title: CNN for very fast ground segmentation in velodyne LiDAR data

abstract: This paper presents a novel method for ground segmentation in Velodyne point clouds. We propose an encoding of sparse 3D data from the Velodyne sensor suitable for training a convolutional neural network (CNN). This general purpose approach is used for segmentation of the sparse point cloud into ground and non-ground points. The LiDAR data are represented as a multi-channel 2D signal where the horizontal axis corresponds to the rotation angle and the vertical axis the indexes channels (i.e. laser beams). Multiple topologies of relatively shallow CNNs (i.e. 3-5 convolutional layers) are trained and evaluated using a manually annotated dataset we prepared. The results show significant improvement of performance over the state-of-the-art method by Zhang et al. in terms of speed and also minor improvements in terms of accuracy.


title: Assessing Octopus vulgaris distribution using presence-only model methods

abstract: The distribution of the common octopus (Octopus vulgaris) in the Mediterranean and Eastern Atlantic waters is evaluated using two presence-only analyses: The maximum entropy model (Maxent) and the ecological niche factor analysis (ENFA). Maxent predicts those geographical areas that satisfy the environmental or abiotic requirements of a species while ENFA explores the niche and habitat preferences of O. vulgaris. The analyses were implemented recovering the spatial information from 213 octopus presence data collected from surveys and bibliographical records. Together, these analyses provided reasonable estimates of the species distribution and the octopus habitat. Among the gathered set of explanatory environmental variables, sea bottom temperature, sea bottom salinity, surface dissolved oxygen and sea surface chlorophyll-α appear as the main variables involved in O. vulgaris distribution. These results were confronted with the available literature.


title: The Unreasonable Fundamental Incertitudes Behind Bitcoin Mining

abstract: Bitcoin is a "crypto currency", a decentralized electronic payment scheme based on cryptography which has recently gained excessive popularity. Scientific research on bitcoin is less abundant. A paper at Financial Cryptography 2012 conference explains that it is a system which "uses no fancy cryptography", and is "by no means perfect". It depends on a well-known cryptographic standard SHA-256. In this paper we revisit the cryptographic process which allows one to make money by producing bitcoins. We reformulate this problem as a Constrained Input Small Output (CISO) hashing problem and reduce the problem to a pure block cipher problem. We estimate the speed of this process and we show that the cost of this process is less than it seems and it depends on a certain cryptographic constant which we estimated to be at most 1.86. These optimizations enable bitcoin miners to save tens of millions of dollars per year in electricity bills. Miners who set up mining operations face many economic incertitudes such as high volatility. In this paper we point out that there are fundamental incertitudes which depend very strongly on the bitcoin specification. The energy efficiency of bitcoin miners have already been improved by a factor of about 10,000, and we claim that further improvements are inevitable. Better technology is bound to be invented, would it be quantum miners. More importantly, the specification is likely to change. A major change have been proposed in May 2013 at Bitcoin conference in San Diego by Dan Kaminsky. However, any sort of change could be flatly rejected by the community which have heavily invested in mining with the current technology. Another question is the reward halving scheme in bitcoin. The current bitcoin specification mandates a strong 4-year cyclic property. We find this property totally unreasonable and harmful and explain why and how it needs to be changed.


title: Associated Activation-Driven Enrichment: Understanding Implicit Information from a Cognitive Perspective

abstract: In this paper, we propose a novel text representation paradigm and a set of follow-up text representation models based on cognitive psychology theories. The intuition of our study is that the knowledge implied in a large collection of documents may improve the understanding of single documents. Based on cognitive psychology theories, we propose a general text enrichment framework, study the key factors to enable activation of implicit information, and develop new text representation methods to enrich text with the implicit information. Our study aims to mimic some aspects of human cognitive procedure in which given stimulant words serve to activate understanding implicit concepts. By incorporating human cognition into text representation, the proposed models advance existing studies by mining implicit information from given text and coordinating with most existing text representation approaches at the same time, which essentially bridges the gap between explicit and implicit information. Experiments on multiple tasks show that the implicit information activated by our proposed models matches human intuition and significantly improves the performance of the text mining tasks as well.


title: On the Discrimination-Generalization Tradeoff in GANs

abstract: Generative adversarial training can be generally understood as minimizing certain moment matching loss defined by a set of discriminator functions, typically neural networks. The discriminator set should be large enough to be able to uniquely identify the true distribution (discriminative), and also be small enough to go beyond memorizing samples (generalizable). In this paper, we show that a discriminator set is guaranteed to be discriminative whenever its linear span is dense in the set of bounded continuous functions. This is a very mild condition satisfied even by neural networks with a single neuron. Further, we develop generalization bounds between the learned distribution and true distribution under different evaluation metrics. When evaluated with neural or Wasserstein distances, our bounds show that generalization is guaranteed as long as the discriminator set is small enough, regardless of the size of the generator or hypothesis set. When evaluated with KL divergence, our bound provides an explanation on the counter-intuitive behaviors of testing likelihood in GAN training. Our analysis sheds lights on understanding the practical performance of GANs.


title: Energy storage management for EV charging stations: Comparison between uncoordinated and statistical charging loads

abstract: Electricity price is essential factor in the deployment of electric vehicles (EVs) on large scale. In wholesale electricity market, EV charging stations(ECS) connected with suitably sized energy storage system (ESS) can save substantial amount of money by managing their time of utilisation (TOU). In this study, a real-time EV charging model at ECS along with ESS degradation model is considered to analyse effect of the ESS for TOU pricing benefits. The objective is to minimize the EVs charging cost. The proposed algorithm focuses on real-time energy management using combination of heuristic approach and deterministic approach. The algorithm analyses electricity pricing trend using historical and forecasted statistical data and controls the power imported from the grid, while feeding dynamic EV charging load. Significant amount of cost saving is seen in results due to deployment of ESS in utilizing TOU pricing benefits of wholesale electricity price.


title: Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science

abstract: Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-freeness), we argue that (contrary to general practice) artificial neural networks, too, should not have fully-connected layers. Here we propose sparse evolutionary training of artificial neural networks, an algorithm which evolves an initial sparse topology (Erdős–Rényi random graph) of two consecutive layers of neurons into a scale-free topology, during learning. Our method replaces artificial neural networks fully-connected layers with sparse ones before training, reducing quadratically the number of parameters, with no decrease in accuracy. We demonstrate our claims on restricted Boltzmann machines, multi-layer perceptrons, and convolutional neural networks for unsupervised and supervised learning on 15 datasets. Our approach has the potential to enable artificial neural networks to scale up beyond what is currently possible.Artificial neural networks are artificial intelligence computing methods which are inspired by biological neural networks. Here the authors propose a method to design neural networks as sparse scale-free networks, which leads to a reduction in computational time required for training and inference.


title: High-Level Language Tools for Reconfigurable Computing

abstract: In the past decade or so we have witnessed a steadily increasing interest in FPGAs as hardware accelerators: they provide an excellent mid-point between the reprogrammability of software devices (CPUs, DSPs, and GPUs) and the performance and low energy consumption of ASICs. However, the programmability of FPGA-based accelerators remains one of the biggest obstacles to their wider adoption. Developing FPGA programs requires extensive familiarity with hardware design and experience with a tedious and complex tool chain. For half a century, layers of abstractions have been developed that simplify the software development process: languages, compilers, dynamically linked libraries, operating systems, APIs, etc. Very little, if any, such abstractions exist in the development of FPGA programs. In this paper, we review the history of using FPGAs as hardware accelerators and summarize the challenges facing the raising of the programming abstraction layers. We survey five High-Level Language tools for the development of FPGA programs: Xilinx Vivado, Altera OpenCL, BluespecBSV, ROCCC, and LegUp to provide an overview of their tool flow, the optimizations they provide, and a qualitative analysis of their hardware implementations of high level code.


title: Cognitive and Energy Harvesting-Based D2D Communication in Cellular Networks: Stochastic Geometry Modeling and Analysis

abstract: While cognitive radio enables spectrum-efficient wireless communication, radio frequency (RF) energy harvesting from ambient interference is an enabler for energy-efficient wireless communication. In this paper, we model and analyze cognitive and energy harvesting-based device-to-device (D2D) communication in cellular networks. The cognitive D2D transmitters harvest energy from ambient interference and use one of the channels allocated to cellular users (in uplink or downlink), which is referred to as the D2D channel, to communicate with the corresponding receivers. We investigate two spectrum access policies for cellular communication in the uplink or downlink, namely, random spectrum access (RSA) policy and prioritized spectrum access (PSA) policy. In RSA, any of the available channels including the channel used by the D2D transmitters can be selected randomly for cellular communication, while in PSA the D2D channel is used only when all of the other channels are occupied. A D2D transmitter can communicate successfully with its receiver only when it harvests enough energy to perform channel inversion toward the receiver, the D2D channel is free, and the signal-to-interference-plus-noise ratio $({\ssr SINR} ) $ at the receiver is above the required threshold; otherwise, an outage occurs for the D2D communication. We use tools from stochastic geometry to evaluate the performance of the proposed communication system model with general path-loss exponent in terms of outage probability for D2D and cellular users. We show that energy harvesting can be a reliable alternative to power cognitive D2D transmitters while achieving acceptable performance. Under the same ${\ssr SINR} $ outage requirements as for the non-cognitive case, cognitive channel access improves the outage probability for D2D users for both the spectrum access policies. When compared with the RSA policy, the PSA policy provides a better performance to the D2D users. Also, using an uplink channel provides improved performance to the D2D users in dense networks when compared to a downlink channel. For cellular users, the PSA policy provides almost the same outage performance as the RSA policy.


title: Millimeter-Wave Multibeam Endfire Dual-Circularly Polarized Antenna Array for 5G Wireless Applications

abstract: A broadband multibeam endfire dual-circularly polarized (CP) antenna array using dielectric-loaded stepped slot antennas for millimeter-wave (mmWave) applications is proposed. First, a dual-CP endfire antenna element is designed for broadband mmWave applications. Additional loaded dielectric is utilized to improve the interelement isolation, the polarization purity, and the gain performance. Second, a substrate integrated waveguide (SIW) feeding network is designed for multibeam array applications. The broadband $4\times 4$ SIW Butler matrix, interconnections, and transitions are designed and applied in the antenna array. Third, mutual coupling and its influence on the array performance are investigated. Additional air gaps between elements are designed for axial ratio performance enhancement. Finally, the proposed multibeam array is designed, fabricated, and measured. The fabricated prototype achieves wide impedance bandwidth of 29.3% with the interbeam isolation greater than 15 dB, and a wide axial ratio bandwidth of 22.5%. The proposed multibeam endfire dual-CP antenna array would be a very attractive candidate for 5G mmWave wireless applications.


title: Longitudinal examination of personal self-efficacy and engagement-related attributes: How do they relate

abstract: A synthesis of the contemporary literature indicates that longitudinal examination of self-efficacy beliefs in educational contexts has been limited to a few notable studies. The present study, utilizing a longitudinal research design, makes attempts to explore the distal impact of students’ enactive learning experiences on their academic self-efficacy beliefs. More importantly, apart from this research focus, we also examine the interrelations between self-efficacy and three major motivation-related attributes of engagement (e.g., absorption) on students’ achievement outcomes in the subject mathematics. This avenue of inquiry, for example, stipulates motivation-related attributes of engagement as potential consequences and antecedents of self-efficacy beliefs. 326 Year 10 students (185 girls, 141 boys) participated in this investigation. We administered a number of Likert-scale questionnaires on multiple occasions over a two-year period, using SEM to analyze the repeated data. MPlus 7.11 yielded some key findings for discussion and educational consideration, for example: the positive influence of Time 1 enactive learning experience on Time 2 self-efficacy and Time 3 motivation-related attributes of engagement; and the positive influence of Time 2 and Time 4 self-efficacy beliefs on Time 5 achievement outcomes. Finally, evidence obtained indicated the mediating mechanisms of both self-efficacy and motivation-related attributes.


title: Exploiting Target Data to Learn Deep Convolutional Networks for Scene-Adapted Human Detection

abstract: The difference between sample distributions of public data sets and specific scenes can be very significant. As a result, the deployment of generic human detectors in real-world scenes most often leads to sub-optimal detection performance. To avoid the labor-intensive task of manual annotations, we propose a semi-supervised approach for training deep convolutional networks on partially labeled data. To exploit a large amount of unlabeled target data, the knowledge learnt from public data sets is transferred to new model training by adapting an auxiliary detector to the target scene. We hypothesize that the components of the auxiliary detector capture essential human characteristics useful for constructing a scene-adapted detector. A selective ensemble algorithm is proposed to select a subset of the components relevant to the target scene for recombination. The resulting model is applied for collecting high-confidence samples from unlabeled target data. Furthermore, a deep convolutional network is trained by progressively labeling and selecting new training samples in a self-paced way. The detailed experimental evaluation verifies the effectiveness and superiority of the proposed approach in scene-specific human detection.


title: Solving the 5 G Mobile Antenna Puzzle

abstract: Advances in antenna technologies for cellular hand-held devices have been synchronous with the evolution of mobile phones over nearly 40 years. Having gone through four major wireless evolutions [1], [2], starting with the analog-based first generation to the current fourth-generation (4G) mobile broadband, technologies from manufacturers and their wireless network capacities today are advancing at unprecedented rates to meet our unrelenting service demands. These ever-growing demands, driven by exponential growth in wireless data usage around the globe [3], have gone hand in hand with major technological milestones achieved by the antenna design community. For instance, realizing the theory regarding the physical limitation of antennas [4]-[6] was paramount to the elimination of external antennas for mobile phones in the 1990s. This achievement triggered a variety of revolutionary mobile phone designs and the creation of new wireless services, establishing the current cycle of cellular advances and advances in mobile antenna technologies.


title: Descriptive and predictive mining on road accidents data

abstract: The paper describes one possibility of how to use the collected data about road accidents to mine frequent patterns and important factors causing different types of accidents. For this purpose was used the real data sample representing road accidents in the United Kingdom (UK) during the years 2005 to 2015. This sample includes more than 1 million records described by 67 attributes divided into three datasets: accidents, casualties and vehicles. Two alternatives were selected as the most suitable: predictive mining through decision trees algorithms and descriptive mining resulted into interesting association rules generated by Apriori algorithm. Obtained results are plausible in comparison with other similar works.


title: Gunrock: GPU Graph Analytics

abstract: For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs, have presented two significant challenges to developing a programmable high-performance graph library. “Gunrock,” our graph-processing system designed specifically for the GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high-performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. We characterize the performance of various optimization strategies and evaluate Gunrock’s overall performance on different GPU architectures on a wide range of graph primitives that span from traversal-based algorithms and ranking algorithms, to triangle counting and bipartite-graph-based algorithms. The results show that on a single GPU, Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives and CPU shared-memory graph libraries, such as Ligra and Galois, and better performance than any other GPU high-level graph library.


title: Fully Convolutional Networks for Ultrasound Image Segmentation of Thyroid Nodules

abstract: Ultrasound image segmentation plays an important role in judgement of benign and malignant thyroid nodules. Compared with the traditional convolutional neural network, the fully convolutional networks has better sparsity, higher precision and faster training speed. In this paper, we develop an 8-layer fully convolutional networks for ultrasound image segmentation of thyroid nodules, which is called FCN-Thyroid Nodules, or FCN-TN for short. We constructed a data set with 300 images to train FCN-TN. Each nodule is delineated by expert and served as ground truth for making comparison. The segmentation accuracy of 91\% is obtained on the proposed network with 100 test images, which indicates that the fully convolutional networks has great potential in the field of ultrasound image segmentation of thyroid nodules.


title: QoE-Oriented Rate Adaptation for DASH With Enhanced Deep Q-Learning

abstract: With the popularity of handheld devices, the development of wireless communication technology and the proliferation of multimedia resources, mobile video has become the main business in LTE networks with explosive traffic demands. How to improve the quality of experience (QoE) of mobile video in the dynamic and complex network environment has become a research focus. Dynamic adaptive streaming over HTTP technology introduces adaptive bitrate (ABR) requests at the client side to improve video QoE and various rate adaptation algorithms are also constantly proposed. In view of the limitations of the existing heuristic or learning-based ABR methods, we propose redirecting enhanced Deep Q-learning toward DASH video QoE (RDQ), a QoE-oriented rate adaptation framework based on enhanced deep Q-learning. First, we establish a chunkwise subjective QoE model and utilize it as the reward function in reinforcement learning so that the strategy can converge toward the direction of maximizing the subjective QoE score. Then, we apply several effective improvements of deep Q-learning to the RDQ agent’s neural network architecture and learning mechanism to achieve faster convergence and higher average reward than other learning-based methods. The proposed RDQ agent has been thoroughly evaluated using trace-based simulation on the real-time LTE network data. For disparate network scenarios and different video contents, the RDQ agent can outperform the existing methods in terms of the QoE score. The breakdown analysis shows that RDQ can suppress the number and the duration of the stalling events to the minimum while maintaining high video bitrate, thus achieving better QoE performance than other methods.


title: Global Stability for the SEIR Model in Epidemiology

abstract: The SEIR model with nonlinear incidence rates in epidemiology is studied. Global stability of the endemic equilibrium is proved using a general criterion for the orbital stability of periodic orbits associated with higher-dimensional nonlinear autonomous systems as well as the theory of competitive systems of differential equations.


title: A New Measure of Word Semantic Similarity Based on WordNet Hierarchy and DAG Theory

abstract: The problem of measuring the semantic similarity between words has been considered a fundamental operation in the field of computational lexical semantics, but the accuracy of existing computational methods is not very close to what humans would perceive. This paper presents a new approach to measure the semantic similarity between words in the hierarchy of WordNet. Our approach considers not only the semantic distance between two words but also the feature information of the DAG (Directed Acyclic Graph). A common data set of word pairs is used to evaluate the proposed approach: we first calculate the semantic similarities of 30 word pairs, then the correlation coefficient between human judgement and six computational measures are calculated, the experiment shows our approach is better than other existing computational models.


title: The Facial Contouring and Support System: An Innovative Method for Midfacial Fat Repositioning

abstract: SUMMARY ::: Minimally invasive rejuvenation procedures are increasingly popular with patients. In the midface, these might involve the introduction of sutures to lift and secure the malar tissue, fat grafts, and fillers to increase volume. This article describes a new facial contouring and support system, which uses an innovative hollow, double-beveled needle to which a 2/0 polypropylene suture may be anchored. Among 102 patients there were no complications, and follow-up at 3-5 years indicates little or no loss of satisfaction with the outcome. The procedure can be combined with other modalities-including fillers and skin peels-to achieve an overall, balanced, natural look for the patient. Potential adjustability and reversibility of the procedure are reassuring for both patient and surgeon and add to the technique's versatility.


title: Detection of purple fringing aberration patterns and its application to forensic analysis of spliced images

abstract: Morphing, fusion and stitching of digital photographs from multiple sources is a common problem in the recent era. While images may depict visual normalcy despite a splicing operation, there are domains in which a consistency or an anomaly check can be performed to detect a covert digital stitching process. Most digital and low-end mobile cameras have certain intrinsic sensor aberrations such as purple fringing (PF), seen in image regions where there are contrast variations and shadowing. This paper proposes an approach based on Fuzzy clustering to first identify regions which contain Purple Fringing and is then used as a forensic tool to detect splicing operations. The accuracy of the Fuzzy clustering approach is comparable with the state-of-the-art PF detection methods and has been shown to penetrate standard interpolation and stitching operations performed using ADOBE PHOTOSHOP.


title: TinyDB: an acquisitional query processing system for sensor networks

abstract: We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to query processing operators. By focusing on the locations and costs of acquiring data, we are able to significantly reduce power consumption over traditional passive systems that assume the a priori existence of data. We discuss simple extensions to SQL for controlling data acquisition, and show how acquisitional issues influence query optimization, dissemination, and execution. We evaluate these issues in the context of TinyDB, a distributed query processor for smart sensor devices, and show how acquisitional techniques can provide significant reductions in power consumption on our sensor devices.


title: Coordinated Reinforcement Learning

abstract: We present several new algorithms for multiagent reinforcement learning. A common feature of these algorithms is a parameterized, structured representation of a policy or value function. This structure is leveraged in an approach we call coordinated reinforcement learning, by which agents coordinate both their action selection activities and their parameter updates. Within the limits of our parametric representations, the agents will determine a jointly optimal action without explicitly considering every possible action in their exponentially large joint action space. Our methods differ from many previous reinforcement learning approaches to multiagent coordination in that structured communication and coordination between agents appears at the core of both the learning algorithm and the execution architecture.


title: A mobility based metric for clustering in mobile ad hoc networks

abstract: We present a novel relative mobility metric for mobile ad hoc networks (MANETs). It is based on the ratio of power levels due to successive receptions at each node from its neighbors. We propose a distributed clustering algorithm, MOBIC, based on the use of this mobility metric for selection of clusterheads, and demonstrate that it leads to more stable cluster formation than the "least clusterhead change" version of the well known Lowest-ID clustering algorithm (Chiang et al., 1997). We show reduction of as much as 33% in the rate of clusterhead changes owing to the use of the proposed technique. In a MANET that uses scalable cluster-based services, network performance metrics such as throughput and delay are tightly coupled with the frequency of cluster reorganization. Therefore, we believe that using MOBIC can result in a more stable configuration, and thus yield better performance.


title: Privacy and security gaps in mitigating Cyber crime: The review

abstract: Our motivation for this study is to provide new information to a researcher based on our field of study: Networking Systems. This is because Cyber crime is becoming very widespread and it is imperative to improve measures used to counteract such threats. There are various research gaps based on awareness of cyber security, and different methods used in enhancing and troubleshooting network security. This research area can offer many opportunities in which justice can be served, creating awareness, making cyber space a more secure area and creating a new business niche based on security. Hence, for our study on Network Security, we read through 24 research papers published between 2008 to 2014. These documents were retrieved online from CAIDA and IEEE, along with other computer journals and articles. The 24 research papers were then summarized into 7 different research gaps to help enlighten a researcher in the topic of Network Security. Therefore, researchers can then take the initiatives to come up with solutions to the issues raised in this paper.


title: A recommender system based on historical usage data for web service discovery

abstract: The tremendous growth in the amount of available web services impulses many researchers on proposing recommender systems to help users discover services. Most of the proposed solutions analyzed query strings and web service descriptions to generate recommendations. However, these text-based recommendation approaches depend mainly on user's perspective, languages, and notations, which easily decrease recommendation's efficiency. In this paper, we present an approach in which we take into account historical usage data instead of the text-based analysis. We apply collaborative filtering technique on user's interactions. We propose and implement four algorithms to validate our approach. We also provide evaluation methods based on the precision and recall in order to assert the efficiency of our algorithms.


title: Scalable Uncertainty-Aware Truth Discovery in Big Data Social Sensing Applications for Cyber-Physical Systems

abstract: Social sensing is a new big data application paradigm for Cyber-Physical Systems (CPS), where a group of individuals volunteer (or are recruited) to report measurements or observations about the physical world at scale. A fundamental challenge in social sensing applications lies in discovering the correctness of reported observations and reliability of data sources without prior knowledge on either of them. We refer to this problem as truth discovery. While prior studies have made progress on addressing this challenge, two important limitations exist: (i) current solutions did not fully explore the uncertainty aspect of human reported data, which leads to sub-optimal truth discovery results; (ii) current truth discovery solutions are mostly designed as sequential algorithms that do not scale well to large-scale social sensing events. In this paper, we develop a Scalable Uncertainty-Aware Truth Discovery (SUTD) scheme to address the above limitations. The SUTD scheme solves a constraint estimation problem to jointly estimate the correctness of reported data and the reliability of data sources while explicitly considering the uncertainty on the reported data. To address the scalability challenge, the SUTD is designed to run a Graphic Processing Unit (GPU) with thousands of cores, which is shown to run two to three orders of magnitude faster than the sequential truth discovery solutions. In evaluation, we compare our SUTD scheme to the state-ofthe- art solutions using three real world datasets collected from Twitter: Paris Attack, Oregon Shooting, and Baltimore Riots, all in 2015. The evaluation results show that our new scheme significantly outperforms the baselines in terms of both truth discovery accuracy and execution time.


title: The Wikipedia Corpus

abstract: Wikipedia, the popular online encyclopedia, has in just six years grown from an adjunct to the now-defunct Nupedia to over 31 million pages and 429 million revisions in 256 languages and spawned sister projects such as Wiktionary and Wikisource. Available under the GNU Free Documentation License, it is an extraordinarily large corpus with broad scope and constant updates. Its articles are largely consistent in structure and organized into category hierarchies. However, the wiki method of collaborative editing creates challenges that must be addressed. Wikipedia’s accuracy is frequently questioned, and systemic bias means that quality and coverage are uneven, while even the variety of English dialects juxtaposed can sabotage the unwary with differences in semantics, diction and spelling. This paper examines Wikipedia from a research perspective, providing basic background knowledge and an understanding of its strengths and weaknesses. We also solve a technical challenge posed by the enormity of text (1.04TB for the English version) made available with a simple, easily-implemented dictionary compression algorithm that permits time-efficient random access to the data with a twenty-eight-fold reduction in size.


Noise Compensation using Spectrogram Morphological Filtering


title: Wetting Resistance of Commercial Membrane Distillation Membranes in Waste Streams Containing Surfactants and Oil

abstract: Water management is becoming increasingly challenging and several technologies, including membrane distillation (MD) are emerging. This technology is less affected by salinity compared to reverse osmosis and is able to treat brines up to saturation. The focus of MD research recently shifted from seawater desalination to industrial applications out of the scope of reverse osmosis. In many of these applications, surfactants or oil traces are present in the feed stream, lowering the surface tension and increasing the risk for membrane wetting. In this study, the technological boundaries of MD in the presence of surfactants are investigated using surface tension, contact angle and liquid entry pressure measurements together with lab-scale MD experiments to predict the wetting resistance of different membranes. Synthetic NaCl solutions mixed with sodium dodecyl sulfate (SDS) were used as feed solution. The limiting surfactant concentration was found to be dependent on the surface chemistry of the membrane, and increased with increasing hydrophobicity and oleophobicity. Additionally, a hexadecane/SDS emulsion was prepared with a composition simulating produced water, a waste stream in the oil and gas sector. When hexadecane is present in the emulsion, oleophobic membranes are able to resist wetting, whereas polytetrafluoretheen (PTFE) is gradually wetted by the feed liquid.


title: Confronting the Challenge of Quality Diversity

abstract: In contrast to the conventional role of evolution in evolutionary computation (EC) as an optimization algorithm, a new class of evolutionary algorithms has emerged in recent years that instead aim to accumulate as diverse a collection of discoveries as possible, yet where each variant in the collection is as fit as it can be. Often applied in both neuroevolution and morphological evolution, these new quality diversity (QD) algorithms are particularly well-suited to evolution's inherent strengths, thereby offering a promising niche for EC within the broader field of machine learning. However, because QD algorithms are so new, until now no comprehensive study has yet attempted to systematically elucidate their relative strengths and weaknesses under different conditions. Taking a first step in this direction, this paper introduces a new benchmark domain designed specifically to compare and contrast QD algorithms. It then shows how the degree of alignment between the measure of quality and the behavior characterization (which is an essential component of all QD algorithms to date) impacts the ultimate performance of different such algorithms. The hope is that this initial study will help to stimulate interest in QD and begin to unify the disparate ideas in the area.


title: Parsing The WSJ Using CCG And Log-Linear Models

abstract: This paper describes and evaluates log-linear parsing models for Combinatory Categorial Grammar (CCG). A parallel implementation of the L-BFGS optimisation algorithm is described, which runs on a Beowulf cluster allowing the complete Penn Treebank to be used for estimation. We also develop a new efficient parsing algorithm for CCG which maximises expected recall of dependencies. We compare models which use all CCG derivations, including non-standard derivations, with normal-form models. The performances of the two models are comparable and the results are competitive with existing wide-coverage CCG parsers.


title: Open innovation and organizational boundaries: task decomposition, knowledge distribution and the locus of innovation

abstract: This chapter contrasts traditional, organization- centered models of innovation with more recent work on open innovation. These fundamentally different and inconsistent innovation logics are associated with contrasting organizational boundaries and organizational designs. We suggest that when critical tasks can be modularized and when problem- solving knowledge is widely distributed and available, open innovation complements traditional innovation logics. We induce these ideas from the literature and with extended examples from Apple, the National Aeronautics and Astronomical Agency (NASA) and LEGO. We suggest that task decomposition and problem- solving knowledge distribution are not deterministic but are strategic choices. If dynamic capabilities are associated with innovation streams, and if different innovation types are rooted in contrasting innovation logics, there are important implications for firm boundaries,


title: CYBER SITUATIONAL AWARENESS – A SYSTEMATIC REVIEW OF THE LITERATURE

abstract: Abstract Cyber situational awareness is attracting much attention. It features prominently in the national cyber strategies of many countries, and there is a considerable body of research dealing with it. However, until now, there has been no systematic and up-to-date review of the scientific literature on cyber situational awareness. This article presents a review of cyber situational awareness, based on systematic queries in four leading scientific databases. 102 articles were read, clustered, and are succinctly described in the paper. The findings are discussed from the perspective of both national cyber strategies and science, and some directions for future research are examined.


LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better


Exploring Semantic Properties of Sentence Embeddings


title: A Review of Internet of Energy Based Building Energy Management Systems: Issues and Recommendations

abstract: A building energy management system (BEMS) is a sophisticated method used for monitoring and controlling a building’s energy requirements. A number of potential studies were conducted in nearly or net zero energy buildings (nZEBs) for the optimization of building energy consumption through efficient and sustainable ways. Moreover, policy makers are approving measures to improve building energy efficiency in order to foster sustainable energy usages. However, the intelligence of existing BEMSs or nZEBs is inadequate, because of the static set points for heating, cooling, and lighting, the complexity of large amounts of BEMS data, data loss, and network problems. To solve these issues, a BEMS or nZEB solution based on the Internet of energy (IoE) provides disruptive opportunities for revolutionizing sustainable building energy management. This paper presents a critical review of the potential of an IoE-based BEMS for enhancing the performance of future generation building energy utilization. The detailed studies of the IoE architecture, typical nZEB configuration, different generations of nZEB, and smart building energy systems for future BEMS are investigated. The operations, advantages, and limitations of the existing BEMSs or nZEBs are illustrated. A comprehensive review of the different types of IoE-based BEMS technologies, such as energy routers, storage systems and materials, renewable sources, and plug-and-play interfaces, is then presented. The rigorous review indicates that existing BEMSs require advanced controllers integrated with IoE-based technologies for sustainable building energy usage. The main objective of this review is to highlight several issues and challenges of the conventional controllers and IoE applications of BEMSs or nZEBs. Accordingly, the review provides several suggestions for the research and development of the advanced optimized controller and IoE of future BEMSs. All the highlighted insights and recommendations of this review will hopefully lead to increasing efforts toward the development of the future BEMS applications.


title: A Byzantine Fault-Tolerant Ordering Service for the Hyperledger Fabric Blockchain Platform

abstract: Hyperledger Fabric is a flexible operating system for permissioned blockchains designed for business applications beyond the basic digital coin addressed by Bitcoin and other existing networks. A key property of this system is its extensibility, and in particular the support for multiple ordering services for building the blockchain. However, version 1 was launched in 2017 without an implementation of a Byzantine fault-tolerant (BFT) ordering service. To overcome this limitation, we designed, implemented, and evaluated a BFT ordering service for this system on top of the BFT-SMART state machine replication/consensus library, with optimizations for wide-area deployment. Our results show that our ordering service can process up to ten thousand transactions per second and write a transaction irrevocably in the blockchain in half a second, even with peers spread across different continents.


title: ARM: Augment-REINFORCE-Merge Gradient for Discrete Latent Variable Models

abstract: To backpropagate the gradients through discrete stochastic layers, we encode the true gradients into a multiplication between random noises and the difference of the same function of two different sets of discrete latent variables, which are correlated with these random noises. The expectations of that multiplication over iterations are zeros combined with spikes from time to time. To modulate the frequencies, amplitudes, and signs of the spikes to capture the temporal evolution of the true gradients, we propose the augment-REINFORCE-merge (ARM) estimator that combines data augmentation, the score-function estimator, permutation of the indices of latent variables, and variance reduction for Monte Carlo integration using common random numbers. The ARM estimator provides low-variance and unbiased gradient estimates for the parameters of discrete distributions, leading to state-of-the-art performance in both auto-encoding variational Bayes and maximum likelihood inference, for discrete latent variable models with one or multiple discrete stochastic layers.


title: Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data

abstract: Fine-grained visual categorization (FGVC) is challenging due in part to the fact that it is often difficult to acquire an enough number of training samples. To employ large models for FGVC without suffering from overfitting, existing methods usually adopt a strategy of pre-training the models using a rich set of auxiliary data, followed by fine-tuning on the target FGVC task. However, the objective of pre-training does not take the target task into account, and consequently such obtained models are suboptimal for fine-tuning. To address this issue, we propose in this paper a new deep FGVC model termed MetaFGNet. Training of MetaFGNet is based on a novel regularized meta-learning objective, which aims to guide the learning of network parameters so that they are optimal for adapting to the target FGVC task. Based on MetaFGNet, we also propose a simple yet effective scheme for selecting more useful samples from the auxiliary data. Experiments on benchmark FGVC datasets show the efficacy of our proposed method.


title: Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search

abstract: Model predictive control (MPC) is an effective method for controlling robotic systems, particularly autonomous aerial vehicles such as quadcopters. However, application of MPC can be computationally demanding, and typically requires estimating the state of the system, which can be challenging in complex, unstructured environments. Reinforcement learning can in principle forego the need for explicit state estimation and acquire a policy that directly maps sensor readings to actions, but is difficult to apply to unstable systems that are liable to fail catastrophically during training before an effective policy has been found. We propose to combine MPC with reinforcement learning in the framework of guided policy search, where MPC is used to generate data at training time, under full state observations provided by an instrumented training environment. This data is used to train a deep neural network policy, which is allowed to access only the raw observations from the vehicle's onboard sensors. After training, the neural network policy can successfully control the robot without knowledge of the full state, and at a fraction of the computational cost of MPC. We evaluate our method by learning obstacle avoidance policies for a simulated quadrotor, using simulated onboard sensors and no explicit state estimation at test time.


title: An ultra-thin, decade (10∶1) Bandwidth, modular “BAVA” array with low cross-polarization

abstract: Rockwell Collins has developed a novel design for a decade bandwidth, dual-polarized Balanced Antipodal Vivaldi Antenna (BAVA) array. The BAVA element height and the array lattice spacing are about λ/2 at the high frequency end of a grating-lobe free operating band. This allows an ultra-thin array profile, and will minimize the number (hence, cost) of transmit/receive (T/R) modules needed for high performance, multifunction phased arrays. The elements are fed directly by a 50Ω stripline. No matching network or balun circuitry is needed to achieve an active VSWR < 3∶1 across the entire bandwidth over wide scan volume. Although the design includes a ground plane, it provides a very low cross-polarization level due to its ultra-thin profile. The BAVA array produces below −25 dB cross-polarization at principal planes, and better than −17 dB at 45° scan-angles along the diagonal plane.


title: The Performance Analysis of a Real-Time Integrated INS/GPS Vehicle Navigation System with Abnormal GPS Measurement Elimination

abstract: The integration of an Inertial Navigation System (INS) and the Global Positioning System (GPS) is common in mobile mapping and navigation applications to seamlessly determine the position, velocity, and orientation of the mobile platform. In most INS/GPS integrated architectures, the GPS is considered to be an accurate reference with which to correct for the systematic errors of the inertial sensors, which are composed of biases, scale factors and drift. However, the GPS receiver may produce abnormal pseudo-range errors mainly caused by ionospheric delay, tropospheric delay and the multipath effect. These errors degrade the overall position accuracy of an integrated system that uses conventional INS/GPS integration strategies such as loosely coupled (LC) and tightly coupled (TC) schemes. Conventional tightly coupled INS/GPS integration schemes apply the Klobuchar model and the Hopfield model to reduce pseudo-range delays caused by ionospheric delay and tropospheric delay, respectively, but do not address the multipath problem. However, the multipath effect (from reflected GPS signals) affects the position error far more significantly in a consumer-grade GPS receiver than in an expensive, geodetic-grade GPS receiver. To avoid this problem, a new integrated INS/GPS architecture is proposed. The proposed method is described and applied in a real-time integrated system with two integration strategies, namely, loosely coupled and tightly coupled schemes, respectively. To verify the effectiveness of the proposed method, field tests with various scenarios are conducted and the results are compared with a reliable reference system.


title: Voice convergin: Speaker de-identification by voice transformation

abstract: Speaker identification might be a suitable answer to prevent unauthorized access to personal data. However we also need to provide solutions to secure transmission of spoken information. This challenge divides into two major aspects. First, the secure transmission of the content of the spoken input and second the secure transmission of the identity of the speaker. In this paper we concentrate on the latter, i.e. how to securely transmit information via voice without revealing the identity of the speaker to unauthorized listeners. In order to make the first steps toward solving this problem we study in this paper the potential of voice transformation for speaker de-identification. We use two speaker identification approaches to verify the success of de-identification with voice transformation, a GMM-based and a Phonetic approach, and study different voice transformation strategies to disguise speaker identity information while preserving understandability.


title: Reduction of Beam-Hardening Artifacts in X-Ray CT

abstract: Our goal is to decrease the importance of beam-hardening artifacts in X-ray computed tomography by addressing the polyenergetic nature of the X-ray source. We use the same physical model as De Man and al (1998). We next adopt an estimation framework for the reconstruction: the attenuation coefficients are determined by a likelihood-based estimator. This approach leads to minimization of an objective function which exhibits a complex structure. Nonetheless, we develop a numerical procedure with satisfactory numerical efficiency: we use a nonlinear conjugate gradient method. The three major contributions of this communication are: the positivity of the solution ensured by a change of variables, the convergence properties of the algorithm, and a satisfying computation time


A Computational Study on Word Meanings and Their Distributed Representations via Polymodal Embedding


title: Differential Geometry Boosts Convolutional Neural Networks for Object Detection

abstract: Convolutional neural networks (CNNs) have had dramatic success in appearance based object recognition tasks such as the ImageNet visual recognition challenge [8]. However, their application to object recognition and detection thus far has focused largely on intensity or color images as inputs. Motivated by demonstrations that depth can enhance the performance of CNN-based approaches [2][5], in this paper we consider the benefits of also including differential geometric shape features. This elementary idea of using zeroth order (depth), first-order (surface normal) and second-order (surface curvature) features in a principled manner boosts the performance of a CNN that has been pretrained on a color image database. Notably, in an object detection task involving 19 categories we achieve 39.30% accuracy on the NYUv2 dataset, which is a 10.4% improvement over the current state-of-the-art accuracy of 35.6% using the method in [5]. In the simpler scenario of turntable style object recognition, our experiments on the University of Washington (UW) RGB-D dataset yield an accuracy of 88.7% correct recognition over 51 object categories, where the best competing result is 87.5% [2]. Taken together, our results provide strong evidence that the abstraction of surface shape benefits object detection and recognition.


title: Nanoantibiotics: Future nanotechnologies to combat antibiotic resistance.

abstract: The discovery of antibiotics was hailed as a historic breakthrough for the human race in the fight against bacterial and malignant infections. However, in a very short time, owing to their acute and aggressive nature, bacteria have developed resistance against antibiotics and other chemotherapeutics agents. Potentially, this situation could again result in bacterial infection outbreaks. Metal and metal oxide nanoparticles have been proven as better alternatives; the combination of antibiotics and metal oxide nanoparticles was shown to decrease the toxicity and enhance the antibacterial, antiviral, and anticancer efficacy of the agents. This review provides a detailed view about the role of metal and metal oxide nanoparticles in the treatment of infections in conjunction with antibiotics, their modes of action, and synergism. In addition, the problems of multidrug resistance are addressed and will allow the development of a comprehensive, reliable, and rational treatment plan. It is expected that this comprehensive review will lead to new research opportunities, which should be helpful for future applications in biomedical science.


title: Incremental Learning From Stream Data

abstract: Recent years have witnessed an incredibly increasing interest in the topic of incremental learning. Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time. Accordingly, it is desirable to be able to abandon the traditional assumption of the availability of representative training data during the training period to develop decision boundaries. Under scenarios of continuous data flow, the challenge is how to transform the vast amount of stream raw data into information and knowledge representation, and accumulate experience over time to support future decision-making process. In this paper, we propose a general adaptive incremental learning framework named ADAIN that is capable of learning from continuous raw data, accumulating experience over time, and using such knowledge to improve future learning and prediction performance. Detailed system level architecture and design strategies are presented in this paper. Simulation results over several real-world data sets are used to validate the effectiveness of this method.


title: Clothing style recognition using fashion attribute detection

abstract: In this paper, a new framework is proposed for clothing style recognition in natural scenes. Clothing region is first detected through the fusion of super-pixel segmentation, saliency detection and Gaussian Mixture Model (GMM). Next, a group of fashion attribute detectors are trained to get the likelihood of each attribute in the clothing image. Finally, the correlation matrix between clothing styles and fashion attributes is adopted to predict the clothing style. For evaluation, we collect a dataset for clothing style recognition which contains 5 styles and 14 fashion attributes. Extensive experiments demonstrate that the proposed framework has a promising ability to recognize the clothing style.


title: Coastal navigation-mobile robot navigation with uncertainty in dynamic environments

abstract: Ships often use the coasts of continents for navigation in the absence of better tools such as GPS, since being close to land allows sailors to determine with high accuracy where they are. Similarly for mobile robots, in many environments global and accurate localization is not always feasible. Environments can lack features, and dynamic obstacles such as people can confuse and block sensors. We demonstrate a technique for generating trajectories that take into account both the information content of the environment, and the density of the people in the environment. These trajectories reduce the average positional certainty as the robot moves, reducing the likelihood the robot will become lost at any point. Our method was successfully implemented and used by the mobile robot Minerva, a museum tourguide robot, for a 2 week period in the Smithsonian National Museum of American History.


title: On multiple foreground cosegmentation

abstract: In this paper, we address a challenging image segmentation problem called multiple foreground cosegmentation (MFC), which concerns a realistic scenario in general Webuser photo sets where a finite number of K foregrounds of interest repeatedly occur cross the entire photo set, but only an unknown subset of them is presented in each image. This contrasts the classical cosegmentation problem dealt with by most existing algorithms, which assume a much simpler but less realistic setting where the same set of foregrounds recurs in every image. We propose a novel optimization method for MFC, which makes no assumption on foreground configurations and does not suffer from the aforementioned limitation, while still leverages all the benefits of having co-occurring or (partially) recurring contents across images. Our method builds on an iterative scheme that alternates between a foreground modeling module and a region assignment module, both highly efficient and scalable. In particular, our approach is flexible enough to integrate any advanced region classifiers for foreground modeling, and our region assignment employs a combinatorial auction framework that enjoys several intuitively good properties such as optimality guarantee and linear complexity. We show the superior performance of our method in both segmentation quality and scalability in comparison with other state-of-the-art techniques on a newly introduced FlickrMFC dataset and the standard ImageNet dataset.


title: A biomimetic robotic platform to study flight specializations of bats

abstract: Bats have long captured the imaginations of scientists and engineers with their unrivaled agility and maneuvering characteristics, achieved by functionally versatile dynamic wing conformations as well as more than 40 active and passive joints on the wings. Wing flexibility and complex wing kinematics not only bring a unique perspective to research in biology and aerial robotics but also pose substantial technological challenges for robot modeling, design, and control. We have created a fully self-contained, autonomous flying robot that weighs 93 grams, called Bat Bot (B2), to mimic such morphological properties of bat wings. Instead of using a large number of distributed control actuators, we implement highly stretchable silicone-based membrane wings that are controlled at a reduced number of dominant wing joints to best match the morphological characteristics of bat flight. First, the dominant degrees of freedom (DOFs) in the bat flight mechanism are identified and incorporated in B2’s design by means of a series of mechanical constraints. These biologically meaningful DOFs include asynchronous and mediolateral movements of the armwings and dorsoventral movements of the legs. Second, the continuous surface and elastic properties of bat skin under wing morphing are realized by an ultrathin (56 micrometers) membranous skin that covers the skeleton of the morphing wings. We have successfully achieved autonomous flight of B2 using a series of virtual constraints to control the articulated, morphing wings.


title: An image-to-map loop closing method for monocular SLAM

abstract: In this paper we present a loop closure method for a handheld single-camera SLAM system based on our previous work on relocalization. By finding correspondences between the current image and the map, our system is able to reliably detect loop closures. We compare our algorithm to existing techniques for loop closure in single-camera SLAM based on both image-to-image and map-to-map correspondences and discuss both the reliability and suitability of each algorithm in the context of monocular SLAM.


abstract: This paper presents the Pulse Induction Metal Detector Using Sample and Hold Method. The proposed method is to measure output voltage from search coil. The system consists of the pulse generator circuit, amplifier circuit, integrator circuit, three mono-stable circuits, sample and hold circuit, comparator circuit and display circuit. The voltage output signal of the proposed technique is proportional to size, shape and type of metal target. The metal target with differential size was used in our experiment as an illustrative case study. Experimental results show that the proposed method can detect metal with satisfied sensitivity and good linearity.


title: Complexity and Extended Phenomenological-Cognitive Systems

abstract: The complex systems approach to cognitive science invites a new understanding of extended cognitive systems. According to this understanding, extended cognitive systems are heterogenous, composed of brain, body, and niche, non-linearly coupled to one another. This view of cognitive systems, as non-linearly coupled brain–body–niche systems, promises conceptual and methodological advances. In this article we focus on two of these. First, the fundamental interdependence among brain, body, and niche makes it possible to explain extended cognition without invoking representations or computation. Second, cognition and conscious experience can be understood as a single phenomenon, eliminating fruitless philosophical discussion of qualia and the so-called hard problem of consciousness. What we call ‘‘extended phenomenological-cognitive systems’’ are relational and dynamical entities, with interactions among heterogeneous parts at multiple spatial and temporal scales.


title: Real-Time Modeling of 3-D Soccer Ball Trajectories From Multiple Fixed Cameras

abstract: In this paper, model-based approaches for real-time 3-D soccer ball tracking are proposed, using image sequences from multiple fixed cameras as input. The main challenges include filtering false alarms, tracking through missing observations, and estimating 3-D positions from single or multiple cameras. The key innovations are: 1. incorporating motion cues and temporal hysteresis thresholding in ball detection; 2. modeling each ball trajectory as curve segments in successive virtual vertical planes so that the 3-D position of the ball can be determined from a single camera view; and 3. introducing four motion phases (rolling, flying, in possession, and out of play) and employing phase-specific models to estimate ball trajectories which enables high-level semantics applied in low-level tracking. In addition, unreliable or missing ball observations are recovered using spatio-temporal constraints and temporal filtering. The system accuracy and robustness are evaluated by comparing the estimated ball positions and phases with manual ground-truth data of real soccer sequences.


title: Named Entity Recognition from Tweets

abstract: Entries in microblogging sites are very short. For example, a 'tweet' (a post or status update on the popular microblogging site Twit- ter) can contain at most 140 characters. To comply with this restric- tion, users frequently use abbreviations to express their thoughts, thus producing sentences that are often poorly structured or ungrammatical. As a result, it becomes a challenge to come up with methods for au- tomatically identifying named entities (names of persons, organizations, locations etc.). In this study, we use a four-step approach to automatic named entity recognition from microposts. First, we do some preprocess- ing of the micropost (e.g. replace abbreviations with actual words). Then we use an off-the-shelf part-of-speech tagger to tag the nouns. Next, we use the Google Search API to retrieve sentences containing the tagged nouns. Finally, we run a standard Named Entity Recognizer (NER) on the retrieved sentences. The tagged nouns are returned along with the tags assigned by the NER. This simple approach, using readily available components, yields promising results on standard benchmark data.


title: Kraken: ultrafast metagenomic sequence classification using exact alignments

abstract: Kraken is an ultrafast and highly accurate program for assigning taxonomic labels to metagenomic DNA sequences. Previous programs designed for this task have been relatively slow and computationally expensive, forcing researchers to use faster abundance estimation programs, which only classify small subsets of metagenomic data. Using exact alignment of k-mers, Kraken achieves classification accuracy comparable to the fastest BLAST program. In its fastest mode, Kraken classifies 100 base pair reads at a rate of over 4.1 million reads per minute, 909 times faster than Megablast and 11 times faster than the abundance estimation program MetaPhlAn. Kraken is available at http://ccb.jhu.edu/software/kraken/.


title: Paradoxical Adipose Hyperplasia After Cryolipolysis

abstract: IMPORTANCE ::: Cryolipolysis is the noninvasive reduction of fat with localized cutaneous cooling. Since initial introduction, over 650,000 cryolipolysis treatment cycles have been performed worldwide. We present a previously unreported, rare adverse effect following cryolipolysis: paradoxical adipose hyperplasia. ::: ::: ::: OBSERVATIONS ::: A man in his 40s underwent a single cycle of cryolipolysis to his abdomen. Three months following his treatment, a gradual enlargement of the treatment area was noted. This enlargement was a large, well-demarcated subcutaneous mass, slightly tender to palpation. Imaging studies revealed accumulation of adipose tissue with normal signal intensity within the treatment area. ::: ::: ::: CONCLUSIONS AND RELEVANCE ::: Paradoxical adipose hyperplasia is a rare, previously unreported adverse effect of cryolipolysis with an incidence of 0.0051%. No single unifying risk factor has been identified. The phenomenon seems to be more common in male patients undergoing cryolipolysis. At this time, there is no evidence of spontaneous resolution. Further studies are needed to characterize the pathogenesis and histologic findings of this rare adverse event.


title: The Barbarians of ancient Europe: realities and interactions

abstract: 1. Classical and barbarian Larissa Bonfante 2. Greek geography of western barbarians Paul Keyser 3. The funeral of Scythian kings: the historical reality and the description of Herodotus (4.71-72) Askold Ivantchik 4. Scythians: between mobility, tomb architecture and early urban structures Renate Rolle 5. Philomele's tongue: pictorial language of myth in ancient Thrace Ivan Marazov 6. In the fabulous Celtic twilight Barry Cunliffe 7. The ancient Germans Peter S. Wells 8. Etruscans and mediators between barbarians and classical civilization Larissa Bonfante 9. The world of situla art Otto-Herman Frey 10. A barbarian myth? The case of the talking head Nancy Thomson de Grummond 11. Romans and/as barbarians John Marincola 12. Late barbarians and wine Walter Stevenson 13. Some final thoughts Barry Cunliffe.


title: Reduced Mimicry to Virtual Reality Avatars in Autism Spectrum Disorder

abstract: Mimicry involves unconsciously copying the actions of others. Increasing evidence suggests that autistic people can copy the goal of an observed action but show differences in their mimicry. We investigated mimicry in autism spectrum disorder (ASD) within a two-dimensional virtual reality environment. Participants played an imitation game with a socially engaged avatar and socially disengaged avatar. Despite being told only to copy the goal of the observed action, autistic participants and matched neurotypical participants mimicked the kinematics of the avatars’ movements. However, autistic participants mimicked less. Social engagement did not modulate mimicry in either group. The results demonstrate the feasibility of using virtual reality to induce mimicry and suggest mimicry differences in ASD may also occur when interacting with avatars.


title: An ontology-based model to determine the automation level of an automated vehicle for co-driving

abstract: Full autonomy of ground vehicles is a major goal of the ITS (Intelligent Transportation Systems) community. However, reaching such highest autonomy level in all situations (weather, traffic, ...) may seem difficult in practice, despite recent results regarding driverless cars (e.g., Google Cars). In addition, an automated vehicle should also self-assess its own perception abilities, and not only perceive its environment. In this paper, we propose an intermediate approach towards full automation, by defining a spectrum of automation layers, from fully manual (the car is driven by a driver) to fully automated (the car is driven by a computer), based on an ontological model for representing knowledge. We also propose a second ontology for situation assessment (what does the automated car perceive?), including the sensors/actuators state, environmental conditions and driver's state. Finally, we also define inference rules to link the situation assessment ontology to the automation level one. Both ontological models have been built and first results are presented.


title: Genetic Algorithm for Solving Simple Mathematical Equality Problem

abstract: This paper explains genetic algorithm for novice in this field. Basic philosophy of genetic algorithm and its flowchart are described. Step by step numerical computation of genetic algorithm for solving simple mathematical equality problem will be briefly explained


title: Incidence of Alcohol Use Disorders Among Hispanic Subgroups in the USA

abstract: Objective: Hispanics are the largest ethnic minority group in the USA and a fairly heterogeneous group. But this heterogen- eity has usually been ignored by grouping them into a single category. The objective of this study is to challenge that practice by providing alcohol use disorders (AUD) incidence and risk estimates across Hispanic subgroups. Methods: A subsample of Hispanics (n= 2371) and non-Hispanic whites (n=7741) at risk for AUD who participated in the first and second wave of the National Epidemiological Survey on Alcohol and Related Conditions was analyzed. Weighted incidence for AUD was calculated across Hispanic subgroups. Aweighted modi- fied Poisson generalized linear model was used to estimate relative risks. Results: Compared with non-Hispanic whites, Puerto Ricans have the highest AUD incidence (3.6%; 95% CI: 0.5, 6.7) followed by Mexican-Americans (2.5%; 95% CI: 1.5, 3.6), Other Hispanics (1.6%; 95% CI: 0.1, 3.1) and Cuban-Americans (0.6%; 95% CI: 0.1, 1.2). In contrast, weighted adjusted relative risk estimates for Mexican-Americans were (RR=3.2; 95% CI: 1.7, 6.2) but for Puerto Ricans (RR=2.2; 95% CI: 1.0, 5.0) it was somewhat attenuated but still significant (P=0.049). No difference was found for Cuban-Americans or Other Hispanics. Conclusions: The annual incidence and risk for AUD varies greatly among Hispanic subgroups. Nonetheless, the practice of categorizing Hispanics as a homogenous ethnic group still continues. Our findings suggest that this practice is not only inappropriate, but also hinders a better understanding of AUD etiology, prevention, treatment and rehabilitation in the largest ethnic minority group in the USA.


title: Definition Extraction with LSTM Recurrent Neural Networks

abstract: Definition extraction is the task to identify definitional sentences automatically from unstructured text. The task can be used in the aspects of ontology generation, relation extraction and question answering. Previous methods use handcraft features generated from the dependency structure of a sentence. During this process, only part of the dependency structure is used to extract features, thus causing information loss. We model definition extraction as a supervised sequence classification task and propose a new way to automatically generate sentence features using a Long Short-Term Memory neural network model. Our method directly learns features from raw sentences and corresponding part-of-speech sequence, which makes full use of the whole sentence. We experiment on the Wikipedia benchmark dataset and obtain 91.2 % on \(F_1\) score which outperforms the current state-of-the-art methods by 5.8 %. We also show the effectiveness of our method in dealing with other languages by testing on a Chinese dataset and obtaining 85.7 % on \(F_1\) score.


title: The Origin of Biases in Face Perception

abstract: Experience with certain types of faces during the first year of development defines which types of faces are more efficiently recognized later in life. In work described here, we found that infants who learned to recognize six monkey faces individually (i.e., each face was individually labeled) over a 3-month period maintained the ability to discriminate monkey faces. However, infants who learned these same six faces categorically (i.e., all faces were labeled “monkey”) or were simply exposed to these faces (i.e., faces were not labeled) showed a decline in the ability to discriminate monkey faces. These results suggest that experience individuating faces from 6 to 9 months of age, via labeling, critically shapes the perceptual representation that is responsible for later recognition and discrimination of faces.


title: An Immersive Visualization Tool for Teaching and Simulation of Smart Grid Technologies

abstract: Intelligent power grid research, i.e. smart grid, involves many simultaneous users spread over a relatively large geographical area. A tool for advancing research and community education is presented utilizing large-scale visualization centers, e.g. planetariums, in simulating smart grid interactions. This approach immerses the user in virtual smart grid visualization and allows the user, with several other users, to interact in real time. This facilitates community education by demonstrating how the power grid functions with smart technologies. The simulation is sophisticated enough to also be used as a research tool for industry and higher education to test software algorithms, deployment strategies, communications protocols, and even new hardware.


title: Parallel Deep Neural Network Training for Big Data on Blue Gene/Q

abstract: Deep Neural Networks (DNNs) have recently been shown to significantly outperform existing machine learning techniques in several pattern recognition tasks. DNNs are the state-of-the-art models used in image recognition, object detection, classification and tracking, and speech and language processing applications. The biggest drawback to DNNs has been the enormous cost in computation and time taken to train the parameters of the networks - often a tenfold increase relative to conventional technologies. Such training time costs can be mitigated by the application of parallel computing algorithms and architectures. However, these algorithms often run into difficulties because of the cost of inter-processor communication bottlenecks. In this paper, we describe how to enable Parallel Deep Neural Network Training on the IBM Blue Gene/Q (BG/Q) computer system. Specifically, we explore DNN training using the data parallel Hessian-free 2nd order optimization algorithm. Such an algorithm is particularly well-suited to parallelization across a large set of loosely coupled processors. BG/Q, with its excellent inter-processor communication characteristics, is an ideal match for this type of algorithm. The paper discusses how issues regarding programming model and data-dependent imbalances are addressed. Results on large-scale speech tasks show that the performance on BG/Q scales linearly up to 4096 processes with no loss in accuracy. This allows us to train neural networks using billions of training examples in a few hours.


title: Global Deconvolutional Networks for Semantic Segmentation

abstract: Semantic image segmentation is a principal problem in computer vision, where the aim is to correctly classify each individual pixel of an image into a semantic label. Its widespread use in many areas, including medical imaging and autonomous driving, has fostered extensive research in recent years. Empirical improvements in tackling this task have primarily been motivated by successful exploitation of Convolutional Neural Networks (CNNs) pre-trained for image classification and object recognition. However, the pixel-wise labelling with CNNs has its own unique challenges: (1) an accurate deconvolution, or upsampling, of low-resolution output into a higher-resolution segmentation mask and (2) an inclusion of global information, or context, within locally extracted features. To address these issues, we propose a novel architecture to conduct the equivalent of the deconvolution operation globally and acquire dense predictions. We demonstrate that it leads to improved performance of state-of-the-art semantic segmentation models on the PASCAL VOC 2012 benchmark, reaching 74.0% mean IU accuracy on the test set.


title: As we may search : Comparison of major features of the Web of Science, Scopus, and Google Scholar citation-based and citation-enhanced databases

abstract: IT may appear blasphemous to paraphrase the title of the classic article of Vannevar Bush but it may be a mitigating factor that it is done to pay tribute to another legendary scientist, Eugene Garfield. His ideas of citationbased searching, resource discovery and quantitative evaluation of publications serve as the basis for many of the most innovative and powerful online information services these days. Bush 60 years ago contemplated – among many other things – an information workstation, the Memex. A researcher would use it to annotate, organize, link, store, and retrieve microfilmed documents. He is acknowledged today as the forefather of the hypertext system, which in turn, is the backbone of the Internet. He outlined his thoughts in an essay published in the Atlantic Monthly. Maybe because of using a nonscientific outlet the paper was hardly quoted and cited in scholarly and professional journals for 30 years. Understandably, the Atlantic Monthly was not covered by the few, specialized abstracting and indexing databases of scientific literature. Such general interest magazines are not source journals in either the Web of Science (WoS), or Scopus databases. However, records for items which cite the ‘As We May Think’ article of Bush (also known as the ‘Memex’ paper) are listed with appropriate bibliographic information. Google Scholar (G-S) lists the records for the Memex paper and many of its citing papers. It is a rather confusing list with many dead links or otherwise dysfunctional links, and a hodge-podge of information related to Bush. It is quite telling that (based on data from the 1945– 2005 edition of WoS) the article of Bush gathered almost 90% of all its 712 citations in WoS between 1975 and 2005, peaking in 1999 with 45 citations in that year alone. Undoubtedly, this proportion is likely to be distorted because far fewer source articles from far fewer journals were processed by the Institute for Scientific Information for 1945–1974 than for 1975–2005. Scopus identifies 267 papers citing the Bush article. The main reason for the discrepancy is that Scopus includes cited references only from 1995 onward, while WoS does so from 1945. Bush’s impatience with the limitations imposed by the traditional classification and indexing tools and practices of the time is palpable. It is worth to quote it as a reminder. Interestingly, he brings up the terms ‘web of trails’ and ‘association of thoughts’ which establishes the link between him and Garfield.


title: Detecting evolutionary financial statement fraud

abstract: A fraudulent financial statement involves the intentional furnishing and/or publishing of false information in it and this has become a severe economic and social problem. We consider Data Mining (DM) based financial fraud detection techniques (such as regression, decision tree, neural networks and Bayesian networks) that help identify fraud. The effectiveness of these DM methods (and their limitations) is examined, especially when new schemes of financial statement fraud adapt to the detection techniques. We then explore a self-adaptive framework (based on a response surface model) with domain knowledge to detect financial statement fraud. We conclude by suggesting that, in an era with evolutionary financial frauds, computer assisted automated fraud detection mechanisms will be more effective and efficient with specialized domain knowledge.


title: Semantic video search by automatic video annotation using TensorFlow

abstract: The paper discusses a tool for video structure analysis, feature extraction, classification and semantic querying suitable for an extremely broad scale of video data set. The tool analyses the video structure to detect shot boundaries where shots in each video are identified using image duplication techniques. A single frame from each shot is passed to a deep learning model implemented using TensorFlow, that is trained for feature extraction and classification of objects in each frame. Subsequently, an automatic textual annotation is generated for each video and finally with the aid of ontology, semantic searching is done using NLP, which allows receiving an efficient result other than manual video annotation of a large scale dataset. While maintaining accurate querying with automatic video content analysis and annotation with semantic searching with around seventy-four percent accuracy rate, this becomes a useful tool in video tagging and annotation.


title: Electronic Stability Control Based on Motor Driving and Braking Torque Distribution for a Four In-Wheel Motor Drive Electric Vehicle

abstract: An electronic stability control (ESC) algorithm is proposed for a four in-wheel motor independent-drive electric vehicle (4MIDEV) utilizing motor driving and regenerative braking torque distribution control to improve vehicle stability. A stability judgment controller, an upper level controller, and a torque distribution algorithm are designed for the ESC system. The stability judgment controller is designed to generate the desired yaw rate and sideslip angle for vehicle stability, and the control mode, which is normal driving mode or ESC mode, is set according to the driver inputs and measurement signal inputs. The upper level controller consists of a speed tracking controller, a yaw moment controller, and four wheel-slip controllers to calculate the desired value of traction force, the desired value of yaw moment, and the four respective net torque inputs of the four in-wheel motors. The torque distribution algorithm is designed to generate each motor driving torque or regenerative braking torque input for each wheel. An average torque distribution strategy, a tire-dynamic-load-based torque distribution strategy, and a minimum-objective-function-based optimal torque distribution strategy are used separately in the torque distribution algorithm to control the motor driving torque or regenerative braking torque for vehicle stability enhancement. The proposed ESC algorithm was implemented and evaluated in a CarSim vehicle model and a MATLAB/Simulink control model. The three proposed torque distribution strategies can be used to regulate the vehicle to perform the following tasks: “single lane change,” “double lane change,” and “snake lane change.” The simulation studies show that the yaw rate error root mean square [RMS $(\gamma-\gamma_\mathrm{-des})$ ] decreased, on average, by 75 percent using the proposed optimal torque distribution algorithm compared with that without using stability control.


title: Social cognitive theory of mass communication

abstract: Social cognitive theory provides an agentic conceptual framework within which to analyze the determinants and psychosocial mechanisms through which symbolic communication influences human thought, affect and action. Communications systems operate through two pathways. In the direct pathway, they promote changes by informing, enabling, motivating, and guiding participants. In the socially mediated pathway, media influences link participants to social networks and community settings that provide natural incentives and continued personalized guidance, for desired change. Social cognitive theory analyzes social diffusion of new styles of behavior in terms of the psychosocial factors governing their acquisition and adoption and the social networks through which they spread and are supported. Structural interconnectedness provides potential diffusion paths; sociocognitive factors largely determine what diffuses through those paths.


title: Development of the snake-like rescue robot "kohga"

abstract: In this paper we report the development of a new rescue robot platform. The robot is constructed by connecting multiple crawler vehicles serially, resulting a long and thin structure so that it can enter narrow space. The units are connected by 2-DOF active joints and 3-DOF passive joints, so that the robot can have both the capability to climb over obstacles as well as adaptation to irregular surfaces. Basic steering and experiments for the robot are also shown.


title: The local mean decomposition and its application to EEG perception data

abstract: This paper describes the local mean decomposition (LMD), a new iterative approach to demodulating amplitude and frequency modulated signals. The new method decomposes such signals into a set of functions, each of which is the product of an envelope signal and a frequency modulated signal from which a time-varying instantaneous frequency can be derived. The LMD method can be used to analyse a wide variety of natural signals such as electrocardiograms, functional magnetic resonance imaging data, and earthquake data. The paper presents the results of applying LMD to a set of scalp electroencephalogram (EEG) visual perception data. The LMD instantaneous frequency and energy structure of the EEG is examined, and compared with results obtained using the spectrogram. The nature of visual perception is investigated by measuring the degree of EEG instantaneous phase concentration that occurs following stimulus onset over multiple trials. The analysis suggests that there is a statistically significant difference between the theta phase concentrations of the perception and no perception EEG data.


Automatically Suggesting Example Sentences of Near-Synonyms for Language Learners


title: Design and prototyping a cable-driven multi-stage telescopic arm for mobile surveillance robots

abstract: Telescopic mechanism has lots of merits and is used in devices and equipment long before the appearance of robots. This paper explores the merits of telescopic mechanism and designs a cable-driven multi-stage telescopic arm with only one actuated degree of freedom (DOF) for mobile surveillance robots. We use pulleys and cables as the transmission system and use a DC motor together with harmonic drive as the actuation system. We analyze the integration of pulleys and cables to reduce cost and carry out numerical analysis by simulation and prototyping. The results show that our arm is fast, portable, stable and has low cost. We expect it to be widely employed for camera lifting tasks in mobile surveillance systems without significantly increasing costs. We also expect its general usage as an arm to support various end-effectors.


title: Machine Comprehension using Rich Semantic Representations

abstract: Machine comprehension tests the system’s ability to understand a piece of text through a reading comprehension task. For this task, we propose an approach using the Abstract Meaning Representation (AMR) formalism. We construct meaning representation graphs for the given text and for each question-answer pair by merging the AMRs of comprising sentences using cross-sentential phenomena such as coreference and rhetorical structures. Then, we reduce machine comprehension to a graph containment problem. We posit that there is a latent mapping of the question-answer meaning representation graph onto the text meaning representation graph that explains the answer. We present a unified max-margin framework that learns to find this mapping (given a corpus of texts and question-answer pairs), and uses what it learns to answer questions on novel texts. We show that this approach leads to state of the art results on the task.


title: Analysis and modeling of conductive EMI noise of power electronics converters in electric and hybrid electric vehicles

abstract: In developing a complex power electronic system, proper modeling at prototype sample level can save the time and cost to develop final product. Hybrid electric vehicle (HEV) is equipped with several high powered electric devices such as DC/DC converters and DC/AC inverters. Considering that there are many restrictions in mounting and wiring the electric power system in engine room, analysis and modeling of electromagnetic interference (EMI) coupling path is crucial in designing optimal electric power system satisfying various EMC regulations. In this paper, conductive common mode (CM) and differential mode (DM) equivalent models for those electric power converters used in HEV were developed and evaluated through the simulation both in time domain and frequency domain using FFT. It is confirmed that the suggested model followed the actual circuit in the point of conducted CM and DM EMI view.


A Reference Model for Anti-Money Laundering in the Financial Sector


title: 2-D Micromachined Thermal Wind Sensors—A Review

abstract: This paper presents an overview and development of 2-D micromachined thermal wind sensors. It first compares different types of wind sensors, then gives a brief introduction to three basic measurement principles of the thermal wind sensors: hot-wire or hot-film, calorimetric, and time of flight. In order to have a good understanding of development of the 2-D micromachined thermal wind sensors, they are first categorized into thermoresistive, thermoelectric, and thermoelectronic wind sensors based on their operations. Then, each category is further divided into different subcategories, according to their measuring principles and sensor configurations. In Section IV , different materials, fabrication processes, packaging techniques, and control modes of the 2-D micromachined thermal wind sensors are analyzed. This paper also evaluates the core technologies on how to improve the sensitivity, measurement range, and power consumption of the thermal wind sensors.


title: Modeling Progress in AI

abstract: Participants in recent discussions of AI-related issues ranging from intelligence explosion to technological unemployment have made diverse claims about the nature, pace, and drivers of progress in AI. However, these theories are rarely specified in enough detail to enable systematic evaluation of their assumptions or to extrapolate progress quantitatively, as is often done with some success in other technological domains. After reviewing relevant literatures and justifying the need for more rigorous modeling of AI progress, this paper contributes to that research program by suggesting ways to account for the relationship between hardware speed increases and algorithmic improvements in AI, the role of human inputs in enabling AI capabilities, and the relationships between different sub-fields of AI. It then outlines ways of tailoring AI progress models to generate insights on the specific issue of technological unemployment, and outlines future directions for research on AI progress.


title: FamiLync: facilitating participatory parental mediation of adolescents' smartphone use

abstract: We consider participatory parental mediation in which children engage with their parents in activities that encourage both parents and children to participate in co-learning of digital media use. To this end, we developed FamiLync, a mobile service that treats use-limiting as a family activity and provides the family with a virtual public space to foster social awareness and improve self-regulation. A three-week user study conducted with twelve families in Korea (17 parents and 18 teenagers) showed that FamiLync improves mutual understanding of usage behavior, thereby providing common grounds for parental mediation. Further, parents actively participated in use-limiting with their children, which significantly increased the children's desire to participate. As a consequence, parental mediation methods and parent-child interaction in relation to smartphone usage changed appreciably, and the participants smartphone usage amount significantly decreased.


title: Blendshapes from commodity RGB-D sensors

abstract: Creating and animating a realistic 3D human face is an important task in computer graphics. The capability of capturing the 3D face of a human subject and reanimate it quickly will find many applications in games, training simulations, and interactive 3D graphics. We demonstrate a system to capture photorealistic 3D faces and generate the blendshape models automatically using only a single commodity RGB-D sensor. Our method can rapidly generate a set of expressive facial poses from a single depth sensor, such as a Microsoft Kinect version 1, and requires no artistic expertise in order to process those scans. The system takes only a matter of seconds to capture and produce a 3D facial pose and only requires a few minutes of processing time to transform it into a blendshape-compatible model. Our main contributions include an end-to-end pipeline for capturing and generating face blendshape models automatically, and a registration method that solves dense correspondences between two face scans by utilizing facial landmarks detection and optical flows. We demonstrate the effectiveness of the proposed method by capturing different human subjects and puppeteering their 3D faces in an animation system with real-time facial performance retargeting.


title: More Than Weighting Cognitive Importance: A Dual-Process Model of Issue Framing Effects

abstract: Issue frames in policy discourse and news reporting regularly influence citizens' political opinions. Yet, we only have a limited understanding of how and among whom these framing effects occur. I propose a dual-process model of issue framing effects arguing that we must understand mediators of framing (the how question) in connection with individual-level moderators of framing (the whom question). Experimental results show that issue framing affects opinion through different psychological processes depending on who the receiver of the frame is. Among the moderately politically aware or those having weak political values, framing effects were mediated through processes of changing importance of considerations as well as changing content of considerations. Among the highly aware, only the importance change process mediated framing effects, while there were no framing effects among those least aware or those having strong values.


title: Telecom industry in the cloud value chain: methods for assessing business opportunities

abstract: Cloud computing represents a major change in how IT resources are delivered and consumed and will influence the telecom industry, its future services and business offerings. Generating new revenues through offering cloud-based services is less discussed in the literature, followed by a lack of commonly agreed and complete methodologies for this purpose. In this paper we propose a three-step method combining qualitative and quantitative models for the assessment of telco's new business opportunities, in particular, a combined role of cloud broker and carrier.


title: Multi-camera parallel tracking and mapping with non-overlapping fields of view

abstract: A novel real-time pose estimation system is presented for solving the visual simultaneous localization and mapping problem using a rigid set of central cameras arranged such that there is no overlap in their fields-of-view. A new parameterization for point feature position using a spherical coordinate update is formulated which isolates system parameters dependent on global scale, allowing the shape parameters of the system to converge despite the scale remaining uncertain. Furthermore, an initialization scheme is proposed from which the optimization will converge accurately using only the measurements from the cameras at the first time step. The algorithm is implemented and verified in experiments with a camera cluster constructed using multiple perspective cameras mounted on a multirotor aerial vehicle and augmented with tracking markers to collect high-precision ground-truth motion measurements from an optical indoor positioning system. The accuracy and performance of the proposed pose estimation system are confirmed for various motion profiles in both indoor and challenging outdoor environments, despite no overlap in the camera fields-of-view.


title: Warp-Knitted Textile as a Strain Sensor: Characterization Procedure and Application in a Comfortable Wearable Goniometer

abstract: Long-term and minimally invasive joint angular measurements are required for monitoring everyday activities, motor rehabilitation sessions, and sport training. In this paper, we designed an innovative wearable device composed of a light, comfortable, and low-cost textile strain-resistance sensor. First, we proposed a methodology to characterize and to calibrate a commercial knitted textile. This characterization procedure represents an interesting result per se. It has been conducted on a single material, but its general characteristics make it suitable for analyzing the behavior of any conductive and stretchable fabrics. Second, we developed a wearable sensor (a goniometer) and we validated it using the humanoid robot SABIAN. Dynamic tests demonstrated that our wearable device is suitable for scenarios, where the accuracy of measurements is less relevant than the ability to continuously track joint movements in a not-obtrusive way.