuous audiovisual speech corpus to compare the performance of visual-only, audio-only and audiovisual speech recognition. The models trained using the Kaldi toolkit are compared with the performance of models trained using conventional hidden Markov models (HMMs). In addition, we compare the performance of a speech recognizer both with and without visual features over nine different SNR levels of babble noise ranging from 20dB down to -20dB. The results show that the DNN outperforms conventional HMMs in all experimental conditions, especially for the lip-reading only system, which achieves a gain of 37.19% accuracy (84.67% absolute word accuracy). Moreover, the DNN provides an effective improvement of 10 and 12dB SNR respectively for both the single modal and bimodal speech recognition systems. However, integrating the visual features using simple feature fusion is only effective in SNRs at 5dB and above. Below this the degradion in accuracy of an audiovisual system is similar to the audio only recognizer.


Real-Time Classification of Dance Gesturesfrom Skeleton Animation

We present a real-time gesture classification system for skeletal wireframe motion. Its key components include an angular representation of the skeleton designed for recognition robustness under noisy input, a cascaded correlation-based classifier for multivariate time-series data, and a distance metric based on dynamic time-warping to evaluate the difference in motion between an acquired gesture and an oracle for the matching gesture. While the first and last tools are generic in nature and could be applied to any gesture-matching scenario, the classifier is conceived based on the assumption that the input motion adheres to a known, canonical time-base: a musical beat. On a benchmark comprising 28 gesture classes, hundreds of gesture instances recorded using the XBOX Kinect platform and performed by dozens of subjects for each gesture class, our classifier has an average accuracy of 96:9%, for approximately 4-second skeletal motion recordings. This accuracy is remarkable given the input noise from the real-time depth sensor.


Passive in-home measurement of stride-to-stride gait variability comparing vision and Kinect sensing

We present an analysis of measuring stride-to-stride gait variability passively, in a home setting using two vision based monitoring techniques: anonymized video data from a system of two web-cameras, and depth imagery from a single Microsoft Kinect. Millions of older adults fall every year. The ability to assess the fall risk of elderly individuals is essential to allowing them to continue living safely in independent settings as they age. Studies have shown that measures of stride-to-stride gait variability are predictive of falls in older adults. For this analysis, a set of participants were asked to perform a number of short walks while being monitored by the two vision based systems, along with a marker based Vicon motion capture system for ground truth. Measures of stride-to-stride gait variability were computed using each of the systems and compared against those obtained from the Vicon.


Older adults' attitudes towards and perceptions of "smart home" technologies: a pilot study.

PRIMARY OBJECTIVE
The study aim is to explore the perceptions and expectations of seniors in regard to "smart home" technology installed and operated in their homes with the purpose of improving their quality of life and/or monitoring their health status.

RESEARCH DESIGN AND METHODS
Three focus group sessions were conducted within this pilot study to assess older adults' perceptions of the technology and ways they believe technology can improve their daily lives. Themes discussed in these groups included participants' perceptions of the usefulness of devices and sensors in health-related issues such as preventing or detecting falls, assisting with visual or hearing impairments, improving mobility, reducing isolation, managing medications, and monitoring of physiological parameters. The audiotapes were transcribed and a content analysis was performed.

RESULTS
A total of 15 older adults participated in three focus group sessions. Areas where advanced technologies would benefit older adult residents included emergency help, prevention and detection of falls, monitoring of physiological parameters, etc. Concerns were expressed about the user-friendliness of the devices, lack of human response and the need for training tailored to older learners.

CONCLUSIONS
All participants had an overall positive attitude towards devices and sensors that can be installed in their homes in order to enhance their lives.


Fast Subsequence Matching in Time-Series Databases

We present an efficient indexing method to locate 1-dimensional subsequences within a collection of sequences, such that the subsequences match a given (query) pattern within a specified tolerance. The idea is to map each data sequences into a small set of multidimensional rectangles in feature space. Then, these rectangles can be readily indexed using traditional spatial access methods, like the R*-tree [9]. In more detail, we use a sliding window over the data sequence and extract its features; the result is a trail in feature space. We propose an efficient and effective algorithm to divide such trails into sub-trails, which are subsequently represented by their Minimum Bounding Rectangles (MBRs). We also examine queries of varying lengths, and we show how to handle each case efficiently. We implemented our method and carried out experiments on synthetic and real data (stock price movements). We compared the method to sequential scanning, which is the only obvious competitor. The results were excellent: our method accelerated the search time from 3 times up to 100 times.


The Recognition of Human Movement Using Temporal Templates

ÐA new view-based approach to the representation and recognition of human movement is presented. The basis of the representation is a temporal templateÐa static vector-image where the vector value at each point is a function of the motion properties at the corresponding spatial location in an image sequence. Using aerobics exercises as a test domain, we explore the representational power of a simple, two component version of the templates: The first value is a binary value indicating the presence of motion and the second value is a function of the recency of motion in a sequence. We then develop a recognition method matching temporal templates against stored instances of views of known actions. The method automatically performs temporal segmentation, is invariant to linear changes in speed, and runs in real-time on standard platforms. Index TermsÐMotion recognition, computer vision.


Churn Prediction

The rapid growth of the market in every sector is leading to a bigger subscriber base for service providers. More competitors, new and innovative business models and better services are increasing the cost of customer acquisition. In this environment service providers have realized the importance of the retention of existing customers. Therefore, providers are forced to put more efforts for prediction and prevention of churn. This paper aims to present commonly used data mining techniques for the identification of churn. Based on historical data these methods try to find patterns which can point out possible churners. Well-known techniques used for this are Regression analysis, Decision Trees, Neural Networks and Rule based learning. In section 1 we give a short introduction describing the current state of the market, then in section 2 a definition of customer churn, its’ types and the imporance of identification of churners is being discussed. Section 3 reviews different techniques used, pointing out advantages and disadvantages. Finally, current state of research and new emerging algorithms are being presented.


High speed operation of electrical machines, a review on technology, benefits and challenges

The High Speed (HS) technology, in electrical machine based drive systems, is reviewed in this paper by investigating around 200 different machine's data that are reported in the literature or open sources. This study includes information regarding clarifying the maximum power vs. speed capability, potential applications, different machine types, design limitation issues consisting of thermal, electro-magnetic, mechanical, control, inverter and power electronics. The limitations of electrical machines in HS technology are classified by introducing HS-index, which is the product of the machine nominal power and nominal speed (= n × Pout). It is shown that HS-index behavior changes by speed, because limitation factors for HS power capability take different physics/nature, as speed increases. It is found that in relatively low speeds, n <; 20 krpm, maximum mechanical stress in the rotor, and in high speeds, n > 20 krpm, additional losses in the machine dictates the maximum power capability. The survey shows that Induction Machine (IM) and Permanent Magnet (PM) machine have relatively superior performance. The maximum rotor surface speed, vmax, is limited to 250-300 m/s (can reach 550 m/s) and is independent of n and output power. The HS-index is a function of vmax2 and almost independent of n for n <; 10-20 krpm. However, for n > 20 krpm is proportional to vmax3/n2 due to thermal limitations. The maximum power is almost cubically (Pout ∝ d2.6) proportional to the rotor diameter.


Limitations of the approximation capabilities of neural networks with one hidden layer

Let s/> 1 be an integer and W be the class of all functions having integrable partial derivatives on [0, 1] s. We are interested in the minimum number of neurons in a neural network with a single hidden layer required in order to provide a mean approximation order of a preassigned e > 0 to each function in W. We prove that this number cannot be O(e -s log(I/e)) if a spline-like localization is required. This cannot be improved even if one allows different neurons to evaluate different activation functions, even depending upon the target function. Nevertheless, for any 6 > 0, a network with O(c -s-6) neurons can be constructed to provide this order of approximation, with localization. Analogous results are also valid for other L p n o r m s .


A 2.5-Gb/s 15-mW clock recovery circuit

This paper describes the design of a 2.5-Gb/s 15mW clock recovery circuit based on the quadricorrelator architecture. Employing both phase and frequency detection, the circuit combines high-speed operations such as differentiation, full-wave rectification, and mixing in one stage to lower the power dissipation. In addition, a two-stage voltage-controlled oscillator is utilized that incorporates both phase shift elements to provide a wide tuning range and isolation techniques to suppress the feedthrough due to input data transitions. Fabricated in a 20GHz 1m BiCMOS technology, the circuit exhibits an rms jitter of 9.5 ps and a capture range of 300 MHz.


A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity

The key challenge in multiagent learning is learning a best response to the behaviour of other agents, which may be non-stationary: if the other agents adapt their strategy as well, the learning target moves. Disparate streams of research have approached nonstationarity from several angles, which make a variety of implicit assumptions that make it hard to keep an overview of the state of the art and to validate the innovation and significance of new works. This survey presents a coherent overview of work that addresses opponent-induced non-stationarity with tools from game theory, reinforcement learning and multi-armed bandits. Further, we reflect on the principle approaches how algorithms model and cope with this non-stationarity, arriving at a new framework and five categories (in increasing order of sophistication): ignore, forget, respond to target models, learn models, and theory of mind. A wide range of state-of-the-art algorithms is classified into a taxonomy, using these categories and key characteristics of the environment (e.g., observability) and adaptation behaviour of the opponents (e.g., smooth, abrupt). To clarify even further we present illustrative variations of one domain, contrasting the strengths and limitations of each category. Finally, we discuss in which environments the different approaches yield most merit, and point to promising avenues of future research.


Neural Machine Translation for Query Construction and Composition

Research on question answering with knowledge base has recently seen an increasing use of deep architectures. In this extended abstract, we study the application of the neural machine translation paradigm for question parsing. We employ a sequence-to-sequence model to learn graph patterns in the SPARQL graph query language and their compositions. Instead of inducing the programs through question-answer pairs, we expect a semi-supervised approach, where alignments between questions and queries are built through templates. We argue that the coverage of language utterances can be expanded using late notable works in natural language generation.


E-government implementation: A bird's eye view of issues relating to costs, opportunities, benefits and risks

After more than a decade of comprehensive research work in the area of electronic government (e-government), no attempt has yet been made to undertake a systematic literature review on the costs, opportunities, benefits and risks that influence the implementation of e-government. This is particularly significant given the various related challenges that governments have faced over the years when implementing e-government initiatives. Hence, the aim of this paper is to undertake a comprehensive analysis of relevant literature addressing these issues using a systematic review of 132 studies identified from the Scopus online database and Google Scholar together with a manual review of relevant papers from journals dedicated to electronic government research such as Electronic Government, an International Journal (EGIJ), International Journal of Electronic Government Research (IJEGR) and Transforming Government: People, Process, and Policy (TGPPP). The overall review indicated that although a large number of papers discuss costs, opportunities, benefits and risks, treatment of these issues have tended to be superficial.Moreover, there is a lack of empirical studies which can statistically evaluate the performance of these constructs in relation to the various egovernment systems. Therefore, this research would help governments to better analyse the impact of costs, opportunities, benefits and risks on the success of e-government systems and its pre-adoption from an implementation perspective.


VOLAP: A Scalable Distributed Real-Time OLAP System for High-Velocity Data

This paper presents VelocityOLAP (VOLAP), a distributed real-time OLAP system for high-velocity data. VOLAP makes use of dimension hierarchies, is highly scalable, exploits both multi-core and multi-processor parallelism, and can guarantee serializable execution of insert and query operations. In contrast to other high performance OLAP systems such as SAP HANA or IBM Netezza that rely on vertical scaling or special purpose hardware, VOLAP supports cost-efficient horizontal scaling on commodity hardware or modest cloud instances. Experiments on 20 Amazon EC2 nodes with TPC-DS data show that VOLAP is capable of bulk ingesting data at over 600 thousand items per second, and processing streams of interspersed insertions and aggregate queries at a rate of approximately 50 thousand insertions and 20 thousand aggregate queries per second with a database of 1 billion items. VOLAP is designed to support applications that perform large aggregate queries, and provides similar high performance for aggregations ranging from a few items to nearly the entire database.


Dynamics co-simulation of a type of spot welding robot by RecurDyn and Simulink

A co-simulation on virtual prototype of a type of spot welding robot has been accomplished by combining MATLAB/Simulink with RecurDyn. The control algorithm is executed under MATLAB to generate command forces, which are then exported to RecurDyn environment and applied to actuators of the virtual prototype at each cycle of time. Dynamic control on joint angles was accomplished through the co-simulation. Torques of each joint and the velocity of the clamp end were also acquired by co-simulation, which can give useful information for optimizing robot design.


A MATLAB-based Convolutional Neural Network Approach for Face Recognition System


DeepGaze II: Reading fixations from deep features trained on object recognition

Here we present DeepGaze II, a model that predicts where people look in images. The model uses the features from the VGG-19 deep neural network trained to identify objects in images. Contrary to other saliency models that use deep features, here we use the VGG features for saliency prediction with no additional fine-tuning (rather, a few readout layers are trained on top of the VGG features to predict saliency). The model is therefore a strong test of transfer learning. After conservative crossvalidation, DeepGaze II explains about 87% of the explainable information gain in the patterns of fixations and achieves top performance in area under the curve metrics on the MIT300 hold-out benchmark. These results corroborate the finding from DeepGaze I (which explained 56% of the explainable information gain), that deep features trained on object recognition provide a versatile feature space for performing related visual tasks. We explore the factors that contribute to this success and present several informative image examples. A web service is available to compute model predictions at http://deepgaze.bethgelab.org.


Identifying Generalization Properties in Neural Networks

While it has not yet been proven, empirical evidence suggests that model generalization is related to local properties of the optima which can be described via the Hessian. We connect model generalization with the local property of a solution under the PAC-Bayes paradigm. In particular, we prove that model generalization ability is related to the Hessian, the higher-order “smoothness” terms characterized by the Lipschitz constant of the Hessian, and the scales of the parameters. Guided by the proof, we propose a metric to score the generalization capability of the model, as well as an algorithm that optimizes the perturbed model accordingly.


Evolution of Memory Architecture

Computer memories continue to serve the role that they first served in the electronic discrete variable automatic computer (EDVAC) machine documented by John von Neumann, namely that of supplying instructions and operands for calculations in a timely manner. As technology has made possible significantly larger and faster machines with multiple processors, the relative distance in processor cycles of this memory has increased considerably. Microarchitectural techniques have evolved to share this memory across ever-larger systems of processors with deep cache hierarchies and have managed to hide this latency for many applications, but are proving to be expensive and energy-inefficient for newer types of problems working on massive amounts of data. New paradigms include scale-out systems distributed across hundreds and even thousands of nodes, in-memory databases that keep data in memory much longer than the duration of a single task, and near-data computation, where some of the computation is off-loaded to the location of the data to avoid wasting energy in the movement of data. This paper provides a historical perspective on the evolution of memory architecture, and suggests that the requirements of new problems and new applications are likely to fundamentally change processor and system architecture away from the currently established von Neumann model.


Data Mining for Cyber Security

This chapter provides an overview of the Minnesota Intrusion Detection System (MINDS), which uses a suite of data mining based algorithms to address different aspects of cyber security. The various components of MINDS such as the scan detector, anomaly detector and the profiling module detect different types of attacks and intrusions on a computer network. The scan detector aims at detecting scans which are the percusors to any network attack. The anomaly detection algorithm is very effective in detecting behavioral anomalies in the network traffic which typically translate to malicious activities such as denial-of-service (DoS) traffic, worms, policy violations and inside abuse. The profiling module helps a network analyst to understand the characteristics of the network traffic and detect any deviations from the normal profile. Our analysis shows that the intrusions detected by MINDS are complementary to those of traditional signature based systems, such as SNORT, which implies that they both can be combined to increase overall attack coverage. MINDS has shown great operational success in detecting network intrusions in two live deployments at the University of Minnesota and as a part of the Interrogator architecture at the US Army Research Labs Center for Intrusion Monitoring and Protection (ARL-CIMP).


Bachelor Degree Project Hierarchical Temporal Memory Software Agent

Artificial general intelligence is not well defined, but attempts such as the recent list of “Ingredients for building machines that think and learn like humans” are a starting point for building a system considered as such [1]. Numenta is attempting to lead the new era of machine intelligence with their research to re-engineer principles of the neocortex. It is to be explored how the ingredients are in line with the design principles of their algorithms. Inspired by Deep Minds commentary about an autonomyingredient, this project created a combination of Numentas Hierarchical Temporal Memory theory and Temporal Difference learning to solve simple tasks defined in a browser environment. An open source software, based on Numentas intelligent computing platform NUPIC and Open AIs framework Universe, was developed to allow further research of HTM based agents on customized browser tasks. The analysis and evaluation of the results show that the agent is capable of learning simple tasks and there is potential for generalization inherent to sparse representations. However, they also reveal the infancy of the algorithms, not capable of learning dynamic complex problems, and that much future research is needed to explore if they can create scalable solutions towards a more general intelligent system.


Hashcash - Amortizable Publicly Auditable Cost-Functions

We present a distributed efficiently amortizable CPU cost-function with no trap–door. The absence of a trap–door allows us to avoid needing to trust any party. Applications for such cost-functions are in distributed document popularity estimation, and metering of web advertising. None of the servers involved have any advantage over users in computing the cost function. The amortized token has a small fixed sized representation independent of the amortized value. The valuation function is efficient. Limits can be placed on the resources which can be expended in computing the cost-function to prevent the user inflating the value of his contribution by using more CPU time than the expected token value. The amortized part of the cost result can be blinded so that clients can not obtain service but avoid contributing to the amortization by expending more resources than the expected token value. Interactive and non-interactive variants of the cost-function can be constructed which can be used in situations where the server can issue a challenge (connection oriented interactive protocol), and where it can't (where the communication is store–and–forward, or packet oriented) respectively.


Linear permanent magnet motor for reciprocating compressor applications

This paper investigates the performance of a three designs of tubular linear permanent magnet motors (TLPMMs) with quasi-Halbach magnetized moving-magnet armature and slotted stator with a single-slot. The motors were developed for reciprocating compressor applications such as household refrigerators. The three designs have different magnet shape structures, and the stator core of all designs have employed a soft magnetic composite (SMC) material, Somaloy 700. Based on finite element analysis (FEA) an extensively analysis on the opencircuit magnetic field distribution, back-EMF, thrust force, fluxlinkage, winding inductance and cogging force in the three designs have been established. The simulation results indicate the effectiveness of the designs to drive a linear reciprocating compressor in household refrigerators, and also the simulation results have shown the superiority of the two designs with T-shape and trapezoidal magnet configurations, over the TLPMM with conventional rectangular magnet. Moreover, the permanent magnets (PMs) array arrangement has significant effects on the motor performance.


Automatic Detection of Fake News

The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collection, annotation, and validation process in detail and present several exploratory analyses on the identification of linguistic differences in fake and legitimate news content. Second, we conduct a set of learning experiments to build accurate fake news detectors, and show that we can achieve accuracies of up to 76%. In addition, we provide comparative analyses of the automatic and manual identification of fake news.


Recovery of Interdependent Networks.

Recent network research has focused on the cascading failures in a system of interdependent networks and the necessary preconditions for system collapse. An important question that has not been addressed is how to repair a failing system before it suffers total breakdown. Here we introduce a recovery strategy for nodes and develop an analytic and numerical framework for studying the concurrent failure and recovery of a system of interdependent networks based on an efficient and practically reasonable strategy. Our strategy consists of repairing a fraction of failed nodes, with probability of recovery γ, that are neighbors of the largest connected component of each constituent network. We find that, for a given initial failure of a fraction 1 - p of nodes, there is a critical probability of recovery above which the cascade is halted and the system fully restores to its initial state and below which the system abruptly collapses. As a consequence we find in the plane γ - p of the phase diagram three distinct phases. A phase in which the system never collapses without being restored, another phase in which the recovery strategy avoids the breakdown, and a phase in which even the repairing process cannot prevent system collapse.


Locality Sensitive Hashing based deepmatching for optical flow estimation

DeepMatching (DM) is one of the state-of-art matching algorithms to compute quasi-dense correspondences between images. Recent optical flow methods use DeepMatching to find initial image correspondences and achieves outstanding performance. However, the key building block of DeepMatching, the correlation map computation, is time-consuming. In this paper, we propose a new algorithm, LSHDM, which addresses the problem by employing Locality Sensitive Hashing (LSH) to DeepMatching. The computational complexity is greatly reduced for the correlation map computation step. Experiments show that image matching can be accelerated by our approach in ten times or more compared to DeepMatching, while retaining comparable accuracy for optical flow estimation.


How Small Labels Create Big Improvements

It is widely believed that identifying communities in an ad hoc mobile communications system, such as a pocket switched network, can reduce the amount of traffic created when forwarding messages, but there has not been any empirical evidence available to support this assumption to date. In this paper, we show through use of real experimental human mobility data, how using a small label, identifying users according to their affiliation, can bring a large improvement in forwarding performance, in term of both delivery ratio and cost.


Reinforcement learning of clothing assistance with a dual-arm robot

This study aims at robotic clothing assistance as it is yet an open field for robotics despite it is one of the basic and important assistance activities in daily life of elderly as well as disabled people. The clothing assistance is a challenging problem since robots must interact with non-rigid clothes generally represented in a high-dimensional space, and with the assisted person whose posture can vary during the assistance. Thus, the robot is required to manage two difficulties to perform the task of the clothing assistance: 1) handling of non-rigid materials and 2) adaptation of the assisting movements to the assisted person's posture. To overcome these difficulties, we propose to use reinforcement learning with the cloth's state which is low-dimensionally represented in topology coordinates, and with the reward defined in the low-dimensional coordinates. With our developed experimental system, for T-shirt clothing assistance, including an anthropomorphic dual-arm robot and a soft mannequin, we demonstrate the robot quickly learns a suitable arm motion for putting the mannequin's head into a T-shirt.


Virtual peers as partners in storytelling and literacy learning

Literacy learning learning how to read and write, begins long before children enter school. One of the key skills to reading and writing is the ability to represent thoughts symbolically and share them in language with an audience who may not necessarily share the same temporal and spatial context for the story. Children learn and practice these important language skills everyday, telling stories with the peers and adults around them. In particular, storytelling in the context of peer collaboration provides a key environment for children to learn language skills important for literacy. In light of this, we designed Sam, an embodied conversational agent who tells stories collaboratively with children. Sam was designed to look like a peer for preschool children, but to tell stories in a developmentally advanced way: modeling narrative skills important for literacy. Results demonstrated that children who played with the virtual peer told stories that more closely resembled the virtual peer’s linguistically advanced stories: using more quoted speech and temporal and spatial expressions. In addition, children listened to Sam's stories carefully, assisting her and suggesting improvements. The potential benefits of having technology play a social role in young children’s literacy learning is discussed.


Diversifying search results

We study the problem of answering ambiguous web queries in a setting where there exists a taxonomy of information, and that both queries and documents may belong to more than one category according to this taxonomy. We present a systematic approach to diversifying results that aims to minimize the risk of dissatisfaction of the average user. We propose an algorithm that well approximates this objective in general, and is provably optimal for a natural special case. Furthermore, we generalize several classical IR metrics, including NDCG, MRR, and MAP, to explicitly account for the value of diversification. We demonstrate empirically that our algorithm scores higher in these generalized metrics compared to results produced by commercial search engines.


Automatic Extraction of Useful Facet Hierarchies from Text Databases

Databases of text and text-annotated data constitute a significant fraction of the information available in electronic form. Searching and browsing are the typical ways that users locate items of interest in such databases. Faceted interfaces represent a new powerful paradigm that proved to be a successful complement to keyword searching. Thus far, the identification of the facets was either a manual procedure, or relied on apriori knowledge of the facets that can potentially appear in the underlying collection. In this paper, we present an unsupervised technique for automatic extraction of facets useful for browsing text databases. In particular, we observe, through a pilot study, that facet terms rarely appear in text documents, showing that we need external resources to identify useful facet terms. For this, we first identify important phrases in each document. Then, we expand each phrase with ";context"; phrases using external resources, such as WordNet and Wikipedia, causing facet terms to appear in the expanded database. Finally, we compare the term distributions in the original database and the expanded database to identify the terms that can be used to construct browsing facets. Our extensive user studies, using the Amazon Mechanical Turk service, show that our techniques produce facets with high precision and recall that are superior to existing approaches and help users locate interesting items faster.


SWAM: Stuxnet Worm Analysis in Metasploit

Nowadays cyber security is becoming a great challenge. Attacker's community is progressing towards making smart and intelligent malwares (viruses, worms and Root kits). They stealth their existence and also use administrator rights without knowing legal user. Stuxnet worm is an example of a recent malware first detected in July 2010. Its variants were also detected earlier. It is the first type of worm that affects the normal functionality of industrial control systems (ICS) having programmable logic controllers (PLC) through PLC Root kit. Its main goal is to modify ICS behavior by changing the code of PLC and make it to behave in a way that attacker wants. It is a complex piece of malware having different operations and functionalities which are achieved by exploiting zero day vulnerabilities. Stuxnet exploits various vulnerable services in Microsoft Windows. In this paper we will show real time simulation of first three vulnerabilities of these through Metasploit Framework 3.2 and analyze results. A real time scenario is established based on some assumptions. We assumed Proteus design (pressure sensor) as PLC and showed after exploitation that the pressure value drops to an unacceptable level by changing Keil code of this design.


The Quest Data Mining System

The goal of the Quest project at the IBM Almaden Research center is to develop technology to enable a new breed of da&intensive decision-support applications. This paper is a capsule summary of the current functionality and architecture of the Quest data mining System. Our overall approach has been to identify basic data mining operations that cut across applications and develop fast, scalable algorithms for their execution (Agrawal, Imielinski, & Swami 1993a). We wanted our algorithms to:


Hierarchical Active Transfer Learning

We describe a unified active transfer learning framework called Hierarchical Active Transfer Learning (HATL). HATL exploits cluster structure shared between different data domains to perform transfer learning by imputing labels for unlabeled target data and to generate effective label queries during active learning. The resulting framework is flexible enough to perform not only adaptive transfer learning and accelerated active learning but also unsupervised and semi-supervised transfer learning. We derive an intuitive and useful upper bound on HATL’s error when used to infer labels for unlabeled target points. We also present results on synthetic data that confirm both intuition and our analysis. Finally, we demonstrate HATL’s empirical effectiveness on a benchmark data set for sentiment classification.


An Interactive Web Mapping Visualization of Urban Air Quality Monitoring Data of China

In recent years, main cities in China have been suffering from hazy weather, which is gaining great attention among the public, government managers and researchers in different areas. Many studies have been conducted on the topic of urban air quality to reveal different aspects of the air quality problem in China. This paper focuses on the visualization problem of the big air quality monitoring data of all main cities on a nationwide scale. To achieve the intuitive visualization of this dataset, this study develops two novel visualization tools for multi-granularity time series visualization (timezoom.js) and a dynamic symbol declutter map mashup layer for thematic mapping (symadpative.js). With the two invented tools, we develops an interactive web map visualization application of urban air quality data of all main cities in China. This application shows us significant air pollution findings at the nationwide scale. These results give us clues for further studies on air pollutant characteristics, forecasting and control in China. As the tools are invented for general visualization purposes of geo-referenced time series data, they can be applied to other environmental monitoring data (temperature, precipitation, etc.) through some configurations.


Dynamic Motions by a Quadruped Musculoskeletal Robot with Angle-Dependent Moment Arms

Current robotics have failed to replicate the great dexterity of the animal’s motions. We believe that it is because most of the works have focused on the controller part rather than on its body structure. Indeed, the musculoskeletal system is essential to produce the animal’s motions and several researchers have attempted to exploit it [1][2][3]. However, most of them have used simple mono-articular muscles and torque control which cannot replicate the nonlinearity of the living tissues, muscles and nerves [4]. In contrast, bipedal robots with monoand bi-articular muscles for performing adaptive motions [5][6][7] have been produced. To further investigate the role of the musculoskeletal system for animal motions, we desgned a quadruped robot with monoand bi-articular muscles. We used artificial pneumatic muscles as actuators due to their high mass/power ratio and their high compliance. Although these actuators replicate several characteristics of biological muscles in terms of damping and elasticity, the length-tension relation differs from them [8][9]. In the result, the angle-torque relation of the robot with pneumatic muscles differed substantially from the one of animals. Thus, we tried to bring the angle-torque relation as close as the one of living tissues by devising the angle-moment arm relation.


Learning Dexterous In-Hand Manipulation

We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed in a simulated environment in which we randomize many of the physical properties of the system like friction coefficients and an object’s appearance. Our policies transfer to the physical robot despite being trained entirely in simulation. Our method does not rely on any human demonstrations, but many behaviors found in human manipulation emerge naturally, including finger gaiting, multi-finger coordination, and the controlled use of gravity. Our results were obtained using the same distributed RL system that was used to train OpenAI Five [43]. We also include a video of our results: https://youtu. be/jwSbzNHGflM.


First response fire combat: Deep leaning based visible fire detection

Visible spectrum video based fire detection using non-stationary cameras has been an overlooked research problem. While many authors have successfully developed algorithms to identify and measure the proportions of uncontrolled fire using thermal or stationary surveillance cameras, the development of non-stationary systems provides a much larger application scope. We present a deep learning based approach, based on Google's Inception v3 and evaluate how the use of different optimizers, learning rates and reduction functions impact the convergence time and the results obtained. The experiment used a balanced two-class dataset, composed of images from videos captured through hand-held and drone attached cameras on real fire hazard situations. The results show that using any choice of algorithms, the Inception v3 architecture is able to converge and obtain results that surpass the state-of-the-art fire detection methods. We show that using a proper choice of learning rate, the network is able to achieve a 99% accuracy in less than one hundred interactions.


Accurate Identification of Ontology Alignments at Different Granularity Levels

As more and more ontologies are defined with different terms, ontology matching plays a crucial role in addressing the semantic heterogeneity problem in many different disciplines. Many efforts have been made to discover correspondences among terms in different ontologies. Most studies directly match two ontologies by utilizing terminological and structural methods that rely on ontologies themselves only. However, the decentralized characteristic of ontologies raises the uncertainty in ontology matching. To address this problem, we propose a four-stage ontology matching framework (FOMF) to enhance ontology matching performance. It is built upon the commonly accepted claim that an external comprehensive knowledge base can be used as a semantic bridge between domain ontologies for ontology matching. First, FOMF semantically maps domain ontologies to a knowledge base and then produces different types of alignments, including equivalence, subclass, sameas, and instance alignments. Similarities between two domain ontologies are next employed to enhance the equivalence and sameas alignments discovery. Finally, based on acquired alignments, inferred alignments are deduced to guarantee the completeness of matching results. Our experimental results show the superiority of the proposed method over the existing ones.


Average cost temporal-difference learning

We propose a variant of temporal-di!erence learning that approximates average and di!erential costs of an irreducible aperiodic Markov chain. Approximations are comprised of linear combinations of "xed basis functions whose weights are incrementally updated during a single endless trajectory of the Markov chain. We present a proof of convergence (with probability 1) and a characterization of the limit of convergence. We also provide a bound on the resulting approximation error that exhibits an interesting dependence on the `mixing timea of the Markov chain. The results parallel previous work by the authors, involving approximations of discounted cost-to-go. ! 1999 Elsevier Science Ltd. All rights reserved.


Game Development for Computer Science Education

Educators have long used digital games as platforms for teaching. Games tend to have several qualities that aren't typically found in homework: they often situate problems within a compelling alternate reality that unfolds through intriguing narrative, they often draw more upon a player's intrinsic motivations than extrinsic ones, they can facilitate deliberate low intensity practice, and they often emphasize a spirit of play instead of work.
 At ITiCSE 2016, this working group convened to survey the landscape of existing digital games that have been used to teach and learn computer science concepts. Our group discovered that these games lacked explicitly defined learning goals and even less evaluation of whether or not the games achieved these goals. As part of this process, we identified and played over 120 games that have been released or described in literature as means for learning computer science concepts. In our report, we classified how these games support the learning objectives outlined in the ACM/IEEE Computer Science Curricula 2013.
 While we found more games than we expected, few games explicitly stated their learning goals and even fewer were evaluated for their capacity to meet these goals. Most of the games we surveyed fell into two categories: short-lived proof-of-concept projects built by academics or closed-source games built by professional developers. Gathering adequate learning data is challenging in either situation. Our original intent for the second year of our working group was to prepare a comprehensive framework for collecting and analyzing learning data from computer science learning games. Upon further discussion, however, we decided that a better next step is to validate the design and development guidelines that we put forth in our final report for ITiCSE 2016.
 We extend this working group to a second year---with a mission to collaboratively develop a game with clearly defined learning objectives and define a methodology for evaluating its capacity to meet its goals.


ITIL-based IT service support process reengineering

The Information Technology Infrastructure Library (ITIL) supports best practices, reengineering activities and IT service support processes. ITIL framework only provides recommendations, and companies need to utilize this framework to improve their IT service support processes and establish best practices. This study provides a methodology on how to apply the ITIL framework for evaluating the IT service support processes, its reengineering and alignment to best practices, and subsequent integration into a decision support system framework. A case study approach was used to identify a set of Key Performance Indicators (KPI) which were monitored by a decision support system (DSS) for triggering on-going reengineering of IT service support processes. This paper focuses on the implementation of the ITIL guidelines at the operational level, improvement of the service desk, and incident, problem, change, release, and configuration management. It also presents the implementation of the ITIL guidelines at the tactical level for the improvement of the service level, capacity, IT service continuity, service availability, and security management. We conclude by providing recommendations for future research.


Small footprint implementation of dual-microphone delay-and-sum beamforming for in-car speech enhancement

For effective speech processing in an automotive environment, speech enhancement is necessary due to significant levels of background noise. In this paper, we present a cost effective small footprint implementation of one particular speech enhancement technique: dual microphone delay-and-sum beamforming. In order to save resources, the implementation utilizes the overlapping frame property used in speech processing systems. The implementation also exhibits a simple interconnection structure leading to even greater resource saving. Experiment results show that the proposed design can produce enhanced output very close to that generated by a theoretical (floating-point) model while only requiring a modest hardware resource usage.


Deep Recurrent Neural Networks for Acoustic Modelling

We present a novel deep Recurrent Neural Network (RNN) model for acoustic modelling in Automatic Speech Recognition (ASR). We term our contribution as a TC-DNN-BLSTM-DNN model, the model combines a Deep Neural Network (DNN) with Time Convolution (TC), followed by a Bidirectional LongShort Term Memory (BLSTM), and a final DNN. The first DNN acts as a feature processor to our model, the BLSTM then generates a context from the sequence acoustic signal, and the final DNN takes the context and models the posterior probabilities of the acoustic states. We achieve a 3.47 WER on the Wall Street Journal (WSJ) eval92 task or more than 8% relative improvement over the baseline DNN models


Gaining Access with Social Engineering: An Empirical Study of the Threat

AbstrAct Recently, research on information security has expanded from its purely technological orientation into striving to understand and explain the role of human behavior in security breaches. However, an area that has been lacking theory-grounded empirical study is in social engineering attacks. While there exists an extensive body of anecdotal literature, factors that account for attack success remains largely speculative. To better understand this increasing phenomenon, we developed a theoretical framework and conducted an empirical field study to investigate social engineering attacks, and from these results, we make recommendations for practice and further research.


A support vector machine classifier with automatic confidence and its application to gender classification

In this paper, we propose a support vector machine with automatic confidence (SVMAC) for pattern classification. The main contributions of this work to learning machines are twofold. One is that we develop an algorithm for calculating the label confidence value of each training sample. Thus, the label confidence values of all of the training samples can be considered in training support vector machines. The other one is that we propose a method for incorporating the label confidence value of each training sample into learning and derive the corresponding quadratic programming problems. To demonstrate the e ff ctiveness of the proposed SVMACs, a series of experiments are performed on three benchmarking pattern classification problems and a challenging gender classification problem. Experimental results show that the generalization performance of our SVMACs is superior to that of traditional SVMs.