title: A Delay Locked Loop With a Feedback Edge Combiner of Duty-Cycle Corrector With a 20%–80% Input Duty Cycle for SDRAMs

abstract: A feedback edge combiner is proposed for the duty-cycle corrector (DCC) of a delay locked loop (DLL) to increase the range of allowed input duty cycle. The feedback edge combiner generates the rising edge of a DCC output at the rising edge of an input clock. It generates the falling edge of the DCC output at the rising edge of a feedback clock that is a half-period-delayed signal of the DCC output. A dual-delay-line digitally controlled delay line (DCDL) is used for seamless boundary switching. The chip area of the DCDL is reduced by around 46% by employing the architecture of two short coarse delay lines followed by a fine phase mixer (FPM) and a long coarse delay line in series instead of the architecture of two long coarse delay lines followed by an FPM. The measurements on the chip fabricated in the 65-nm CMOS show the allowed input duty cycle in the range from 20% to 80%; root-mean-square and peak-to-peak jitters of 2.69 and 14.0 ps, respectively, at 2 GHz and 1.2 V; and the operating frequency range from 0.12 to 2.0 GHz at 1.2 V. The measured power consumption is 3.3 mW/GHz at 1.2 V. The chip area is 0.059 mm 2.


title: A duality between clause width and clause density for SAT

abstract: We consider the relationship between the complexities of k-SAT and those of SAT restricted to formulas of constant density. Let s/sub k/ be the infimum of those c /spl ges/ 0 such that k-SAT on n variables can be decided in time O(2/sup cn/) and d/sub /spl Delta// be the infimum of those c /spl ges/ 0 such that SAT on n variables and /spl les/ /spl Delta/n clauses can be decided in time O(2/sup cn/). We show that lim/sub k/spl rarr//spl infin// s/sub k/ = lim/sub /spl Delta//spl rarr//spl infin//d/sub /spl Delta//. So, for any /spl epsi/ > 0, k-SAT can be solved in 2/sup (1-/spl epsi/)n/ time independent of k if and only if the same is true for SAT with any fixed density of clauses to variables. We derive some interesting consequences from this. For example, assuming that 3-SAT is exponentially hard (that is, s/sub 3/ > 0), SAT of any fixed density can be solved in time whose exponent is strictly less than that for general SAT. We also give an improvement to the sparsification lemma of Impagliazzo et al. (1998) showing that instances of k-SAT of density slightly more than exponential in k are almost the hardest instances of k-SAT. The previous result showed this for densities doubly exponential in k.


title: An overview on the existing language models for prediction systems as writing assistant tools

abstract: The prediction task in national language processing means to guess the missing letter, word, phrase, or sentence that likely follow in a given segment of a text. Since 1980s many systems with different methods were developed for different languages. In this paper an overview of the existing prediction methods that have been used for more than two decades are described and a general classification of the approaches is presented. The three main categories of the classification are statistical modeling, knowledge-based modeling, and heuristic modeling (adaptive).


title: An improved shoe-mounted inertial navigation system

abstract: This paper proposes an improved shoe-mounted inertial navigation system for pedestrian tracking. The improvements consist of a fuzzy logic (FL) procedure for better foot stance phase detection and an indirect Kalman filter (IKF) for drift correction based on the typical zero-updating (ZUPT) measurement and our proposed angular updating (AUPT) pseudo-measurement. We illustrate our findings using a real time implementation of the proposed approach.


title: Inferring translation candidates for multilingual dictionary generation with multi-way neural machine translation

abstract: This publication has emanated from research supported in part by a research grant ::: from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289, ::: co-funded by the European Regional Development Fund, and the European ::: Union’s Horizon 2020 research and innovation programme under grant agreement ::: No 731015, ELEXIS - European Lexical Infrastructure.


Formalizing the Design Space and Product Development Cycle for Socially Interactive Robots


title: Precise and Robust Line Detection for Highly Distorted and Noisy Images

abstract: This article presents a method to detect lines in fisheye and distorted perspective images. The detection is performed with subpixel accuracy. By detecting lines in the original images without warping the image with a reverse distortion, the detection accuracy can be noticeably improved. The combination of the edge detection and the line detection to a single step provides a more robust and more reliable detection of larger line segments.


title: Non-specific effects of methylphenidate (Ritalin) on cognitive ability and decision-making of ADHD and healthy adults

abstract: Introduction ::: The effect of a single dose of methylphenidate (MPH) on cognitive measures and decision-making processes was assessed in a sample of adults with ADHD and in a control sample.


title: Local Similarity-Aware Deep Feature Embedding

abstract: Existing deep embedding methods in vision tasks are capable of learning a compact Euclidean space from images, where Euclidean distances correspond to a similarity metric. To make learning more effective and efficient, hard sample mining is usually employed, with samples identified through computing the Euclidean feature distance. However, the global Euclidean distance cannot faithfully characterize the true feature similarity in a complex visual feature space, where the intraclass distance in a high-density region may be larger than the interclass distance in low-density regions. In this paper, we introduce a Position-Dependent Deep Metric (PDDM) unit, which is capable of learning a similarity metric adaptive to local feature structure. The metric can be used to select genuinely hard samples in a local neighborhood to guide the deep embedding learning in an online and robust manner. The new layer is appealing in that it is pluggable to any convolutional networks and is trained end-to-end. Our local similarity-aware feature embedding not only demonstrates faster convergence and boosted performance on two complex image retrieval datasets, its large margin nature also leads to superior generalization results under the large and open set scenarios of transfer learning and zero-shot learning on ImageNet 2010 and ImageNet-10K datasets.


title: Crowd-centric Requirements Engineering

abstract: Requirements engineering is a preliminary and crucial phase for the correctness and quality of software systems. Despite the agreement on the positive correlation between user involvement in requirements engineering and software success, current development methods employ a too narrow concept of that user and rely on a recruited set of users considered to be representative. Such approaches might not cater for the diversity and dynamism of the actual users and the context of software usage. This is especially true in new paradigms such as cloud and mobile computing. To overcome these limitations, we propose crowd-centric requirements engineering (CCRE) as a revised method for requirements engineering where users become primary contributors, resulting in higher-quality requirements and increased user satisfaction. CCRE relies on crowd sourcing to support a broader user involvement, and on gamification to motivate that voluntary involvement.


Exposures and their control in radiographic film processing in British Columbia


title: Patterns and Pattern Diagrams for Access Control

abstract: Access control is a fundamental aspect of security. There are many variations of the basic access control models and it is confusing for a software developer to select an appropriate model for her application. The result in practice is that only basic models are used and the power of more advanced models is thus lost. We try to clarify this panorama here through the use of patterns. In particular, we use pattern diagrams to navigate the pattern space. A pattern diagram shows relationships between patterns and we can see how different models relate to each other. A subproduct of our work is the analysis of which patterns are available for use and which need to be written. Pattern maps are also useful to perform semi-automatic model transformations as required for Model-Driven Development (MDD). The idea is to provide the designer of a secure system with a navigation tool that she can use to select an appropriate pattern from a catalog of security patterns. We also indicate how to compose new access control models by adding features to an existing pattern and how to define patterns by analogy.


title: The strict avalanche criterion randomness test

abstract: A new statistical test for randomness, the strict avalanche criterion (SAC) test, is presented, together with its results over some well-known generators in the literature. These results are analyzed and some possible applications of the test, as for measuring the strength of cryptographic primitives including block ciphers, stream ciphers and pseudorandom number generators, especially during the design and analysis phase, are detailed. Finally, the source code for a basic version of the SAC test is provided, which proves some of its other advantages: it is easy to implement, and very fast so well-suited for practical applications.


title: Trend Filtering Methods for Momentum Strategies ∗

abstract: This paper studies trend filtering methods. These methods are widely used in momentum strategies, which correspond to an investment style based only on the history of past prices. For example, the CTA strategy used by hedge funds is one of the best-known momentum strategies. In this paper, we review the different econometric estimators to extract a trend of a time series. We distinguish between linear and nonlinear models as well as univariate and multivariate filtering. For each approach, we provide a comprehensive presentation, an overview of its advantages and disadvantages and an application to the S\&P 500 index. We also consider the calibration problem of these filters. We illustrate the two main solutions, the first based on prediction error, and the second using a benchmark estimator. We conclude the paper by listing some issues to consider when implementing a momentum strategy.


Random Sampling and Locality Constraint for Fast Face Sketch Synthesis


title: DESIGN OF A COMPACT UWB ANTENNA INTEGRATED WITH GSM/WCDMA/WLAN BANDS

abstract: In this research, a compact printed antenna design operating on ultra-wideband (UWB) and three extra wireless communication bands is proposed. An ellipse-shaped monopole is utilized to realize UWB application (3.1{10.6GHz). The modifled ground employs three folded Capacitive Loaded Line Resonators (CLLRs) to obtain triple relatively lower communication bands, including parts of global System for Mobile Communications (GSM) band at the centre frequency of 1.78GHz, Wideband Code Division Multiple Access (WCDMA) band at the centre frequency of 2.15GHz, and Wireless Local Area Networks (WLAN) band at the centre frequency of 2.4GHz. The CLLRs are designed with quarter- wavelength to control the corresponding frequencies independently. Good agreement is achieved between the simulation and measurement to verify our presented design. The basic, dual-, triple-band UWB antennas are also simulated and good results are obtained. Small group delay variations across UWB frequencies are obtained for the presented antenna and reference antennas, with some level of distortion observed.


title: A comparative study of text classification algorithms on user submitted bug reports

abstract: In software industries, various open source projects utilize the services of Bug Tracking Systems that let users submit software issues or bugs and allow developers to respond to and fix them. The users label the reports as bugs or any other relevant class. This classification helps to decide which team or personnel would be responsible for dealing with an issue. A major problem here is that users tend to wrongly classify the issues, because of which a middleman called a bug triager is required to resolve any misclassifications. This ensures no time is wasted at the developer end. This approach is very time consuming and therefore it has been of great interest to automate the classification process, not only to speed things up, but to lower the amount of errors as well. In the literature, several approaches including machine learning techniques have been proposed to automate text classification. However, there has not been an extensive comparison on the performance of different natural language classifiers in this field. In this paper we compare general natural language data classifying techniques using five different machine learning algorithms: Naive Bayes, kNN, Pegasos, Rocchio and Perceptron. The performance comparison of these algorithms was done on the basis of their apparent error rates. The data-set involved four different projects, Httpclient, Jackrabbit, Lucene and Tomcat5, that used two different Bug Tracking Systems — Bugzilla and Jira. An experimental comparison of pre-processing techniques was also performed.


title: Functional connectivity of the anterior cingulate cortex within the human frontal lobe: a brain-mapping meta-analysis

abstract: A database of positron-emission-tomography studies published between January 1993 and November 1996 was created to address several questions regarding the function and connectivity of the human anterior cingulate cortex (ACC). Using this database, we have previously reported on the relationship between behavioural variables and the probability of blood-flow response in distinct subdivisions of the ACC. The goal of the current analysis was to discover which areas of the frontal cortex show increased blood-flow co-occurring consistently with increased blood-flow in the ACC. Analyses of the frequency distributions of peaks in the ACC and the remaining frontal cortex (FC) yielded several important findings. First, FC peaks in the precentral gyrus, superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, medial frontal gyrus and orbitomedial frontal gyri were more frequent in subtractions that also yielded a peak in the ACC than in those that did not yield an ACC peak. Second, regional differences in the frequency distribution of these FC peaks were observed when the ACC peaks were subdivided into the rostral versus caudal ACC and supracallosal versus subcallosal ACC. Peaks in the precentral gyrus and in the vicinity of the supplementary motor area were more prevalent in subtractions with co-occurring peaks in the caudal than with the rostral ACC. Peaks in the middle frontal gyrus were more frequent in subtractions with co-occurring peaks in the paralimbic part of the supracallosal ACC, relative to the subcallosal or limbic supracallosal ACC. These observations are consistent with known differences in the anatomic connectivity in these cortical regions, as defined in non-human primates. Further analyses of the influence of behavioural variables on the relationships between the ACC and other regions of the frontal cortex suggested that this type of meta-analysis may provide testable hypotheses about functional and effective connectivity within the human frontal lobe.


Sustainable Management of the Water-Energy-Food Nexus : From Traditional to Integrated Hydro-Irrigation-Restoration Systems 1


Socialization Processes toward Children and Adolescents for Developing Empathy, Sympathy and Prosocial Behaviors


title: A survey on predicting the popularity of web content

abstract: Social media platforms have democratized the process of web content creation allowing mere consumers to become creators and distributors of content. But this has also contributed to an explosive growth of information and has intensified the online competition for users attention, since only a small number of items become popular while the rest remain unknown. Understanding what makes one item more popular than another, observing its popularity dynamics, and being able to predict its popularity has thus attracted a lot of interest in the past few years. Predicting the popularity of web content is useful in many areas such as network dimensioning (e.g., caching and replication), online marketing (e.g., recommendation systems and media advertising), or real-world outcome prediction (e.g., economical trends). In this survey, we review the current findings on web content popularity prediction. We describe the different popularity prediction models, present the features that have shown good predictive capabilities, and reveal factors known to influence web content popularity.


title: A Computational Framework for Conceptual Blending

abstract: Abstract We present a computational framework for conceptual blending , a concept invention method that is advocated in cognitive science as a fundamental and uniquely human engine for creative thinking. Our framework treats a crucial part of the blending process, namely the generalisation of input concepts, as a search problem that is solved by means of modern answer set programming methods to find commonalities among input concepts. We also address the problem of pruning the space of possible blends by introducing metrics that capture most of the so-called optimality principles , described in the cognitive science literature as guidelines to produce meaningful and serendipitous blends. As a proof of concept, we demonstrate how our system invents novel concepts and theories in domains where creativity is crucial, namely mathematics and music.


title: Multi-Tier Drone Architecture for 5G/B5G Cellular Networks: Challenges, Trends, and Prospects

abstract: Drones (or unmanned aerial vehicles [UAVs]) are expected to be an important component of fifth generation (5G)/beyond 5G (B5G) cellular architectures that can potentially facilitate wireless broadcast or point-to-multipoint transmissions. The distinct features of various drones such as the maximum operational altitude, communication, coverage, computation, and endurance impel the use of a multi-tier architecture for future drone-cell networks. In this context, this article focuses on investigating the feasibility of multi-tier drone network architecture over traditional single-tier drone networks and identifying the scenarios in which drone networks can potentially complement the traditional RF-based terrestrial networks. We first identify the challenges associated with multi-tier drone networks as well as drone-assisted cellular networks. We then review the existing state-of-the-art innovations in drone networks and drone-assisted cellular networks. We then investigate the performance of a multi-tier drone network in terms of spectral efficiency of downlink transmission while illustrating the optimal intensity and altitude of drones in different tiers numerically. Our results demonstrate the specific network load conditions (i.e., ratio of user intensity and base station intensity) where deployment of drones can be beneficial (in terms of spectral efficiency of downlink transmission) for conventional terrestrial cellular networks.


title: Cheap Data Analytics using Cold Storage Devices

abstract: Enterprise databases use storage tiering to lower capital and operational expenses. In such a setting, data waterfalls from an SSD-based high-performance tier when it is "hot" (frequently accessed) to a disk-based capacity tier and finally to a tape-based archival tier when "cold" (rarely accessed). To address the unprecedented growth in the amount of cold data, hardware vendors introduced new devices named Cold Storage Devices (CSD) explicitly targeted at cold data workloads. With access latencies in tens of seconds and cost/GB as low as $0.01/GB/month, CSD provide a middle ground between the low-latency (ms), high-cost, HDD-based capacity tier, and high-latency (min to h), low-cost, tape-based, archival tier. ::: ::: Driven by the price/performance aspect of CSD, this paper makes a case for using CSD as a replacement for both capacity and archival tiers of enterprise databases. Although CSD offer major cost savings, we show that current database systems can suffer from severe performance drop when CSD are used as a replacement for HDD due to the mismatch between design assumptions made by the query execution engine and actual storage characteristics of the CSD. We then build a CSD-driven query execution framework, called Skipper, that modifies both the database execution engine and CSD scheduling algorithms to be aware of each other. Using results from our implementation of the architecture based on PostgreSQL and OpenStack Swift, we show that Skipper is capable of completely masking the high latency overhead of CSD, thereby opening up CSD for wider adoption as a storage tier for cheap data analytics over cold data.


title: The Effect Evaluation of Coastal Engineering to Hydrodynamics a Numerical Modelling Approach

abstract: In order to evaluate the effect of coastal engineering on hydrodynamics,a robust three-dimensional hydrodynamic numerical model with 6-node triangular element and two order shape function is developed.This model can simulate and predicate the hydrodynamics variation caused by the engineering construct based on the unconstruct grid meshing technology.The application of this model to the EIA of Songyu harbor project shows it can be implemented to the feasible and EIA research of the marine engineering.


title: A modified bat algorithm for the Quadratic Assignment Problem

abstract: Quadratic Assignment Problem (QAP) is a discrete search space problem where one has to find the perfect feasible permutation of locations which minimizes the objective function. This paper solves the quadratic assignment problem using bat algorithm. Bat algorithm cannot be directly applied for solving QAP as it generates a solution for continuous search space problems. Therefore, we use a heuristic rule called Smallest Position Value (SPV) to enable our algorithm for finding solutions for sequencing problems. The modified algorithm completely outperforms the particle swarm optimization algorithm in all the cases considered.


title: Compression of Deep Neural Networks on the Fly

abstract: Thanks to their state-of-the-art performance, deep neural networks are increasingly used for object recognition. To achieve the best results, they use millions of parameters to be trained. However, when targetting embedded applications the size of these models becomes problematic. As a consequence, their usage on smartphones or other resource limited devices is prohibited. In this paper we introduce a novel compression method for deep neural networks that is performed during the learning phase. It consists in adding an extra regularization term to the cost function of fully-connected layers. We combine this method with Product Quantization (PQ) of the trained weights for higher savings in storage consumption. We evaluate our method on two data sets (MNIST and CIFAR10), on which we achieve significantly larger compression rates than state-of-the-art methods.


title: Factorization Machines with libFM

abstract: Factorization approaches provide high accuracy in several important prediction problems, for example, recommender systems. However, applying factorization approaches to a new prediction problem is a nontrivial task and requires a lot of expert knowledge. Typically, a new model is developed, a learning algorithm is derived, and the approach has to be implemented. Factorization machines (FM) are a generic approach since they can mimic most factorization models just by feature engineering. This way, factorization machines combine the generality of feature engineering with the superiority of factorization models in estimating interactions between categorical variables of large domain. libFM is a software implementation for factorization machines that features stochastic gradient descent (SGD) and alternating least-squares (ALS) optimization, as well as Bayesian inference using Markov Chain Monto Carlo (MCMC). This article summarizes the recent research on factorization machines both in terms of modeling and learning, provides extensions for the ALS and MCMC algorithms, and describes the software tool libFM .


Performance Analysis of Motion-Sensor Behavior for User Authentication on Smartphones


title: Application of Public Knowledge Discovery Tool (PKDE4J) to Represent Biomedical Scientific Knowledge

abstract: In today’s era of information explosion, extracting entities and their relations in large-scale, unstructured collections of text to better represent knowledge has emerged as a daunting challenge in biomedical text mining. To respond to the demand to automatically extract scientific knowledge with higher precision, the public knowledge discovery tool PKDE4J (Song et al., 2015) was proposed as a flexible text-mining tool. In this study, we propose an extended version of PKDE4J to represent scientific knowledge for literature-based knowledge discovery. Specifically, we assess the performance of PKDE4J in terms of three extraction tasks: entity, relation, and event detection. We also suggest applications of PKDE4J along three lines: 1) knowledge search, 2) knowledge linking, and 3) knowledge inference. We first describe the updated features of PKDE4J and report on tests of its performance. With additional options in the processes of named entity extraction, verb expansion, and event detection, we expect that the enhanced PKDE4J can be utilized for literature-based knowledge discovery.


title: Can a Chatbot Determine My Diet?: Addressing Challenges of Chatbot Application for Meal Recommendation

abstract: Poor nutrition can lead to reduced immunity, increased susceptibility to disease, impaired physical and mental development, and reduced productivity. A conversational agent can support people as a virtual coach, however building such systems still have its associated challenges and limitations. This paper describes the background and motivation for chatbot systems in the context of healthy nutrition recommendation. We discuss current challenges associated with chatbot application, we tackled technical, theoretical, behavioural, and social aspects of the challenges. We then propose a pipeline to be used as guidelines by developers to implement theoretically and technically robust chatbot systems.


Aiming for resilience and adaptation in managing environment An emerging environmental and emergency leadership in the twenty-first century


Stock Market Prediction and Analysis Using Naïve Bayes


title: A Map-Reduce Model of Decision Tree Classifier using Attribute Partitioning

abstract: Data mining is a process of analyzing data to extract the patterns in large datasets in the field of artificial intelligence, machine learning and statistics. Decision tree is one of the well-established classification models in data mining. The size and dimensionality of the data of today’s world are increasing exponentially, thus finding of informative patterns is an important and crucial task. The organizations require distributed systems for storing and processing huge amount of data. The proposed method is the parallel implementation of Decision Tree methods based on the idea of attribute partitioning, where dataset is partitioned into multiple subsets of dimensions. We develop a Map-Reduce programming model for processing data using decision tree classifier based on attribute partitioning. The experimental results show an improvement in classification accuracy as compared to a traditional decision tree method.


title: On Multi-Sensor Radar Configurations for Vehicle Tracking in Autonomous Driving Environments

abstract: Multi-sensor radar tracking systems can provide better system performance, visibility and complementary information than single sensor tracking systems. One way to utilize multiple sensors for target tracking is to generate individual sensor detections and fuse these detections to produce optimized system tracks. All or a subset of detections from the sensors are fed into a tracker algorithm, allowing the filter to choose the best possible measurement association. This paper discusses the implementation of a multi-sensor target tracking system using three different sensor configurations for providing vehicle detections. The three sensor configurations compare the accuracy of one vs two radar sensors, operating in either a stepped frequency waveform or a hybrid stepped frequency and continuous waveform. The purpose of this paper is to provide insight into the possibilities of multi-sensor radar tracking for the advancement of autonomous driving.


title: A CNN Accelerator on FPGA Using Depthwise Separable Convolution

abstract: Convolutional neural networks (CNNs) have been widely deployed in the fields of computer vision and pattern recognition because of their high accuracy. However, large convolution operations are computing intensive and often require a powerful computing platform such as a graphics processing unit. This makes it difficult to apply CNNs to portable devices. The state-of-the-art CNNs, such as MobileNetV2 and Xception, adopt depthwise separable convolution to replace the standard convolution for embedded platforms, which significantly reduces operations and parameters with only limited loss in accuracy. This highly structured model is very suitable for field-programmable gate array (FPGA) implementation. In this brief, a scalable high performance depthwise separable convolution optimized CNN accelerator is proposed. The accelerator can be fit into an FPGA of different sizes, provided the balancing between hardware resources and processing speed. As an example, MobileNetV2 is implemented on Arria 10 SoC FPGA, and the results show this accelerator can classify each picture from ImageNet in 3.75 ms, which is about 266.6 frames per second. The FPGA design achieves 20x speedup if compared to CPU.


title: Securing the Internet of Things (IoT): A Security Taxonomy for IoT

abstract: In Internet of Things (IoT), there is a vast number of connected devices that exist. These devices are collecting and transmitting great volumes of data from device to device, device to enterprise systems, and occasionally from device to humans. Due to the billions of connected devices, there is a great risk of identity and data theft, device manipulation, data falsification, server/network manipulation, and subsequent impact to application platforms. While the number of these interconnected devices continues to grow every day, so does the number of security threats and vulnerabilities posed to these devices. Security is one of the most paramount technological research problems that exist today for IoT. Security has many facets - security built within the device, security of data transmission, and data storage within the systems and its applications. There is an extensive amount of literature that exists on the subject with countless problems as well as proposed solutions; however, most of the existing work does not provide a holistic view of security and data privacy issues within the IoT. The primary goal of this research work is to advance the current state of the art in IoT research by identifying (a) the critical domains where IoT is heavily used, (b) the security requirements and challenges that IoT is currently facing, and (c) the existing security solutions that have been proposed or implemented with their limitations


title: Deep Audio-Visual Speech Recognition

abstract: The goal of this work is to recognise phrases and sentences being spoken by a talking face, with or without the audio. Unlike previous works that have focussed on recognising a limited number of words or phrases, we tackle lip reading as an open-world problem -- unconstrained natural language sentences, and in the wild videos. Our key contributions are: (1) we compare two models for lip reading, one using a CTC loss, and the other using a sequence-to-sequence loss. Both models are built on top of the transformer self-attention architecture; (2) we investigate to what extent lip reading is complementary to audio speech recognition, especially when the audio signal is noisy; (3) we introduce and publicly release two new datasets for audio-visual speech recognition: LRS2-BBC, consisting of thousands of natural sentences from British television; and LRS3-TED, consisting of hundreds of hours of TED and TEDx talks obtained from YouTube. The models that we train surpass the performance of all previous work on lip reading benchmark datasets by a significant margin.


title: Evaluating Syntactic Properties of Seq2seq Output with a Broad Coverage HPSG: A Case Study on Machine Translation

abstract: Sequence to sequence (seq2seq) models are often employed in settings where the target output is natural language. However, the syntactic properties of the language generated from these models are not well understood. We explore whether such output belongs to a formal and realistic grammar, by employing the English Resource Grammar (ERG), a broad coverage, linguistically precise HPSG-based grammar of English. From a French to English parallel corpus, we analyze the parseability and grammatical constructions occurring in output from a seq2seq translation model. Over 93\% of the model translations are parseable, suggesting that it learns to generate conforming to a grammar. The model has trouble learning the distribution of rarer syntactic rules, and we pinpoint several constructions that differentiate translations between the references and our model.


title: Vehicle speed dependent assistant control for electric power steering systems

abstract: This paper considers vehicle speed dependent assistant control problems for electric power steering systems (EPSs). According to the requirement analysis of the vehicle speed dependent assistant control, a mathematical model of EPSs is established, and a double closed-loop control system structure is proposed. Based on the assist characteristic of the desired steering wheel torque, the function model of desired steering wheel torque on vehicle speed is designed. Theory analyses show the double closed-loop control system structure can make the output torque applied to the steering wheel at different vehicle speeds are always close to the desired steering wheel torque, and tracking control without steady error for the EPS is realized. The EPS simulation model is established by using Simulink, and the PI control strategy of the torque controller is used to simulate the EPS. Simulation results show the control system structure and the vehicle speed dependent assistant control strategy are easy to implement, and the control effect is better.


title: Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond

abstract: We look at the eigenvalues of the Hessian of a loss function before and after training. The eigenvalue distribution is seen to be composed of two parts, the bulk which is concentrated around zero, and the edges which are scattered away from zero. We present empirical evidence for the bulk indicating how over-parametrized the system is, and for the edges that depend on the input data.


title: An Analytical Model for False Turn-On Evaluation of High-Voltage Enhancement-Mode GaN Transistor in Bridge-Leg Configuration

abstract: Compared with the state-of-the-art Si-based power devices, enhancement-mode Gallium Nitride (E-mode GaN) transistors have better figures of merit and exhibit great potential in enabling higher switching frequency, higher efficiency, and higher power density for power converters. The bridge-leg configuration circuit, consisting of a controlling switch and a synchronous switch, is a critical component in many power converters. However, owing to the low threshold voltage and fast switching speed, E-mode GaN devices are more prone to false turn-on phenomenon in bridge-leg configuration, leading to undesirable results, such as higher switching loss, circuit oscillation, and shoot through. In order to expand gate terminal's safe operating margin without increasing reverse conduction loss during deadtime, negative gate voltage bias for turn-off and antiparallel diode could be applied to E-mode GaN device. In this paper, with consideration of strong nonlinearities in C–V and I–V characteristics of high-voltage (650 V) E-mode GaN transistors, analytical device models are first developed. Then, we develop an analytical circuit model that combines the circuit parameters with intrinsic characteristics of the high-voltage GaN transistor and antiparallel diode. Thus, key transient waveforms with regard to the false turn-on problem can be acquired, including displacement current and false triggering voltage pulse on gate terminal. The simulated waveforms are then verified on a testing board with GaN-based bridge-leg circuit. In contrast to piecewise switching process models and PSpice simulation, the proposed model exhibits outstanding performances. To provide design guidelines for mitigating false turn-on of GaN transistor, the impacts of different circuit parameters, along with the optimum negative gate voltage bias, are investigated based on the proposed model.


title: A framework for estimating information security risk assessment method completeness

abstract: In general, an information security risk assessment (ISRA) method produces risk estimates, where risk is the product of the probability of occurrence of an event and the associated consequences for the given organization. ISRA practices vary among industries and disciplines, resulting in various approaches and methods for risk assessments. There exist several methods for comparing ISRA methods, but these are scoped to compare the content of the methods to a predefined set of criteria, rather than process tasks to be carried out and the issues the method is designed to address. It is the lack of an all-inclusive and comprehensive comparison that motivates this work. This paper proposes the Core Unified Risk Framework (CURF) as an all-inclusive approach to compare different methods, all-inclusive since we grew CURF organically by adding new issues and tasks from each reviewed method. If a task or issue was present in surveyed ISRA method, but not in CURF, it was appended to the model, thus obtaining a measure of completeness for the studied methods. The scope of this work is primarily functional approaches risk assessment procedures, which are the formal ISRA methods that focus on assessments of assets, threats, vulnerabilities, and protections, often with measures of probability and consequence. The proposed approach allowed for a detailed qualitative comparison of processes and activities in each method and provided a measure of completeness. This study does not address aspects beyond risk identification, estimation, and evaluation; considering the total of all three activities, we found the “ISO/IEC 27005 Information Security Risk Management” to be the most complete approach at present. For risk estimation only, we found the Factor Analysis of Information Risk and ISO/IEC 27005:2011 as the most complete frameworks. In addition, this study discovers and analyzes several gaps in the surveyed methods.


title: A Short-term Traffic Flow Forecasting Method Based on the Hybrid PSO-SVR

abstract: Accurate short-term flow forecasting is important for the real-time traffic control, but due to its complex nonlinear data pattern, getting a high precision is difficult. The support vector regression model (SVR) has been widely used to solve nonlinear regression and time series predicting problems. To get a higher precision with less learning time, this paper presents a Hybrid PSO-SVR forecasting method, which uses particle swarm optimization (PSO) to search optimal SVR parameters. In order to find a PSO that is more proper for SVR parameters searching, this paper proposes three kinds of strategies to handle the particles flying out of the searching space Through the comparison of three strategies, we find one of the strategies can make PSO get the optimal parameters more quickly. The PSO using this strategy is called fast PSO. Furthermore, aiming at the problem about the decrease of prediction accuracy caused by the noises in the original data, this paper proposes a hybrid PSO-SVR method with historical momentum based on the similarity of historical short-term flow data. The results of extensive comparison experiments indicate that the proposed model can get more accurate forecasting results than other state-of-the-art algorithms, and when the data contain noises, the method with historical momentum still gets accurate forecasting results.


title: Millennials, narcissism, and social networking: What narcissists do on social networking sites and why.

abstract: The rise in levels of narcissism among Millennials together with the increased usage of social networking sites (SNSs) raises the question of whether there is a connection between the two. The current study examined the link between narcissism and both SNS activities and motivation for SNSs activities. Results indicated that narcissism did not relate to the amount of time spent on SNS, frequency of status updates, posting picture of others, or checking up on SNS friends. However, narcissism predicted reasons why Millennials use SNSs, such as having as many SNS friends as possible, wanting their SNS friends to know what they were doing, believing their SNS friends were interested in what they are doing, and having their SNS profiles project a positive image. Findings suggest that Millennials’ general usage of SNSs is a sign of the times. While narcissists in the Millennial generation do not appear to use SNSs more often than non-narcissists, their reasons for doing so are different.


title: Transforming models with ATL

abstract: This paper presents ATL (ATLAS Transformation Language): a hybrid model transformation language that allows both declarative and imperative constructs to be used in transformation definitions. The paper describes the language syntax and semantics by using examples. ATL is supported by a set of development tools such as an editor, a compiler, a virtual machine, and a debugger. A case study shows the applicability of the language constructs. Alternative ways for implementing the case study are outlined. In addition to the current features, the planned future ATL features are briefly discussed.


title: Metamagnetics with rainbow colors

abstract: A family of coupled nanostrips with varying dimensions is demonstrated exhibiting optical magnetic responses across the whole visible spectrum, from red to blue. We refer to such a phenomenon as rainbow magnetism. The experimental and analytical studies of such structures provide us with a universal building block and a general recipe for producing controllable optical magnetism for various practical implementations.


title: ANALISIS FAKTOR – FAKTOR YANG MEMPENGARUHI MAHASISWA DALAM MEMILIH PROGRAM STUDI PADA JURUSAN ADMINISTRASI BISNIS POLITEKNIK NEGERI BANJARMASIN

abstract: T his study aims to identify and analyze factors affecting the decisions of the students in s e l ec t i n g courses (Y) with a focus on Brand Equity factor (X1) consist ing of Brand Awareness, Brand Loyalty, Perceive Quality, Brand Association and Factors of Reference Group (X2 ) which are Normative and Informational. Then, they are analyzed using factor analysis resulting in the formation of new factors at the Department of Business Administration, State Polytechnic Banjarmasin. T his research method used explanatory research by distributing questionnaires to the s a m p l es of respondents, students of the Department of Business Administration. The department consists of three study programs: Business Administration, Informative Management and Banking. The number of samples were 100 people (purposive sampling). Then the data were analyzed using factor analysis which resulted in the formation of new factors that indicated factors of brand loyalty (X1), Normative and Informational reference groups (X2), brand awareness (X3), Brand Asosiation (X4), the influence of external environment (X5), quality of human resources (X6), and atmosphere and learning facility factors (X7), and then the analysis was followed by multiple linear regression analysis to determine the effects simultaneously and partially of the newfactors to the decisions in choosing a study program at the Department of Business Admninistration, State Polytechnic Banjarmasin. T h e results showed that factors of of brand loyalty (X1), normative and informational r e f e r e n c e groups (X2), brand awareness (X3), brand association (X4), the influence of external environment (X5), quality of human resources (X6), and atmosphere and learning facility factors (X7) simultaneously gave significant effects on the decisions in choosing study programs in Business Administration Department, State Polytechnic Banjarmasin. Partially, brand loyalty (X1), normative and informational reference groups (X2), brand awareness (X3), the influence of external environment (X5) significantly influence the decisions in choosing study programs (Y). and brand asosiation (X4), quality of human resources (X6) and atmosphere and learning facility factors (X7) partially did not affect the decisions in selecting a study program (Y) .


title: The use, benefits and challenges of using the Internet of Things (IoT) in retail businesses: A literature review

abstract: This paper presents the use, benefits and challenges of using of the Internet of Things (IoT) in retail businesses. The paper aims to inform Chief Information Officers (CIOs), digital strategists or Head of Information Technology in retail of the use, benefits and challenges of using IoT. IoT is a new paradigm that uses different technologies to achieve various tasks. The use of IoT is growing among business and individuals and businesses that are not currently using IoT are set to invest in its use in the coming years. The use of IoT is increasing in the retail market, giving to the vast benefits that IoT offers. For businesses to fully use IoT they will need to change their business processes to realise its benefits. However, businesses to fully realise the benefits of IoT they need to overcome the challenges that it presents, this includes but not limited to security, privacy and network challenges. The Technology Organisation Environment (TOE) Framework was used to study the use, benefit and challenges of using IoT in retail businesses.


title: A novel shoulder-elbow exoskeleton with series elastic actuators

abstract: This work presents a new shoulder-elbow exoskeleton (NESM) for upper-limb neurological rehabilitation. The system has four active degrees of freedom, namely shoulder adduction/abduction, flexion/extension and intra/extra rotation and elbow flexion/extension, together with eight additional passive degrees of freedom for the alignment of the motor axes to the human joint axes, regardless the user's specific anthropometry sizes. All the four active joints employ series elastic actuators: such compliant architecture makes the device both controllable in position mode and in torque mode. In order to realize a safe human-machine interface in the rehabilitation treatment, an algorithm for detecting spastic user contractions, or equivalently collisions with external objects, based on the torque measured in real-time by the device, has been preliminary tested.


title: A resonant switched-capacitor converter for voltage balancing of series-connected capacitors

abstract: This paper describes a resonant switched-capacitor converter (RSCC) for voltage balancing of several capacitor or battery modules connected in series. This voltage balancing circuit transfers electric charge between the capacitor modules with a soft switching operation, resulting in a reduction of power loss and EMI. Moreover, a phase-shift control method enables to improve its voltage-regulation performance. Its current loops are analyzed in a circuit configuration which has more than three capacitor modules. Based on the analysis, a new controller is designed to improve the transient response. Experimental results confirm the validity and effectiveness of the proposed control method.


The Coffee Shop Effect: Investigating the Relationship between Ambient Noise and Cognitive Flexibility


title: Measuring the Quality of Interaction in Mobile Robotic Telepresence: A Pilot’s Perspective

abstract: This article presents a method for measuring the quality of interaction in social mobile robotic telepresence. The methodology is in part based on Adam Kendon’s theory of F-formations. The theory is based on observations of how bodies naturally orient themselves during interaction between people in real life settings. In addition, two presence questionnaires (Temple Presence Inventory and Networked Minds Social Presence Inventory), designed to measure the users’ perceptions of others and the environment when experienced through a communication medium are used. The perceived presence and ease of use are correlated to the spatial formations between the robot and an actor. The proposed methodology is validated experimentally on a dataset consisting of interactions between an elder (actor) and 21 different users being trained in piloting a mobile robotic telepresence unit. The evaluation has shown that these tools are suitable for evaluating mobile robotic telepresence and also that correlations between the tools used exist. Further, these results give important guidelines on how to improve the interface in order to increase the quality of interaction.


title: Fuzzy Neural Network-Based Adaptive Control for a Class of Uncertain Nonlinear Stochastic Systems

abstract: This paper studies an adaptive tracking control for a class of nonlinear stochastic systems with unknown functions. The considered systems are in the nonaffine pure-feedback form, and it is the first to control this class of systems with stochastic disturbances. The fuzzy-neural networks are used to approximate unknown functions. Based on the backstepping design technique, the controllers and the adaptation laws are obtained. Compared to most of the existing stochastic systems, the proposed control algorithm has fewer adjustable parameters and thus, it can reduce online computation load. By using Lyapunov analysis, it is proven that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded in probability and the system output tracks the reference signal to a bounded compact set. The simulation example is given to illustrate the effectiveness of the proposed control algorithm.


title: Extracting deep bottleneck features using stacked auto-encoders

abstract: In this work, a novel training scheme for generating bottleneck features from deep neural networks is proposed. A stack of denoising auto-encoders is first trained in a layer-wise, unsupervised manner. Afterwards, the bottleneck layer and an additional layer are added and the whole network is fine-tuned to predict target phoneme states. We perform experiments on a Cantonese conversational telephone speech corpus and find that increasing the number of auto-encoders in the network produces more useful features, but requires pre-training, especially when little training data is available. Using more unlabeled data for pre-training only yields additional gains. Evaluations on larger datasets and on different system setups demonstrate the general applicability of our approach. In terms of word error rate, relative improvements of 9.2% (Cantonese, ML training), 9.3% (Tagalog, BMMI-SAT training), 12% (Tagalog, confusion network combinations with MFCCs), and 8.7% (Switchboard) are achieved.


title: Migrating Away from a Seasonal Famine: A Randomized Intervention in Bangladesh

abstract: The rural northwestern districts of Bangladesh, home to 10 million people, experience a preharvest seasonal famine, locally known as Monga, with disturbing regularity. Surprisingly, outmigration from the Monga-prone districts is not all that common. This research tests whether migration could play any role in Monga mitigation. We implemented a randomized intervention that provided monetary incentives to individuals in Monga-prone regions to seasonally outmigrate during the pre-harvest season. We experimentally varied the conditionalities attached to the incentives, such as a requirement to form a group and migrate jointly (as opposed to migrating individually), sometimes assigning migration partners and the destination, and varying group size. This paper reports just the first stage results of this randomized intervention project, where we focus on household responsiveness to our incentive offers in terms of their decision to migrate. Our cash and credit incentives had a very large effect on migration propensity: over 40% of those receiving an incentive choose to migrate, whereas only 13% of control households do. This large effect is consistent with the presence of savings or borrowing constraints for these households, since providing information on wages and employment conditions at destinations only has a negligible 2 percentage point impact on the propensity to migrate relative to the control group.


title: Multiresolution EO/IR target tracking and identification

abstract: Simultaneous target tracking and identification through feature association, attribute matching, or blob analysis is dependent on spatio-temporal measurements. Improved track maintenance should be achievable by maintaining coarse sensor resolutions on maneuvering targets and utilizing finer sensor resolutions to resolve closely-spaced targets. There are inherent optimal resolutions for sensors and restricted altitudes that constrain operational performance that a sensor manager must optimize for both wide-area surveillance and precision tracking. The advent of better optics, coordinated sensor management, and fusion strategies provide an opportunity to enhance simultaneous tracking and identification algorithms. We investigate utilizing electro-optical (EO) and infrared (IR) sensors operating at various resolutions to optimize target tracking and identification. We use a target-dense maneuvering scenario to highlight the performance gains with the multiresolution EO/IR data association (MEIDA) algorithm in tracking crossing targets.


title: Epidemic Spreading in Scale-Free Networks

abstract: The Internet has a very complex connectivity recently modeled by the class of scale-free networks. This feature, which appears to be very efficient for a communications network, favors at the same time the spreading of computer viruses. We analyze real data from computer virus infections and find the average lifetime and persistence of viral strains on the Internet. We define a dynamical model for the spreading of infections on scale-free networks, finding the absence of an epidemic threshold and its associated critical behavior. This new epidemiological framework rationalizes data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks.


title: Plane-Wave Coupling to a Twisted-Wire Pair Above Ground

abstract: A transmission-line model is developed for predicting the response of a twisted-wire pair (TWP) circuit in the presence of a ground plane, illuminated by a plane-wave electromagnetic field. The twisted pair is modeled as an ideal bifilar helix, the total coupling is separated into differential- (DM) and common-mode (CM) contributions, and closed-form expressions are derived for the equivalent induced sources. Approximate upper bounds to the terminal response of electrically long lines are obtained, and a simplified low-frequency circuit model is used to explain the mechanism of field-to-wire coupling in a TWP above ground, as well as the role of load balancing on the DM and CM electromagnetic noise induced in the terminal loads.


title: Sub-MICs of Mentha piperita essential oil and menthol inhibits AHL mediated quorum sensing and biofilm of Gram-negative bacteria

abstract: Bacterial quorum sensing (QS) is a density dependent communication system that regulates the expression of certain genes including production of virulence factors in many pathogens. Bioactive plant extract/compounds inhibiting QS regulated gene expression may be a potential candidate as antipathogenic drug. In this study anti-QS activity of peppermint (Mentha piperita) oil was first tested using the Chromobacterium violaceum CVO26 biosensor. Further, the findings of the present investigation revealed that peppermint oil (PMO) at sub-Minimum Inhibitory Concentrations (sub-MICs) strongly interfered with acyl homoserine lactone (AHL) regulated virulence factors and biofilm formation in Pseudomonas aeruginosa and Aeromonas hydrophila. The result of molecular docking analysis attributed the QS inhibitory activity exhibited by PMO to menthol. Assessment of ability of menthol to interfere with QS systems of various Gram-negative pathogens comprising diverse AHL molecules revealed that it reduced the AHL dependent production of violacein, virulence factors, and biofilm formation indicating broad-spectrum anti-QS activity. Using two Escherichia coli biosensors, MG4/pKDT17 and pEAL08-2, we also confirmed that menthol inhibited both the las and pqs QS systems. Further, findings of the in vivo studies with menthol on nematode model Caenorhabditis elegans showed significantly enhanced survival of the nematode. Our data identified menthol as a novel broad spectrum QS inhibitor.


title: Probabilistic Linear Discriminant Analysis for Inferences About Identity

abstract: Many current face recognition algorithms perform badly when the lighting or pose of the probe and gallery images differ. In this paper we present a novel algorithm designed for these conditions. We describe face data as resulting from a generative model which incorporates both within-individual and between-individual variation. In recognition we calculate the likelihood that the differences between face images are entirely due to within-individual variability. We extend this to the non-linear case where an arbitrary face manifold can be described and noise is position-dependent. We also develop a "tied" version of the algorithm that allows explicit comparison across quite different viewing conditions. We demonstrate that our model produces state of the art results for (i) frontal face recognition (ii) face recognition under varying pose.


title: Assessment of chaos-based FM signals for range-Doppler imaging

abstract: The authors analysed a set of random frequency modulated (FM) signals for wideband radar imaging and assessed their resolution capability and sidelobe distribution on the range–Doppler plane. To this effect deterministic, bounded, nonlinear iterated maps were first considered. The initial condition of each chaotic map was assigned to a random variable to obtain statistically independent samples with invariant probability density function. The resulting sequences, which have white time–frequency representations, are used to construct wideband stochastic FM signals. These FM signals are ergodic and stationary. The autocorrelation, spectrum and the ambiguity surface associated with each of the FM signals were characterised. It was also demonstrated that the ambiguity surface of an FM signal generated via a chaotic map with uniform sample distribution and tail-shifted chaotic attractor is comparable to the ambiguity function of a Gaussian FM signal.


title: Credibility and trust of information in online environments: The use of cognitive heuristics

abstract: Abstract Networked digital media present new challenges for people to locate information that they can trust. At the same time, societal reliance on information that is available solely or primarily via the Internet is increasing. This article discusses how and why digitally networked communication environments alter traditional notions of trust, and presents research that examines how information consumers make judgments about the credibility and accuracy of information they encounter online. Based on this research, the article focuses on the use of cognitive heuristics in credibility evaluation. Findings from recent studies are used to illustrate the types of cognitive heuristics that information consumers employ when determining what sources and information to trust online. The article concludes with an agenda for future research that is needed to better understand the role and influence of cognitive heuristics in credibility evaluation in computer-mediated communication contexts.


title: OPEB: Open physical environment benchmark for artificial intelligence

abstract: Artificial Intelligence methods to solve continuous- control tasks have made significant progress in recent years. However, these algorithms have important limitations and still need significant improvement to be used in industry and real- world applications. This means that this area is still in an active research phase. To involve a large number of research groups, standard benchmarks are needed to evaluate and compare proposed algorithms. In this paper, we propose a physical environment benchmark framework to facilitate collaborative research in this area by enabling different research groups to integrate their designed benchmarks in a unified cloud-based repository and also share their actual implemented benchmarks via the cloud. We demonstrate the proposed framework using an actual implementation of the classical mountain-car example and present the results obtained using a Reinforcement Learning algorithm.


title: Mobility prediction model-based service migration procedure for follow me cloud to support QoS and QoE

abstract: Follow me cloud (FMC) is one of the solutions to the limitations of the service quality and type of functionality due to the inherent constraints of mobility management. In this paper, we address the tradeoff between the overhead and Quality of Experience (QoE) by proposing a mobility-based services migration prediction (MSMP). The aim of MSMP is to plan, for a given service, the sequence of data to transfer from distinct micro data centers (MDC) to the user according to his mobility pattern and the estimated load of data centers. MSMP consists of (1) a data transfer throughput estimation scheme, called DTT, that aims to estimate, in advance, the throughput that the user could receive; (2) MDC service area handoff time estimation scheme, called AHT, that aims to estimate the time windows when the user will perform MDC service area handoffs; and (3) service migration management scheme, called SMM, that aims to select optimal MDCs in terms of offered throughput and to split user requested service into several portions to be processed by these MDCs. We evaluate MSMP and compare it against the most recent related approaches; the simulation results show that MSMP outperforms existing approaches in terms of data latency.


title: AdaDNNs: Adaptive Ensemble of Deep Neural Networks for Scene Text Recognition

abstract: Recognizing text in the wild is a really challenging task because of complex backgrounds, various illuminations and diverse distortions, even with deep neural networks (convolutional neural networks and recurrent neural networks). In the end-to-end training procedure for scene text recognition, the outputs of deep neural networks at different iterations are always demonstrated with diversity and complementarity for the target object (text). Here, a simple but effective deep learning method, an adaptive ensemble of deep neural networks (AdaDNNs), is proposed to simply select and adaptively combine classifier components at different iterations from the whole learning system. Furthermore, the ensemble is formulated as a Bayesian framework for classifier weighting and combination. A variety of experiments on several typical acknowledged benchmarks, i.e., ICDAR Robust Reading Competition (Challenge 1, 2 and 4) datasets, verify the surprised improvement from the baseline DNNs, and the effectiveness of AdaDNNs compared with the recent state-of-the-art methods.


title: SHape REtrieval Contest 2007: Watertight Models Track

abstract: In this brief communication, we provide an overview of the density-based shape description framework in and the details of the runs we have submitted to the Watertight Models Track of the SHREC’07.


title: Next generation radar sensors in automotive sensor fusion systems

abstract: During the last decades, radar sensors have become an established component in assisted driving systems. Light detection and ranging (LIDAR) sensors are commonly perceived as the key technology to enable fully autonomous driving, but are still expensive. Recent advances in radar technology, enable modern radar sensors to sense the environment in higher detail, thus adding significant value to the environment perception quality of a sensor fusion system. Due to their performance, reliability, and cost-efficiency, modern radar sensors will be a crucial element in future autonomous vehicles. In this paper we give an overview of the current state-of-the-art of automotive radar sensors and their potential in future vehicles and data fusion systems. We want to show that modern radar sensors add significant value to automotive environment perception in order to enable fully automated driving. Additionally, challenges arising with the integration of new radar sensors and remaining issues are discussed.


title: Chance-constrained dynamic programming with application to risk-aware robotic space exploration

abstract: Existing approaches to constrained dynamic programming are limited to formulations where the constraints share the same additive structure of the objective function (that is, they can be represented as an expectation of the summation of one-stage costs). As such, these formulations cannot handle joint probabilistic (chance) constraints, whose structure is not additive. To bridge this gap, this paper presents a novel algorithmic approach for joint chance-constrained dynamic programming problems, where the probability of failure to satisfy given state constraints is explicitly bounded. Our approach is to (conservatively) reformulate a joint chance constraint as a constraint on the expectation of a summation of indicator random variables, which can be incorporated into the cost function by considering a dual formulation of the optimization problem. As a result, the primal variables can be optimized by standard dynamic programming, while the dual variable is optimized by a root-finding algorithm that converges exponentially. Error bounds on the primal and dual objective values are rigorously derived. We demonstrate algorithm effectiveness on three optimal control problems, namely a path planning problem, a Mars entry, descent and landing problem, and a Lunar landing problem. All Mars simulations are conducted using real terrain data of Mars, with four million discrete states at each time step. The numerical experiments are used to validate our theoretical and heuristic arguments that the proposed algorithm is both (i) computationally efficient, i.e., capable of handling real-world problems, and (ii) near-optimal, i.e., its degree of conservatism is very low.


title: Accessibility and adaptability of learning objects: responding to metadata, learning patterns and profiles of needs and preferences

abstract: The case for learning patterns as a design method for accessible and adaptable learning objects is explored. Patterns and templates for the design of learning objects can be derived from successful existing learning resources. These patterns can then be reused in the design of new learning objects. We argue that by attending to criteria for reuse in the definition of these patterns and in the subsequent design of new learning objects, those new resources can be themselves reusable and also adaptable to different learning contexts. Finally, if the patterns identified can be implemented as templates for standard authoring tools, the design of effective, reusable and adaptable resources can be made available to those with limited skills in multimedia authoring and result in learning resources that are more widely accessible.


title: A Sex Difference in the Specificity of Sexual Arousal

abstract: Sexual arousal is category-specific in men; heterosexual men are more aroused by female than by male sexual stimuli, whereas homosexual men show the opposite pattern. There is reason to believe that female sexual arousal is organized differently. We assessed genital and subjective sexual arousal to male and female sexual stimuli in women, men, and postoperative male-to-female transsexuals. In contrast to men, women showed little category specificity on either the genital or the subjective measure. Both heterosexual and homosexual women experienced strong genital arousal to both male and female sexual stimuli. Transsexuals showed a category-specific pattern, demonstrating that category specificity can be detected in the neovagina using a photoplethysmographic measure of female genital sexual arousal. In a second study, we showed that our results for females are unlikely to be explained by ascertainment biases. These findings suggest that sexual arousal patterns play fundamentally different roles in male an...


title: Monocular 3D scene modeling and inference: understanding multi-object traffic scenes

abstract: Scene understanding has (again) become a focus of computer vision research, leveraging advances in detection, context modeling, and tracking. In this paper, we present a novel probabilistic 3D scene model that encompasses multi-class object detection, object tracking, scene labeling, and 3D geometric relations. This integrated 3D model is able to represent complex interactions like inter-object occlusion, physical exclusion between objects, and geometric context. Inference allows to recover 3D scene context and perform 3D multiobject tracking from a mobile observer, for objects of multiple categories, using only monocular video as input. In particular, we show that a joint scene track-let model for the evidence collected over multiple frames substantially improves performance. The approach is evaluated for two different types of challenging on-board sequences. We first show a substantial improvement to the state-of-the-art in 3D multi-people tracking. Moreover, a similar performance gain is achieved for multi-class 3D tracking of cars and trucks on a new, challenging dataset.


title: AgreementMaker: Efficient Matching for Large Real-World Schemas and Ontologies

abstract: We present the AgreementMaker system for matching real-world schemas and ontologies, which may consist of hundreds or even thousands of concepts. The end users of the system are sophisticated domain experts whose needs have driven the design and implementation of the system: they require a responsive, powerful, and extensible framework to perform, evaluate, and compare matching methods. The system comprises a wide range of matching methods addressing different levels of granularity of the components being matched (conceptual vs. structural), the amount of user intervention that they require (manual vs. automatic), their usage (stand-alone vs. composed), and the types of components to consider (schema only or schema and instances). Performance measurements (recall, precision, and runtime) are supported by the system, along with the weighted combination of the results provided by those methods. The AgreementMaker has been used and tested in practical applications and in the Ontology Alignment Evaluation Initiative (OAEI) competition. We report here on some of its most advanced features, including its extensible architecture that facilitates the integration and performance tuning of a variety of matching methods, its capability to evaluate, compare, and combine matching results, and its user interface with a control panel that drives all the matching methods and evaluation strategies.


title: A game-theoretic approach for achieving k-anonymity in Location Based Services

abstract: Location Based Service (LBS), although it greatly benefits the daily life of mobile device users, has introduced significant threats to privacy. In an LBS system, even under the protection of pseudonyms, users may become victims of inference attacks, where an adversary reveals a user's real identity and complete moving trajectory with the aid of side information, e.g., accidental identity disclosure through personal encounters. To enhance privacy protection for LBS users, a common approach is to include extra fake location information associated with different pseudonyms, known as dummy users, in normal location reports. Due to the high cost of dummy generation using resource constrained mobile devices, self-interested users may free-ride on others' efforts. The presence of such selfish behaviors may have an adverse effect on privacy protection. In this paper, we study the behaviors of self-interested users in the LBS system from a game-theoretic perspective. We model the distributed dummy user generation as Bayesian games in both static and timing-aware contexts, and analyze the existence and properties of the Bayesian Nash Equilibria for both models. Based on the analysis, we propose a strategy selection algorithm to help users achieve optimized payoffs. Leveraging a beta distribution generalized from real-world location privacy data traces, we perform simulations to assess the privacy protection effectiveness of our approach. The simulation results validate our theoretical analysis for the dummy user generation game models.


title: A Multicriteria Approach for Distributed Planning and Conflict Resolution for Multi-Agent Systems

abstract: A multicriteria approach for distributed planning and conflict resolution for multiagent systems is presented in the paper. A graph representation is used to model the plans of each agent and a multicriteria "best" path procedure can be used in order to obtain the "best" path for each agent considering its private and local goals. In our approach private and local goals do not necessarily coincide. A conflict and/or positive cooperation may occur and a detection procedure is triggered as soon as the agents broadcast their "best plans". A negotiation process is then established if necessary. Such a process iterates the use of the graph representation and of the "best" path algorithm to a level including the agents that have to negotiate. The parameters of the multicriteria model used to evaluate the paths are then negotiated or even the model itself. Some open problems conclude the paper.


title: Persistent activity in the prefrontal cortex during working memory

abstract: The dorsolateral prefrontal cortex (DLPFC) plays a crucial role in working memory. Notably, persistent activity in the DLPFC is often observed during the retention interval of delayed response tasks. The code carried by the persistent activity remains unclear, however. We critically evaluate how well recent findings from functional magnetic resonance imaging studies are compatible with current models of the role of the DLFPC in working memory. These new findings suggest that the DLPFC aids in the maintenance of information by directing attention to internal representations of sensory stimuli and motor plans that are stored in more posterior regions. Working memory refers to the temporary representation of information that was just experienced or just retrieved from long-term memory. These active representations are short-lived, but can be maintained for longer periods of time through active rehearsal strategies, and can be subjected to various operations that manipulate the information in such a way that makes it useful for goaldirected behavior. Most definitions of working memory include both storage and (executive) control components [1]. Cognitive neuroscientists are searching for ways to disassociate the separate components of working memory in attempts to localize and clearly characterize their neural implementation. The prefrontal cortex (PFC) is thought to be the most important substrate for working memory (Fig. 1). Two key findings from studies of monkeys performing delayed response tasks suggest a crucial role for the PFC in working memory. First, experimental lesions of the principal sulcus in the dorsolateral prefrontal cortex (DLPFC) cause delay-dependent impairments [2‐4]. That is, forgetting increases not only when a delay is imposed but increases with the length of the delay. Second, neurophysiological unit recordings from the DLPFC often show persistent, sustained levels of neuronal firing during the retention interval of delayed response Fig. 1. Lateral surface of (a) macaque and (b) human brain. The PFC is composed of lateral, medial, and orbital sectors that are believed to be functionally distinct given the selective effects of damage and distribution of afferent and efferent projections. The tinted areas correspond to those defined by Petrides and Pandya [71] based on cytoarchitecture and connectivity. Notably, the mid-DLPFC comprises areas 46 and 9/46 and the mid-VLPFC comprises areas 45 and 47/12. Note that much of area 46 lies in the depths of the principle sulcus of the monkey and the intermediate frontal sulcus of the human. Frontal premotor regions are also highlighted. The frontal eye field (F) in the macaque lies in the anterior bank of the arcuate sulcus in area 8A. In the human, F is found in the vicinity of the precentral sulcus and superior frontal sulcus junction (area 6 and maybe the caudal-most portion of 8A). The frontal eye field is a premotor region involved in the control of eye movements. Broca’s area (B, area 44) is also a premotor area that is involved in the production of speech. The dotted line represents the principle sulcus in the macaque and the inferior frontal sulcus in the human. TRENDS in Cognitive Sciences


title: Understanding deep learning requires rethinking generalization

abstract: Despite their massive size, successful deep artificial neural networks can ::: exhibit a remarkably small difference between training and test performance. ::: Conventional wisdom attributes small generalization error either to properties ::: of the model family, or to the regularization techniques used during training. ::: ::: Through extensive systematic experiments, we show how these traditional ::: approaches fail to explain why large neural networks generalize well in ::: practice. Specifically, our experiments establish that state-of-the-art ::: convolutional networks for image classification trained with stochastic ::: gradient methods easily fit a random labeling of the training data. This ::: phenomenon is qualitatively unaffected by explicit regularization, and occurs ::: even if we replace the true images by completely unstructured random noise. We ::: corroborate these experimental findings with a theoretical construction ::: showing that simple depth two neural networks already have perfect finite ::: sample expressivity as soon as the number of parameters exceeds the ::: number of data points as it usually does in practice. ::: ::: We interpret our experimental findings by comparison with traditional models.


Global Inference for Entity and Relation Identification via a Linear Programming Formulation


title: Fast and approximate stream mining of quantiles and frequencies using graphics processors

abstract: We present algorithms for fast quantile and frequency estimation in large data streams using graphics processors (GPUs). We exploit the high computation power and memory bandwidth of graphics processors and present a new sorting algorithm that performs rasterization operations on the GPUs. We use sorting as the main computational component for histogram approximation and construction of e-approximate quantile and frequency summaries. Our algorithms for numerical statistics computation on data streams are deterministic, applicable to fixed or variable-sized sliding windows and use a limited memory footprint. We use GPU as a co-processor and minimize the data transmission between the CPU and GPU by taking into account the low bus bandwidth. We implemented our algorithms on a PC with a NVIDIA GeForce FX 6800 Ultra GPU and a 3.4 GHz Pentium IV CPU and applied them to large data streams consisting of more than 100 million values. We also compared the performance of our GPU-based algorithms with optimized implementations of prior CPU-based algorithms. Overall, our results demonstrate that the graphics processors available on a commodity computer system are efficient stream-processor and useful co-processors for mining data streams.


title: Code Transformation by Direct Transformation of ASTs

abstract: Software evolves to be adapted to the environment, due to bugs, new features and design changes. Code transformations can be done manually, but that is a tedious and error-prone task. Therefore automated tools are used to assist developers in this maintenance operation. The Pharo environment includes its own refactoring tool --- the Rewrite Engine --- that allows one to transform methods by directly specifying parts of the AST to be rewritten. In addition, it proposes a parse tree transformation engine. However this tool and the used DSL to express the patterns for matching and transforming trees are complex to understand and master. In this context, writing a transformation rule is not a trivial task. We present a graphical tool built on the top of the Rewrite Engine --- the Rewrite Tool --- that abstracts the creation of transformation rules and proposes high-level AST operations that are simpler to understand than syntactic descriptions. It helps to automate the process of code transformation with a user-friendly interface.


title: Cross-layer optimization policy for QoS scheduling in computational grid

abstract: This paper presents a cross-layer quality of service (QoS) optimization policy for computational grid. Efficient QoS management is critical for computational grid to meet heterogeneity and dynamics of resources and users' requirements. There are different QoS metrics at different layers of computational grid. To improve perceived QoS by end users over computational grid, QoS supports can be addressed in different layers, including application layer, collective layer, fabric layer and so forth. The paper tackles cross-layer grid QoS optimization as optimization decomposition, each layer corresponds to a decomposed subproblem. The proposed policy produces an optimal set of grid resources, service compositions and user's payments at the fabric layer, collective layer and application layer respectively to maximize global grid QoS. The cross-layer optimization problem decomposes into three subproblems: grid resource allocation problem, service composing and user satisfaction degree maximization problem, all of which interact through the optimal variables for capacities of grid resources and service demand. In order to coordinate the subproblems, cross-layer QoS feedback mechanism is established to ensure different layer interactions. The simulations are conducted to validate the efficiency of the proposed policy.


title: Analysis of coronary heart disease and prediction of heart attack in coal mining regions using data mining techniques

abstract: Heart disease (HD) is a major cause of morbidity and mortality in the modern society. Medical diagnosis is extremely important but complicated task that should be performed accurately and efficiently. This study analyzes the Behavioral Risk Factor Surveillance System, survey to test whether self-reported cardiovascular disease rates are higher in Singareni coal mining regions in Andhra Pradesh state, India, compared to other regions after control for other risks. Dependent variables include self-reported measures of being diagnosed with cardiovascular disease (CVD) or with a specific form of CVD including (1) chest pain (2) stroke and (3) heart attack. Heart care study specifies 15 attributes to predict the morbidity. Beside regular attributes other general attributes BMI (Body Mass Index), physician supply, age, ethnicity, education, income, and others are used for prediction. An automated system for medical diagnosis would enhance medical care and reduce costs. In this paper popular data mining techniques namely, Decision Trees, Naive Bayes and Neural Network are used for prediction of heart disease.


title: The effect of prosthetic foot push-off on mechanical loading associated with knee osteoarthritis in lower extremity amputees

abstract: Abstract Lower extremity amputation not only limits mobility, but also increases the risk of knee osteoarthritis of the intact limb. Dynamic walking models of non-amputees suggest that pushing-off from the trailing limb can reduce collision forces on the leading limb. These collision forces may determine the peak knee external adduction moment (EAM), which has been linked to the development of knee OA in the general population. We therefore hypothesized that greater prosthetic push-off would lead to reduced loading and knee EAM of the intact limb in unilateral transtibial amputees. Seven unilateral transtibial amputees were studied during gait under three prosthetic foot conditions that were intended to vary push-off. Prosthetic foot-ankle push-off work, intact limb knee EAM and ground reaction impulses for both limbs during step-to-step transition were measured. Overall, trailing limb prosthetic push-off work was negatively correlated with leading intact limb 1st peak knee EAM (slope = −.72 ± .22; p = .011). Prosthetic push-off work and 1st peak intact knee EAM varied significantly with foot type. The prosthetic foot condition with the least push-off demonstrated the largest knee EAM, which was reduced by 26% with the prosthetic foot producing the most push-off. Trailing prosthetic limb push-off impulse was negatively correlated with leading intact limb loading impulse (slope = −.34 ± .14; p = .001), which may help explain how prosthetic limb push-off can affect intact limb loading. Prosthetic feet that perform more prosthetic push-off appear to be associated with a reduction in 1st peak intact knee EAM, and their use could potentially reduce the risk and burden of knee osteoarthritis in this population.


title: A Review of Cyber Threats and Defence Approaches in Emergency Management

abstract: Emergency planners, first responders and relief workers increasingly rely on computational and communication systems that support all aspects of emergency management, from mitigation and preparedness to response and recovery. Failure of these systems, whether accidental or because of malicious action, can have severe implications for emergency management. Accidental failures have been extensively documented in the past and significant effort has been put into the development and introduction of more resilient technologies. At the same time researchers have been raising concerns about the potential of cyber attacks to cause physical disasters or to maximise the impact of one by intentionally impeding the work of the emergency services. Here, we provide a review of current research on the cyber threats to communication, sensing, information management and vehicular technologies used in emergency management. We emphasise on open issues for research, which are the cyber threats that have the potential to affect emergency management severely and for which solutions have not yet been proposed in the literature.


title: Theano-based Large-Scale Visual Recognition with Multiple GPUs

abstract: In this report, we describe a Theano-based AlexNet (Krizhevsky et al., 2012) implementation and its naive data parallelism on multiple GPUs. Our performance on 2 GPUs is comparable with the state-of-art Caffe library (Jia et al., 2014) run on 1 GPU. To the best of our knowledge, this is the first open-source Python-based AlexNet implementation to-date.


title: Comparing Tweet Classifications by Authors' Hashtags, Machine Learning, and Human Annotators

abstract: Over the last years, many papers have been published about how to use machine learning for classifying postings on microblogging platforms like Twitter, e.g., in order to assist users to reach tweets that interest them. Typically, the automatic classification results are then evaluated against a gold standard classification which consists of either (i) the hashtags of the tweets' authors, or (ii) manual annotations of independent human annotators. In this paper, we show that there are fundamental differences between these two kinds of gold standard classifications, i.e., human annotators are more likely to classify tweets like other human annotators than like the tweets' authors. Furthermore, we discuss how these differences may influence the evaluation of automatic classifications, like they may be achieved by Latent Dirichlet Allocation (LDA). We argue that researchers who conduct machine learning experiments for tweet classification should pay particular attention to the kind of gold standard they use. One may even argue that hashtags are not appropriate as a gold standard for tweet classification.


title: A Compact Flicker-Free Transformer-Less LED Driver With an Enhanced Power Factor for Omnidirectional Multichannel Smart Bulb Applications

abstract: In this paper, a flicker-free transformer-less multichannel light-emitting diode (LED) driver that adopts a new power factor enhancement technique is presented. The proposed LED driver divides the transmission path of the current to the LED string in two ways depending on the instantaneous input voltage in order to improve the PF relative to that of a conventional compact flicker-free LED driver that uses a fixed LED current regulation scheme with only one electrolytic capacitor. The proposed driver can be implemented with a minimum number of external components leading to a compact module that allows a light bulb with omnidirectional light emission. In order to enhance the expandability of the smart bulb, a conventional buck converter with 3-V output is integrated with the proposed LED driver to supply the peripherals of the module. The proposed LED driver IC is implemented in a 0.35-μm ultrahigh voltage CMOS process and occupies an area of 2.9 mm × 2.0 mm. A 10-W smart bulb prototype that adopts the proposed LED driver is flicker free and shows a power factor of 0.715 and power conversion efficiency of 77.5% for an ac supply of 120 V.


title: Spatial-Aware Collaborative Representation for Hyperspectral Remote Sensing Image Classification

abstract: Representation-residual-based classifiers have attracted much attention in recent years in hyperspectral image (HSI) classification. How to obtain the optimal representa-tion coefficients for the classification task is the key problem of these methods. In this letter, spatial-aware collaborative representation (CR) is proposed for HSI classification. In order to make full use of the spatial–spectral information, we propose a closed-form solution, in which the spatial and spectral features are both utilized to induce the distance-weighted regularization terms. Different from traditional CR-based HSI classification algorithms, which model the spatial feature in a preprocessing or postprocessing stage, we directly incorporate the spatial information by adding a spatial regularization term to the representation objective function. The experimental results on three HSI data sets verify that our proposed approach outperforms the state-of-the-art classifiers.


title: Detection of Wordpress Content Injection Vulnerability

abstract: The popularity of content management software (CMS) is growing vastly to the web developers and the business people because of its capacity for easy accessibility, manageability and usability of the distributed website contents. As per the statistics of Built with, 32% of the web applications are developed with WordPress(WP) among all other CMSs [1]. It is obvious that quite a good number of web applications were built with WP in version 4.7.0 and 4.7.1. A recent research reveals that content injection vulnerability was found available in the above two versions of WP [2]. Unauthorized content injection by an intruder in a CMS managed application is one of the serious problems for the business as well as for the web owner.Therefore, detection of the vulnerability becomes a critical issue for this time. In this paper, we have discussed about the root cause of WP content injection of the above versions and have also proposed a detection model for the given vulnerability. A tool, SAISAN has been implemented as per our anticipated model and conducted an examination on 176 WP developed web applications using SAISAN. We achieved the accuracy of 92% of the result of SAISAN as compared to manual black box testing outcome.


title: Feedback-error-learning for stability of Double Inverted Pendulum

abstract: Double Inverted Pendulum is a nonlinear system, unstable and fast reaction system. Double Inverted Pendulum is stable when its two Pendulums allocated in vertically position and have no oscillation and movement and also insertion force should be zero. The main target of this research is design a controller based on Neuro-Fuzzy methods by using feedback-error-learning for controlling double inverted Pendulum.


title: Bisimulation can't be traced

abstract: In the concurrent language CCS, two programs are considered the same if they are bisimilar. Several years and many researchers have demonstrated that the theory of bisimulation is mathematically appealing and useful in practice. However, bisimulation makes too many distinctions between programs. We consider the problem of adding operations to CCS to make bisimulation fully abstract. We define the class of GSOS operations, generalizing the style and technical advantages of CCS operations. We characterize GSOS congruence in as a bisimulation-like relation called ready-simulation. Bisimulation is strictly finer than ready simulation, and hence not a congruence for any GSOS language.


title: A Big Data-as-a-Service Framework: State-of-the-Art and Perspectives

abstract: Due to the rapid advances of information technologies, Big Data, recognized with 4Vs characteristics (volume, variety, veracity, and velocity), bring significant benefits as well as many challenges. A major benefit of Big Data is to provide timely information and proactive services for humans. The primary purpose of this paper is to review the current state-of-the-art of Big Data from the aspects of organization and representation, cleaning and reduction, integration and processing, security and privacy, analytics and applications, then present a novel framework to provide high-quality so called Big Data-as-a-Service. The framework consists of three planes, namely sensing plane, cloud plane and application plane, to systemically address all challenges of the above aspects. Also, to clearly demonstrate the working process of the proposed framework, a tensor-based multiple clustering on bicycle renting and returning data is illustrated, which can provide several suggestions for rebalancing of the bicycle-sharing system. Finally, some challenges about the proposed framework are discussed.


title: Grid-Based Crime Prediction Using Geographical Features

abstract: Machine learning is useful for grid-based crime prediction. Many previous studies have examined factors including time, space, and type of crime, but the geographic characteristics of the grid are rarely discussed, leaving prediction models unable to predict crime displacement. This study incorporates the concept of a criminal environment in grid-based crime prediction modeling, and establishes a range of spatial-temporal features based on 84 types of geographic information by applying the Google Places API to theft data for Taoyuan City, Taiwan. The best model was found to be Deep Neural Networks, which outperforms the popular Random Decision Forest, Support Vector Machine, and K-Near Neighbor algorithms. After tuning, compared to our design’s baseline 11-month moving average, the F1 score improves about 7% on 100-by-100 grids. Experiments demonstrate the importance of the geographic feature design for improving performance and explanatory ability. In addition, testing for crime displacement also shows that our model design outperforms the baseline.


title: A compact printed wide-slot UWB antenna with band-notched characteristics

abstract: In this paper, we present an offset microstrip-fed ultrawideband antenna with band notched characteristics. The antenna structure consists of rectangular radiating patch and ground plane with rectangular shaped slot, which increases impedance bandwidth upto 123.52%(2.6–11GHz). A new modified U slot is etched in the radiating patch to create band-notched properties in the WiMAX (3.3–3.7GHz) and C-band satellite communication (3.7–4.15GHz). Furthermore, parametric studies have been conducted using EM simulation software CADFEKO suite(7.0). A prototype of antenna is fabricated on 1.6mm thick FR-4 substrate with dielectric constant of 4.4 and loss tangent of 0.02. The proposed antenna exhibits directional and omnidirectional radiation patterns along E and H-plane with stable efficiency over the frequency band from 2.6GHz to 11GHz with VSWR less than 2, except 3.3–4.15GHz notched frequency band. The proposed antenna shows good time domain analysis.


Blockchain to Rule the Waves - Nascent Design Principles for Reducing Risk and Uncertainty in Decentralized Environments


title: Industrial Control System Network Intrusion Detection by Telemetry Analysis

abstract: Until recently, industrial control systems (ICSs) used “air-gap” security measures, where every node of the ICS network was isolated from other networks, including the Internet, by a physical disconnect. Attaching ICS networks to the Internet benefits companies and engineers who use them. However, as these systems were designed for use in the air-gapped security environment, protocols used by ICSs contain little to no security features and are vulnerable to various attacks. This paper proposes an approach to detect the intrusions into network attached ICSs by measuring and verifying data that is transmitted through the network but is not inherently the data used by the transmission protocol—network telemetry. Using simulated PLC units, the developed IDS was able to achieve 94.3 percent accuracy when differentiating between machines of an attacker and engineer on the same network, and 99.5 percent accuracy when differentiating between attacker and engineer on the Internet.