title: iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects

abstract: We address the task of 6D pose estimation of known rigid objects from single input images in scenarios where the objects are partly occluded. Recent RGB-D-based methods are robust to moderate degrees of occlusion. For RGB inputs, no previous method works well for partly occluded objects. Our main contribution is to present the first deep learning-based system that estimates accurate poses for partly occluded objects from RGB-D and RGB input. We achieve this with a new instance-aware pipeline that decomposes 6D object pose estimation into a sequence of simpler steps, where each step removes specific aspects of the problem. The first step localizes all known objects in the image using an instance segmentation network, and hence eliminates surrounding clutter and occluders. The second step densely maps pixels to 3D object surface positions, so called object coordinates, using an encoder-decoder network, and hence eliminates object appearance. The third, and final, step predicts the 6D pose using geometric optimization. We demonstrate that we significantly outperform the state-of-the-art for pose estimation of partly occluded objects for both RGB and RGB-D input.


title: The ASRA evidence-based medicine assessment of ultrasound-guided regional anesthesia and pain medicine: Executive summary.

abstract: OBJECTIVES ::: The American Society of Regional Anesthesia and Pain Medicine charged an expert panel to examine the evidence basis for ultrasound guidance as a nerve localization tool in the clinical practices of regional anesthesia and interventional pain medicine. ::: ::: ::: METHODS ::: The panel searched, examined, and assessed the literature of ultrasound-guided regional anesthesia (UGRA) from the past 20 years. The qualities of studies were graded using the Jadad score. Strength of evidence and recommendations were graded using an accepted rating tool. ::: ::: ::: RESULTS ::: The panel made specific literature-based assessments concerning the relative advantages and limitations of UGRA relative to traditional nerve localization methods as they pertained to block characteristics and complications. Assessments and recommendations were made for upper and lower extremity, neuraxial, and truncal blocks and include pediatrics and interventional pain medicine. ::: ::: ::: CONCLUSIONS ::: Ultrasound guidance improves block characteristics (particularly performance time and surrogate measures of success) that are often block specific and that may impart an efficiency advantage depending on individual practitioner circumstances. Evidence for UGRA impacting patient safety is currently limited to the demonstration of improvements in the frequency of surrogate events for serious complications.


title: Simulation tools for model-based robotics: Comparison of Bullet, Havok, MuJoCo, ODE and PhysX

abstract: There is growing need for software tools that can accurately simulate the complex dynamics of modern robots. While a number of candidates exist, the field is fragmented. It is difficult to select the best tool for a given project, or to predict how much effort will be needed and what the ultimate simulation performance will be. Here we introduce new quantitative measures of simulation performance, focusing on the numerical challenges that are typical for robotics as opposed to multi-body dynamics and gaming. We then present extensive simulation results, obtained within a new software framework for instantiating the same model in multiple engines and running side-by-side comparisons. Overall we find that each engine performs best on the type of system it was designed and optimized for: MuJoCo wins the robotics-related tests, while the gaming engines win the gaming-related tests without a clear leader among them. The simulations are illustrated in the accompanying movie.


title: Detecting Distributed Denial of Service Attacks Using Source IP Address Monitoring

abstract: In this paper, we propose a simple but robust scheme to detect denial of service attacks (including distributed denial of service attacks) by monitoring the increase of new IP addresses. Unlike previous proposals for bandwidth attack detection schemes which are based on monitoring the traffic volume, our scheme is very effective for highly distributed denial of service attacks. Our scheme exploits an inherent feature of DDoS attacks, which makes it hard for the attacker to counter this detection scheme by changing their attack signature. Our scheme uses a sequential nonparametric change point detection method to improve the detection accuracy without requiring a detailed model of normal and attack traffic. Furthermore, we show that with the combination of monitoring per flow speed, we can detect all types of DDoS attacks. We demonstrate that we can achieve high detection accuracy on a range of different network packet traces.


title: Cluster kernels for semi-supervised learning

abstract: We propose a framework to incorporate unlabeled data in kernel classifier, based on the idea that two points in the same cluster are more likely to have the same label. This is achieved by modifying the eigenspectrum of the kernel matrix. Experimental results assess the validity of this approach.


title: Distinguishing cause from effect using observational data: methods and benchmarks

abstract: The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X, Y. An example is to decide whether altitude causes temperature, or vice versa, given only joint measurements of both variables. Even under the simplifying assumptions of no confounding, no feedback loops, and no selection bias, such bivariate causal discovery problems are challenging. Nevertheless, several approaches for addressing those problems have been proposed in recent years. We review two families of such methods: Additive Noise Methods (ANM) and Information Geometric Causal Inference (IGCI). We present the benchmark CauseEffectPairs that consists of data for 100 different cause-effect pairs selected from 37 datasets from various domains (e.g., meteorology, biology, medicine, engineering, economy, etc.) and motivate our decisions regarding the"ground truth"causal directions of all pairs. We evaluate the performance of several bivariate causal discovery methods on these real-world benchmark data and in addition on artificially simulated data. Our empirical results on real-world data indicate that certain methods are indeed able to distinguish cause from effect using only purely observational data, although more benchmark data would be needed to obtain statistically significant conclusions. One of the best performing methods overall is the additive-noise method originally proposed by Hoyer et al. (2009), which obtains an accuracy of 63+-10 % and an AUC of 0.74+-0.05 on the real-world benchmark. As the main theoretical contribution of this work we prove the consistency of that method.


title: Compressed Sensing of Block-Sparse Signals: Uncertainty Relations and Efficient Recovery

abstract: We consider compressed sensing of block-sparse signals, i.e., sparse signals that have nonzero coefficients occurring in clusters. An uncertainty relation for block-sparse signals is derived, based on a block-coherence measure, which we introduce. We then show that a block-version of the orthogonal matching pursuit algorithm recovers block $k$-sparse signals in no more than $k$ steps if the block-coherence is sufficiently small. The same condition on block-coherence is shown to guarantee successful recovery through a mixed $\ell_2/\ell_1$-optimization approach. This complements previous recovery results for the block-sparse case which relied on small block-restricted isometry constants. The significance of the results presented in this paper lies in the fact that making explicit use of block-sparsity can provably yield better reconstruction properties than treating the signal as being sparse in the conventional sense, thereby ignoring the additional structure in the problem.


title: What Item Response Theory Can Tell Us About the Complex Span Tasks

abstract: Working memory capacity is an important construct in psychology because of its relationship with many higher-order cognitive abilities and psychopathologies. Working memory capacity is often measured using a type of paradigm known as complex span. Some recent work has focused on shortening the administration time of the complex span tasks, resulting in different versions of these tasks being used (Foster et al., 2015; Oswald, McAbee, Redick, & Hambrick, 2015). Variations in the complex span tasks, such as the number of set sizes, can lead to varying power to discriminate individuals at different ability levels. Thus, research findings may be inconsistent across populations due to differing appropriateness for the ability levels. The present study uses a combination of item response theory and correlational analyses to better understand the psychometric properties of the operation span, symmetry span, and rotation span. The findings show that the typical administration of these tasks, particularly the operation span, is not suitable for above average ability samples (Study 1; n = 573). When larger set sizes are added to the tasks (Study 2; n = 351), predictive validity and discriminability is improved for all complex span tasks, however the operation span is still inferior to the spatial tasks. The authors make several conclusions about which tasks and set sizes should be used depending on the intended population, and further suggest avoiding the standard-length operation span for average or higher ability populations. (PsycINFO Database Record


title: Workplace Internet Use Monitoring and Employee Behavior Intention Change

abstract: The use of Internet in workplace has brought about concerns for companies. Previous researches focused on the effect of electronic control on employee productivity and job satisfaction. This paper uses the Theory of Planned Behavior and Deterrence Theory to understand the factors that could affect the workplace Internet use behavior intention. The results will provide insightful suggestions for improving workplace Internet use management with less costly and proactive approaches that can cultivate employee voluntary avoidance of Internet misuse. Keyword: workplace Internet use monitoring, Internet use, Internet use monitoring, behavior intention change Liao, Luo, Gurung & Li, Workplace Internet monitoring and Behavior Intention


title: Shortest path algorithms: An evaluation using real road networks

abstract: The classic problem of finding the shortest path over a network has been the target of many research efforts over the years. These research efforts have resulted in a number of different algorithms and a considerable amount of empirical findings with respect to performance. Unfortunately, prior research does not provide a clear direction for choosing an algorithm when one faces the problem of computing shortest paths on real road networks. Most of the computational testing on shortest path algorithms has been based on randomly generated networks, which may not have the characteristics of real road networks. In this paper, we provide an objective evaluation of 15 shortest path algorithms using a variety of real road networks. Based on the evaluation, a set of recommended algorithms for computing shortest paths on real road networks is identified. This evaluation should be particularly useful to researchers and practitioners in operations research, management science, transportation, and Geographic Information Systems.


title: Concussion Recovery Time Among High School and Collegiate Athletes: A Systematic Review and Meta-Analysis

abstract: Background ::: Concussion diagnosis and management is made through the clinical exam using assessment tools that include self-report symptomatology, postural control, and cognitive evaluations. The specific timing of concussion resolution varies between individuals. However, despite a lack of research in concussion recovery, it is widely accepted that the majority of young adults will recover in 7–10 days, with youth athletes taking longer.


title: Opinum: statistical sentiment analysis for opinion classification

abstract: The classification of opinion texts in positive and negative can be tackled by evaluating separate key words but this is a very limited approach. We propose an approach based on the order of the words without using any syntactic and semantic information. It consists of building one probabilistic model for the positive and another one for the negative opinions. Then the test opinions are compared to both models and a decision and confidence measure are calculated. In order to reduce the complexity of the training corpus we first lemmatize the texts and we replace most named-entities with wildcards. We present an accuracy above 81% for Spanish opinions in the financial products domain.


title: Validation of behavior specifications of production systems within different phases of the engineering process

abstract: Production systems are tending to be more and more complex. Thus, the engineering of such systems creates increasing requirements to the engineers. To enable the engineers to improve engineering process quality in recent times the use of simulation techniques established. By its application engineers can validate engineering result correctness. But up to now within particular engineering phases different simulation tools are used. In addition the created and exploited models are usually neither compatible nor consistent with each other. To overcome this limitation in this paper a modular, adaptable, and extensible simulation framework will be presented applicable along different phases of the engineering process of production systems based on common engineering process artifact libraries.


title: Vehicle-to-vehicle wireless communication protocols for enhancing highway traffic safety

abstract: This article presents an overview of highway cooperative collision avoidance (CCA), which is an emerging vehicular safety application using the IEEE- and ASTM-adopted Dedicated Short Range Communication (DSRC) standard. Along with a description of the DSRC architecture, we introduce the concept of CCA and its implementation requirements in the context of a vehicle-to-vehicle wireless network, primarily at the Medium Access Control (MAC) and the routing layer. An overview is then provided to establish that the MAC and routing protocols from traditional Mobile Ad Hoc networks arc not directly applicable for CCA and similar safety-critical applications. Specific constraints and future research directions are then identified for packet routing protocols used to support such applications in the DSRC environment. In order to further explain the interactions between CCA and its underlying networking protocols, we present an example of the safety performance of CCA using simulated vehicle crash experiments. The results from these experiments arc also used to demonstrate the need for network data prioritization for safety-critical applications such as CCA. Finally, the performance sensitivity of CCA to unreliable wireless channels is discussed based on the experimental results.


title: Sequence to Sequence Transduction with Hard Monotonic Attention

abstract: We present a supervised sequence to sequence transduction model with a hard attention mechanism which combines the more traditional statistical alignment methods with the power of recurrent neural networks. We evaluate the model on the task of morphological inflection generation and show that it provides state of the art results in various setups compared to the previous neural and non-neural approaches. Eventually we present an analysis of the learned representations for both hard and soft attention models, shedding light on the features such models extract in order to solve the task.


title: Physico-chemical pretreatment and fungal biotreatment for park wastes and cattle dung for biogas production

abstract: AbstractWith the rising demand for renewable energy and environmental protection, anaerobic digestion of biogas technology has attracted considerable attention within the scientific community. The effect of physico-chemical pretreatment on cellulose degradation followed by fungal treatment by Aspergillus terreus and Trichoderma viride to treat cellulosic biomass for enhancing its digestibility was investigated. The tested substrate was digested with and without physical, chemical, and biological treatment. Fresh leaves, dry leaves and cattle dung were characterized by a total solids content 35, 84 and 17 %, volatile solids content 81.2, 59.49 and 64.5 % and C/N ratio 31, 45.4 and 13.6, respectively. Biogas total volume was determined using water replacement technique, while methane volume was determined using precipitation of CO2 in 20 % NaOH solution. Pretreatment steps were carried out by using mechanical and chemical pretreatments using 2.5 % NaOH mixed with 2.5 % NH4OH for 15 days, followed by biological treatment of A. terreus and T. viride. The potential of pretreatment of substrate was studied at regular intervals of 0, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days determining the change in chemical and physical compositions of used substrates. Biogas production was 102.6 and 125.9 L/KgVS from untreated and pretreated substrate, respectively. On the other hand, methane production was 61.4 and 79.8 L/KgVS from untreated and pretreated substrate, respectively. In conclusion, Physical (milling), chemical (NaOH and NH4OH) ::: pretreatment in addition to fungal (A. terreus and T. viride) treatment for the tested substrate prior to AD was an efficient process for improvement of biogas and methane production.


title: Colorimetric Detection of Mercuric Ion (Hg2+) in Aqueous Media using DNA-Functionalized Gold Nanoparticles

abstract: Mercury is a widespread pollutant with distinct toxicological profiles, and it exists in a variety of different forms (metallic, ionic, and as part of organic and inorganic salts and complexes). Solvated mercuric ion (Hg), one of the most stable inorganic forms of mercury, is a caustic and carcinogenic material with high cellular toxicity. The most common organic source of mercury, methyl mercury, can accumulate in the human body through the food chain and cause serious and permanent damage to the brain with both acute and chronic toxicity. Methyl mercury is generated by microbial biomethylation in aquatic sediments from water-soluble mercuric ion (Hg). Therefore, routine detection of Hg is central to the environmental monitoring of rivers and larger bodies of water and for evaluating the safety of aquatically derived food supplies. Several methods for the detection of Hg, based upon organic fluorophores or chromophores, semiconductor nanocrystals, cyclic voltammetry, polymeric materials, proteins, and microcantilevers, have been developed. Colorimetric methods, in particular, are extremely attractive because they can be easily read out with the naked eye, in some cases at the point of use. Although there are now several chromophoric colorimetric sensors for Hg, all of them are either limited with respect to sensitivity (current limit of detection 1 mm) and selectivity, kinetically unstable, or incompatible with aqueous environments. Recently, DNA-functionalized gold nanoparticles (DNA– Au NPs) have been used in a variety of forms for the detection of proteins, oligonucleotides, certain metal ions, and other small molecules. DNA–Au NPs have high extinction coefficients (3–5 orders of magnitude higher than those of organic dye molecules) and unique distancedependent optical properties that can be chemically programmed through the use of specific DNA interconnects, which allows one, in certain cases, to detect targets of interest through colorimetric means. Moreover, these structures, when hybridized to complementary particles, exhibit extremely sharp melting transitions, which have been used to enhance the selectivity of detection systems based upon them. By using such an approach, one can typically detect nucleic acid targets in the low nanomolar to high picomolar target concentration range in colorimetric format. The ability to use such particles to detect Hg in the nanomolar concentration range in colorimetric format would be a significant advance, especially when one considers that commercial systems for detecting Hg rely on cumbersome inductively coupled plasma approaches that are not suitable for point-of-use applications. Herein, we present a highly selective and sensitive colorimetric detection method for Hg that relies on thymidine–Hg–thymidine coordination chemistry and complementary DNA–Au NPs with deliberately designed T–T mismatches. When two complementary DNA–Au NPs are combined, they form DNA-linked aggregates that can dissociate reversibly with a concomitant purple-to-red color change. 28] For our novel colorimetric Hg assay, however, we prepared two types of Au NPs (designated as probe A and probe B, see the Supporting Information), each functionalized with different thiolated-DNA sequences (probe A: 5’HS-C10-A10-T-A103’, probe B: 5’HS-C10-T10-T-T103’), which are complementary except for a single thymidine–thymidine mismatch (shown in bold; Scheme 1). Importantly, these particles also form stable aggregates and exhibit the characteristic sharp melting transitions (full width at half maximum< 1 8C) associated with aggregates formed from perfectly complementary particles, but with a lower melting temperature Tm. [17, 18] Since it is known that Hg will coordinate selectively to the bases that make up a T–T mismatch, we hypothesized that Hg would


title: Factorization Machines for Hybrid Recommendation Systems Based on Behavioral, Product, and Customer Data

abstract: This study creates a hybrid recommendation system for online offer personalization of an e-commerce company. The system goes beyond existing literature by combining four different data sources, i.e. customer data, product data, implicit and explicit behavioral data, in a single algorithm. Factorization machines are employed as model-based algorithm and have as advantage that the four data sources are incorporated in a single model by feature combination. Results show that hybridization of the four distinct data sources improves accuracy compared to (i) factorization machines based on a single data source and (ii) a real-life company benchmark model using collaborative filtering.


title: An Energy-Efficient Differential Flip-Flop for Deeply Pipelined Systems

abstract: Deeply pipelined systems require flip-flops with low latency and power consumption. Often, the flip-flop must supply both inverted and non-inverted signals to subsequent logic. Generating both outputs at the same time improves performance by equalizing the worst-case delays. In this paper, we present a novel differential flip-flop for deeply pipelined systems. The circuit uses cross-coupled p-transistors as pull-up devices to achieve high energy efficiency. We simulated the design in 90-nm CMOS technology to determine the delay and power consumption. We then repeated the analysis with four other differential flip-flops that produce symmetric outputs. The proposed design achieves the best power-delay product of the five alternatives.


title: Frequency-Domain Gray-Box Identification of Industrial Robots

abstract: This paper considers identification of unknown parameters in elastic dynamic models of industrial robots. Identifying such models is a challenging task since an industrial robot is a multivariable, ...


title: Message Passing Multi-Agent GANs

abstract: Communicating and sharing intelligence among agents is an important facet of achieving Artificial General Intelligence. As a first step towards this challenge, we introduce a novel framework for image generation: Message Passing Multi-Agent Generative Adversarial Networks (MPM GANs). While GANs have recently been shown to be very effective for image generation and other tasks, these networks have been limited to mostly single generator-discriminator networks. We show that we can obtain multi-agent GANs that communicate through message passing to achieve better image generation. The objectives of the individual agents in this framework are two fold: a co-operation objective and a competing objective. The co-operation objective ensures that the message sharing mechanism guides the other generator to generate better than itself while the competing objective encourages each generator to generate better than its counterpart. We analyze and visualize the messages that these GANs share among themselves in various scenarios. We quantitatively show that the message sharing formulation serves as a regularizer for the adversarial training. Qualitatively, we show that the different generators capture different traits of the underlying data distribution.


title: The use of CAD/CAM technology in prosthetics and orthotics--current clinical models and a view to the future.

abstract: This report examines the current clinical uses of CAD/CAM in prosthetics and orthotics. We conducted interviews to contrast patterns of CAD/CAM use in different private practice settings, at two different teaching institutions, and within two large Prosthetic and Orthotic delivery systems. Investigation into these patterns of use has revealed several lessons. First, there currently exist several very different models of use in clinical practice and these different models will most likely continue. The clinical models range from all traditional techniques with no use of CAD/CAM, to full in-house suites of CAD/CAM equipment with extensive utilization, to a simplified office with minimal in-house equipment and minimal fabrication and a near total dependency on central fabrication. Second, a growing number of prosthetic and orthotic devices are successfully being fabricated and fit with CAD/CAM technology after starting the process with simple measurements instead of casted, scanned, or digitized exact anatomic data. Starting the CAD process with "by-the-numbers approach" has revealed the reality that for some devices, the "input" needed to define the shape of the residual limb or torso, may not need to be as accurate as originally thought. Third, the fabrication techniques that are currently being used with CAD/CAM systems are still rather traditional techniques. Most devices are still laminated or formed over computer carved models. Although research continues into advanced fabrication techniques, the prosthetics and orthotics industry has not yet taken advantage of the possibilities in the computer-assisted manufacturing side of the equation. Finally, the business of manufacturing and selling up-to-date CAD/CAM equipment and software has a tremendous impact on how this technology is used in prosthetics and orthotics. The size of the prosthetics and orthotics industry and the potential number of customers for major equipment are relatively small. Being in the business of providing the advanced CAD/CAM systems of tomorrow is an expensive and difficult proposition. Current users of full in-house CAD systems have expressed concern that upgrading equipment and software might not be economical with today's decreasing revenues. For all of these reasons, many believe that the number of practitioners who use a central fabrication model will grow more rapidly than the number of practitioners who own and operate a full in-house system.


title: A branch-and-bound algorithm for globally optimal hand-eye calibration

abstract: This paper introduces a novel solution to hand-eye calibration problem. It is the first method that uses camera measurements directly and at the same time requires neither prior knowledge of the external camera calibrations nor a known calibration device. Our algorithm uses branch-and-bound approach to minimize an objective function based on the epipolar constraint. Further, it employs Linear Programming to decide the bounding step of the algorithm. The presented technique is able to recover both the unknown rotation and translation simultaneously and the solution is guaranteed to be globally optimal with respect to the L ∞ -norm.


title: Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm

abstract: We propose an extensive analysis of the behavior of majority votes in binary classification. In particular, we introduce a risk bound for majority votes, called the C-bound, that takes into account the average quality of the voters and their average disagreement. We also propose an extensive PAC-Bayesian analysis that shows how the C-bound can be estimated from various observations contained in the training data. The analysis intends to be self-contained and can be used as introductory material to PAC-Bayesian statistical learning theory. It starts from a general PAC-Bayesian perspective and ends with uncommon PAC-Bayesian bounds. Some of these bounds contain no Kullback-Leibler divergence and others allow kernel functions to be used as voters (via the sample compression setting). Finally, out of the analysis, we propose the MinCq learning algorithm that basically minimizes the C-bound. MinCq reduces to a simple quadratic program. Aside from being theoretically grounded, MinCq achieves state-of-the-art performance, as shown in our extensive empirical comparison with both AdaBoost and the Support Vector Machine.


title: Deep neural support vector machines for speech recognition

abstract: A new type of deep neural networks (DNNs) is presented in this paper. Traditional DNNs use the multinomial logistic regression (softmax activation) at the top layer for classification. The new DNN instead uses a support vector machine (SVM) at the top layer. Two training algorithms are proposed at the frame and sequence-level to learn parameters of SVM and DNN in the maximum-margin criteria. In the frame-level training, the new model is shown to be related to the multiclass SVM with DNN features; In the sequence-level training, it is related to the structured SVM with DNN features and HMM state transition features. Its decoding process is similar to the DNN-HMM hybrid system but with frame-level posterior probabilities replaced by scores from the SVM. We term the new model deep neural support vector machine (DNSVM). We have verified its effectiveness on the TIMIT task for continuous speech recognition.


title: RRAM based synaptic devices for neuromorphic visual systems

abstract: Neuromorphic computing is an attractive computation paradigm with the features of massive parallelism, adaptivity to the complex input information, and tolerance to errors. As one of the most crucial components in a neuromorphic system, the electronic synapse requires high device integration density and low-energy consumption. Oxide-based resistive switching devices (RRAM) have emerged as the leading candidate to realize the synapse functions due to the extra-low energy loss per spike. This work will address the design and optimization of oxide-based RRAM synaptic devices and the impacts of the synaptic devices parameters on the performance of neuromorphic visual system. Possible solutions are also provided to suppress the intrinsic variation of the oxide-RRAM based synaptic devices to achieve high recognition accuracy and efficiency of neuromorphic visual systems


title: A high-performance network intrusion detection system

abstract: In this paper we present a new approach for network intrusion detection based on concise specifications that characterize normal and abnormal network packet sequences. Our specification language is geared for a robust network intrusion detection by enforcing a strict type discipline via a combination of static and dynamic type checking. Unlike most previous approaches in network intrusion detection, our approach can easily support new network protocols as information relating to the protocols are not hard-coded into the system. Instead, we simply add suitable type definitions in the specifications and define intrusion patterns on these types. We compile these specifications into a high-performance network intrusion detection system. Important components of our approach include efficient algorithms for pattern-matching and information aggregation on sequences of network packets. In particular, our techniques ensure that the matching time is insensitive to the number of patterns characterizing different network intrusions, and that the aggregation operations typically take constant time per packet. Our system participated in an intrusion detection evaluation organized by MIT Lincoln Labs, where our system demonstrated its effectiveness (96% detection rate on low-level network attacks) and performance (real-time detection at 500Mbps), while producing very few false positives (0.05 to 0.1 per attack).


title: Facial Expression Recognition Based on 3D Dynamic Range Model Sequences

abstract: Traditionally, facial expression recognition (FER) issues have been studied mostly based on modalities of 2D images, 2D videos, and 3D static models. In this paper, we propose a spatio-temporal expression analysis approach based on a new modality, 3D dynamic geometric facial model sequences, to tackle the FER problems. Our approach integrates a 3D facial surface descriptor and Hidden Markov Models (HMM) to recognize facial expressions. To study the dynamics of 3D dynamic models for FER, we investigated three types of HMMs: temporal 1D-HMM, pseudo 2D-HMM (a combination of a spatial HMM and a temporal HMM), and real 2D-HMM. We also created a new dynamic 3D facial expression database for the research community. The results show that our approach achieves a 90.44% person-independent recognition rate for distinguishing six prototypic facial expressions. The advantage of our method is demonstrated as compared to methods based on 2D texture images, 2D/3D Motion Units, and 3D static range models. Further experimental evaluations also verify the benefits of our approach with respect to partial facial surface occlusion, expression intensity changes, and 3D model resolution variations.


title: JADE – A FIPA-compliant agent framework

abstract: JADE is a software framework to develop agent applications in compliance with the FIPA specifications for interoperable intelligent multi-agent systems. The goal is to simplify development while ensuring standard compliance through a comprehensive set of system services and agents. JADE can then be considered an agent middle-ware that implements an Agent Platform and a development framework. It deals with all those aspects that are not peculiar of the agent internals and that are independent of the applications, such as message transport, encoding and parsing, or agent life-cycle. This paper presents the JADE software describing its intended uses, as well as being a walkthrough of JADE internal architecture. The main architectural issues are discussed, and the major design decisions are outlined.


title: On the control of automatic processes: A parallel distributed processing account of the Stroop effect

abstract: Abstract : A growing body of evidence suggests that traditional views of automaticity are in need of revision. For example, automaticity has often been treated as an all-or-none phenomenon, and traditional theories have held that automatic processes are independent of attention. Yet recent empirial data suggests that automatic processes are continuous, and furthermore are subject to attentional control. In this paper we present a model of attention which addresses these issues. Using a parallel distributed processing framework we propose that the attributes of automaticity depend upon the strength of a process and that strength increases with training. Using the Stroop effect as an example, we show how automatic processes are continuous and emerge gradually with practice. Specifically, we present a computational model of the Stroop task which simulates the time course of processing as well as the effects of learning.


title: Community Detection via Random and Adaptive Sampling

abstract: In this paper, we consider networks consisting of a finite number of non-overlapping communities. To extract these communities, the interaction between pairs of nodes may be sampled from a large available data set, which allows a given node pair to be sampled several times. When a node pair is sampled, the observed outcome is a binary random variable, equal to 1 if nodes interact and to 0 otherwise. The outcome is more likely to be positive if nodes belong to the same communities. For a given budget of node pair samples or observations, we wish to jointly design a sampling strategy (the sequence of sampled node pairs) and a clustering algorithm that recover the hidden communities with the highest possible accuracy. We consider both non-adaptive and adaptive sampling strategies, and for both classes of strategies, we derive fundamental performance limits satisfied by any sampling and clustering algorithm. In particular, we provide necessary conditions for the existence of algorithms recovering the communities accurately as the network size grows large. We also devise simple algorithms that accurately reconstruct the communities when this is at all possible, hence proving that the proposed necessary conditions for accurate community detection are also sufficient. The classical problem of community detection in the stochastic block model can be seen as a particular instance of the problems consider here. But our framework covers more general scenarios where the sequence of sampled node pairs can be designed in an adaptive manner. The paper provides new results for the stochastic block model, and extends the analysis to the case of adaptive sampling.


title: Reliability of power cycling for IGBT power semiconductor modules

abstract: Power cycling capability is one of the most important reliability items in application semiconductor modules. This paper describes the mechanism of power cycling by analysis of the structure of lead-based solder and joint failure due to fatigue was studied to ascertain improvement in the power cycling lifetime. Lead-based solder has a relatively weaker yield-strength which means that plastic deformation leads to deterioration. Therefore, tin-silver based solder was selected as a higher yield-strength material, and a new composition which has both excellent mechanical properties and wettability was developed. Concerning the failure mechanism of power cycling for this newly developed solder material, solder joint failure and aluminum wire bond failure were clearly distinguished, and the lifetime prediction curve was clarified by FEM analysis and detailed failure analysis. Furthermore, it was proved that the deterioration of new tin-silver based solder is caused by thermal fatigue with the grain growth of tin. Thus, longer power cycling capability is achieved by using this newly developed solder instead of conventional lead-based solder.


title: Gain Improvement of Rectangular Dielectric Resonator Antenna by Engraving Grooves on Its Side Walls

abstract: A new technique for increasing the boresight gain of a rectangular dielectric resonator antenna (DRA) operating at its fundamental radiating TEy111 mode is introduced. The idea is to increase the radiations from the side walls of the DRA compared to that of its top wall by engraving grooves on the side walls. A model based on the array theory is developed to explain the high-gain nature of the antenna. The measured results demonstrate that the proposed antenna achieves an impedance bandwidth of 21% over a band of 3.24-4 GHz, with a maximum gain of 9.6 dB. This is significantly higher with respect to available data in the literature.


title: Real-time object classification and novelty detection for collaborative video surveillance

abstract: To conduct real-time video surveillance using low-cost commercial off-the-shelf hardware, system designers typically define the classifiers prior to the deployment of the system so that the performance of the system can be optimized for a particular mission. This implies the system is restricted to interpreting activity in the environment in terms of the original context specified. Ideally the system should allow the user to provide additional context in an incremental fashion as conditions change. Given the volumes of data produced by the system, it is impractical for the user to periodically review and label a significant fraction of the available data. We explore a strategy for designing a real-time object classification process that aids the user in identifying novel, informative examples for efficient incremental learning.


title: Do Private and Portable Web Browsers Leave Incriminating Evidence? A Forensic Analysis of Residual Artifacts from Private and Portable Web Browsing Sessions

abstract: The Internet is an essential tool for everyday tasks. Aside from common use, the option to browse the Internet privately is a desirable attribute. However, this can create a problem when private Internet sessions become hidden from computer forensic investigators in need of evidence. Our primary focus in this research is to discover residual artifacts from private and portable web browsing sessions. In addition, the artifacts must contain more than just file fragments and enough to establish an affirmative link between user and session. Certain aspects of this topic have triggered many questions, but there have never been enough authoritative answers to follow. As a result, we propose a new methodology for analyzing private and portable web browsing artifacts. Our research will serve to be a significant resource for law enforcement, computer forensic investigators, and the digital forensics research community.


title: Ripple II: faster communication through physical vibration

abstract: We envision physical vibration as a new modality of data communication. In NSDI 2015, our paper reported the feasibility of modulating the vibration of a smartphone's vibra-motor. When in physical contact with another smartphone, the accelerometer of the second phone was able to decode the vibrations at around 200 bits/s. This paper builds on our first prototype, but redesigns the entire radio stack to now achieve 30 kbps. The core redesign includes (1) a new OFDM-based physical layer that uses the microphone as a receiver (instead of the accelerometer), and (2) a MAC layer that detects collision at the transmitter and performs proactive symbol retransmissions. We also develop two example applications on top of the vibratory radio: (1) a finger ring that transmits vibratory passwords through the finger bone to enable touch based authentication, and (2) surface communication between devices placed on the same table. The overall system entails unique challenges and opportunities, including ambient sound cancellation, OFDM over vibrations, back-EMF based carrier sensing, predictive retransmissions, bone conduction, etc. We call our system Ripple II to suggest the continuity from the NSDI 2015 paper. We close the paper with a video demo that streams music as OFDM packets through vibrations and plays it in real time through the receiver's speaker.


title: Deep Learning with Limited Numerical Precision

abstract: Training of large-scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data representation and computation on neural network training. Within the context of low-precision fixed-point computations, we observe the rounding scheme to play a crucial role in determining the network's behavior during training. Our results show that deep networks can be trained using only 16-bit wide fixed-point number representation when using stochastic rounding, and incur little to no degradation in the classification accuracy. We also demonstrate an energy-efficient hardware accelerator that implements low-precision fixed-point arithmetic with stochastic rounding.


title: Occlusion invariant face recognition using selective local non-negative matrix factorization basis images

abstract: In this paper, we propose a novel occlusion invariant face recognition algorithm based on Selective Local Non-negative Matrix Factorization (S-LNMF) technique. The proposed algorithm is composed of two phases; the occlusion detection phase and the selective LNMF-based recognition phase. We use a local approach to effectively detect partial occlusions in an input face image. A face image is first divided into a finite number of disjointed local patches, and then each patch is represented by PCA (Principal Component Analysis), obtained by corresponding occlusion-free patches of training images. And the 1-NN threshold classifier is used for occlusion detection for each patch in the corresponding PCA space. In the recognition phase, by employing the LNMF-based face representation, we exclusively use the LNMF bases of occlusion-free image patches for face recognition. Euclidean nearest neighbor rule is applied for the matching. We have performed experiments on AR face database that includes many occluded face images by sunglasses and scarves. The experimental results demonstrate that the proposed local patch-based occlusion detection technique works well and the S-LNMF method shows superior performance to other conventional approaches.


title: Finding software license violations through binary code clone detection

abstract: Software released in binary form frequently uses third-party packages without respecting their licensing terms. For instance, many consumer devices have firmware containing the Linux kernel, without the suppliers following the requirements of the GNU General Public License. Such license violations are often accidental, e.g., when vendors receive binary code from their suppliers with no indication of its provenance. To help find such violations, we have developed the Binary Analysis Tool (BAT), a system for code clone detection in binaries. Given a binary, such as a firmware image, it attempts to detect cloning of code from repositories of packages in source and binary form. We evaluate and compare the effectiveness of three of BAT's clone detection techniques: scanning for string literals, detecting similarity through data compression, and detecting similarity by computing binary deltas.


title: An IoT Cloud System for Traffic Monitoring and Vehicular Accidents Prevention Based on Mobile Sensor Data Processing

abstract: The sudden traffic slowdown especially in fast scrolling roads and highways characterized by a scarce visibility is one of the major causes of accidents among motorized vehicles. It can be caused by other accidents, work-in-progress on roads, excessive motorized vehicles especially at peak times and so on. Typically, fixed traffic sensors installed on roads that interact with drivers’ mobile App through the 4G network can mitigate such a problem, but unfortunately not all roads and highways are equipped with such devices. In this paper, we discuss a possible alternative solution for addressing such an issue considering mobile traffic sensors directly installed in private and/or public transportation and volunteer vehicles. In this scenario a fast real-time processing of big traffic data is fundamental to prevent accidents. In particular, we discuss an IoT Cloud system for traffic monitoring and alert notification based on OpenGTS and MongoDB. Our IoT Cloud system, besides for private drivers, it is very useful for drivers of critical rescue vehicles such as ambulances. Experiments prove that our system provides acceptable response times that allows drivers to receive alert messages in useful time so as to avoid the risk of possible accidents.


title: Towards Security-Aware Virtual Environments for Digital Twins

abstract: Digital twins open up new possibilities in terms of monitoring, simulating, optimizing and predicting the state of cyber-physical systems (CPSs). Furthermore, we argue that a fully functional, virtual replica of a CPS can also play an important role in securing the system. In this work, we present a framework that allows users to create and execute digital twins, closely matching their physical counterparts. We focus on a novel approach to automatically generate the virtual environment from specification, taking advantage of engineering data exchange formats. From a security perspective, an identical (in terms of the system's specification), simulated environment can be freely explored and tested by security professionals, without risking negative impacts on live systems. Going a step further, security modules on top of the framework support security analysts in monitoring the current state of CPSs. We demonstrate the viability of the framework in a proof of concept, including the automated generation of digital twins and the monitoring of security and safety rules.


A Proposition-Based Abstractive Summariser


title: Weekly Training Frequency Effects on Strength Gain: A Meta-Analysis

abstract: BACKGROUND ::: The current recommendations for resistance training (RT) frequency range from 2 to 5 days per week (days week- 1) depending on the subjects' training status. However, the relationship between RT frequency and muscular strength remains controversial with reported variances existing across different population groups. We conducted a meta-analysis that (1) quantified the effects of low (LF; 1 day week- 1), medium (MF; 2 days week- 1), or high (HF; ≥ 3 days week- 1) RT frequency on muscular strength per exercise; (2) examined the effects of different RT frequency on one repetition maximum (1RM) strength gain profiles (multi-joint exercises and single joint exercises); (3) examined the effects of different RT frequency on 1RM strength gain when RT volume is equated; and (4) examined the effects of different RT frequency on 1RM strength gains on upper and lower body. ::: ::: ::: METHODS ::: Computerised searches were performed using the terms 'strength training frequency', 'resistance training frequency', 'training frequency', and 'weekly training frequency'. After review, 12 studies were deemed suitable according to pre-set eligibility criteria. Primary data were pooled using a random-effects model. Outcomes analysed for main effects were pre- to post strength change with volume-equated studies that combined multi-joint and isolation exercise; isolation-only exercise and untrained subjects only. Heterogeneity between studies was assessed using I2 and Cochran's Q statistics with funnel plots used to assess publication bias and sensitivity analyses calculated for subgroups. ::: ::: ::: RESULTS ::: Pre- versus post-training strength analysis comprised of 74 treatment groups from 12 studies. For combined multi-joint and isolation exercises, there was a trend towards higher RT frequency compared with lower frequency [mean effect size (ES) 0.09 (95% CI - 0.06-0.24)] however not significant (p = 0.25). Volume-equated pre- to post-intervention strength gain was similar when LF was compared to HF [mean ES 0.03 (95% CI - 0.20-0.27); p = 0.78]. Upper body pre- to post-intervention strength gain was greater when HF was compared with LF [mean ES 0.48 (95% CI 0.20-0.76)] with significant differences between frequencies (p < 0.01). Upper body pre- to post-intervention strength gain was similar when MF was compared with LF (ES 0.12; 95% CI - 0.22-0.47); p = 0.48]. There was no significant difference in lower body mean ES between HF and LF [mean ES 0.21(95% CI - 0.55-0.13); p = 0.22]. There was a trend towards a difference in mean ES between MF and HF [mean ES 0.41(95% CI - 0.26-1.09); however, the effect was not significant (p = 0.23). ::: ::: ::: CONCLUSIONS ::: The existing data does not provide a strong correlation between increased weekly training frequency (HF) and maximal strength gain in upper and lower body resistance exercises for a mixed population group. When RT is volume-equated for combined multi-joint and isolation exercises, there is no significant effect of RT frequency on muscular strength gain. More investigations are required to explore the effects of varying weekly training frequencies adequately.


Character-level Supervision for Low-resource POS Tagging


title: Practical Methods to Assess Chronic Constipation

abstract: Defining the pathophysiology of chronic constipation is critical for planning management. After excluding organic diseases, physician should speculate whether the constipated patient has a defecatory disorder or not. Recently, American Gastroenterology Association suggested an updated algorithm in diagnosis of chronic constipation.1 The major change was anorectal manometry and a rectal balloon expulsion test as the initial step which should be considered even before conventional laxative trial especially in patients who are highly suspected as having a defecatory disorder. This change inevitably posed the measurement of colon transit time in the last step of the algorithm and assessment of colonic transit is not recommended as early study any more. What is the rationale of this change? The American Gastroenterology Association medical position statement provided two reasonable issues. First, because near half of patients with defecatory disorders show concomitant slow colonic transit, slow transit time cannot rule out the presence of defecatory disorders and omit anorectal studies. Defecatory disorders are treated with pelvic floor retraining behavioral therapy, namely biofeedback, even if slow colonic transit is concomitant. Second reason is that the administration of laxative is the main initial therapy regardless of colon transit if there is no evidence of defecatory disorders. Normal transit constipation and slow transit constipation are treated similarly. ::: ::: Then, what is the clinical impact of slow colonic transit? We select very exceptional case of refractory chronic constipation as a candidate of surgical treatment according to the colonic transit. Colectomy could be effective in medically refractory constipated patients with slow colonic transit if defecatory disorders are not concomitant. The overall satisfaction rate of surgery was about 86% in a systematic review involving 39 reports and 1423 patients.2 Confirming slow colonic transit and ruling out defecatory disorders are mandatory for the successful outcome. The present study by Park et al3 suggested that the gas volume scoring could be a practical method assessing colonic transit. Even though the authors cannot find a positive correlation of colon transit time and colon gas volume score, they demonstrated that the difference of gas volume existed between slow transit and normal transit, defined as 45 hours (mean value + 1 standard deviation). The difference of gas volume score was small, 5.66% vs 4.15%, and the sensitivity and specificity of the cut-off value to diagnose slow colon transit was disappointing. The failure of presenting valuable cut-off colon gas volume score to differentiate slow colon transit is a big drawback of this study, because there was only small mean difference of colon gas volume score between the groups and this small difference can be easily made by manual processing of gas volume scoring or by confusing small bowel gas as colon gas. Also the range of colon gas volume score largely overlaps each other. Therefore the present outcome has insufficient clinical impact to replace conventional method using radiopaque markers to assess colon transit time. ::: ::: But primary physicians can roughly discriminate slow transit constipation based on this tendency of colon gas on simple x-ray and colon transit. When primary physicians encounter a constipated patient with a relatively larger amount of colon gas on simple abdominal x-ray, they can suspect the patient may have slow colon transit. There are some practical methods to assess constipated patient and this include digital rectal exam and Bristol stool scale. We can consider additional anorectal tests in patients of abnormal digital rectal exam.4 Abnormal digital rectal exam by skillful examiner can practically suspect defecatory disorder. Also Bristol stool scale is a practical indicator of colon transit and this simple illustration can be useful in a clinical setting of discrepancy between the bowel frequency and stool hardness.5 Besides of this physical exam and simple illustration, simple abdominal radiography may give some additional information to primary physician.


title: A 21-Gbit/s 1.63-pJ/bit Adaptive CTLE and One-Tap DFE With Single Loop Spectrum Balancing Method

abstract: This brief presents an adaptive continuous-time linear equalizer (CTLE) and one-tap decision feedback equalizer (DFE) using the spectrum balancing (SB) method. The SB method is extended for not only CTLE but also DFE with the aid of gain characteristics of one-tap DFE. Thus, adaptation loops for each equalizer type are merged to a single loop. As a result, the complexity and power consumption of the adaptation circuits are reduced significantly. The test chip consumes 34.2 mW from 1.2 V supply with 65-nm CMOS process.


title: Genetic foundations of human intelligence

abstract: Individual differences in intelligence (cognitive abilities) are a prominent aspect of human psychology, and play a substantial role in influencing important life outcomes. Their phenotypic structure—as described by the science of psychometrics—is well understood and well replicated. Approximately half of the variance in a broad range of cognitive abilities is accounted by a general cognitive factor (g), small proportions of cognitive variance are caused by separable broad domains of mental function, and the substantial remainder is caused by variance that is unique to highly specific cognitive skills. The heritability of g is substantial. It increases from a low value in early childhood of about 30%, to well over 50% in adulthood, which continues into old age. Despite this, there is still almost no replicated evidence concerning the individual genes, which have variants that contribute to intelligence differences. Here, we describe the human intelligence phenotype, summarise the evidence for its heritability, provide an overview of and comment on molecular genetic studies, and comment on future progress in the field.


Why TTIP Should Have an Investment Chapter Including ISDS


title: VNF-FG design and VNF placement for 5G mobile networks

abstract: Network function virtualization (NFV) is envisioned as one of the critical technologies in 5th-Generation (5G) mobile networks. This paper investigates the virtual network function forwarding graph (VNF-FG) design and virtual network function (VNF) placement for 5G mobile networks. We first propose a two-step method composed of flow designing and flow combining for generating VNF-FGs according to network service requests. For mapping VNFs in the generated VNF-FG to physical resources, we then modify the hybrid NFV environment with introducing more types of physical nodes and mapping modes for the sake of completeness and practicality, and formulate the VNF placement optimization problem for achieving lower bandwidth consumption and lower maximum link utilization simultaneously. To resolve this problem, four genetic algorithms are proposed on the basis of the frameworks of two existing algorithms (multiple objective genetic algorithm and improved non-dominated sorting genetic algorithm). Simulation results show that Greedy-NSGA-II achieves the best performance among our four algorithms. Compared with three non-genetic algorithms (random, backtracking mapping and service chains deployment with affiliation-aware), Greedy-NSGA-II reduces 97.04%, 87.76% and 88.42% of the average total bandwidth consumption, respectively, and achieves only 13.81%, 25.04% and 25.41% of the average maximum link utilization, respectively. Moreover, using our VNF-FG design method and Greedy-NSGA-II together can also reduce the total bandwidth consumption remarkably.


title: An overview of the tasks used to test working memory in rodents

abstract: In rodents, working memory is a representation of an object, stimulus, or spatial location that is typically used within a testing session, but not between sessions, to guide behaviour. In this review we consider a number of the tasks used to assess this type of memory in the rodent, and highlight some of their limitations. Although the concept of working memory as applied to rodents has its origin in the experiments of David Olton and Werner Honig in the 1970s, many earlier experiments assessed the same type of memory under the guise of delayed reaction or alternation paradigms. We revisit these early tasks, and also consider the nature of working memory used on maze tasks, operant box based tasks, and non-spatial delayed non-matching to sample paradigms.


title: A Public-Key Cryptosystem Based On Stochastic Petri Net

abstract: In this paper, we present a new method to build public-key Cryptosystem. The method is based on the state explosion problem occurred in the computing of average number of tokens in the places of Stochastic Petri Net (SPN). The reachable markings in the coverability tree of SPN are used as the encryption keys. Accordingly, multiple encryption keys can be generated, thus we can perform multiple encryption to get as strong security as we expect. The decryption is realized through solving a group of ordinary differential equations from Continuous Petri Net (CPN), which has the same underlying Petri net as that of SPN. The decipherment difficulty for attackers is in exponential order. The contribution of this paper is that we can use continuous mathematics to design cryptosystems besides discrete mathematics.


title: Software Development Processes for Games: A Systematic Literature Review

abstract: This paper describes the methodology and results from a Systematic Literature Review (SLR) of the software processes used in game development. A total of 404 papers were analyzed as part of the review and the various process models that are used in industry and academia/research are presented. Software Process Improvement (SPI) initiatives for game development are discussed. The factors that promote or deter the adoption of process models, and implementing SPI in practice are highlighted. Our findings indicate that there is no single model that serves as a best practice process model for game development and it is a matter of deciding which model is best suited for a particular game. Agile models such as Scrum and XP are suited to the knowledge intensive domain of game development where innovation and speed to market are vital. Hybrid approaches such as reuse can also be suitable for game development where the risk of the upfront investment in terms of time and cost is mitigated with a game that has stable requirements and a longer lifespan.


Media, society, world: Social theory and digital media practice


title: A model for coupling under coil misalignment for DD pads and circular pads of WPT system

abstract: The coupling coefficient of a transformer is a key parameter affecting the performance of a wireless power transmission system. Based on the magnetic reluctance model under aligned condition, this paper gives an effective method for calculating the null coupling position, as well as a practical and accurate model for finding the coupling coefficient under coil misalignment and varying air gap. The null coupling position is determined by the horizontal offset when the flux of opposite direction that is intersected with the receiving coil is cancelled out. An empirical formula is given to correct the null coupling position. Based on the physics of coupling under misaligned conditions, a practical model for coupling is constructed. Transformers with DD structure and typical circular structure are fabricated for verification. Experimental measurement validates the model.


title: Locating faults through automated predicate switching

abstract: Typically debugging begins when during a program execution a point is reached at which an obviously incorrect value is observed. A general and powerful approach to automated debugging can be based upon identifying modifications to the program state that will bring the execution to a successful conclusion. However, searching for arbitrary changes to the program state is difficult due to the extremely large search space. In this paper we demonstrate that by forcibly switching a predicate's outcome at runtime and altering the control flow, the program state can not only be inexpensively modified, but in addition it is often possible to bring the program execution to a successful completion (i.e., program produces the desired output). By examining the switched predicate, also called the critical predicate, the cause of the bug can then be identified. Since the outcome of a branch can only be either true or false, the number of modified states resulting by predicate switching is far less than those possible through arbitrary state changes. Thus, it is possible to automatically search through modified states to find one that leads to the correct output. We have developed an implementation based upon dynamic instrumentation to perform this search through program re-execution -- the program is executed from the beginning and a predicate's outcome is switched to produce the desired change in control flow. To evaluate our approach, we tried our technique on several reported bugs for a number of UNIX utility programs. Our technique was found to be practical (i.e., acceptable in time taken) and effective (i.e., we were able to automatically identify critical predicates). Moreover we show that bidirectional dynamic slices of critical predicates capture the faulty code.


title: A Study on Dual-Scale Data Charts

abstract: We present the results of a user study that compares different ways of representing Dual-Scale data charts. Dual-Scale charts incorporate two different data resolutions into one chart in order to emphasize data in regions of interest or to enable the comparison of data from distant regions. While some design guidelines exist for these types of charts, there is currently little empirical evidence on which to base their design. We fill this gap by discussing the design space of Dual-Scale cartesian-coordinate charts and by experimentally comparing the performance of different chart types with respect to elementary graphical perception tasks such as comparing lengths and distances. Our study suggests that cut-out charts which include collocated full context and focus are the best alternative, and that superimposed charts in which focus and context overlap on top of each other should be avoided.


title: Less is More: Filtering Abnormal Dimensions in GloVe

abstract: GloVe, global vectors for word representation, performs well in some word analogy and semantic relatedness tasks. However, we find that some dimensions of the trained word embedding are abnormal. We verify our conjecture via removing these abnormal dimensions using Kolmogorov-Smimov test and experiment on several benchmark datasets for semantic relatedness measurement. The experimental results confirm our finding. Interestingly, some of the tasks outperform the state-of-the-art model SensEmbed by simply removing these abnormal dimensions. The novel rule of thumb technique which leads to better performance is expected to be useful in practice.


title: Joint learning of words and meaning representations for open-text semantic parsing

abstract: Open-text semantic parsers are designed to interpret any statement in natural language by inferring a corresponding meaning representation (MR – a formal representation of its sense). Unfortunately, large scale systems cannot be easily machine-learned due to a lack of directly supervised data. We propose a method that learns to assign MRs to a wide range of text (using a dictionary of more than 70,000 words mapped to more than 40,000 entities) thanks to a training scheme that combines learning from knowledge bases (e.g. WordNet) with learning from raw text. The model jointly learns representations of words, entities and MRs via a multi-task training process operating on these diverse sources of data. Hence, the system ends up providing methods for knowledge acquisition and wordsense disambiguation within the context of semantic parsing in a single elegant framework. Experiments on these various tasks indicate the promise of the approach.


title: Could on-line voting boost desire to vote? - Technology acceptance perceptions of young Hungarian citizens

abstract: Abstract In our paper we develop and test the argument that intent to i-vote (to use on-line voting systems) drives intent to vote, while intent to i-vote is influenced by four key attitudes: performance expectation, perception on ease of use, trust in the internet and trust in the government. We show that these findings contradict those which exclusively identified economical, legal, and cultural drivers to enhance democratic participation in the Central and Eastern European region. Rooted cardinally in the Technology Acceptance Model (TAM) six hypotheses were set, and then tested with partial least square (PLS) structural equation modelling. In the context of young, educated and internet-ready Hungarian voters the testing of the hypotheses has shown high level of on-line voting intent and that perception of on-line voting would enhance voting desire amongst young Hungarian internet users. Also, our findings show that performance expectation, perception on ease of use and trust in the internet are positively associated with i-voting intent.


title: Usage of optical correlator in video surveillance system for abandoned luggage

abstract: This article is focused on new methods of image processing used in modern video surveillance systems. The aim of this article is usage of the latest knowledge in the field of digital signal processing (image and video) and optical correlation in the design and experimental verification of video surveillance system that should detect lost or abandoned luggage.


title: Road surface 3d reconstruction based on dense subpixel disparity map estimation

abstract: Various 3D reconstruction methods have enabled civil engineers to detect damage on a road surface. To achieve the millimeter accuracy required for road condition assessment, a disparity map with subpixel resolution needs to be used. However, none of the existing stereo matching algorithms are specially suitable for the reconstruction of the road surface. Hence in this paper, we propose a novel dense subpixel disparity estimation algorithm with high computational efficiency and robustness. This is achieved by first transforming the perspective view of the target frame into the reference view, which not only increases the accuracy of the block matching for the road surface but also improves the processing speed. The disparities are then estimated iteratively using our previously published algorithm, where the search range is propagated from three estimated neighboring disparities. Since the search range is obtained from the previous iteration, errors may occur when the propagated search range is not sufficient. Therefore, a correlation maxima verification is performed to rectify this issue, and the subpixel resolution is achieved by conducting a parabola interpolation enhancement. Furthermore, a novel disparity global refinement approach developed from the Markov random fields and fast bilateral stereo is introduced to further improve the accuracy of the estimated disparity map, where disparities are updated iteratively by minimizing the energy function that is related to their interpolated correlation polynomials. The algorithm is implemented in C language with a near real-time performance. The experimental results illustrate that the absolute error of the reconstruction varies from 0.1 to 3 mm.


title: Contextual Priming and Feedback for Faster R-CNN

abstract: The field of object detection has seen dramatic performance improvements in the last few years. Most of these gains are attributed to bottom-up, feedforward ConvNet frameworks. However, in case of humans, top-down information, context and feedback play an important role in doing object detection. This paper investigates how we can incorporate top-down information and feedback in the state-of-the-art Faster R-CNN framework. Specifically, we propose to: (a) augment Faster R-CNN with a semantic segmentation network; (b) use segmentation for top-down contextual priming; (c) use segmentation to provide top-down iterative feedback using two stage training. Our results indicate that all three contributions improve the performance on object detection, semantic segmentation and region proposal generation.


title: Understanding consumers' purchase intention towards online group buying in China

abstract: Many new online business models have emerged in recent years. In China, online group buying (OGB) is widely used as an effective marketing method. This paper provides a research model to examine the technology acceptance factors, social and psychological factors on Chinese customer's purchase intention towards online group buying. Questionnaire survey and statistical analysis method are used in this paper and 232 valid samples are collected. With the help of software SPSS 17.0, the research model and hypotheses are tested and some key factors affecting Chinese consumers' purchase intention towards OGB are found out. The results demonstrate that perceived usefulness, perceived ease of use, subjective norm, word-of-mouth, perceived low-price, and perceived playfulness positively affect consumers' purchase intention towards OGB in China, perceived risk has not a negative influence on purchase intention towards OGB in China and perceived low-price does not show the influence on perceived risks. Finally, based on the above results, some management inspirations are put forward. This study extends the application of technology adoption model (TAM) to study the adoption behavior in developing countries. Also, the findings provide specific directions for online group buying websites to improve their service and enhance their competitions.


title: Planar antennas

abstract: This article reviews the state of the art in broadband antennas for emerging UWB applications and addresses the important issues of the broadband antenna design for UWB applications. First, a variety of planar monopoles with finite ground planes are reviewed. Next, the roll antennas with enhanced radiation performance are outlined. After that, the planar antennas printed on PCBs are described. A directional antipodal Vivaldi antenna is also presented for UWB applications. Last, a UWB antenna for wearable applications is exemplified


title: An ultra low power UHF RFID tag front-end for EPCglobal Gen2 with novel clock-free decoder

abstract: In this paper, an ultra low power front-end circuit for an UHF RFID tag is presented. In order to minimize the power consumption, a novel data decoder is proposed, which removes the need for high frequency oscillator. In addition, a dual voltage multiplier scheme is employed, which increases the power efficiency. Simulation results shows that the proposed circuit reduces the power consumption by an order magnitude compared to conventional RFID front-end circuits that use high frequency oscillators and single voltage multiplier.


title: Broad and load-aware anycast mapping with verfploeter

abstract: IP anycast provides DNS operators and CDNs with automatic fail-over and reduced latency by breaking the Internet into catchments, each served by a different anycast site. Unfortunately, understanding and predicting changes to catchments as anycast sites are added or removed has been challenging. Current tools such as RIPE Atlas or commercial equivalents map from thousands of vantage points (VPs), but their coverage can be inconsistent around the globe. This paper proposes Verfploeter, a new method that maps anycast catchments using active probing. Verfploeter provides around 3.8M passive VPs, 430x the 9k physical VPs in RIPE Atlas, providing coverage of the vast majority of networks around the globe. We then add load information from prior service logs to provide calibrated predictions of anycast changes. Verfploeter has been used to evaluate the new anycast deployment for B-Root, and we also report its use of a nine-site anycast testbed. We show that the greater coverage made possible by Verfploeter's active probing is necessary to see routing differences in regions that have sparse coverage from RIPE Atlas, like South America and China.


title: Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach

abstract: This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations between stations to predict station-level hourly demand in a large-scale bike-sharing network. Two architectures of the GCNN-DDGF model are explored; GCNNreg-DDGF is a regular GCNN-DDGF model which contains the convolution and feedforward blocks, and GCNNrec-DDGF additionally contains a recurrent block from the Long Short-term Memory neural network architecture to capture temporal dependencies in the bike-sharing demand series. Furthermore, four types of GCNN models are proposed whose adjacency matrices are based on various bike-sharing system data, including Spatial Distance matrix (SD), Demand matrix (DE), Average Trip Duration matrix (ATD), and Demand Correlation matrix (DC). These six types of GCNN models and seven other benchmark models are built and compared on a Citi Bike dataset from New York City which includes 272 stations and over 28 million transactions from 2013 to 2016. Results show that the GCNNrec-DDGF performs the best in terms of the Root Mean Square Error, the Mean Absolute Error and the coefficient of determination (R2), followed by the GCNNreg-DDGF. They outperform the other models. Through a more detailed graph network analysis based on the learned DDGF, insights are obtained on the black box of the GCNN-DDGF model. It is found to capture some information similar to details embedded in the SD, DE and DC matrices. More importantly, it also uncovers hidden heterogeneous pairwise correlations between stations that are not revealed by any of those matrices.


title: Symmetric-Aperture Antenna for Broadband Circular Polarization

abstract: A novel symmetric-aperture antenna is proposed for broadband circularly polarized (CP) radiation using a coplanar waveguide (CPW) feed. The proposed antenna consists of a CPW-feed and a wide symmetric aperture along the diagonal axis. A symmetric-aperture consists of two diamond-shaped sections. The symmetric-aperture and CPW-feeding structure are fabricated on the same plane of a dielectric substrate. Wideband CP radiation is achieved by changing the arms of symmetric-aperture and stripline length. The measured bandwidths of 3-dB axial ratio (AR) and 10-dB return loss are around 68% (2.4-4.85 GHz) and 107% (1.6-5.25 GHz), respectively. The measured boresight gain is more than 3.0 dBic over the CP bandwidth. Overall antenna size is around 0.7λo × 0.7λo × 0.019λo at 3.5 GHz.


title: Factors Influencing Fast-Food Consumption Among Adolescents in Tehran: A Qualitative Study

abstract: Background:The consumption of different types of fast food is increasingly growing in all parts of the world, both in developed and developing countries. Because of the changes and transitions in the lifestyle and dietary habits of people, an increasing number of people from different age groups, particularly adolescents and young adults, are inclined toward consuming fast food. ::: Objectives:The objective of this study was to investigate the factors influencing fast-food consumption among adolescents in Tehran, Iran. ::: Patients and Methods:The present qualitative study was conducted in 2012 - 2013 in Tehran, the capital of Iran. To achieve the objective of this study, 42 adolescents were enrolled in this study through a purposive sampling method, and the required data was collected via individual semi-structured in-depth interviews. Data collection and analysis were carried out simultaneously, and the collected data was analyzed via a thematic content analysis and using MAXQDA 10 software. ::: Results:In this study after coding the transcribed interviews, the findings were categorized into three main themes as follows: personal views, social factors, and family factors. Each theme included several categories and subcategories, and the coded sentences and phrases were placed under each category and subcategory. ::: Conclusions:The results of this study showed that the number of factors promoting fast-food consumption appeared to be more than the inhibiting factors and that the diverse factors at the individual and social level influenced fast-food consumption among adolescents. ::: Keywords: Fast Foods, Adolescent, Health Food, Iran


title: Challenges of Internet of Things and Big Data Integration

abstract: The Internet of Things anticipates the conjunction of physical gadgets to the In-ternet and their access to wireless sensor data which makes it expedient to restrain the physical world. Big Data convergence has put multifarious new opportunities ahead of business ventures to get into a new market or enhance their operations in the current market. considering the existing techniques and technologies, it is probably safe to say that the best solution is to use big data tools to provide an analytical solution to the Internet of Things. Based on the current technology deployment and adoption trends, it is envisioned that the Internet of Things is the technology of the future, while to-day's real-world devices can provide real and valuable analytics, and people in the real world use many IoT devices. Despite all the advertisements that companies offer in connection with the Internet of Things, you as a liable consumer, have the right to be suspicious about IoT advertise-ments. The primary question is: What is the promise of the Internet of things con-cerning reality and what are the prospects for the future.


title: Leveraging dynamic slicing to enhance indirect branch prediction

abstract: An indirect branch specifies where the address of the next instruction to execute is located. The address to be jumped to is not known until the instruction is executed. Indirect branches can incur a significant fraction of branch misprediction overhead even though they remain less frequent than the more predictable conditional branches. Indirect branch instructions implement multiway branch statements and virtual function calls in object-oriented languages. Multiple targets indirect branch instructions are difficult to predict.


title: Speech Recognition Using Vector Quantization through Modified K-meansLBG Algorithm

abstract: In the Vector Quantization, the main task is to generate a good codebook. The distortion measure between the original pattern and the reconstructed pattern should be minimum. In this paper, a proposed algorithm called Modified K-meansLBG algorithm used to obtain a good codebook. The system has shown good performance on limited vocabulary tasks. Keywords: K-means algorithm, LBG algorithm, Vector Quantization, Speech Recognition


title: Deep Convolutional Neural Networks for Action Recognition Using Depth Map Sequences

abstract: Recently, deep learning approach has achieved promising results in various fields of computer vision. In this paper, a new framework called Hierarchical Depth Motion Maps (HDMM) + 3 Channel Deep Convolutional Neural Networks (3ConvNets) is proposed for human action recognition using depth map sequences. Firstly, we rotate the original depth data in 3D pointclouds to mimic the rotation of cameras, so that our algorithms can handle view variant cases. Secondly, in order to effectively extract the body shape and motion information, we generate weighted depth motion maps (DMM) at several temporal scales, referred to as Hierarchical Depth Motion Maps (HDMM). Then, three channels of ConvNets are trained on the HDMMs from three projected orthogonal planes separately. The proposed algorithms are evaluated on MSRAction3D, MSRAction3DExt, UTKinect-Action and MSRDailyActivity3D datasets respectively. We also combine the last three datasets into a larger one (called Combined Dataset) and test the proposed method on it. The results show that our approach can achieve state-of-the-art results on the individual datasets and without dramatical performance degradation on the Combined Dataset.


title: When context hinders! Learn–test compatibility in face recognition

abstract: Some theories of holistic face processing propose that parts in faces (eyes, nose, mouth, etc.) are not explicitly represented. So far, the empirical evidence has shown that whole-to-part superiority is found when wholes are learned. We substantiated this using photographic faces. More importantly, we investigated whether learning parts also reveals holistic effects. This has not been attempted before. Four experiments showed that after learning facial parts, recognition of these parts was disrupted when the part was shown in the full face. This distraction effect was strongest when perceivers were not directed to focus on a particular facial feature. Thus, it is very difficult to ignore irrelevant parts in faces. In fact, this might be the essence of holistic face processing.


title: Convolutional 2D Knowledge Graph Embeddings

abstract: Link prediction for knowledge graphs is the task of predicting missing relationships between entities. Previous work on link prediction has focused on shallow, fast models which can scale to large knowledge graphs. However, these models learn less expressive features than deep, multi-layer models -- which potentially limits performance. In this work, we introduce ConvE, a multi-layer convolutional network model for link prediction, and report state-of-the-art results for several established datasets. We also show that the model is highly parameter efficient, yielding the same performance as DistMult and R-GCN with 8x and 17x fewer parameters. Analysis of our model suggests that it is particularly effective at modelling nodes with high indegree -- which are common in highly-connected, complex knowledge graphs such as Freebase and YAGO3. In addition, it has been noted that the WN18 and FB15k datasets suffer from test set leakage, due to inverse relations from the training set being present in the test set -- however, the extent of this issue has so far not been quantified. We find this problem to be severe: a simple rule-based model can achieve state-of-the-art results on both WN18 and FB15k. To ensure that models are evaluated on datasets where simply exploiting inverse relations cannot yield competitive results, we investigate and validate several commonly used datasets -- deriving robust variants where necessary. We then perform experiments on these robust datasets for our own and several previously proposed models and find that ConvE achieves state-of-the-art Mean Reciprocal Rank across most datasets.


title: Wireless Sensor Network Based Smart Grid Communications: Challenges, Protocol Optimizations, and Validation Platforms

abstract: Smart grids, the next generation of electric grids, require the deployment of sophisticated monitoring and control systems to enhance their operational efficiency. Wireless sensor networks (WSNs) have been considered as a promising communication technology for the monitoring and control of smart grid operation. They bring significant advantages such as, rapid deployment, low cost and scalability. However, the deployment of WSNs in smart grids brought new challenges mainly due to the electric grid features. Consequently, traditional WSN communication protocols have been shown inadequate and several recent research efforts were dedicated for their optimization. This paper provides a comprehensive survey on related literature, discusses the still-open research issues, and identifies the most common validation platforms for experimenting WSN communications in smart grid. We believe this survey will pave the way for the research community to (i) understand important concepts related to WSN-based smart grid communications, (ii) identify gaps and make valuable contributions in this timely and exiting field and (iii) choose the convenient experimental platform for the validation of proposed solutions.


title: Use of Roadway Scene Semantic Information and Geometry-Preserving Landmark Pairs to Improve Visual Place Recognition in Changing Environments

abstract: Visual place recognition (VPR) in changing environments is an urgent challenge for long-term autonomous navigation. One recent ConvNet landmark-based approach exploits region landmarks coupled with ConvNet features to match images, and the approach has shown promising results under significant environmental and viewpoint changes. In this paper, we propose a robust ConvNet landmark-based system for VPR in changing outdoor roadway environments by extension of this approach from the following two aspects. First, our method utilizes more discriminative landmarks obtained by a novel refinement method called SemLandmarks , which leverages roadway scene semantic information to screen landmarks directly detected by an existing object proposal method. Second, our method improves the accuracy of image matching by introducing consistent spatial constraints based on the use of geometry-preserving landmark pairs. Experimental results demonstrate that our method significantly improves the state of the art in VPR in terms of recognition accuracy on three challenging benchmark data sets with various environmental and viewpoint changes.


title: The Austrian eID Ecosystem in the Public Cloud: How to Obtain Privacy While Preserving Practicality

abstract: The Austrian eID system constitutes a main pillar within the Austrian e-Government strategy. The eID system ensures unique identification and secure authentication for citizens protecting access to applications where sensitive and personal data is involved. In particular, the Austrian eID system supports three main use cases: Identification and authentication of Austrian citizens, electronic representation, and foreign citizen authentication at Austrian public sector applications. For supporting all these use cases, several components -- either locally deployed in the applications' domain or centrally deployed -- need to communicate with each other. While local deployments have some advantages in terms of scalability, still a central deployment of all involved components would be advantageous, e.g. due to less maintenance efforts. However, a central deployment can easily lead to load bottlenecks because theoretically the whole Austrian population as well as -- for foreign citizens -- the whole EU population could use the provided services. To mitigate the issue on scalability, in this paper we propose the migration of main components of the ecosystem into a public cloud. However, a move of trusted services into a public cloud brings up new obstacles, particular with respect to privacy. To bypass the issue on privacy, in this paper we propose an approach on how the complete Austrian eID ecosystem can be moved into a public cloud in a privacy-preserving manner by applying selected cryptographic technologies (in particular using proxy re-encryption and redactable signatures). Applying this approach, no sensitive data will be disclosed to a public cloud provider by still supporting all three main eID system use cases. We finally discuss our approach based on selected criteria.


title: FSMRank: Feature Selection Algorithm for Learning to Rank

abstract: In recent years, there has been growing interest in learning to rank. The introduction of feature selection into different learning problems has been proven effective. These facts motivate us to investigate the problem of feature selection for learning to rank. We propose a joint convex optimization formulation which minimizes ranking errors while simultaneously conducting feature selection. This optimization formulation provides a flexible framework in which we can easily incorporate various importance measures and similarity measures of the features. To solve this optimization problem, we use the Nesterov's approach to derive an accelerated gradient algorithm with a fast convergence rate O(1/T2). We further develop a generalization bound for the proposed optimization problem using the Rademacher complexities. Extensive experimental evaluations are conducted on the public LETOR benchmark datasets. The results demonstrate that the proposed method shows: 1) significant ranking performance gain compared to several feature selection baselines for ranking, and 2) very competitive performance compared to several state-of-the-art learning-to-rank algorithms.


0 Acceleration of Convergence of the Alternating Least Squares Algorithm for Nonlinear Principal Components Analysis


Diet and nutrition : A literature review of factors influencing blue-collar apprentices


title: The Gesture Recognition Toolkit

abstract: The Gesture Recognition Toolkit is a cross-platform open-source C++ library designed to make real-time machine learning and gesture recognition more accessible for non-specialists. Emphasis is placed on ease of use, with a consistent, minimalist design that promotes accessibility while supporting flexibility and customization for advanced users. The toolkit features a broad range of classification and regression algorithms and has extensive support for building real-time systems. This includes algorithms for signal processing, feature extraction and automatic gesture spotting.


title: Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases

abstract: Background: Deep learning (DL) is a representation learning approach ideally suited for image analysis challenges in digital pathology (DP). The variety of image analysis tasks in the context of DP includes detection and counting (e.g., mitotic events), segmentation (e.g., nuclei), and tissue classification (e.g., cancerous vs. non-cancerous). Unfortunately, issues with slide preparation, variations in staining and scanning across sites, and vendor platforms, as well as biological variance, such as the presentation of different grades of disease, make these image analysis tasks particularly challenging. Traditional approaches, wherein domain-specific cues are manually identified and developed into task-specific handcrafted features, can require extensive tuning to accommodate these variances. However, DL takes a more domain agnostic approach combining both feature discovery and implementation to maximally discriminate between the classes of interest. While DL approaches have performed well in a few DP related image analysis tasks, such as detection and tissue classification, the currently available open source tools and tutorials do not provide guidance on challenges such as (a) selecting appropriate magnification, (b) managing errors in annotations in the training (or learning) dataset, and (c) identifying a suitable training set containing information rich exemplars. These foundational concepts, which are needed to successfully translate the DL paradigm to DP tasks, are non-trivial for (i) DL experts with minimal digital histology experience, and (ii) DP and image processing experts with minimal DL experience, to derive on their own, thus meriting a dedicated tutorial. Aims: This paper investigates these concepts through seven unique DP tasks as use cases to elucidate techniques needed to produce comparable, and in many cases, superior to results from the state-of-the-art hand-crafted feature-based classification approaches. Results : Specifically, in this tutorial on DL for DP image analysis, we show how an open source framework (Caffe), with a singular network architecture, can be used to address: (a) nuclei segmentation ( F -score of 0.83 across 12,000 nuclei), (b) epithelium segmentation ( F -score of 0.84 across 1735 regions), (c) tubule segmentation ( F -score of 0.83 from 795 tubules), (d) lymphocyte detection ( F -score of 0.90 across 3064 lymphocytes), (e) mitosis detection ( F -score of 0.53 across 550 mitotic events), (f) invasive ductal carcinoma detection ( F -score of 0.7648 on 50 k testing patches), and (g) lymphoma classification (classification accuracy of 0.97 across 374 images). Conclusion: This paper represents the largest comprehensive study of DL approaches in DP to date, with over 1200 DP images used during evaluation. The supplemental online material that accompanies this paper consists of step-by-step instructions for the usage of the supplied source code, trained models, and input data.


title: Argumentation Quality Assessment: Theory vs. Practice

abstract: Argumentation quality is viewed differently in argumentation theory and in practical assessment approaches. This paper studies to what extent the views match empirically. We find that most observations on quality phrased spontaneously are in fact adequately represented by theory. Even more, relative comparisons of arguments in practice correlate with absolute quality ratings based on theory. Our results clarify how the two views can learn from each other.


title: Graph Metrics for Temporal Networks

abstract: Temporal networks, i.e., networks in which the interactions among a set of elementary units change over time, can be modelled in terms of time-varying graphs, which are time-ordered sequences of graphs over a set of nodes. In such graphs, the concepts of node adjacency and reachability crucially depend on the exact temporal ordering of the links. Consequently, all the concepts and metrics proposed and used for the characterisation of static complex networks have to be redefined or appropriately extended to time-varying graphs, in order to take into account the effects of time ordering on causality. In this chapter we discuss how to represent temporal networks and we review the definitions of walks, paths, connectedness and connected components valid for graphs in which the links fluctuate over time. We then focus on temporal node–node distance, and we discuss how to characterise link persistence and the temporal small-world behaviour in this class of networks. Finally, we discuss the extension of classic centrality measures, including closeness, betweenness and spectral centrality, to the case of time-varying graphs, and we review the work on temporal motifs analysis and the definition of modularity for temporal graphs.