title: SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals

abstract: Indoor localization typically relies on measuring a collection of RF signals, such as Received Signal Strength (RSS) from WiFi, in conjunction with spatial maps of signal fingerprints. A new technology for localization could arise with the use of 4G LTE telephony small cells, with limited range but with rich signal strength information, namely Reference Signal Received Power (RSRP). In this paper, we propose to combine an ensemble of available sources of RF signals to build multi-modal signal maps that can be used for localization or for network deployment optimization. We primarily rely on Simultaneous Localization and Mapping (SLAM), which provides a solution to the challenge of building a map of observations without knowing the location of the observer. SLAM has recently been extended to incorporate signal strength from WiFi in the so-called WiFi-SLAM. In parallel to WiFi-SLAM, other localization algorithms have been developed that exploit the inertial motion sensors and a known map of either WiFi RSS or of magnetic field magnitude. In our study, we use all the measurements that can be acquired by an off-the-shelf smartphone and crowd-source the data collection from several experimenters walking freely through a building, collecting time-stamped WiFi and Bluetooth RSS, 4G LTE RSRP, magnetic field magnitude, GPS reference points when outdoors, Near-Field Communication (NFC) readings at specific landmarks and pedestrian dead reckoning based on inertial data. We resolve the location of all the users using a modified version of Graph-SLAM optimization of the users poses with a collection of absolute location and pairwise constraints that incorporates multi-modal signal similarity. We demonstrate that we can recover the user positions and thus simultaneously generate dense signal maps for each WiFi access point and 4G LTE small cell, “from the pocket”. Finally, we demonstrate the localization performance using selected single modalities, such as only WiFi and the WiFi signal maps that we generated.


title: Hand Rehabilitation Learning System With an Exoskeleton Robotic Glove

abstract: This paper presents a hand rehabilitation learning system, the SAFE Glove, a device that can be utilized to enhance the rehabilitation of subjects with disabilities. This system is able to learn fingertip motion and force for grasping different objects and then record and analyze the common movements of hand function including grip and release patterns. The glove is then able to reproduce these movement patterns in playback fashion to assist a weakened hand to accomplish these movements, or to modulate the assistive level based on the user's or therapist's intent for the purpose of hand rehabilitation therapy. Preliminary data have been collected from healthy hands. To demonstrate the glove's ability to manipulate the hand, the glove has been fitted on a wooden hand and the grasping of various objects was performed. To further prove that hands can be safely driven by this haptic mechanism, force sensor readings placed between each finger and the mechanism are plotted. These experimental results demonstrate the potential of the proposed system in rehabilitation therapy.


title: Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Web

abstract: Over the past couple of years, anecdotal evidence has emerged linking coordinated campaigns by state-sponsored actors with efforts to manipulate public opinion on the Web, often around major political events, through dedicated accounts, or “trolls.” Although they are often involved in spreading disinformation on social media, there is little understanding of how these trolls operate, what type of content they disseminate, and most importantly their influence on the information ecosystem. In this paper, we shed light on these questions by analyzing 27K tweets posted by 1K Twitter users identified as having ties with Russia’s Internet Research Agency and thus likely state-sponsored trolls. We compare their behavior to a random set of Twitter users, finding interesting differences in terms of the content they disseminate, the evolution of their account, as well as their general behavior and use of Twitter. Then, using Hawkes Processes, we quantify the influence that trolls had on the dissemination of news on social platforms like Twitter, Reddit, and 4chan. Overall, our findings indicate that Russian trolls managed to stay active for long periods of time and to reach a substantial number of Twitter users with their tweets. When looking at their ability of spreading news content and making it viral, however, we find that their effect on social platforms was minor, with the significant exception of news published by the Russian state-sponsored news outlet RT (Russia Today).


title: A Survey on the Security of Pervasive Online Social Networks (POSNs)

abstract: Pervasive Online Social Networks (POSNs) are the extensions of Online Social Networks (OSNs) which facilitate connectivity irrespective of the domain and properties of users. POSNs have been accumulated with the convergence of a plethora of social networking platforms with a motivation of bridging their gap. Over the last decade, OSNs have visually perceived an altogether tremendous amount of advancement in terms of the number of users as well as technology enablers. A single OSN is the property of an organization, which ascertains smooth functioning of its accommodations for providing a quality experience to their users. However, with POSNs, multiple OSNs have coalesced through communities, circles, or only properties, which make service-provisioning tedious and arduous to sustain. Especially, challenges become rigorous when the focus is on the security perspective of cross-platform OSNs, which are an integral part of POSNs. Thus, it is of utmost paramountcy to highlight such a requirement and understand the current situation while discussing the available state-of-the-art. With the modernization of OSNs and convergence towards POSNs, it is compulsory to understand the impact and reach of current solutions for enhancing the security of users as well as associated services. This survey understands this requisite and fixates on different sets of studies presented over the last few years and surveys them for their applicability to POSNs...


title: An embedded controller for a 7-degree of freedom prosthetic arm

abstract: We present results from an embedded real-time hardware system capable of decoding surface myoelectric signals (sMES) to control a seven degree of freedom upper limb prosthesis. This is one of the first hardware implementations of sMES decoding algorithms and the most advanced controller to-date. We compare decoding results from the device to simulation results from a real-time PC-based operating system. Performance of both systems is shown to be similar, with decoding accuracy greater than 90% for the floating point software simulation and 80% for fixed point hardware and software implementations.


title: Modeling and Analysis of Coupling Performance Between Passive Magnetic Bearing and Hybrid Magnetic Radial Bearing for Magnetically Suspended Flywheel

abstract: In this paper, the design and development of a novel magnetic bearing (MB) system which consists of a passive magnetic bearing (PMB) with two pairs of passive permanent magnet rings and hybrid magnetic radial bearing (HMRB) used in magnetic bearing flywheel (MBFW) of control moment gyroscope (CMG) for agile satellite application are presented. The models of PMB and HMRB are proposed and their performances of force-current, force-position, and torque-angle are analyzed using the equivalent magnetic circuit method, finite element method (FEM), and equivalent magnetic charge method. The coupling forces between x and y position degrees of freedom (DOFs) of the HMRB, between the coil current in x-axis and y-axis, between the radial and the axial position DOF of the HMRB, and the coupling force and torque between the translating motion along the x (or y) axis and the rotary motion around x (or y) axis. These coupling problems of the novel MB system are proposed and clarified in this paper. An example is given to clarify the mathematical models and the coupling problems, and the linearized model is proposed for controller design of the MB system.


title: PFID: Pittsburgh fast-food image dataset

abstract: We introduce the first visual dataset of fast foods with a total of 4,545 still images, 606 stereo pairs, 303 360° videos for structure from motion, and 27 privacy-preserving videos of eating events of volunteers. This work was motivated by research on fast food recognition for dietary assessment. The data was collected by obtaining three instances of 101 foods from 11 popular fast food chains, and capturing images and videos in both restaurant conditions and a controlled lab setting. We benchmark the dataset using two standard approaches, color histogram and bag of SIFT features in conjunction with a discriminative classifier. Our dataset and the benchmarks are designed to stimulate research in this area and will be released freely to the research community.


title: Multiple-target deep learning for LSTM-RNN based speech enhancement

abstract: In this study, we explore long short-term memory recurrent neural networks (LSTM-RNNs) for speech enhancement. First, a regression LSTM-RNN approach for a direct mapping from the noisy to clean speech features is presented and verified to be more effective than deep neural network (DNN) based regression techniques in modeling long-term acoustic context. Then, a comprehensive comparison between the proposed direct mapping based LSTM-RNN and ideal ratio mask (IRM) based LSTM-RNNs is conducted. We observe that the direct mapping framework achieves better speech intelligibility at low signal-to-noise ratios (SNRs) while the IRM approach shows its superiority at high SNRs. Accordingly, to fully utilize this complementarity, a novel multiple-target joint learning approach is designed. The experiments under unseen noises show that the proposed framework can consistently and significantly improve the objective measures for both speech quality and intelligibility.


title: Microgrid planning and operation: Solar energy and wind energy

abstract: Economic, technology and environmental incentives are changing the features of electricity generation and transmission. Centralized power systems are giving way to local scale distributed generations. At present, there is a need to assess the effects of large numbers of distributed generators and short-term storage in Microgrid. To accommodate the high demand of renewable energy and the environment policy, the planning and operation of Micro-source generators has been studied using HOMER. Simulation results show a case study of an optimal microgrid configuration on Ontario area in Canada. Sensitivity variables are specified to examine the effect of uncertainties (e.g. diesel price and average wind speed), especially in a long-term planning. The effect of air emission penalties on Microgrid planning is also well presented.


title: A novel dual-mode bandpass filter with wide stopband using the properties of microstrip open-loop resonator

abstract: A novel dual-mode microstrip square loop resonator is proposed using the slow-wave and dispersion features of the microstrip slow-wave open-loop resonator. It is shown that the designed and fabricated dual-mode microstrip filter has a wide stopband including the first spurious resonance frequency. Also, it has a size reduction of about 50% at the same center frequency, as compared with the dual-mode bandpass filters such as microstrip patch, cross-slotted patch, square loop, and ring resonator filter.


title: Person Re-identification with Deep Similarity-Guided Graph Neural Network

abstract: The person re-identification task requires to robustly estimate visual similarities between person images. However, existing person re-identification models mostly estimate the similarities of different image pairs of probe and gallery images independently while ignores the relationship information between different probe-gallery pairs. As a result, the similarity estimation of some hard samples might not be accurate. In this paper, we propose a novel deep learning framework, named Similarity-Guided Graph Neural Network (SGGNN) to overcome such limitations. Given a probe image and several gallery images, SGGNN creates a graph to represent the pairwise relationships between probe-gallery pairs (nodes) and utilizes such relationships to update the probe-gallery relation features in an end-to-end manner. Accurate similarity estimation can be achieved by using such updated probe-gallery relation features for prediction. The input features for nodes on the graph are the relation features of different probe-gallery image pairs. The probe-gallery relation feature updating is then performed by the messages passing in SGGNN, which takes other nodes’ information into account for similarity estimation. Different from conventional GNN approaches, SGGNN learns the edge weights with rich labels of gallery instance pairs directly, which provides relation fusion more precise information. The effectiveness of our proposed method is validated on three public person re-identification datasets.


title: Distributed trajectory estimation with privacy and communication constraints: A two-stage distributed Gauss-Seidel approach

abstract: We propose a distributed algorithm to estimate the 3D trajectories of multiple cooperative robots from relative pose measurements. Our approach leverages recent results [1] which show that the maximum likelihood trajectory is well approximated by a sequence of two quadratic subproblems. The main contribution of the present work is to show that these subproblems can be solved in a distributed manner, using the distributed Gauss-Seidel (DGS) algorithm. Our approach has several advantages. It requires minimal information exchange, which is beneficial in presence of communication and privacy constraints. It has an anytime flavor: after few iterations the trajectory estimates are already accurate, and they asymptotically convergence to the centralized estimate. The DGS approach scales well to large teams, and it has a straightforward implementation. We test the approach in simulations and field tests, demonstrating its advantages over related techniques.


Chronos : Serverless Multi-User Chat Over NDN


title: Productive Parallel Linear Algebra Programming with Unstructured Topology Adaption

abstract: Sparse linear algebra is a key component of many scientific computations such as computational fluid dynamics, mechanical engineering or the design of new materials to mention only a few. The discretization of complex geometries in unstructured meshes leads to sparse matrices with irregular patterns. Their distribution in turn results in irregular communication patterns within parallel operations. In this paper, we show how sparse linear algebra can be implemented effortless on distributed memory architectures. We demonstrate how simple it is to incorporate advanced partitioning, network topology mapping, and data migration techniques into parallel HPC programs by establishing novel abstractions. For this purpose, we developed a linear algebra library - Parallel Matrix Template Library 4 - based on generic and meta-programming introducing a new paradigm: meta-tuning. The library establishes its own domain-specific language embedded in C++. The simplicity of software development is not paid by lower performance. Moreover, the incorporation of topology mapping demonstrated performance improvements up to 29%.


title: The imaginative mind.

abstract: The astounding capacity for the human imagination to be engaged across a wide range of contexts is limitless and fundamental to our day-to-day experiences. Although processes of imagination are central to human psychological function, they rarely occupy center stage in academic discourse or empirical study within psychological and neuroscientific realms. The aim of this paper is to tackle this imbalance by drawing together the multitudinous facets of imagination within a common framework. The processes fall into one of five categories depending on whether they are characterized as involving perceptual/motor related mental imagery, intentionality or recollective processing, novel combinatorial or generative processing, exceptional phenomenology in the aesthetic response, or altered psychological states which range from commonplace to dysfunctional. These proposed categories are defined on the basis of theoretical ideas from philosophy as well as empirical evidence from neuroscience. By synthesizing the findings across these domains of imagination, this novel five-part or quinquepartite classification of the human imagination aids in systematizing, and thereby abets, our understanding of the workings and neural foundations of the human imagination. It would serve as a blueprint to direct further advances in the field of imagination while also promoting crosstalk with reference to stimulus-oriented facets of information processing. A biologically and ecologically valid psychology is one that seeks to explain fundamental aspects of human nature. Given the ubiquitous nature of the imaginative operations in our daily lives, there can be little doubt that these quintessential aspects of the mind should be central to the discussion. Hum Brain Mapp 37:4197–4211, 2016. © 2016 Wiley Periodicals, Inc.


title: A ROBUST VARIABLE STEP-SIZE FREQUENCY- DOMAIN ADAPTIVE FILTER ALGORITHM FOR ACOUS- TIC ECHO CANCELLATION

abstract: In acoustic echo cancellation (AEC), frequency-domain adaptive filter (FDAF) algorithm is commonly used to improve the computational efficiency and the convergence rate. However, the choice of step size has to compromise between the convergence rate and the steady-state performance. A variable step-size algorithm was presented by Nitsch to tackle this problem, where the spectral convergence state is estimated to control the step size. In this paper, a new approach for the estimation of the optimal step size is proposed. The relationship between the time-domain system mismatch and the frequency-domain spectral convergence state is de


title: Complex network measures of brain connectivity: Uses and interpretations

abstract: article i nfo Brain connectivity datasets comprise networks of brain regions connected by anatomical tracts or by functional associations. Complex network analysis—a new multidisciplinary approach to the study of complex systems—aims to characterize these brain networks with a small number of neurobiologically meaningful and easily computable measures. In this article, we discuss construction of brain networks from connectivity data and describe the most commonly used network measures of structural and functional connectivity. We describe measures that variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, characterize patterns of local anatomical circuitry, and test resilience of networks to insult. We discuss the issues surrounding comparison of structural and functional network connectivity, as well as comparison of networks across subjects. Finally, we describe a Matlab toolbox (http://www.brain-connectivity-toolbox.net) accompanying this article and containing a collection of complex network measures and large-scale neuroanatomical connectivity datasets.


title: Assessing Pairing and Data Exchange Mechanism Security in the Wearable Internet of Things

abstract: The consumer wearable economy is a rapidly growing sector with an ever increasingly number of use cases mostly focused on the quantified self. Whether used for fitness tracking, mobile health monitoring, or as remote controllers for connected smartphone apps, wearables typically come equipped with a wide variety of different sensors such as accelerometers, pulsometors, and thermometers to capture data such as, respectively, the user's movements, heart-rate, and temperature. Once data is captured it is then typically wirelessly transmitted, using Bluetooth LE (low energy) to an awaiting smartphone. Since the data may be sensitive and/or personally identifiable, it is critical that this exchange and the pairing mechanisms used to set up the connection remain secure and resilient to eavesdropping attacks. This paper empirically evaluates the data exchange mechanisms of a variety of major commercial wearable products to determine if, and how well, the products live up to this security constraint. As part of this effort, the work also investigates the three different types of Bluetooth LE pairing strategies at a packet and protocol level. The results show presumably secure pairing strategies have glaring security vulnerabilities that affect all of the devices examined. In addition to this publication, efforts are underway to report these vulnerabilities to US-CERT.


title: Programming Pluralism: Using Learning Analytics to Detect Patterns in the Learning of Computer Programming

abstract: New high-frequency, automated data collection and analysis algorithms could offer new insights into complex learning processes, especially for tasks in which students have opportunities to generate unique open-ended artifacts such as computer programs. These approaches should be particularly useful because the need for scalable project-based and student-centered learning is growing considerably. In this article, we present studies focused on how students learn computer programming, based on data drawn from 154,000 code snapshots of computer programs under development by approximately 370 students enrolled in an introductory undergraduate programming course. We use methods from machine learning to discover patterns in the data and try to predict final exam grades. We begin with a set of exploratory experiments that use fully automated techniques to investigate how much students change their programming behavior throughout all assignments in the course. The results show that students’ change in programming ...


title: Auction-based multi-robot routing

abstract: Recently, auction methods have been investigated as effective, decentralized methods for multi-robot coordination. Experimental research has shown great potential, but has not been complemented yet by theoretical analysis. In this paper we contribute a theoretical analysis of the performance of auction methods for multi-robot routing. We suggest a generic framework for auction-based multi-robot routing and analyze a variety of bidding rules for different team objectives. This is the first time that auction methods are shown to offer theoretical guarantees for such a variety of bidding rules and team objectives.


title: Antimicrobial Activity of Emilia sonchifolia DC., Tridax procumbens L. and Vernonia cinerea L. of Asteracea Family: Potential as Food Preservatives.

abstract: Chemical preservatives have been used in the food industry for many years. However, with increased health concerns, consumers prefer additive-free products or food preservatives based on natural products. This study evaluated antimicrobial activities of extracts from Emilia sonchifolia L. (Common name: lilac tassel flower), Tridax procumbens L. (Common name: tridax daisy) and Vernonia cinerea L. (Common name: Sahadevi), belonging to the Asteracea family, to explore their potential for use against general food spoilage and human pathogens so that new food preservatives may be developed. Three methanol extracts of these plants were tested in vitro against 20 bacterial species, 3 yeast species, and 12 filamentous fungi by the agar diffusion and broth dilution methods. The V. cinerea extract was found to be most effective against all of the tested organisms and the methanol fraction showed the most significant (p < 0.05) antimicrobial activity among all the soluble fractions tested. The minimum inhibitory concentrations (MICs) of extracts determined by the broth dilution method ranged from 1.56 to 100.00mg/mL. The MIC of methanol fraction was the lowest in comparison to the other four extracts. The study findings indicate that bioactive natural products from these plants may be isolated for further testing as leads in the development of new pharmaceuticals in food preservation as well as natural plant-based medicine.


LO-PHI: Low-Observable Physical Host Instrumentation for Malware Analysis


title: A Comprehensive Isolated Farsi / Arabic Character Database for Handwritten OCR Research

abstract: This paper presents a new comprehensive database for isolated offline handwritten Farsi/Arabic numbers and characters for use in optical character recognition research. The database is freely available for academic use. So far no such a freely database in Farsi language is available. Grayscale images of 52,380 characters and 17,740 numerals are included. Each image was scanned from Iranian school entrance exam forms during the years 2004-2006 at 300 dpi. The only restriction imposed on the writers is to write each character within a rectangular box. The number of samples in each class of the database is non-uniform corresponding to their real life distributions. Also, for comparison purposes, each dataset has been properly divided into respective training and test sets.


Development of a Bio-Inspired Underwater Robot Prototype with Undulatory Fin Propulsion


title: 4D crop monitoring: Spatio-temporal reconstruction for agriculture

abstract: Autonomous crop monitoring at high spatial and temporal resolution is a critical problem in precision agriculture. While Structure from Motion and Multi-View Stereo algorithms can finely reconstruct the 3D structure of a field with low-cost image sensors, these algorithms fail to capture the dynamic nature of continuously growing crops. In this paper we propose a 4D reconstruction approach to crop monitoring, which employs a spatio-temporal model of dynamic scenes that is useful for precision agriculture applications. Additionally, we provide a robust data association algorithm to address the problem of large appearance changes due to scenes being viewed from different angles at different points in time, which is critical to achieving 4D reconstruction. Finally, we collected a high-quality dataset with ground-truth statistics to evaluate the performance of our method. We demonstrate that our 4D reconstruction approach provides models that are qualitatively correct with respect to visual appearance and quantitatively accurate when measured against the ground truth geometric properties of the monitored crops.


title: Key Enabling Technologies for Secure and Scalable Future Fog-IoT Architecture: A Survey

abstract: Fog or Edge computing has recently attracted broad attention from both industry and academia. It is deemed as a paradigm shift from the current centralized cloud computing model and could potentially bring a"Fog-IoT"architecture that would significantly benefit the future ubiquitous Internet of Things (IoT) systems and applications. However, it takes a series of key enabling technologies including emerging technologies to realize such a vision. In this article, we will survey these key enabling technologies with specific focuses on security and scalability, which are two very important and much-needed characteristics for future large-scale deployment. We aim to draw an overall big picture of the future for the research and development in these areas.


title: Efficient query processing framework for big data warehouse: an almost join-free approach

abstract: The rapidly increasing scale of data warehouses is challenging today's data analytical technologies. A conventional data analytical platform processes data warehouse queries using a star schema -- it normalizes the data into a fact table and a number of dimension tables, and during query processing it selectively joins the tables according to users' demands. This model is space economical. However, it faces two problems when applied to big data. First, join is an expensive operation, which prohibits a parallel database or a MapReduce-based system from achieving efficiency and scalability simultaneously. Second, join operations have to be executed repeatedly, while numerous join results can actually be reused by different queries. ::: ::: In this paper, we propose a new query processing framework for data warehouses. It pushes the join operations partially to the pre-processing phase and partially to the post-processing phase, so that data warehouse queries can be transformed into massive parallelized filter-aggregation operations on the fact table. In contrast to the conventional query processing models, our approach is efficient, scalable and stable despite of the large number of tables involved in the join. It is especially suitable for a large-scale parallel data warehouse. Our empirical evaluation on Hadoop shows that our framework exhibits linear scalability and outperforms some existing approaches by an order of magnitude.


title: Efficient trace monitoring

abstract: A wealth of recent research involves generating program monitors from declarative specifications. Doing this efficiently has proved challenging, and available implementations often produce infeasibly slow monitors. We demonstrate how to dramatically improve performance -- typically reducing overheads to within an order of magnitude of the program's normal runtime.


title: Data-Free Knowledge Distillation for Deep Neural Networks

abstract: Recent advances in model compression have provided procedures for compressing large neural networks to a fraction of their original size while retaining most if not all of their accuracy. However, all of these approaches rely on access to the original training set, which might not always be possible if the network to be compressed was trained on a very large dataset, or on a dataset whose release poses privacy or safety concerns as may be the case for biometrics tasks. We present a method for data-free knowledge distillation, which is able to compress deep neural networks trained on large-scale datasets to a fraction of their size leveraging only some extra metadata to be provided with a pretrained model release. We also explore different kinds of metadata that can be used with our method, and discuss tradeoffs involved in using each of them.


title: Safe futures for Java

abstract: A future is a simple and elegant abstraction that allows concurrency to be expressed often through a relatively small rewrite of a sequential program. In the absence of side-effects, futures serve as benign annotations that mark potentially concurrent regions of code. Unfortunately, when computation relies heavily on mutation as is the case in Java, its meaning is less clear, and much of its intended simplicity lost.This paper explores the definition and implementation of safe futures for Java. One can think of safe futures as truly transparent annotations on method calls, which designate opportunities for concurrency. Serial programs can be made concurrent simply by replacing standard method calls with future invocations. Most significantly, even though some parts of the program are executed concurrently and may indeed operate on shared data, the semblance of serial execution is nonetheless preserved. Thus, program reasoning is simplified since data dependencies present in a sequential program are not violated in a version augmented with safe futures.Besides presenting a programming model and API for safe futures, we formalize the safety conditions that must be satisfied to ensure equivalence between a sequential Java program and its future-annotated counterpart. A detailed implementation study is also provided. Our implementation exploits techniques such as object versioning and task revocation to guarantee necessary safety conditions. We also present an extensive experimental evaluation of our implementation to quantify overheads and limitations. Our experiments indicate that for programs with modest mutation rates on shared data, applications can use futures to profitably exploit parallelism, without sacrificing safety.


title: Field‐oriented developments for LineScout Technology and its deployment on large water crossing transmission lines

abstract: Condition assessments for power transmission line infrastructure and conductors are essential to maintain a reliable and efficient system. LineScout is a high-performance robot designed to undertake detailed, comprehensive inspections of transmission lines, which contributes to maintaining the integrity of equipment and the safety of inspection crews. This paper outlines Hydro-Quebec's LineScout Technology, describing briefly the main systems and key design issues and discussing the technology's impact and benefits on power transmission line inspection and maintenance practices. The paper then focuses on field results gathered over the past 2 years with LineScout Technology. Very important lessons were learned from more than 20 field deployments of the robot on live transmission lines. An appreciable amount of significant feedback came from these field visits and was decisive in improving the technology. Specifically, transportation and installation methods and onboard energy management strategy are presented. Also discussed, for the first time, is a simplified wheel contact radius estimate, useful for improving the wheel odometer readings. Finally, one field deployment on a large water crossing transmission line is presented in detail. The paper discusses the importance of the work done and of newly implemented features in terms of the quality and efficiency of inspections, the reliability of systems, and the safe operation of the technology. © 2011 Wiley Periodicals, Inc.


title: A Survey of Hardware Trojan Taxonomy and Detection

abstract: Editor's note:Today's integrated circuits are vulnerable to hardware Trojans, which are malicious alterations to the circuit, either during design or fabrication. This article presents a classification of hardware Trojans and a survey of published techniques for Trojan detection.


title: Knowledge Graph-Based Query Rewriting in a Relational Data Harmonization Framework

abstract: There are diverse data providers, storage formats and data schemas in many modern application domains from sales and marketing to health care. This leads to a high demand for a harmonized data management platform that hides the heterogeneity of the system from the end user. Using an abstraction layer that provides a single logical view of all data located in disparate data sources, makes the implementation of a harmonized data management platform effortless. This abstraction layer is called Data Virtualization and it can query the data sources via a single query which is usually in the common SQL format. However, the user needs to know where the desired data is located before submitting the SQL query to the virtualization middleware. In this paper, we present how to capture inter-database associations of relational data stores as a Resource Description Framework (RDF) graph for the purpose of automation in Data Virtualization. Furthermore, we propose an approach to enrich a naive input SPARQL which can query the RDF graph and translate it to a SQL query that will be fed into the virtualization layer. Combining these two approaches results in the transparency of the data harmonization framework.


title: Understanding information systems security policy compliance: An integration of the theory of planned behavior and the protection motivation theory

abstract: This research investigated information systems security policy (ISSP) compliance by drawing upon two relevant theories i.e. the theory of planned behavior (TPB) and the protection motivation theory (PMT). A research model that fused constituents of the aforementioned theories was proposed and validated. Relevant hypotheses were developed to test the research conceptualization. Data analysis was performed using the partial least squares (PLS) technique. Using a survey of 124 business managers and IS professionals, this study showed that factors such as self-efficacy, attitude toward compliance, subjective norms, response efficacy and perceived vulnerability positively influence ISSP behavioral compliance intentions of employees. The data analysis did not support perceived severity and response cost as being predictors of ISSP behavioral compliance intentions. The study's implications for research and practice are discussed.


title: Ultra-low power 50/60Hz notch filter for biomedical signal acquisition using 32nm ± 0.15V Bulk-Driven subthreshold CMOS OTAs

abstract: This paper deals with the design of continuous-time analog band-reject filter in 32 nm CMOS technology intended for biomedical applications using analog front-end (AFE). The work focuses on miniaturization and ultra low power consumption, needed in implantable devices running on battery. The circuit can process signals in the frequency range of 0.01 Hz to 300 Hz while filtering AC mains frequency (50 or 60 Hz). A sixth-order notch filter is implemented using Bulk-Driven Operational Transconductance Amplifiers in an OTA-C topology. The designed OTA has an ultra low transconductance: 65.4pA/V. The notch produces an attenuation of 17 dB at the mains frequency. The complete AFE was observed to consume 18.81pW of power at ±0.15V supply.


The economic justification of investing in energy efficiency.


title: The Evolution of C 2 1 Where Have We Been ? Where Are We Going ? Distributed Auction Algorithms for the Assignment Problem with Partial Information 1 15 th ICCRTS The Evolution of C 2 C 2 Approaches and Organization

abstract: Abstract : Task-asset assignment is a fundamental problem paradigm in a wide variety of applications. Typical problem setting involves a single decision maker (DM) who has complete knowledge of the weight (reward, benefit, accuracy) matrix and who can control any of the assets to execute the tasks. Motivated by planning problems arising in distributed organizations, this paper introduces a novel variation of the assignment problem, wherein there are multiple DMs and each DM knows only a part of the weight matrix and/or controls a subset of the assets. We extend the auction algorithm to such realistic settings with various partial information structures using a blackboard coordination structure. We show that by communicating the bid, the best and the second best profits among DMs and with a coordinator, the DMs can reconstruct the centralized assignment solution. The auction setup provides a nice analytical framework for formalizing how team members build internal models of other DMs and achieve team cohesiveness over time.


The Future of Bitcoin: a Synchrosqueezing Wavelet Transform to Predict Search Engine Query Trends


Mechanisms of Bone Remodeling


title: Solar energy driven Arduino based smart mosquito repeller system

abstract: Mosquitoes and other insects are harmful for humans as they can cause many diseases which are harmful for human health. In order to avoid the mosquitoes, people use chemical repellents which affect the environment adversely or mosquito repeller skin creams can create skin problems. This paper proposes the smart ultrasonic insect repelling system driven by harvesting energy from solar panels. The ultrasonic waves have frequencies more than 20,000 Hz which are inaudible to humans but when insects come in the contact with ultrasonic waves, they sense the waves from special hair or sensilla present on the antennae of mosquitoes which will create stress on the nervous system and jam their own ultrasonic frequency and force them to leave that area. The frequency ranges of 38–44 kHz can be used effectively to repel the mosquitoes and flies. The system employs solar panel as energy source to charge the rechargeable 12V battery which is used to drive the Arduino UNO to turn on and turn off the mosquito repeller system. The proposed smart mosquito repeller system can be used to repel mosquitos from within the area of 125 square meters.


title: Hippocampal–diencephalic–cingulate networks for memory and emotion: An anatomical guide

abstract: This review brings together current knowledge from tract tracing studies to update and reconsider those limbic connections initially highlighted by Papez for their presumed role in emotion. These connections link hippocampal and parahippocampal regions with the mammillary bodies, the anterior thalamic nuclei, and the cingulate gyrus, all structures now strongly implicated in memory functions. An additional goal of this review is to describe the routes taken by the various connections within this network. The original descriptions of these limbic connections saw their interconnecting pathways forming a serial circuit that began and finished in the hippocampal formation. It is now clear that with the exception of the mammillary bodies, these various sites are multiply interconnected with each other, including many reciprocal connections. In addition, these same connections are topographically organised, creating further subsystems. This complex pattern of connectivity helps explain the difficulty of interpreting the functional outcome of damage to any individual site within the network. For these same reasons, Papez’s initial concept of a loop beginning and ending in the hippocampal formation needs to be seen as a much more complex system of hippocampal–diencephalic–cingulate connections. The functions of these multiple interactions might be better viewed as principally providing efferent information from the posterior medial temporal lobe. Both a subcortical diencephalic route (via the fornix) and a cortical cingulate route (via retrosplenial cortex) can be distinguished. These routes provide indirect pathways for hippocampal interactions with prefrontal cortex, with the preponderance of both sets of connections arising from the more posterior hippocampal regions. These multi-stage connections complement the direct hippocampal projections to prefrontal cortex, which principally arise from the anterior hippocampus, thereby creating longitudinal functional differences along the anterior–posterior plane of the hippocampus.


title: The place of the literature review in grounded theory research

abstract: TSRM_A_494930.sg m For those employing grounded theory as a research methodology, the issue of how and when to engage with existing literature is often problematic, especially for PhD students. With this in mind, the current article seeks to offer some clarity on the topic and provide novice grounded theory researchers in particular with advice on how to approach the issue of the literature review in grounded theory. This is done by reviewing the origins of grounded theory, exploring the original stance taken by the founders of the methodology with regard to the literature review, tracking how this position has changed over time, outlining the rationale associated with specific positions and discussing ideas for reconciling opposing perspectives. Coupled with this, the author draws on his own experience of using grounded theory for his PhD research to explain how extant literature may be used and discusses how the nature of engagement with existing literature may impact upon the overall written presentation of a grounded theory study.


title: Evaluating shallow and deep networks for ransomware detection and classification

abstract: Ransomware is one type of malware that covertly installs and executes a cryptovirology attack on a victims computer to demand a ransom payment for restoration of the infected resources. This kind of malware has been growing largely in recent days and causes tens of millions of dollars losses to consumers. In this paper, we evaluate shallow and deep networks for the detection and classification of ransomware. To characterize and distinguish ransomware over benign and various other families of ransomwares, we leverage the dominance of application programming interface (API) invocations. To select a best architecture for the multi-layer perceptron (MLP), we done various experiments related to network parameters and structures. All the experiments are run up to 500 epochs with a learning rate in the range [0.01-0.5]. Result obtained on our data set is more promising to distinguish ransomware not only from benign from its families too. On distinguishing the .EXE as either benign or ransomware, MLP has attained highest accuracy 1.0 and classifying the ransomware to their categories obtained highest accuracy 0.98. Moreover, MLP has performed well in detecting and classifying ransomwares in comparison to the other classical machine learning classifiers.


title: Deep Representation Learning for Clustering of Health Tweets

abstract: Twitter has been a prominent social media platform for mining population-level health data and accurate clustering of health-related tweets into topics is important for extracting relevant health insights. In this work, we propose deep convolutional autoencoders for learning compact representations of health-related tweets, further to be employed in clustering. We compare our method to several conventional tweet representation methods including bag-of-words, term frequency-inverse document frequency, Latent Dirichlet Allocation and Non-negative Matrix Factorization with 3 different clustering algorithms. Our results show that the clustering performance using proposed representation learning scheme significantly outperforms that of conventional methods for all experiments of different number of clusters. In addition, we propose a constraint on the learned representations during the neural network training in order to further enhance the clustering performance. All in all, this study introduces utilization of deep neural network-based architectures, i.e., deep convolutional autoencoders, for learning informative representations of health-related tweets.


title: A cognitive model of posttraumatic stress disorder.

abstract: Abstract Posttraumatic stress disorder (PTSD) is a common reaction to traumatic events. Many people recover in the ensuing months, but in a significant subgroup the symptoms persist, often for years. A cognitive model of persistence of PTSD is proposed. It is suggested that PTSD becomes persistent when individuals process the trauma in a way that leads to a sense of serious, current threat. The sense of threat arises as a consequence of: (1) excessively negative appraisals of the trauma and/or its sequelae and (2) a disturbance of autobiographical memory characterised by poor elaboration and contextualisation, strong associative memory and strong perceptual priming. Change in the negative appraisals and the trauma memory are prevented by a series of problematic behavioural and cognitive strategies. The model is consistent with the main clinical features of PTSD, helps explain several apparently puzzling phenomena and provides a framework for treatment by identifying three key targets for change. Recent studies have provided preliminary support for several aspects of the model.


title: Creativity in amateur multimedia: Popular culture, critical theory, and HCI

abstract: The last decade has witnessed the emergence and aesthetic maturation of amateur multimedia on an unprecedented scale, from video podcasts to machinima, and Flash animations to user-created metaverses. Today, especially in academic circles, this pop culture phenomenon is little recognized and even less understood. This paper explores creativity in amateur multimedia using three theorizations of creativity—those of HCI, postructuralism, and technological determinism. These theorizations frame a semiotic analysis of numerous commonly used multimedia authoring platforms, which demonstrates a deep convergence of multimedia authoring tool strategies that collectively project a conceptualization and practice of digital creativity. This conceptualization of digital creativity in authoring tools is then compared with hundreds of amateur-created artifacts. These analyses reveal relationships among emerging amateur multimedia aesthetics, common software authoring tools, and the three theorizations of creativity discussed.


title: Functional topography of the cerebellum for motor and cognitive tasks: An fMRI study

abstract: Anatomical, clinical and imaging findings suggest that the cerebellum is engaged in cognitive and affective functions as well as motor control. Evidence from converging modalities also indicates that there is a functional topography in the human cerebellum for overt control of movement vs. higher functions, such that the cerebellum can be divided into zones depending on connectivity with sensorimotor vs. multimodal association cortices. Using functional MRI, we show that regions active during overt movement differ from those involved in higher-level language, spatial processing and working memory tasks. Nine healthy participants each completed five tasks in order to determine the relative activation patterns for the different paradigms. Right-handed finger-tapping activated right cerebellar lobules IV-V and VIII, consistent with descriptions of the cerebellar homunculi. Verb generation engaged right cerebellar lobules VI-Crus I and a second cluster in lobules VIIB-VIIIA. Mental rotation activation peaks were localized to medial left cerebellar lobule VII (Crus II). A 2-back working memory task activated bilateral regions of lobules VI-VII. Viewing arousing vs. neutral images did not reliably activate the cerebellum or cerebral limbic areas in this study. The cerebellar functional topography identified in this study reflects the involvement of different cerebro-cerebellar circuits depending on the demands of the task being performed: overt movement activated sensorimotor cortices along with contralateral cerebellar lobules IV-VI and VIII, whereas more cognitively demanding tasks engaged prefrontal and parietal cortices along with cerebellar lobules VI and VII. These findings provide further support for a cerebellar role in both motor and cognitive tasks, and better establish the existence of functional subregions in the cerebellum. Future studies are needed to determine the exact contribution of the cerebellum – and different cerebro-cerebellar circuits – to task performance.


title: BioFed: federated query processing over life sciences linked open data

abstract: BackgroundBiomedical data, e.g. from knowledge bases and ontologies, is increasingly made available following open linked data principles, at best as RDF triple data. This is a necessary step towards unified access to biological data sets, but this still requires solutions to query multiple endpoints for their heterogeneous data to eventually retrieve all the meaningful information. Suggested solutions are based on query federation approaches, which require the submission of SPARQL queries to endpoints. Due to the size and complexity of available data, these solutions have to be optimised for efficient retrieval times and for users in life sciences research. Last but not least, over time, the reliability of data resources in terms of access and quality have to be monitored. Our solution (BioFed) federates data over 130 SPARQL endpoints in life sciences and tailors query submission according to the provenance information. BioFed has been evaluated against the state of the art solution FedX and forms an important benchmark for the life science domain.MethodsThe efficient cataloguing approach of the federated query processing system ’BioFed’, the triple pattern wise source selection and the semantic source normalisation forms the core to our solution. It gathers and integrates data from newly identified public endpoints for federated access. Basic provenance information is linked to the retrieved data. Last but not least, BioFed makes use of the latest SPARQL standard (i.e., 1.1) to leverage the full benefits for query federation. The evaluation is based on 10 simple and 10 complex queries, which address data in 10 major and very popular data sources (e.g., Dugbank, Sider).ResultsBioFed is a solution for a single-point-of-access for a large number of SPARQL endpoints providing life science data. It facilitates efficient query generation for data access and provides basic provenance information in combination with the retrieved data. BioFed fully supports SPARQL 1.1 and gives access to the endpoint’s availability based on the EndpointData graph. Our evaluation of BioFed against FedX is based on 20 heterogeneous federated SPARQL queries and shows competitive execution performance in comparison to FedX, which can be attributed to the provision of provenance information for the source selection.ConclusionDeveloping and testing federated query engines for life sciences data is still a challenging task. According to our findings, it is advantageous to optimise the source selection. The cataloguing of SPARQL endpoints, including type and property indexing, leads to efficient querying of data resources over the Web of Data. This could even be further improved through the use of ontologies, e.g., for abstract normalisation of query terms.


title: RDMA-Based Direct Transfer of File Data to Remote Page Cache

abstract: The performance of a distributed file system significantly affects data-intensive applications that frequently execute I/O operations on large amounts of data. Although many modern distributed file systems are geared to provide highly efficient I/O performance, their operations are nonetheless affected by runtime overhead in data transfer between client nodes and I/O servers. A large part of the overhead is caused by memory copies executed by the client interface using the FUSE framework or a special kernel module. In this paper, we propose a method based on InfiniBand RDMA that improves data transfer performance between client and server in a distributed file system. The major characteristic of the method is that it transfers file data directly from a server's memory to the page cache of a client node. The method minimizes memory copies that are otherwise executed in the client interface or the operating system kernel. We implemented the proposed method in the Gfarm distributed file system and tested it using I/O benchmark software and real applications. The experimental results showed that our method effected a performance improvement of up to 78.4% and 256.0% in sequential and random file reads, respectively, and an improvement of up to 6.3% in data-intensive applications.


title: Search-based software test data generation for string data using program-specific search operators

abstract: This paper presents a novel approach to automatic software test data generation, where the test data is intended to cover program branches which depend on string predicates such as string equality, string ordering and regular expression matching. A search-based approach is assumed and some potential search operators and corresponding evaluation functions are assembled. Their performance is assessed empirically by using them to generate test data for a number of test programs. A novel approach of using search operators based on programming language string operators and parameterized by string literals from the program under test is introduced. These operators are also assessed empirically in generating test data for the test programs and are shown to provide a significant increase in performance. Copyright © 2006 John Wiley & Sons, Ltd.


title: Using coupled vias for band-pass filters in multilayered printed-circuit boards

abstract: This contribution extends previous work of the authors on microwave couplers using vias (plated through holes). In [1] a novel concept for microwave coupler design using vias in multilayered printed circuit boards (PCBs) was presented. By placing thru vias inside a ground via cage, high coupling between the vias can be observed. In this work it is shown that appropriate definition of via ports leads to a filter behavior of the structure. By adjusting the dimensions of the structure and by adding further thru vias the filter behavior can be controlled. Results are validated by physics-based and full-wave simulations up to 40 GHz.


ZooKeeper ’ s atomic broadcast protocol : Theory and practice


title: KP-S: A Spark-Based Design of the K-Prototypes Clustering for Big Data

abstract: Big data is often characterized by a huge volume and a mixed types of attributes namely, numeric and categorical. K-prototypes is one of the most well-known clustering methods to deal with mixed data. Several parallel alternatives based on MapReduce have been proposed to enable this method to handle large scale of mixed data. However, these solutions are not suitable when dealing with Big data, due to time and memory restrictions. To address this issue, we propose in this paper a new Spark-based k-prototypes clustering method which uses the reclustering technique. We take advantage of the in-memory operations of Spark to build grouping from large scale of mixed data. Experiments performed on simulated and real data sets show that the proposed method is scalable and improves the efficiency of the existing k-prototypes methods.


title: On discovery of gathering patterns from trajectories

abstract: The increasing pervasiveness of location-acquisition technologies has enabled collection of huge amount of trajectories for almost any kind of moving objects. Discovering useful patterns from their movement behaviours can convey valuable knowledge to a variety of critical applications. In this light, we propose a novel concept, called gathering, which is a trajectory pattern modelling various group incidents such as celebrations, parades, protests, traffic jams and so on. A key observation is that these incidents typically involve large congregations of individuals, which form durable and stable areas with high density. Since the process of discovering gathering patterns over large-scale trajectory databases can be quite lengthy, we further develop a set of well thought out techniques to improve the performance. These techniques, including effective indexing structures, fast pattern detection algorithms implemented with bit vectors, and incremental algorithms for handling new trajectory arrivals, collectively constitute an efficient solution for this challenging task. Finally, the effectiveness of the proposed concepts and the efficiency of the approaches are validated by extensive experiments based on a real taxicab trajectory dataset.


title: Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering

abstract: A number of studies have found that today's Visual Question Answering (VQA) models are heavily driven by superficial correlations in the training data and lack sufficient image grounding. To encourage development of models geared towards the latter, we propose a new setting for VQA where for every question type, train and test sets have different prior distributions of answers. Specifically, we present new splits of the VQA v1 and VQA v2 datasets, which we call Visual Question Answering under Changing Priors (VQA-CP v1 and VQA-CP v2 respectively). First, we evaluate several existing VQA models under this new setting and show that their performance degrades significantly compared to the original VQA setting. Second, we propose a novel Grounded Visual Question Answering model (GVQA) that contains inductive biases and restrictions in the architecture specifically designed to prevent the model from 'cheating' by primarily relying on priors in the training data. Specifically, GVQA explicitly disentangles the recognition of visual concepts present in the image from the identification of plausible answer space for a given question, enabling the model to more robustly generalize across different distributions of answers. GVQA is built off an existing VQA model -- Stacked Attention Networks (SAN). Our experiments demonstrate that GVQA significantly outperforms SAN on both VQA-CP v1 and VQA-CP v2 datasets. Interestingly, it also outperforms more powerful VQA models such as Multimodal Compact Bilinear Pooling (MCB) in several cases. GVQA offers strengths complementary to SAN when trained and evaluated on the original VQA v1 and VQA v2 datasets. Finally, GVQA is more transparent and interpretable than existing VQA models.


title: Flow Control and Scheduling for Shared FIFO Queues over Wireless Networks

abstract: We investigate the performance of First-In, First-Out (FIFO) queues over wireless networks. We characterize the stability region of a general scenario where an arbitrary number of FIFO queues, which are served by a wireless medium, are shared by an arbitrary number of flows. In general, the stability region of this system is non-convex. Thus, we develop a convex inner-bound on the stability region, which is provably tight in certain cases. The convexity of the inner bound allows us to develop a resource allocation scheme; dFC. Based on the structure of dFC, we develop a stochastic flow control and scheduling algorithm; qFC. We show that qFC achieves optimal operating point in the convex inner bound. Simulation results show that our algorithms significantly improve the throughput of wireless networks with FIFO queues, as compared to the well-known queue-based flow control and max-weight scheduling.


title: Bankruptcy prediction using neural networks

abstract: Abstract Prediction of firm bankruptcies have been extensively studied in accounting, as all stakeholders in a firm have a vested interest in monitoring its financial performance. This paper presents an exploratory study which compares the predictive capabilities for firm bankruptcy of neural networks and classical multivariate discriminant analysis. The predictive accuracy of the two techniques is presented within a comprehensive, statistically sound framework, indicating the value added to the forecasting problem by each technique. The study indicates that neural networks perform significantly better than discriminant analysis at predicting firm bankruptcies. Implications of our results for the accounting professional, neural networks researcher and decision support system builders are highlighted.


title: Loss of transcriptional activation of three sterol-regulated genes in mutant hamster cells.

abstract: Cholesterol biosynthesis and uptake are controlled by a classic end product-feedback mechanism whereby elevated cellular sterol levels suppress transcription of the genes encoding 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) synthase, HMG-CoA reductase, and the low-density lipoprotein receptor. The 5'-flanking region of each gene contains a common cis-acting element, designated the sterol regulatory element (SRE), that is required for transcriptional regulation. In this report, we describe mutant Chinese hamster ovary (CHO) cell lines that lack SRE-dependent transcription. Mutant cell lines were isolated on the basis of their ability to survive treatment with amphotericin B, a polyene antibiotic that kills cells by interacting with cholesterol in the plasma membrane. Four mutant lines (SRD-6A, -B, -C, and -D) were found to be cholesterol auxotrophs and demonstrated constitutively low levels of mRNA for all three sterol-regulated genes even under conditions of sterol deprivation. The mutant cell lines were found to be genetically recessive, and all four lines belonged to the same complementation group. When transfected with a plasmid containing a sterol-regulated promoter fused to a bacterial reporter gene, SRD-6B cells demonstrated constitutively low levels of transcription, in contrast to wild-type CHO cells, which increased transcription under conditions of sterol deprivation. Mutation of the SREs in this plasmid prior to transfection reduced the level of expression in wild-type CHO cells deprived of sterols to the level of expression found in SRD-6B cells. The defect in SRD-6 cells is limited to transcriptional regulation, since posttranscriptional mechanisms of sterol-mediated regulation were intact: the cells retained the ability to posttranscriptionally suppress HMG-CoA reductase activity and to stimulate acyl-CoA:cholesterol acyltransferase activity. These results suggest that SRD-6 cells lack a factor required for SRE-dependent transcriptional activation. We contrast these cells with a previously isolated oxysterol-resistant cell line (SRD-2) that lacks a factor required for SRE-dependent transcriptional suppression and propose a model for the role of these genetically defined factors in sterol-mediated transcriptional regulation.


Introduction to leadership


title: Crowdsourcing for Reminiscence Chatbot Design

abstract: In this work-in-progress paper we discuss the challenges in identifying effective and scalable crowd-based strategies for designing content, conversation logic, and meaningful metrics for a reminiscence chatbot targeted at older adults. We formalize the problem and outline the main research questions that drive the research agenda in chatbot design for reminiscence and for relational agents for older adults in general.


title: Moving Chipless RFID Tag Detection Using Adaptive Wavelet-Based Detection Algorithm

abstract: The detection of a chipless radio frequency identification (RFID) tag in stepped motion is proposed. The performance is evaluated through experiment and measured using the vector network analyzer. The postprocessing of the measured results is performed using MATLAB. A novel adaptive wavelet-based detection algorithm is used for the decoding of the tag ID. An error analysis is conducted on the experimental results to study the variation of magnitude and frequency due to the movement of a chipless RFID tag. As far as the tag signature has not been disappeared from the reading range it has the same confidence level for the detection of its tag ID. This opens a new avenue toward developing the detection algorithms and decoding techniques when the chipless RFID tag is in motion. It also opens the doors for the localization of moving chipless RFID tags and prediction of the tagged objects moving trajectory modeling in the future.


title: Discriminative Deep Metric Learning for Face Verification in the Wild

abstract: This paper presents a new discriminative deep metric learning (DDML) method for face verification in the wild. Different from existing metric learning-based face verification methods which aim to learn a Mahalanobis distance metric to maximize the inter-class variations and minimize the intra-class variations, simultaneously, the proposed DDML trains a deep neural network which learns a set of hierarchical nonlinear transformations to project face pairs into the same feature subspace, under which the distance of each positive face pair is less than a smaller threshold and that of each negative pair is higher than a larger threshold, respectively, so that discriminative information can be exploited in the deep network. Our method achieves very competitive face verification performance on the widely used LFW and YouTube Faces (YTF) datasets.


title: A Practical Point Cloud Based Road Curb Detection Method for Autonomous Vehicle

abstract: Robust and quick road curb detection under various situations is critical in developing intelligent vehicles. However, the road curb detection is easily affected by the obstacles in the road area when Lidar based method is applied. A practical road curb detection method using point cloud from a three-dimensional Lidar for autonomous vehicle is reported in this paper. First, a multi-feature, loose-threshold, varied-scope ground segmentation method is presented to increase the robustness of ground segmentation with which obstacles above the ground can be detected. Second, the road curb is detected by applying the global road trend and an extraction-update mechanism. Experiments show the robustness and efficiency of the road curb detection under various environments. The road curb detection method is 10 times the speed of traditional method and the accuracy is much higher than existing methods.


title: Pharmaceutical Company Websites as Sources of Information for Consumers: How Appropriate and Informative Are They?

abstract: Although the advantages of disseminating health information on the world wide web are well recognized, there has been considerable concern about the quality of online information. There has also been some debate as to whether organizations with a vested interest in a product, such as pharmaceutical companies, can provide balanced and appropriate information on the web. However, there is very little published information about the content, quality or impact of pharmaceutical websites. This paper considers the issues, arguments and empirical evidence for and against the practice of direct-to-consumer advertising by pharmaceutical companies. It also describes a selected set of pharmaceutical company sites that provide consumer information about major public health problems or about health conditions corresponding to frequently used search engine terms. The pharmaceutical sites surveyed were characterized both by positive attributes (such as a breadth and depth of information, specification of medication adverse effects, and inclusion of potentially useful consumer aids such as symptom diaries and screening tests) and negative features (such as failure to disclose authorship and scientific sources, failure to provide clear and accessible information about the absolute and relative efficacy of the treatments mentioned, and absence of an editorial board or independent review process).


title: An Ontology for Context-Aware Pervasive Computing Environments

abstract: This document describes COBRA-ONT, an ontology for supporting pervasive context-aware systems. COBRA-ONT, expressed in the Web Ontology Language OWL, is a collection of ontologies for describing places, agents and events and their associated properties in an intelligent meeting-room domain. This ontology is developed as a part of the Context Broker Architecture (CoBrA), a broker-centric agent architecture that provides knowledge sharing, context reasoning and privacy protection supports for pervasive context-aware systems. We also describe an inference engine for reasoning with information expressed using the COBRA-ONT ontology and the ongoing research in using the DAML-Time ontology for context reasoning.


Neural plasticity after spinal cord injury☆


title: Artificial intelligence, alienation and ontological problems of other minds: A critical investigation into the future of man and machines

abstract: While most contemporary researchers argue that Artificial Intelligence (AI) provides overwhelming and profound advancements in technology and beneficial tools used daily to advance human life on earth, there exist a host of other researchers who hold contrary opinions to this view, with regard to the rising adverse ontological and existential consequences which the products of super-intelligent technologies has on an alarming number of mankind in the 21st century. Scholars like Vardi, Tegmark and Greene likened this scenario to a time-bomb waiting to go off any moment. The recent endorsement of 23 AI principles by 1200 AI/Robotics researchers and over 2342 other researchers from diverse disciplines, in a just concluded Future of Life (FLI) Conference, adds credence to the worries which most researchers have about the presumed benefits of AI to mankind. The study draws from a combination of Marxian Alienation and ontological theories which basically holds that: rising advancements in AI technologies, continues to alienate mankind from his existential human nature. The ex-post facto research design in the social sciences and Deriders' deconstructive and critical reconstructive analytic method in philosophy, for interrogating the meaning of concepts, arguments and current debates on the relevance and risks of IA, were adopted for the study. The study identified justifiable grounds and reasons for the alarm raised over current innovations in the field of AI technologies. The study strengthen the resolve of current researchers to identify ways of reducing or avoiding the impending adverse consequences of evolving conscious AI and machines in the feature. The research proposes a radical legal enforcement and adoption of the 23 newly established AI principles as one of the pertinent measure for saving mankind from the impending threats posed by innovations powered by advances in AI technology.


title: A physically based, scalable MOS varactor model and extraction methodology for RF applications

abstract: A physically based scalable model for MOS varactors, including analytical surface potential based charge modeling and physical geometry and process parameter based parasitic modeling, is proposed. Key device performances of capacitance and quality factor Q are validated over a wide voltage, frequency, and geometrical space. The model, implemented in Verilog-A for simulator portability, provides for robust and accurate RF simulation of MOS varactors.


title: Personas, people and participation: challenges from the trenches of local government

abstract: In the early days of digital technology development, design was done 'for', 'with' or 'by' the users based on the assumption that users were real people. Today 'users' have become a component in mass-market production and are seen as 'customers', rather than people. Still designers need to address use, and personas have been introduced for this purpose. The paper uses research on user participation and research-based personas from the eGov+ project to discuss whether personas help designers engage with users. In this project, design was carried out in the domain of municipal services through involvement of clerks, management and citizens from three different municipalities. Through four cases we discuss if applying personas in participatory design settings is productive to designers' understanding of users' use situations. Does deployment of personas bring designers closer to the actual use situation? In which ways do personas help design for, with or by the users? Do personas support participatory design?


Introduction to k Nearest Neighbour Classification and Condensed Nearest Neighbour Data Reduction


title: Classification and Characteristics of Electronic Payment Systems

abstract: Electronic commerce and electronic business greatly need new payment systems that will support their further development. To better understand problems and perspectives of the electronic payment systems this article describes a classification and different characteristic aspects of payment systems. It suggests distinctions between payment systems and mediating systems, and is trying to illustrate advantages and limitations of diverse categories of payment systems using the defined characteristics. It is highlighting importance of user-related aspects in design and introduction of electronic payment systems for mass customers.


title: Feature selection in UNSW-NB15 and KDDCUP'99 datasets

abstract: Machine learning and data mining techniques have been widely used in order to improve network intrusion detection in recent years. These techniques make it possible to automate anomaly detection in network traffics. One of the major problems that researchers are facing is the lack of published data available for research purposes. The KDD'99 dataset was used by researchers for over a decade even though this dataset was suffering from some reported shortcomings and it was criticized by few researchers. In 2009, Tavallaee M. et al. proposed a new dataset (NSL-KDD) extracted from the KDD'99 dataset in order to improve the dataset where it can be used for carrying out research in anomaly detection. The UNSW-NB15 dataset is the latest published dataset which was created in 2015 for research purposes in intrusion detection. This research is analysing the features included in the UNSW-NB15 dataset by employing machine learning techniques and exploring significant features (curse of high dimensionality) by which intrusion detection can be improved in network systems. Therefore, the existing irrelevant and redundant features are omitted from the dataset resulting not only faster training and testing process but also less resource consumption while maintaining high detection rates. A subset of features is proposed in this study and the findings are compared with the previous work in relation to features selection in the KDD'99 dataset.


title: A prediction algorithm for coexistence problem in multiple WBANs environment

abstract: Coexistence problem occurs when a single WBAN(Wireless Body Area Network) locates in multiple WBANs environment. In that case, WBANs are suffered from serious channel interferences which may degrade the performance of each WBAN due to failure of data transmission. Because WBAN handles physical signal or emergency data affecting human life, WBAN requires the detection of coexistence condition to guarantee reliable communication continuously for each sensor node of WBAN. In this paper, we present a prediction algorithm to detect coexistence problem efficiently in multiple WBANs environment. The algorithm measures PRR(Packet Reception Ratio) and SINR(Signal to Interference and Noise Ratio) to detect interference reliably. In order to handle coexistence problem efficiently, the algorithm employs the naive Bayesian classifier which is one of machine learning techniques to classify the coexistence condition into four states. We conduct extensive simulations for coexistence detection with various packet transmit rates of sender node and speeds of mobile WBAN by using Castalia 3.2 simulator based on OMNet++ platform. Consequently, we demonstrate that the proposed algorithm provides more reliable and accurate performance than existing studies to detect coexistence in multiple WBANs environment.


title: Experiments With Open-Domain Textual Question Answering

abstract: This paper describes the integration of several knowledge-based natural language processing techniques into a Question Answering system, capable of mining textual answers from large collections of texts. Surprizing quality is achieved when several lightweight knowledge-based NLP techniques complement mostly shallow, surface-based approaches.


title: A Deep Learning Perspective on the Origin of Facial Expressions

abstract: Facial expressions play a significant role in human communication and behavior. Psychologists have long studied the relationship between facial expressions and emotions. Paul Ekman et al., devised the Facial Action Coding System (FACS) to taxonomize human facial expressions and model their behavior. The ability to recognize facial expressions automatically, enables novel applications in fields like human-computer interaction, social gaming, and psychological research. There has been a tremendously active research in this field, with several recent papers utilizing convolutional neural networks (CNN) for feature extraction and inference. In this paper, we employ CNN understanding methods to study the relation between the features these computational networks are using, the FACS and Action Units (AU). We verify our findings on the Extended Cohn-Kanade (CK+), NovaEmotions and FER2013 datasets. We apply these models to various tasks and tests using transfer learning, including cross-dataset validation and cross-task performance. Finally, we exploit the nature of the FER based CNN models for the detection of micro-expressions and achieve state-of-the-art accuracy using a simple long-short-term-memory (LSTM) recurrent neural network (RNN).


title: First Impressions: A Survey on Computer Vision-Based Apparent Personality Trait Analysis.

abstract: Personality analysis has been widely studied in psychology, neuropsychology, signal processing fields, among others. From the computing point of view, by far speech and text have been the most analyzed cues of information for analyzing personality. However, recently there has been an increasing interest form the computer vision community in analyzing personality starting from visual information. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing computer vision-based visual and multimodal approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features. More importantly, future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed. Hence, the survey provides an up-to-date review of research progress in a wide range of aspects of this research theme.


title: Class Probability Estimates are Unreliable for Imbalanced Data (and How to Fix Them)

abstract: Obtaining good probability estimates is imperative for many applications. The increased uncertainty and typically asymmetric costs surrounding rare events increases this need. Experts (and classification systems) often rely on probabilities to inform decisions. However, we demonstrate that class probability estimates attained via supervised learning in imbalanced scenarios systematically underestimate the probabilities for minority class instances, despite ostensibly good overall calibration. To our knowledge, this problem has not previously been explored. Motivated by our exposition of this issue, we propose a simple, effective and theoretically motivated method to mitigate the bias of probability estimates for imbalanced data that bags estimators calibrated over balanced bootstrap samples. This approach drastically improves performance on the minority instances without greatly affecting overall calibration. We show that additional uncertainty can be exploited via a Bayesian approach by considering posterior distributions over bagged probability estimates.


title: Detection of sinkhole attacks for supporting secure routing on 6LoWPAN for Internet of Things

abstract: The Internet of Things (IoT) networks are vulnerable to various kinds of attacks, being the sinkhole attack one of the most destructive since it prevents communication among network devices. In general, existing solutions are not effective to provide protection and security against attacks sinkhole on IoT, and they also introduce high consumption of resources de memory, storage and processing. Further, they do not consider the impact of device mobility, which in essential in urban scenarios, like smart cities. This paper proposes an intrusion detection system, called INTI (Intrusion detection of SiNkhole attacks on 6LoWPAN for InterneT of ThIngs), to identify sinkhole attacks on the routing services in IoT. Moreover, INTI aims to mitigate adverse effects found in IDS that disturb its performance, like false positive and negative, as well as the high resource cost. The system combines watchdog, reputation and trust strategies for detection of attackers by analyzing the behavior of devices. Results show the INTI performance and its effectiveness in terms of attack detection rate, number of false positives and false negatives.


title: Smartphone use and smartphone addiction in middle school students in Korea: Prevalence, social networking service, and game use

abstract: This study aimed to examine smartphone use patterns, smartphone addiction characteristics, and the predictive factors of the smartphone addiction in middle school students in South Korea. According to the Smartphone Addiction Proneness Scale scores, 563 (30.9%) were classified as a risk group for smartphone addiction and 1261 (69.1%) were identified as a normal user group. The adolescents used mobile messengers for the longest, followed by Internet surfing, gaming, and social networking service use. The two groups showed significant differences in smartphone use duration, awareness of game overuse, and purposes of playing games. The predictive factors of smartphone addiction were daily smartphone and social networking service use duration, and the awareness of game overuse.


title: 300-GHz Step-Profiled Corrugated Horn Antennas Integrated in LTCC

abstract: This paper presents 300-GHz step-profiled corrugated horn antennas, aiming at their integration in low-temperature co-fired ceramic (LTCC) packages. Using substrate integrated waveguide technology, the cavity inside the multi-layer LTCC substrate and a surrounding via fence are used to form a feeding hollow waveguide and horn structure. Owing to the vertical configuration, we were able to design the corrugations and stepped profile of horn antennas to approximate smooth metallic surface. To verify the design experimentally, the LTCC waveguides and horn antennas were fabricated with an LTCC multi-layer process. The LTCC waveguide exhibits insertion loss of 0.6 dB/mm, and the LTCC horn antenna exhibits 18-dBi peak gain and 100-GHz bandwidth with more than 10-dB return loss. The size of the horn antenna is only 5×5×2.8 mm ::: 3 ::: , which makes it easy to integrate it in LTCC transceiver modules.


title: Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines

abstract: To allow the hidden units of a restricted Boltzmann machine to model the transformation between two successive images, Memisevic and Hinton (2007) introduced three-way multiplicative interactions that use the intensity of a pixel in the first image as a multiplicative gain on a learned, symmetric weight between a pixel in the second image and a hidden unit. This creates cubically many parameters, which form a three-dimensional interaction tensor. We describe a low-rank approximation to this interaction tensor that uses a sum of factors, each of which is a three-way outer product. This approximation allows efficient learning of transformations between larger image patches. Since each factor can be viewed as an image filter, the model as a whole learns optimal filter pairs for efficiently representing transformations. We demonstrate the learning of optimal filter pairs from various synthetic and real image sequences. We also show how learning about image transformations allows the model to perform a simple visual analogy task, and we show how a completely unsupervised network trained on transformations perceives multiple motions of transparent dot patterns in the same way as humans.


Automatic Bass Line Transcription from Polyphonic Music


title: A multi-scale cascade fully convolutional network face detector

abstract: Face detection is challenging as faces in images could be present at arbitrary locations and in different scales. We propose a three-stage cascade structure based on fully convolutional neural networks (FCNs). It first proposes the approximate locations where the faces may be, then aims to find the accurate location by zooming on to the faces. Each level of the FCN cascade is a multi-scale fully-convolutional network, which generates scores at different locations and in different scales. A score map is generated after each FCN stage. Probable regions of face are selected and fed to the next stage. The number of proposals is decreased after each level, and the areas of regions are decreased to more precisely fit the face. Compared to passing proposals directly between stages, passing probable regions can decrease the number of proposals and reduce the cases where first stage doesn't propose good bounding boxes. We show that by using FCN and score map, the FCN cascade face detector can achieve strong performance on public datasets.


title: Different perspectives on emotional and behavioural problems in unaccompanied refugee children and adolescents

abstract: Objective. This study aims, firstly, to investigate the prevalence of emotional and behavioural problems in unaccompanied refugee children and adolescents living in Belgium. Secondly, this study compares the perspectives of the adolescents with those of social workers on the adolescents’ emotional well-being. Design. A total of 166 unaccompanied refugee children and adolescents, living in different large- and small-scale centres, in foster care or alone, participated in the study. Of them, 142 completed self-report questionnaires on emotional and behavioural problems (HSCL-37A, SDQ-self and RATS) and traumatic experiences (SLE), and for 124 refugee youths, social workers filled in two questionnaires on emotional and behavioural problems (CBCL/6-18 and SDQ-parent). Results. Between 37 and 47% of the unaccompanied refugee youths have severe or very severe symptoms of anxiety, depression and post-traumatic stress. Girls and those having experienced many traumatic events are at even higher risk for the develo...


title: Summarization from Medical Documents: A Survey

abstract: Objective:: The aim of this paper is to survey the recent work in medical documents summarization. Background:: During the last decade, documents summarization got increasing attention by the AI research community. More recently it also attracted the interest of the medical research community as well, due to the enormous growth of information that is available to the physicians and researchers in medicine, through the large and growing number of published journals, conference proceedings, medical sites and portals on the World Wide Web, electronic medical records, etc. Methodology:: This survey gives first a general background on documents summarization, presenting the factors that summarization depends upon, discussing evaluation issues and describing briefly the various types of summarization techniques. It then examines the characteristics of the medical domain through the different types of medical documents. Finally, it presents and discusses the summarization techniques used so far in the medical domain, referring to the corresponding systems and their characteristics. Discussion and conclusions:: The paper discusses thoroughly the promising paths for future research in medical documents summarization. It mainly focuses on the issue of scaling to large collections of documents in various languages and from different media, on personalization issues, on portability to new sub-domains, and on the integration of summarization technology in practical applications.


title: Flexible 3D localization of planar objects for industrial bin-picking with monocamera vision system

abstract: In this paper, we present a robust and flexible vision system for 3D localization of planar parts for industrial robots. Our system is able to work with nearly any object with planar shape, randomly placed inside a standard industrial bin or on a conveyor belt. Differently from most systems based on 2D image analysis, which usually can manage parts disposed in single layers, our approach can estimate the 6 degrees of freedom (DoF) pose of planar objects from a single 2D image. The choice of a single camera solution makes our system cheaper and faster with respect to systems using expensive industrial 3D cameras, or laser triangulation systems, or laser range finders. Our system can work virtually with any planar piece, without changing the software parameters, because the input for the recognition and localization algorithm is the CAD data of the planar part. The localization software is based on a two step strategy: i) a candidates selection step based on a well-engineered voting scheme ii) a refinement and best match selection step based on a robust iterative optimize-and-score procedure. During this second step, we employ a novel strategy we called search-in-the-stack that avoids the optimization from being stuck on local minima (representing false positives) created when objects are almost regularly stacked. Our system is currently installed in seven real world industrial plants, with different setups, working with hundreds of different models and successfully guiding the manipulators to pick several hundreds of thousands of pieces per year. In the experiment section, we report statistics about our system at work in real production plants on more than 60000 cycles.


Wireless World Research Forum (WWRF) Short-Range Optical Wireless Communications


title: Semantic Compositionality through Recursive Matrix-Vector Spaces

abstract: Single-word vector space models have been very successful at learning lexical information. However, they cannot capture the compositional meaning of longer phrases, preventing them from a deeper understanding of language. We introduce a recursive neural network (RNN) model that learns compositional vector representations for phrases and sentences of arbitrary syntactic type and length. Our model assigns a vector and a matrix to every node in a parse tree: the vector captures the inherent meaning of the constituent, while the matrix captures how it changes the meaning of neighboring words or phrases. This matrix-vector RNN can learn the meaning of operators in propositional logic and natural language. The model obtains state of the art performance on three different experiments: predicting fine-grained sentiment distributions of adverb-adjective pairs; classifying sentiment labels of movie reviews and classifying semantic relationships such as cause-effect or topic-message between nouns using the syntactic path between them.


title: Firmament: fast, centralized cluster scheduling at scale

abstract: Centralized datacenter schedulers can make high-quality placement decisions when scheduling tasks in a cluster. Today, however, high-quality placements come at the cost of high latency at scale, which degrades response time for interactive tasks and reduces cluster utilization. ::: ::: This paper describes Firmament, a centralized scheduler that scales to over ten thousand machines at sub-second placement latency even though it continuously reschedules all tasks via a min-cost max-flow (MCMF) optimization. Firmament achieves low latency by using multiple MCMF algorithms, by solving the problem incrementally, and via problem-specific optimizations. ::: ::: Experiments with a Google workload trace from a 12,500-machine cluster show that Firmament improves placement latency by 20× over Quincy [22], a prior centralized scheduler using the same MCMF optimization. Moreover, even though Firmament is centralized, it matches the placement latency of distributed schedulers for workloads of short tasks. Finally, Firmament exceeds the placement quality of four widely-used centralized and distributed schedulers on a real-world cluster, and hence improves batch task response time by 6×.


title: A Statistical Perspective on Data Mining

abstract: Abstract Data mining can be regarded as a collection of methods for drawing inferences from data. The aims of data mining, and some of its methods, overlap with those of classical statistics. However, there are some philosophical and methodological differences. We examine these differences, and we describe three approaches to machine learning that have developed largely independently: classical statistics, Vapnik's statistical learning theory, and computational learning theory. Comparing these approaches, we conclude that statisticians and data miners can profit by studying each other's methods and using a judiciously chosen combination of them.


title: Substructure similarity measurement in chinese recipes

abstract: Improving the precision of information retrieval has been a challenging issue on Chinese Web. As exemplified by Chinese recipes on the Web, it is not easy/natural for people to use keywords (e.g. recipe names) to search recipes, since the names can be literally so abstract that they do not bear much, if any, information on the underlying ingredients or cooking methods. In this paper, we investigate the underlying features of Chinese recipes, and based on workflow-like cooking procedures, we model recipes as graphs. We further propose a novel similarity measurement based on the frequent patterns, and devise an effective filtering algorithm to prune unrelated data so as to support efficient on-line searching. Benefiting from the characteristics of graphs, frequent common patterns can be mined from a cooking graph database. So in our prototype system called RecipeView, we extend the subgraph mining algorithm FSG to cooking graphs and combine it with our proposed similarity measurement, resulting in an approach that well caters for specific users' needs. Our initial experimental studies show that the filtering algorithm can efficiently prune unrelated cooking graphs without affecting the retrieval performance and the similarity measurement gets a relatively higher precision/recall against its counterparts


title: Electromagnetic Shielding Analysis of Multiple Slits on a Metal Plate Coated With a Ferrite Sheet

abstract: This paper presents a rigorous analysis of electromagnetic (EM) field penetration into multiple slits on a metal plate loaded with a ferrite sheet having frequency-dependent complex permeability. The Fourier transform is used to represent the EM fields in the spectral domain, and the mode-matching technique is enforced to obtain a solution in rapidly converging series form, leading to numerically efficient results. Electric and magnetic shielding characteristics in near-field region are investigated over wideband frequency range in terms of the presence or absence of a ferrite sheet with frequency-dependent magnetic permeability. It is observed that the shielding characteristics of electric and magnetic fields are different from each other at the low-frequency range in the near-field region. The number of slits and the ferrite sheet have significant influence on the magnetic shielding performance. In addition, the effect of nonperiodic multiple slits on the shielding effectiveness is examined. The proposed method is verified by comparing the results with those of a commercial EM solver, and good agreement is achieved using the feature selective validation.