title: A Cross-Modal Distillation Network for Person Re-identification in RGB-Depth

abstract: Person re-identification involves the recognition over time of individuals captured using multiple distributed sensors. With the advent of powerful deep learning methods able to learn discriminant representations for visual recognition, cross-modal person re-identification based on different sensor modalities has become viable in many challenging applications in, e.g., autonomous driving, robotics and video surveillance. Although some methods have been proposed for re-identification between infrared and RGB images, few address depth and RGB images. In addition to the challenges for each modality associated with occlusion, clutter, misalignment, and variations in pose and illumination, there is a considerable shift across modalities since data from RGB and depth images are heterogeneous. In this paper, a new cross-modal distillation network is proposed for robust person re-identification between RGB and depth sensors. Using a two-step optimization process, the proposed method transfers supervision between modalities such that similar structural features are extracted from both RGB and depth modalities, yielding a discriminative mapping to a common feature space. Our experiments investigate the influence of the dimensionality of the embedding space, compares transfer learning from depth to RGB and vice versa, and compares against other state-of-the-art cross-modal re-identification methods. Results obtained with BIWI and RobotPKU datasets indicate that the proposed method can successfully transfer descriptive structural features from the depth modality to the RGB modality. It can significantly outperform state-of-the-art conventional methods and deep neural networks for cross-modal sensing between RGB and depth, with no impact on computational complexity.


title: A Blockchain-Based Risk and Information System Control Framework

abstract: Risk and Information System Control Framework in business includes the methods and processes to manage risks and seize opportunities which involve identifying particular risk events relevant to the objectives, assessing them in terms of likelihood and magnitude of impact, determining a response strategy, and monitoring progress. In order to provide better support for the backtracking, traceability, irreversibility, and credible requirements of risk registration table data in the framework, this paper proposes a blockchain-based risk and information system control framework. A risk association tree is designed for combining summarized risk item ledgers with risk assessment ledgers and risk response ledgers based on the Merkle Tree. Three proposed smart contracts are used in risk identification, risk assessment, risk response and mitigation, and risk and control monitoring and reporting processes. We implement a prototype for this framework.


title: Semantic Mapping with Simultaneous Object Detection and Localization

abstract: We present a filtering-based method for semantic mapping to simultaneously detect objects and localize their 6 degree-of-freedom pose. For our method, called Contextual Temporal Mapping (or CT-Map), we represent the semantic map as a belief over object classes and poses across an observed scene. Inference for the semantic mapping problem is then modeled in the form of a Conditional Random Field (CRF). CT-Map is a CRF that considers two forms of relationship potentials to account for contextual relations between objects and temporal consistency of object poses, as well as a measurement potential on observations. A particle filtering algorithm is then proposed to perform inference in the CT-Map model. We demonstrate the efficacy of the CT-Map method with a Michigan Progress Fetch robot equipped with a RGB-D sensor. Our results demonstrate that the particle filtering based inference of CT-Map provides improved object detection and pose estimation with respect to baseline methods that treat observations as independent samples of a scene.


title: Applications of Metaheuristics in Reservoir Computing Techniques: A Review

abstract: Reservoir computing approaches have been around for almost two decades. They were developed to solve the difficult gradient-descent training of recurrent neural networks. However, in reservoir computing, the choice of parameters and architecture often leads to an optimization challenge. Most early applications have used trial and error with expert knowledge to select the right value for parameter(s)/architecture. This approach is usually cumbersome and difficult considering the large search space. Metaheuristics have been known to perform well in solving such kinds of problems. This review discusses areas where metaheuristics are used in the echo state network—a pioneer in the reservoir computing field. In addition, trends and research gaps are also discussed.


title: Vision based system for Vacant Parking Lot Detection: VPLD

abstract: The proposed system comes in the context of intelligent parking lots management and presents an approach for vacant parking spots detection and localization. Our system provides a camera-based solution, which can deal with outdoor parking lots. It returns the real time states of the parking lots providing the number of available vacant places and its specific positions in order to guide the drivers through the roads. In order to eliminate the real world challenges, we propose a combination of the Adaptive Background Subtraction algorithm to overcome the problems of changing lighting and shadow effects with the Speeded Up Robust Features algorithm to benefit from its robustness to the scale changes and the rotation. Our approach presents also a new state “Transition” for the classification of the parking places states.


title: Compact Metal Mountable UHF RFID Tag on a Barium Titanate Based Substrate

abstract: The development of a compact metal mountable Radio- Frequency IDentiflcation (RFID) tag antenna on a ceramic substrate based on Barium Titanate is presented. The performance limitations and design trade-ofis of metal mountable RFID tag antennas are reviewed and the favorable features of a high-permittivity antenna substrate for the development of antennas for metal mountable RFID tags are discussed. The simulation-based tag antenna design process is outlined and the measured read range of the developed metal mountable tag on conductive platforms of various sizes is presented.


Vehicular Ad Hoc Networks ( VANETs ) , Past Present and Future : A survey


title: New educational tools and curriculum enhancements for motivating engineering students to design and realize bio-inspired products

abstract: The use of bio-inspiration to create new products and devices requires the development of new design tools and manufacturing technologies, as well as the education of students capable of using them. At the University of Maryland, we have developed new educational tools that emphasize bioinspired product realization. These tools include the development of a bioinspired design repository, a concurrent fabrication and assembly technology that focuses on the use of multi-piece, multi-stage molds to affordably manufacture large numbers of bio-inspired products, and a series of undergraduate curriculum enhancements. This paper provides an overview of these two technologies and describes how these technologies are integrated into the Mechanical Engineering curriculum. The new educational tools provide students with the fundamental design and manufacturing principles needed in bio-inspired product and device development. The curriculum changes culminate in an elective course undertaken in their senior year.


title: Beyond Point Clouds: Scene Understanding by Reasoning Geometry and Physics

abstract: In this paper, we present an approach for scene understanding by reasoning physical stability of objects from point cloud. We utilize a simple observation that, by human design, objects in static scenes should be stable with respect to gravity. This assumption is applicable to all scene categories and poses useful constraints for the plausible interpretations (parses) in scene understanding. Our method consists of two major steps: 1) geometric reasoning: recovering solid 3D volumetric primitives from defective point cloud, and 2) physical reasoning: grouping the unstable primitives to physically stable objects by optimizing the stability and the scene prior. We propose to use a novel disconnectivity graph (DG) to represent the energy landscape and use a Swendsen-Wang Cut (MCMC) method for optimization. In experiments, we demonstrate that the algorithm achieves substantially better performance for i) object segmentation, ii) 3D volumetric recovery of the scene, and iii) better parsing result for scene understanding in comparison to state-of-the-art methods in both public dataset and our own new dataset.


title: A Multilevel Mixture-of-Experts Framework for Pedestrian Classification

abstract: Notwithstanding many years of progress, pedestrian recognition is still a difficult but important problem. We present a novel multilevel Mixture-of-Experts approach to combine information from multiple features and cues with the objective of improved pedestrian classification. On pose-level, shape cues based on Chamfer shape matching provide sample-dependent priors for a certain pedestrian view. On modality-level, we represent each data sample in terms of image intensity, (dense) depth, and (dense) flow. On feature-level, we consider histograms of oriented gradients (HOG) and local binary patterns (LBP). Multilayer perceptrons (MLP) and linear support vector machines (linSVM) are used as expert classifiers.


title: Focus-group interview and data analysis

abstract: In recent years focus-group interviews, as a means of qualitative data collection, have gained popularity amongst professionals within the health and social care arena. Despite this popularity, analysing qualitative data, particularly focus-group interviews, poses a challenge to most practitioner researchers. The present paper responds to the needs expressed by public health nutritionists, community dietitians and health development specialists following two training sessions organised collaboratively by the Health Development Agency, the Nutrition Society and the British Dietetic Association in 2003. The focus of the present paper is on the concepts and application of framework analysis, especially the use of Krueger’s framework. It provides some practical steps for the analysis of individual data, as well as focus-group data using examples from the author’s own research, in such a way as to assist the newcomer to qualitative research to engage with the methodology. Thus, it complements the papers by Draper (2004) and Fade (2004) that discuss in detail the complementary role of qualitative data in researching human behaviours, feelings and attitudes. Draper (2004) has provided theoretical and philosophical bases for qualitative data analysis. Fade (2004) has described interpretative phenomenology analysis as a method of analysing individual interview data. The present paper, using framework analysis concentrating on focus-group interviews, provides another approach to qualitative data analysis. Focus-group interviews: Qualitative data analysis


title: Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study

abstract: Fog computing (FC) and Internet of Everything (IoE) are two emerging technological paradigms that, to date, have been considered standing-alone. However, because of their complementary features, we expect that their integration can foster a number of computing and network-intensive pervasive applications under the incoming realm of the future Internet. Motivated by this consideration, the goal of this position paper is fivefold. First, we review the technological attributes and platforms proposed in the current literature for the standing-alone FC and IoE paradigms. Second, by leveraging some use cases as illustrative examples, we point out that the integration of the FC and IoE paradigms may give rise to opportunities for new applications in the realms of the IoE, Smart City, Industry 4.0, and Big Data Streaming, while introducing new open issues. Third, we propose a novel technological paradigm, the Fog of Everything (FoE) paradigm, that integrates FC and IoE and then we detail the main building blocks and services of the corresponding technological platform and protocol stack. Fourth, as a proof-of-concept, we present the simulated energy-delay performance of a small-scale FoE prototype, namely, the V-FoE prototype. Afterward, we compare the obtained performance with the corresponding one of a benchmark technological platform, e.g., the V-D2D one. It exploits only device-to-device links to establish inter-thing “ad hoc” communication. Last, we point out the position of the proposed FoE paradigm over a spectrum of seemingly related recent research projects.


title: Realtime shape-enhanced shading of large scale skinned meshes with fast normal updating

abstract: Shape exaggeration is highly expected in many applications such as cultural heritage, cartography, educational demos, game etc. Although there have been some research on shape-exaggerated shading, most of them are too slow for real-time applications when large scale meshes are involved. We notice the bottleneck is updating of surface normal. Based on this observation, we present a real-time shape-exaggerated shading method for large scale skinned meshes. The key component is a fast normal updating method tailored for skinned meshes. Experimental results show that our method can generate shading results that are comparable to existing methods in real-time.


title: An intelligent PE-malware detection system based on association mining

abstract: The proliferation of malware has presented a serious threat to the security of computer systems. Traditional signature-based anti-virus systems fail to detect polymorphic/metamorphic and new, previously unseen malicious executables. Data mining methods such as Naive Bayes and Decision Tree have been studied on small collections of executables. In this paper, resting on the analysis of Windows APIs called by PE files, we develop the Intelligent Malware Detection System (IMDS) using Objective-Oriented Association (OOA) mining based classification. IMDS is an integrated system consisting of three major modules: PE parser, OOA rule generator, and rule based classifier. An OOA_Fast_FP-Growth algorithm is adapted to efficiently generate OOA rules for classification. A comprehensive experimental study on a large collection of PE files obtained from the anti-virus laboratory of KingSoft Corporation is performed to compare various malware detection approaches. Promising experimental results demonstrate that the accuracy and efficiency of our IMDS system outperform popular anti-virus software such as Norton AntiVirus and McAfee VirusScan, as well as previous data mining based detection systems which employed Naive Bayes, Support Vector Machine (SVM) and Decision Tree techniques. Our system has already been incorporated into the scanning tool of KingSoft’s Anti-Virus software.


Adversarial Feature Adaptation for Cross-lingual Relation Classification


title: Designing Digital Workplace Environments An Agile Framework for Large-Scale End-User Participation

abstract: For companies, knowledge is the central and decisive resource for success. Today, however, knowledge must flow more than ever before in order to meet the requirements of shorter innovation cycles and an increasingly agile working environment. The distribution and generation of knowledge requires a digital workplace environment that facilitates communication and collaboration between employees and their environment. The digital workplace acts as both a technical enabler and an attractive feature, thus making a major contribution to organizational performance and recruiting, retaining and motivating staff. Such an infrastructure, which connects users, makes them capable of working ubiquitously. Due to the complexity and ambiguities of such an endeavour it can only be rolled out and developed on an ongoing basis in close interaction with users. Therefore, this contribution proposes an agile framework for large-scale end-user participation in developing digital workplace environments based on an agile design approach that actively and continuously involves employees.


title: Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images

abstract: In this paper we address the memory demands that come with the processing of 3-dimensional, high-resolution, multi-channeled medical images in deep learning. We exploit memory-efficient backpropagation techniques, to reduce the memory complexity of network training from being linear in the network's depth, to being roughly constant $ - $ permitting us to elongate deep architectures with negligible memory increase. We evaluate our methodology in the paradigm of Image Quality Transfer, whilst noting its potential application to various tasks that use deep learning. We study the impact of depth on accuracy and show that deeper models have more predictive power, which may exploit larger training sets. We obtain substantially better results than the previous state-of-the-art model with a slight memory increase, reducing the root-mean-squared-error by $ 13\% $. Our code is publicly available.


title: Light Field Stereo Matching Using Bilateral Statistics of Surface Cameras

abstract: In this paper, we introduce a bilateral consistency metric on the surface camera (SCam) [26] for light field stereo matching to handle significant occlusions. The concept of SCam is used to model angular radiance distribution with respect to a 3D point. Our bilateral consistency metric is used to indicate the probability of occlusions by analyzing the SCams. We further show how to distinguish between on-surface and free space, textured and non-textured, and Lambertian and specular through bilateral SCam analysis. To speed up the matching process, we apply the edge preserving guided filter [14] on the consistency-disparity curves. Experimental results show that our technique outperforms both the state-of-the-art and the recent light field stereo matching methods, especially near occlusion boundaries.


title: Query optimizers: time to rethink the contract?

abstract: Query Optimization is expected to produce good execution plans for complex queries while taking relatively small optimization time. Moreover, it is expected to pick the execution plans with rather limited knowledge of data and without any additional input from the application. We argue that it is worth rethinking this prevalent model of the optimizer. Specifically, we discuss how the optimizer may benefit from leveraging rich usage data and from application input. We conclude with a call to action to further advance query optimization technology.


Pen-on-Paper Flexible Electronics


title: Security of biometric authentication systems

abstract: This paper outlines our views of actual security of biometric ::: authentication and encryption systems. The attractiveness of ::: some novel approaches like cryptographic key generation from ::: biometric data is in some respect understandable, yet so far ::: has lead to various shortcuts and compromises on security. This ::: paper starts with an introductory section that is followed by a ::: section about variability of biometric characteristics, with a ::: particular attention paid to biometrics used in large systems. ::: The following sections then discuss the potential for biometric ::: authentication systems and for the use of biometrics in support ::: of cryptographic applications as they are typically used in ::: computer systems.


title: Perceptron Reranking for CCG Realization

abstract: This paper shows that discriminative reranking with an averaged perceptron model yields substantial improvements in realization quality with CCG. The paper confirms the utility of including language model log probabilities as features in the model, which prior work on discriminative training with log linear models for HPSG realization had called into question. The perceptron model allows the combination of multiple n-gram models to be optimized and then augmented with both syntactic features and discriminative n-gram features. The full model yields a state-of-the-art BLEU score of 0.8506 on Section 23 of the CCGbank, to our knowledge the best score reported to date using a reversible, corpus-engineered grammar.


title: Multiobjective evolutionary algorithms for electric power dispatch problem

abstract: The potential and effectiveness of the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem are comprehensively discussed and evaluated in this paper. Specifically, nondominated sorting genetic algorithm, niched Pareto genetic algorithm, and strength Pareto evolutionary algorithm (SPEA) have been developed and successfully applied to an environmental/economic electric power dispatch problem. A new procedure for quality measure is proposed in this paper in order to evaluate different techniques. A feasibility check procedure has been developed and superimposed on MOEA to restrict the search to the feasible region of the problem space. A hierarchical clustering algorithm is also imposed to provide the power system operator with a representative and manageable Pareto-optimal set. Moreover, an approach based on fuzzy set theory is developed to extract one of the Pareto-optimal solutions as the best compromise one. These multiobjective evolutionary algorithms have been individually examined and applied to the standard IEEE 30-bus six-generator test system. Several optimization runs have been carried out on different cases of problem complexity. The results of MOEA have been compared to those reported in the literature. The results confirm the potential and effectiveness of MOEA compared to the traditional multiobjective optimization techniques. In addition, the results demonstrate the superiority of the SPEA as a promising multiobjective evolutionary algorithm to solve different power system multiobjective optimization problems.


title: State Machine Replication for the Masses with BFT-SMART

abstract: The last fifteen years have seen an impressive amount of work on protocols for Byzantine fault-tolerant (BFT) state machine replication (SMR). However, there is still a need for practical and reliable software libraries implementing this technique. BFT-SMART is an open-source Java-based library implementing robust BFT state machine replication. Some of the key features of this library that distinguishes it from similar works (e.g., PBFT and UpRight) are improved reliability, modularity as a first-class property, multicore-awareness, reconfiguration support and a flexible programming interface. When compared to other SMR libraries, BFT-SMART achieves better performance and is able to withstand a number of real-world faults that previous implementations cannot.


title: Design and Implementation of Views: Isolated Perspectives of a File System

abstract: We present Views, a file system architecture that provides isolation between system components for the purposes of access control, regulatory compliance, and sandboxing. Views allows for discrete I/O entities, such as users, groups, or processes, to have a logically complete yet fully isolated perspective (view) of the file system. This ensures that each entity’s file system activities only modify that entity’s view of the file system, but in a transparent fashion that does not limit or restrict the entity’s functionality. Views can therefore be used to monitor system activity based on user accounts for access control (as required by federal regulations such as HIPAA), provide a reliable sandbox for arbitrary applications without inducing any noticeable loss in performance, and enable traditional snapshotting functionality by manipulating and transplanting views as snapshots in time. Views’ architecture is designed to be file system independent, extremely easy to use and manage, and flexible in defining isolation and sharing polices. Our implementation of Views is built on ext3cow, which additionally provides versioning capabilities to all entities. Benchmarking results show that the performance of Views is nearly identical to other traditional file systems such as ext3.


title: Solving mixed integer nonlinear programs by outer approximation

abstract: A wide range of optimization problems arising from engineering applications can be formulated as Mixed Integer NonLinear Programming problems (MINLPs). Duran and Grossmann (1986) suggest an outer approximation scheme for solving a class of MINLPs that are linear in the integer variables by a finite sequence of relaxed MILP master programs and NLP subproblems.


title: Thickness ratio and d 33 effects on flexible piezoelectric unimorph energy conversion

abstract: Piezoelectric unimorphs are bilayer structures where a blanket piezoelectric film (with top and bottom electrodes) is uniformly laminated on an inactive but flexible substrate. Because of their simple construction and flexibility, unimorphs are widely used as a key element in flexible sensors and actuators. The response of a unimorph is governed by the material properties of the film and the substrate as well as their geometric parameters. For low frequency biological energy harvesting, structural optimization is critical due to the dimensional confinement imposed by curvilinear and deformable bio-tissues. Here we report a comprehensive theoretical framework to investigate the effects of the film-to-substrate thickness ratio on voltage, charge, and energy outputs when the unimorph is subjected to eight different boundary/loading conditions. A broad class of power generators can be designed using such a framework under the assumption that the unimorph length is very large compared to its thickness, where the only dimensionless variable is the film-to-substrate thickness ratio. We show that the analytical and finite element modeling results are in excellent agreement. For not so thin unimorphs, there is non-zero normal stress in the thickness direction (σ 3) and d 33 can play a significant role in this case. Non-monotonic dependence of voltage and energy generation on thickness ratio has been found in some cases and optimum thickness ratio for unimorph generator can be predicted. When the unimorph is actuated by voltage applied across the piezo-film thickness, non-monotonic maximum deflection versus thickness ratio is also found. This work provides new physical insights on unimorphs and analytical solutions that can be used for the structural design and optimization of unimorphs under different boundary/loading conditions.


title: Integrin signaling aberrations in prostate cancer

abstract: Integrins are cell surface receptors for extracellular matrix proteins and play a key role in cell survival, proliferation, migration and gene expression. Integrin signaling has been shown to be deregulated in several types of cancer, including prostate cancer. This review is focused on integrin signaling pathways known to be deregulated in prostate cancer and known to promote prostate cancer progression.


title: A variable stiffness joint using leaf springs for robot manipulators

abstract: Safety of a manipulator designed to be used at home requires different approach than industrial robots, where safety is achieved mainly by decreasing the interaction with humans. Robots for applications at home, however, require frequent interaction with humans. Introducing compliant component gives the answer to the safety issue at the cost of performance degradation. In order to reduce the performance degradation, manipulators equipped with variable stiffness have been studied by many researchers. This paper presents a variable stiffness joint(VSJ) designed for a robot manipulator. The stiffness is generated by leaf springs and two actuators are used to control the position and stiffness of the joint. Changing the effective length of the spring results in change in stiffness. The position of the joint is controlled via rotating two actuators at the same speed in the same direction. The stiffness is controlled when the two actuators rotate in the different speed. Experiments are conducted to show that the position and stiffness are controlled independent with each other and having less stiffness at the joint helps in making unexpected collision with object safer.


title: vMES: Virtualization aware manufacturing execution system

abstract: Abstract The large scale emergence in the last decade of various cloud solutions, ranging from software-as-a-service (SaaS) based solutions for business process management and implementation to very sophisticated private cloud solutions capable of high performance computing (HPC) and efficient virtualization, constitute the building blocks for engineering the next generation of flexible enterprise systems that can respond with great agility to changes in their environment. These new technologies are adopted at a certain level by manufacturing enterprises in order to advance in a new era of mass customization where flexibility, scalability and agility are the differentiating factors. In this context, this paper introduces the virtualized manufacturing execution system (vMES), an intermediate layer in the manufacturing stack, and discusses the advantages and limitations offered by this approach for manufacturing enterprises. A classification of MES workloads based on the ISA-95 function model is presented, focusing on the virtualization techniques suitable for each workload, considering the algorithms and technologies used and the virtualization overhead. A pilot vMES implementation using a parallel process for smart resource provisioning and automatic scaling is also presented. The pilot implementation using six Adept robots and one IBM CloudBurst 2.1 private cloud and an ISA-95 based MES is described; the virtualization sequence is analyzed in several scenarios of resource workload collocation on physical cloud blades with and without perturbations.


title: Mobile telephones, distracted attention, and pedestrian safety.

abstract: Driver distraction is a major cause of traffic accidents, with mobile telephones as a key source of distraction. In two studies, we examined distraction of pedestrians associated with mobile phone use. The first had 60 participants walk along a prescribed route, with half of them conversing on a mobile phone, and the other half holding the phone awaiting a potential call, which never came. Comparison of the performance of the groups in recalling objects planted along the route revealed that pedestrians conversing recalled fewer objects than did those not conversing. The second study had three observers record pedestrian behavior of mobile phone users, i-pod users, and pedestrians with neither one at three crosswalks. Mobile phone users crossed unsafely into oncoming traffic significantly more than did either of the other groups. For pedestrians as with drivers, cognitive distraction from mobile phone use reduces situation awareness, increases unsafe behavior, putting pedestrians at greater risk for accidents, and crime victimization.


title: Towards Future THz Communications Systems

abstract: Carrier frequencies beyond 300 GHz, belonging to the so-called THz range, have received attention for considering this frequency band for future multi-gigabit short-range communication systems. This review paper gives an overview of current issues in the emerging field of THz communications targeting to deliver wireless 100 Gbps over short distances. The paper will start by introducing scenarios and applications requiring such high data rates followed by a discussion on the radio channel characteristics. In the 300 GHz frequency band, the path loss is even more significant than at 60 GHz and appropriate measures to mitigate effects in none-line-of-sight (NLOS) cases, caused e. g. by the influence of moving people, are required. Advanced antenna techniques like beam forming or beam switching are a pre-requisite to guarantee seamless service. In order to consider such techniques in the standards development, the propagation channel operating at these mm- and sub-mm wave bands in realistic environments must be well understood. Therefore, intensive channel measurement and modeling activities have been done at 300 GHz, which started a couple of years ago at Terahertz Communications Lab (TCL). Due to the short wave length, the influence of surface roughness of typical building materials plays an important role. Hence, the modeling of rough surface scattering has been one of the main areas in these investigations. In this contribution, the main results of the propagation research activities at TCL are summarized. In the last part of the paper, an overview of the state-of-the-art in technology development and successful demonstrations of data transmission will be given together with a report on the status quo of ongoing activities in standardization at IEEE 802.15 IG THz and the regulation of the spectrum beyond 300 GHz.


title: Digital halftoning technique using a blue-noise mask

abstract: A novel digital halftoning technique, by which the halftoning is achieved by a pixelwise comparison of the gray-scale image to an array (halftone screen), the blue-noise mask, is presented. This mask is designed so that the halftone image has blue-noise (high-frequency) characteristics in the frequency domain. The algorithm for the construction of the blue-noise mask and an algorithm for the construction of binary patterns with the same first-order but different second-order statistics are presented. Two psychovisual tests in which human subjects rated halftone patterns and images according to various criteria are also described.


title: Profile hidden Markov models and metamorphic virus detection

abstract: Metamorphic computer viruses “mutate” by changing their internal structure and, consequently, different instances of the same virus may not exhibit a common signature. With the advent of construction kits, it is easy to generate metamorphic strains of a given virus. In contrast to standard hidden Markov models (HMMs), profile hidden Markov models (PHMMs) explicitly account for positional information. In principle, this positional information could yield stronger models for virus detection. However, there are many practical difficulties that arise when using PHMMs, as compared to standard HMMs. PHMMs are widely used in bioinformatics. For example, PHMMs are the most effective tool yet developed for finding family related DNA sequences. In this paper, we consider the utility of PHMMs for detecting metamorphic virus variants generated from virus construction kits. PHMMs are generated for each construction kit under consideration and the resulting models are used to score virus and non-virus files. Our results are encouraging, but several problems must be resolved for the technique to be truly practical.


title: Analysis of customer churn prediction in telecom industry using decision trees and logistic regression

abstract: Customer churn prediction in Telecom industry is one of the most prominent research topics in recent years. It consists of detecting customers who are likely to cancel a subscription to a service. Recently, the mobile telecommunication market has changed from a rapidly growing market into a state of saturation and fierce competition. The focus of telecommunication companies has therefore shifted from building a large customer base into keeping customers in house. For that reason, it is valuable to know which customers are likely to switch to a competitor in the near future. The data extracted from telecom industry can help analyze the reasons of customer churn and use that information to retain the customers. We have proposed to build a model for churn prediction for telecommunication companies using data mining and machine learning techniques namely logistic regression and decision trees. A comparison is made based on efficiency of these algorithms on the available dataset.


SQuAD Reading Comprehension with Coattention


title: It Is All About Perspective: An Exploration of Mitigating Selective Exposure with Aspect Indicators

abstract: Selective exposure, the preferential seeking of confirmatory information, can potentially exacerbate fragmentation of online opinions and lead to biased decisions. We tested whether features that allowed users to better distinguish information about different issue aspects would encourage them to take different perspectives, thereby moderating the negative influence of pre-existing beliefs on information seeking. Using an information aggregator that provided drug related comments, we conducted an experiment to study the impact of aspect indicators (indicating whether the comment was about effectiveness or side effects) on moderating selective exposure. We found that, when participants were asked to decide between medications for high-risk diseases, and had preexisting biased beliefs in their effectiveness (one medication was less effective than the other), without aspect indicators they exhibited selective exposure to both types of comments (effectiveness and side effects) and were biased to choose the medication in confirmation of their pre-existing beliefs. With aspect indicators, we found reduced selective exposure to information about side effects of the medications, and as a result their overall decision bias was mitigated. However, the effect of aspect indicators in reducing selective exposure was moderated by the decision contexts, including the perceived risk of the diseases and whether the aspect was perceived to be critical to the decision.


title: Stock price prediction using genetic algorithms and evolution strategies

abstract: Stock market is a very challenging and an interesting field. In this paper, we are trying to predict the target prices of the stocks for the short term. We are predicting the target priceof script individually for eight different scripts. For each script, six attributes are used which help us to find, whether the prices are going up or down. The evolutionary techniques used for this experiment are the genetic algorithms and evolution strategies. By using these algorithms, we are trying to find the connection weight for each attribute, which helps us in predicting the target price of the stock. An input for each attribute is given to a sigmoid function after it is amplified based on its connection weight. The experimental results show that using this approach, predicting the stock price is promising. In each case, the algorithms were able to predict with an accuracy of at least 70.00.


title: Reducing Confusion about Grounded Theory and Qualitative Content Analysis: Similarities and Differences

abstract: Introduction Using an appropriate research method for inquiry is critical to successful research. Grounded theory and qualitative content analysis share similarities. Both are based on naturalistic inquiry that entails identifying themes and patterns and involves rigorous coding. They are both used to analyze and interpret qualitative data; however, the similarities and differences in grounded theory and qualitative content analysis have not been clarified in the literature (Priest, Roberts, & Woods, 2002), nor have they been consistently considered. To illustrate, both have been considered equivalent approaches to interpret qualitative data (e.g., Priest et al., 2002). Grounded theory was treated as a research methodology, and content analysis as a method (e.g., Crotty, 2003); furthermore, grounded theory was considered a theoretical framework and content analysis a research method of textual data analysis (e.g., Patton, 2002). Qualitative content analysis was considered a strategy for the analysis of qualitative descriptive studies (Sandelowski, 2000) and a technique with overtones of other research methods, such as ethnographic and grounded theory (Altheide, 1987). Qualitative content analysis was unknown as a research method until recently, especially in English-speaking countries, because of the dominance of quantitative content analysis (Schreier, 2012). Moreover, a researcher's approach purportedly following grounded theory actually seems closer to qualitative content analysis or other methods (Sandelowski & Barroso, 2003; Suddaby, 2006). Sandelowski and Barroso (2003) cited the discrepancy between "method claims and the actual use of methods" (p. 905) in research papers. Novice researchers, especially students who want to conduct qualitative research, are often confused by the characteristics of the two as result of the lack of comparative references. Some researchers who stated they had used grounded theory actually used qualitative content analysis, which incorporates some procedures of grounded theory, such as open coding or memoing (Sandelowski & Barroso, 2003). Thus, the purpose of this paper is to clarify ambiguities about the characteristics of grounded theory and qualitative content analysis. Using our own research as examples, we have discussed the similarities and differences in the two in the following six areas: a) background and philosophical basis, b) unique characteristics of each method, c) goals and rationale of each method, d) data analysis process, e) outcomes of the research, and f) evaluation of trustworthiness of research. We have also discussed the strengths and weaknesses of each. Through this paper, we expect to provide knowledge that can assist novice researchers in the selection of appropriate research methods for their inquiries. Background and Philosophical Basis Grounded Theory The term grounded theory was introduced in The Discovery of Grounded Theory (1967) by Glaser and Strauss as "the discovery of theory from data--systematically obtained and analyzed in social research" (p. 1). Instead of verification of theories, they introduced a research method to arrive at a "theory suited to its supposed uses" contrasting with a "theory generated by logical deduction from a priori assumptions" (p. 3). According to Strauss and Corbin (1994) it is "a general methodology, a way of thinking about and conceptualizing data" (p. 275). The Grounded Theory Institute, run by Glaser, one of the founders of grounded theory, defined it as follows: Grounded Theory is an inductive methodology. Although many call Grounded Theory a qualitative method, it is not. It is a general method. It is the systematic generation of theory from systematic research. It is a set of rigorous research procedures leading to the emergence of conceptual categories. …


Instruments and measurement


title: Facebook use and academic performance among college students: A mixed-methods study with a multi-ethnic sample

abstract: A mixed-methods study of academic performance and Facebook among U.S.-based college students.A qualitative account of how a U.S.-sample of college students conferred meanings to their academic lives on Facebook.Academic performance may determine college students' Facebook use, rather than the reverse.Those with lower GPAs may disclose negative affective states in their academic disclosure on Facebook. This paper uses a mixed-methods approach to examine the relation between online academic disclosure and academic performance. A multi-ethnic sample of college students (N=261; male=66; female=195; M age ź 22years) responded to open-ended questions about their Facebook use. Thematic analysis revealed that over 14% of the Facebook wall posts/status updates (N=714) contained academic themes; positive states were more frequent than negative and neutral states and students with lower GPAs expressed negative states more often. A path analysis suggested that academic performance may determine college students' Facebook use, rather than the reverse. Implications for student support services are discussed.


title: Religiosity of elite college athletes.

abstract: This study was a pilot examination of the religiousness of student athletes as compared to nonathletes. Participants were 248 undergraduate students (84 athletes) at the University of Florida who were enrolled in randomly chosen courses within the Department of Counselor Education. To assess the organizational, nonorganizational, and intrinsic dimensions of religion, the Duke Religion Index was used. Findings indicate that male and female athletes reported higher degrees of organizational, nonorganizational, and intrinsic religiousness than male nonathletes. Implications of these Findings on future research and applied sport practice are discussed.


title: Cheating in Online Games: A Social Network Perspective

abstract: Online gaming is a multi-billion dollar industry that entertains a large, global population. One unfortunate phenomenon, however, poisons the competition and spoils the fun: cheating. The costs of cheating span from industry-supported expenditures to detect and limit it, to victims’ monetary losses due to cyber crime. This article studies cheaters in the Steam Community, an online social network built on top of the world’s dominant digital game delivery platform. We collected information about more than 12 million gamers connected in a global social network, of which more than 700 thousand have their profiles flagged as cheaters. We also observed timing information of the cheater flags, as well as the dynamics of the cheaters’ social neighborhoods. We discovered that cheaters are well embedded in the social and interaction networks: their network position is largely indistinguishable from that of fair players. Moreover, we noticed that the number of cheaters is not correlated with the geographical, real-world population density, or with the local popularity of the Steam Community. Also, we observed a social penalty involved with being labeled as a cheater: cheaters lose friends immediately after the cheating label is publicly applied. Most importantly, we observed that cheating behavior spreads through a social mechanism: the number of cheater friends of a fair player is correlated with the likelihood of her becoming a cheater in the future. This allows us to propose ideas for limiting cheating contagion.


title: Security, privacy and efficiency of Internet banking transactions

abstract: ABbstract The last decade has witnessed the emergence of a plethora of approaches for securing financial transactions over the Internet. During the same period, attacks have matured from isolated exploits to an organized e-criminal industry. In the midst of this evolution stood the End User, whose instances have often been neglected under the assumption that refunding financial losses is all that mattered. This paper analyzes the existing deployments of Internet banking services from the perspective of the End User, whose main goal is completing the online transaction. The sole use on the client side of so-called “trusted” hardware devices will be discussed and shown to fall short of the requirements for truly secure Internet banking. Evidence will be provided in support of the need to protect the client components using connected devices and applying software hardening techniques to lower the hacking ROI and help rebalance forces in the fight against cyber criminals. A new metric for gauging the effectiveness of security software will be described and applied to measure the practical security of existing Internet banking systems. Finally, a number of guidelines will be provided for assuring that reasonable care is exercised in the design and deployment of Internet banking systems.


title: A review of the literature on citation impact indicators

abstract: Citation impact indicators nowadays play an important role in research evaluation, and consequently these indicators have received a lot of attention in the bibliometric and scientometric literature. This paper provides an in-depth review of the literature on citation impact indicators. First, an overview is given of the literature on bibliographic databases that can be used to calculate citation impact indicators (Web of Science, Scopus, and Google Scholar). Next, selected topics in the literature on citation impact indicators are reviewed in detail. The first topic is the selection of publications and citations to be included in the calculation of citation impact indicators. The second topic is the normalization of citation impact indicators, in particular normalization for field differences. Counting methods for dealing with co-authored publications are the third topic, and citation impact indicators for journals are the last topic. The paper concludes by offering some recommendations for future research.


title: Modelling Semantic Context of OOV Words in Large Vocabulary Continuous Speech Recognition

abstract: The diachronic nature of broadcast news data leads to the problem of out-of-vocabulary (OOV) words in large vocabulary continuous speech recognition (LVCSR) systems. Analysis of OOV words reveals that a majority of them are proper names (PNs). However, PNs are important for automatic indexing of audio–video content and for obtaining reliable automatic transcriptions. In this paper, we focus on the problem of OOV PNs in diachronic audio documents. To enable the recovery of the PNs missed by the LVCSR system, relevant OOV PNs are retrieved by exploiting the semantic context of the LVCSR transcriptions. For retrieval of OOV PNs, we explore topic and semantic context derived from latent Dirichlet allocation (LDA) topic models, continuous word vector representations and the neural bag-of-words (NBOW) model which is capable of learning task specific word and context representations. We propose a neural bag-of-weighted words (NBOW2) model which learns to assign higher weights to words that are important for retrieval of an OOV PN. With experiments on French broadcast news videos, we show that the NBOW and NBOW2 models outperform the methods based on raw embeddings from LDA and Skip-gram models. Combining the NBOW and NBOW2 models gives a faster convergence during training. Second pass speech recognition experiments, in which the LVCSR vocabulary and language model are updated with the retrieved OOV PNs, demonstrate the effectiveness of the proposed context models.


title: TreadMarks: Shared memory computing on networks of workstations

abstract: Shared memory facilitates the transition from sequential to parallel processing. Since most data structures can be retained, simply adding synchronization achieves correct, efficient programs for many applications. We discuss our experience with parallel computing on networks of workstations using the TreadMarks distributed shared memory system. DSM allows processes to assume a globally shared virtual memory even though they execute on nodes that do not physically share memory. We illustrate a DSM system consisting of N networked workstations, each with its own memory. The DSM software provides the abstraction of a globally shared memory, in which each processor can access any data item without the programmer having to worry about where the data is or how to obtain its value.


title: Adaptive noise cancelling: Principles and applications

abstract: This paper describes the concept of adaptive noise cancelling, an alternative method of estimating signals corrupted by additive noise or interference. The method uses a "primary" input containing the corrupted signal and a "reference" input containing noise correlated in some unknown way with the primary noise. The reference input is adaptively filtered and subtracted from the primary input to obtain the signal estimate. Adaptive filtering before subtraction allows the treatment of inputs that are deterministic or stochastic, stationary or time variable. Wiener solutions are developed to describe asymptotic adaptive performance and output signal-to-noise ratio for stationary stochastic inputs, including single and multiple reference inputs. These solutions show that when the reference input is free of signal and certain other conditions are met noise in the primary input can be essentiany eliminated without signal distortion. It is further shown that in treating periodic interference the adaptive noise canceller acts as a notch filter with narrow bandwidth, infinite null, and the capability of tracking the exact frequency of the interference; in this case the canceller behaves as a linear, time-invariant system, with the adaptive filter converging on a dynamic rather than a static solution. Experimental results are presented that illustrate the usefulness of the adaptive noise cancelling technique in a variety of practical applications. These applications include the cancelling of various forms of periodic interference in electrocardiography, the cancelling of periodic interference in speech signals, and the cancelling of broad-band interference in the side-lobes of an antenna array. In further experiments it is shown that a sine wave and Gaussian noise can be separated by using a reference input that is a delayed version of the primary input. Suggested applications include the elimination of tape hum or turntable rumble during the playback of recorded broad-band signals and the automatic detection of very-low-level periodic signals masked by broad-band noise.


title: Fast Trajectory Optimization for Agile Quadrotor Maneuvers with a Cable-Suspended Payload.

abstract: Reference EPFL-CONF-232508 URL: http://rpg.ifi.uzh.ch/docs/RSS17_Foehn.pdf Record created on 2017-11-21, modified on 2017-11-21


title: Fast quantum modular exponentiation architecture for Shor's factoring algorithm

abstract: We present a novel and efficient, in terms of circuit depth, design for Shor's quantum factorization algorithm. The circuit effectively utilizes a diverse set of adders based on the Quantum Fourier transform (QFT) Draper's adders to build more complex arithmetic blocks: quantum multiplier/accumulators by constants and quantum dividers by constants. These arithmetic blocks are effectively architected into a quantum modular multiplier which is the fundamental block for the modular exponentiation circuit, the most computational intensive part of Shor's algorithm. The proposed modular exponentiation circuit has a depth of about 2000n2 and requires 9n + 2 qubits, where n is the number of bits of the classic number to be factored. The total quantum cost of the proposed design is 1600n3. The circuit depth can be further decreased by more than three times if the approximate QFT implementation of each adder unit is exploited.


title: Design and fabrication of underground fault distance locator using arduino and GSM

abstract: This paper introduces an underground fault location model of cable using Arduino and GSM (Global system for mobile communication) module, used for remote indication to diminish power outages and heavy loss of profit by sending SMS to main control room so the field engineer can resolve the issue of an exact fault location. In underground system, occurrence of fault is rare and is difficult to locate the exact position of fault to overcome this problem, we came up with the idea presented in paper. The main aim is to diagnose the open circuit and three phase short circuit faults distance from base-station in kilometers. This prototype is congregated with a set of resistors showing length of cable in kilometers and a set of switches are used to create fault at every known kilometer to determine the exactness and results regarding fault readings, is displayed on a LCD connected with the Arduino UNO board. Further NI Multisim software is used to simulate the fault. This project is beneficial in reducing the cost, production losses by saving the time and efforts for sectionalization of faults and improving the power availability to consumers through an enhancement of overall efficiency of power nets.


title: Conceptualizing smart city with dimensions of technology, people, and institutions

abstract: This conceptual paper discusses how we can consider a particular city as a smart one, drawing on recent practices to make cities smart. A set of the common multidimensional components underlying the smart city concept and the core factors for a successful smart city initiative is identified by exploring current working definitions of smart city and a diversity of various conceptual relatives similar to smart city. The paper offers strategic principles aligning to the three main dimensions (technology, people, and institutions) of smart city: integration of infrastructures and technology-mediated services, social learning for strengthening human infrastructure, and governance for institutional improvement and citizen engagement.


title: Towards the Right Assistance at the Right Time for Using Complex Interfaces

abstract: Many users struggle when they have to use complex interfaces to complete everyday computing tasks. Offering intelligent, proactive assistance is becoming commonplace yet determining the right time to provide help is still difficult. We conducted an empirical study that aimed to uncover what user factors influenced following advice. Our results describe a user's background and expectations that appear to play a role in heeding assistance. Our work is a step towards understanding how to provide the right assistance at the right time and build proactive assistance systems that are personalized for individual users.


Generic Tasks in Knowledge-Based Reasoning: High-Level Building Blocks for Expert System Design


title: Classification of Music-Induced Emotions Based on Information Fusion of Forehead Biosignals and Electrocardiogram

abstract: Emotion recognition systems have been developed to assess human emotional states during different experiences. In this paper, an approach is proposed for recognizing music-induced emotions through the fusion of three-channel forehead biosignals (the left temporalis, frontalis, and right temporalis channels) and an electrocardiogram. The classification of four emotional states in an arousal–valence space (positive valence/low arousal, positive valence/high arousal, negative valence/high arousal, and negative valence/low arousal) was performed by employing two parallel support vector machines as arousal and valence classifiers. The inputs of the classifiers were obtained by applying a fuzzy-rough model feature evaluation criterion and sequential forward floating selection algorithm. An average classification accuracy of 88.78 % was achieved, corresponding to an average valence classification accuracy of 94.91 % and average arousal classification accuracy of 93.63 %. The proposed emotion recognition system may be useful for interactive multimedia applications or music therapy.


title: Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos

abstract: Analyzing videos of human activities involves not only recognizing actions (typically based on their appearances), but also determining the story/plot of the video. The storyline of a video describes causal relationships between actions. Beyond recognition of individual actions, discovering causal relationships helps to better understand the semantic meaning of the activities. We present an approach to learn a visually grounded storyline model of videos directly from weakly labeled data. The storyline model is represented as an AND-OR graph, a structure that can compactly encode storyline variation across videos. The edges in the AND-OR graph correspond to causal relationships which are represented in terms of spatio-temporal constraints. We formulate an Integer Programming framework for action recognition and storyline extraction using the storyline model and visual groundings learned from training data.


title: Blind Deblurring Using Internal Patch Recurrence

abstract: Recurrence of small image patches across different scales of a natural image has been previously used for solving ill-posed problems (e.g. super- resolution from a single image). In this paper we show how this multi-scale property can also be used for “blind-deblurring”, namely, removal of an unknown blur from a blurry image. While patches repeat ‘as is’ across scales in a sharp natural image, this cross-scale recurrence significantly diminishes in blurry images. We exploit these deviations from ideal patch recurrence as a cue for recovering the underlying (unknown) blur kernel. More specifically, we look for the blur kernel k, such that if its effect is “undone” (if the blurry image is deconvolved with k), the patch similarity across scales of the image will be maximized. We report extensive experimental evaluations, which indicate that our approach compares favorably to state-of-the-art blind deblurring methods, and in particular, is more robust than them.


title: The Effect of Trust and Perceived Risk on Consumers' Online Purchase Intention

abstract: With the rapid growth of e-commerce in Taiwan, B2C domain of e-commerce has been the significant issue recently. According to the importance to understand and predict consumers’ online purchase intention. Based on the Technology Acceptance Model (TAM), this study aims to predict the consumers’ purchase intention by integrating trust and perceived risk into the model to empirically examine the impacts of key drivers. With 705 samples obtained from users of Yahoo! Kimo online vendor, this study applied the Structural Equation Model (SEM) to examine the proposed model. The results indicate that perceived ease of use (PEOU) and perceived usefulness (PU), trust, as well as perceived risk, significantly impact purchase intention. Moreover, trust significantly reduced perceived risk.


title: CryptoPage: An Efficient Secure Architecture with Memory Encryption, Integrity and Information Leakage Protection

abstract: Several secure computing hardware architectures using memory encryption and memory integrity checkers have been proposed during the past few years to provide applications with a tamper resistant environment. Some solutions, such as HIDE, have also been proposed to solve the problem of information leakage on the address bus. We propose the CRYPTOPAGE architecture which implements memory encryption, memory integrity protection checking and information leakage protection together with a low performance penalty (3% slowdown on average) by combining the Counter Mode of operation, local authentication values and Merkle trees.


title: Feminine to smell but masculine to touch? Multisensory congruence and its effect on the aesthetic experience☆

abstract: We draw upon literature examining cross-modal sensory interactions and congruence to explore the impact of smell on touch. In line with our predictions, two experiments show that smell can impact touch in meaningful ways. Specifically, we show that multisensory semantic congruence between smell and touch properties of a stimulus enhances haptic perception and product evaluation. We explore this relationship in the context of two properties of touch, namely texture and temperature, and demonstrate that both smell and touch can have semantic associations, which can affect haptic perception and product evaluation depending on whether they match or not. In study 1, we focus on the semantic association of smell and touch (texture) with gender and in study 2 with temperature. Our results extend prior work on smell and touch within consumer behavior, and further contribute to emerging literature on multisensory interactions.


title: The Rocky 7 rover: a Mars sciencecraft prototype

abstract: This paper describes the design and implementation at the Jet Propulsion Laboratory of a small rover for future Mars missions requiring long traverses and rover-based science experiments. The small rover prototype, called Rocky 7, is capable of long traverses, autonomous navigation, and science instrument control. This rover carries three science instruments, and can be commanded from any computer platform from any location using the World Wide Web. In this paper we describe the mobility system, the sampling system, the sensor suite, navigation and control, onboard science instruments, and the ground command and control system. We also present key accomplishments of a recent field test of Rocky 7 in the Mojave Desert in California.


title: Ontologies: Principles, methods and applications

abstract: This paper is intended to serve as a comprehensive introduction to the emerging field concerned with the design and use of ontologies. We observe that disparate backgrounds, languages, tools and techniques are a major barrier to effective communication among people, organisations and/or software understanding (i.e. an “ontology”) in a given subject area, can improve such communication, which in turn, can give rise to greater reuse and sharing, inter-operability, and more reliable software. After motivating their need, we clarify just what ontologies are and what purpose they serve. We outline a methodology for developing and evaluating ontologies, first discussing informal techniques, concerning such issues as scoping, handling ambiguity, reaching agreement and producing definitions. We then consider the benefits and describe, a more formal approach. We re-visit the scoping phase, and discuss the role of formal languages and techniques in the specification, implementation and evalution of ontologies. Finally, we review the state of the art and practice in this emerging field, considering various case studies, software tools for ontology development, key research issues and future prospects.


title: Software quality in the clouds: a cloud-based solution

abstract: Cloud computing, an on-demand computation model that consists of large data-centers (Clouds) managed by cloud providers, offers storage and computation needs for cloud users based on service level agreements (SLAs). Services in cloud computing are offered at relatively low cost. The model, therefore, forms a great target for many applications, such as startup businesses and e-commerce applications. The area of cloud computing has grown rapidly in the last few years; yet, it still faces some obstacles. For example, there is a lack of mechanisms that guarantee for cloud users the quality that they are actually getting, compared to the quality of service that is specified in SLAs. Another example is the concern of security, privacy and trust, since users lose control over their data and programs once they are sent to cloud providers. In this paper, we introduce a new architecture that aids the design and implementation of attestation services. The services monitor cloud-based applications to ensure software quality, such as security, privacy, trust and usability of cloud-based applications. Our approach is a user-centric approach through which users have more control on their own data/applications. Further, the proposed approach is a cloud-based approach where the powers of the clouds are utilized. Simulation results show that many services can be designed based on our architecture, with limited performance overhead.


title: Design of a Highly Efficient 2–4-GHz Octave Bandwidth GaN-HEMT Power Amplifier

abstract: In this paper, the design, implementation, and experimental results of a high-efficiency wideband GaN-HEMT power amplifier are presented. A method based on source-pull/load-pull simulation has been used to find optimum source and load impedances across the bandwidth and then used with a systematic approach to design wideband matching networks. Large-signal measurement results show that, across 1.9-4.3 GHz, 9-11-dB power gain and 57%-72% drain efficiency are obtained while the corresponding power-added efficiency (PAE) is 50%-62%. Moreover, an output power higher than 10 W is maintained over the band. Linearized modulated measurements using a 20-MHz long-term evolution signal with 11.2-dB peak-to-average ratio show an average PAE of 27% and 25%, an adjacent channel leakage ratio of -44 and -42 dBc at 2.5 and 3.5 GHz, respectively.


title: Fast and inexpensive color image segmentation for interactive robots

abstract: Vision systems employing region segmentation by color are crucial in real-time mobile robot applications. With careful attention to algorithm efficiency, fast color image segmentation can be accomplished using commodity image capture and CPU hardware. This paper describes a system capable of tracking several hundred regions of up to 32 colors at 30 Hz on general purpose commodity hardware. The software system consists of: a novel implementation of a threshold classifier, a merging system to form regions through connected components, a separation and sorting system that gathers various region features, and a top down merging heuristic to approximate perceptual grouping. A key to the efficiency of our approach is a new method for accomplishing color space thresholding that enables a pixel to be classified into one or more, up to 32 colors, using only two logical AND operations. The algorithms and representations are described, as well as descriptions of three applications in which it has been used.


title: Person re-identification by probabilistic relative distance comparison

abstract: Matching people across non-overlapping camera views, known as person re-identification, is challenging due to the lack of spatial and temporal constraints and large visual appearance changes caused by variations in view angle, lighting, background clutter and occlusion. To address these challenges, most previous approaches aim to extract visual features that are both distinctive and stable under appearance changes. However, most visual features and their combinations under realistic conditions are neither stable nor distinctive thus should not be used indiscriminately. In this paper, we propose to formulate person re-identification as a distance learning problem, which aims to learn the optimal distance that can maximises matching accuracy regardless the choice of representation. To that end, we introduce a novel Probabilistic Relative Distance Comparison (PRDC) model, which differs from most existing distance learning methods in that, rather than minimising intra-class variation whilst maximising intra-class variation, it aims to maximise the probability of a pair of true match having a smaller distance than that of a wrong match pair. This makes our model more tolerant to appearance changes and less susceptible to model over-fitting. Extensive experiments are carried out to demonstrate that 1) by formulating the person re-identification problem as a distance learning problem, notable improvement on matching accuracy can be obtained against conventional person re-identification techniques, which is particularly significant when the training sample size is small; and 2) our PRDC outperforms not only existing distance learning methods but also alternative learning methods based on boosting and learning to rank.


title: On the Use of Improved Imaging Techniques for the Development of a Multistatic Three-Dimensional Millimeter-Wave Portal for Personnel Screening

abstract: The design and evaluation of an active three dimensional (3D) millimeter wave imaging system for personnel security screening is presented in this work. The system is able to produce a high- resolution 3D reconstruction of the whole human body surface and reveal concealed objects under clothing. Innovative multistatic millimeter wave radar designs and algorithms, which have been previously validated, are combined to signiflcantly improve the previous reconstruction results. In addition, the system makes use of a reduced amount of information, thus simplifying portal design. Representative simulation results showing good performance of the proposed system are provided and supported by sample measurements.


title: Babies driving robots: self-generated mobility in very young infants

abstract: Self-generated mobility via locomotion is a key for the cognitive, social and motor development of young infants. For certain children with special needs, self-generated mobility is only attained via assistive technology such as a power wheelchair. Up until recently, infants under 24 months of age were not considered candidates for training in power mobility. Recent work in our labs and others suggest that younger infants can utilize their reaching and grasping ability to learn power mobility. This interdisciplinary study combines our previous work in motor development and learning in infants with special needs, and the application of robot technology for rehabilitation to determine whether young infants without structured training, would drive a mobile robot, and if so, to determine how their driving would change over multiple sessions. The two infants that were seen for the most sessions were the focus of this pilot study. Both infants increased their total session time, percentage of session time spent driving, and total path length. These results suggest that, without training, young infants will independently move themselves using a mobile robot. These results provide the foundation for training studies to advance the self-generated mobility in young infants with special needs. Our future studies will explore the multiple training and technology combinations to reduce the barriers to exploration via self-generated mobility, and advance the general development of infants with special needs.


title: Spam filtering for short messages in adversarial environment

abstract: The unsolicited bulk messages are widespread in the applications of short messages. Although the existing spam filters have satisfying performance, they are facing the challenge of an adversary who misleads the spam filters by manipulating samples. Until now, the vulnerability of spam filtering technique for short messages has not been investigated. Different from the other spam applications, a short message only has a few words and its length usually has an upper limit. The current adversarial learning algorithms may not work efficiently in short message spam filtering. In this paper, we investigate the existing good word attack and its counterattack method, i.e. the feature reweighting, in short message spam filtering in an effort to understand whether, and to what extent, they can work efficiently when the length of a message is limited. This paper proposes a good word attack strategy which maximizes the influence to a classifier with the least number of inserted characters based on the weight values and also the length of words. On the other hand, we also proposes the feature reweighting method with a new rescaling function which minimizes the importance of the feature representing a short word in order to require more inserted characters for a successful evasion. The methods are evaluated experimentally by using the SMS and the comment spam dataset. The results confirm that the length of words is a critical factor of the robustness of short message spam filtering to good word attack.


title: Detection of covert attacks and zero dynamics attacks in cyber-physical systems

abstract: A new approach to detect covert attacks and zero dynamics attacks on cyber-physical systems is presented in this paper. These attack strategies are very sophisticated and rely on perfect system knowledge. The resulting attack signal is completely invisible in the sensor readings. Hence an ordinary fault diagnosis system is not able to detect such attacks and to trigger an alarm. Our goal is to insert a modulation matrix in the path of the control variables. By altering the input behavior of the process, the adversary will lose his perfect system knowledge and therefore his attacks can be revealed. We design the modulation matrix by focusing on covert attacks. By guaranteeing that the modulation changes the input directions, we unmask zero dynamics attacks as well, since multivariable zeros depend on frequency and direction.


title: On-board velocity estimation and closed-loop control of a quadrotor UAV based on optical flow

abstract: Robot vision became a field of increasing importance in micro aerial vehicle robotics with the availability of small and light hardware. While most approaches rely on external ground stations because of the need of high computational power, we will present a full autonomous setup using only on-board hardware. Our work is based on the continuous homography constraint to recover ego-motion from optical flow. Thus we are able to provide an efficient fall back routine for any kind of UAV (Unmanned Aerial Vehicles) since we rely solely on a monocular camera and on on-board computation. In particular, we devised two variants of the classical continuous 4-point algorithm and provided an extensive experimental evaluation against a known ground truth. The results show that our approach is able to recover the ego-motion of a flying UAV in realistic conditions and by only relying on the limited on-board computational power. Furthermore, we exploited the velocity estimation for closing the loop and controlling the motion of the UAV online.


title: The α BB Global Optimization Algorithm for Nonconvex Problems: An Overview

abstract: The αBB algorithm, a deterministic global optimization algorithm for constrained twice-differentiable NLPs, is presented. It is based on a branch-and-bound approach in which a convex relaxation of the original nonconvex problem is obtained through a dual procedure. First, all nonconvex terms of special structure (i.e., bilinear, trilinear, fractional, fractional trilinear, univariate concave) are replaced by customized tight convex lower bounding functions. Second, valid convex underestirnators are generated for general nonconvex terms by using a diagonal shift matrix A. A is related to the Hessian matrix of the nonconvex term and four rigorous methods, based on the interval Hessian matrix, are proposed for its computation. Branching variable selection strategies that improve the quality of the convex relaxations are presented. Variable bound updates are also found to affect the convergence rate positively. An implementation of the algorithm is used to solve three examples: a small but highly nonconvex problem, a stability problem and a large-scale problem involving the design of batch plant under uncertainty.


title: Energy harvesting technologies for low-power electronics

abstract: Power consumption is one of the most critical issues when designing low-cost electronic devices, such as sensing nodes in wireless sensor networks. To support their operation, such systems usually contain a battery; however, when the battery has consumed all its energy, the node (e.g. the sensor) must be retrieved and the battery replaced. If the node is located in a remote and non-accessible placement, battery replacement can become an expensive (and even impossible) task. This way, energy harvesting has emerged as a suitable alternative to supply low-power electronic systems, by converting ambient energy into electric power. Scavenged energy can be used to directly supply the circuits, or stored to be used when needed. This paper summarises the power needs of a general wireless sensor node and describes the main principles of most representative energy harvesting technologies. Copyright © 2012 John Wiley & Sons, Ltd.


title: Unsupervised Domain Adaptation by Domain Invariant Projection

abstract: Domain-invariant representations are key to addressing the domain shift problem where the training and test examples follow different distributions. Existing techniques that have attempted to match the distributions of the source and target domains typically compare these distributions in the original feature space. This space, however, may not be directly suitable for such a comparison, since some of the features may have been distorted by the domain shift, or may be domain specific. In this paper, we introduce a Domain Invariant Projection approach: An unsupervised domain adaptation method that overcomes this issue by extracting the information that is invariant across the source and target domains. More specifically, we learn a projection of the data to a low-dimensional latent space where the distance between the empirical distributions of the source and target examples is minimized. We demonstrate the effectiveness of our approach on the task of visual object recognition and show that it outperforms state-of-the-art methods on a standard domain adaptation benchmark dataset.


title: A Novel Signal Processing Technique for Clutter Reduction in GPR Measurements of Small, Shallow

abstract: A signal processing technique is developed to reduce clutter due to ground bounce in ground penetrating radar (GPR) measurements. This technique is especially useful when a GPR is used to detect subsurface antipersonnel mines. The GPR clutter is modeled using a simple parametric model. Buried mine and clutter contributions are separated through a pair of coupled iterative procedures. The algorithm outperforms existing clutter reduction approaches and also yields target features that are useful for detection and identification of these mines. The proposed technique effectively reduces clutter resulting in a significant decrease in false alarm rates.


title: Texture classification for rail surface condition evaluation

abstract: Rail surface defects threaten train and passenger safety. Hence rail surfaces must be restored using different processes depending on measurement of the severity of the defects. In this paper, we propose a new method for automatic classification of rail surface defect severity from images collected by rail inspection vehicles. It contains 2 components: a rail surface segmentation module, which utilizes structured random forests to generate an edge map and a Generalized Hough Transform to locate the boundaries of the rail surface; and a defect severity classification module, which combines multiple classifiers through a stacked ensemble model. The first-level learners are trained using descriptors of the rail surface images extracted by texton forests andtexton dictionaries, with x2-kernel SVM classifiers. The probability estimation output of the first-level learners is the input to a second level linear-kernel SVM. Our experiments on a dataset of 939 images categorized into 8 severity levels achieved 82% accuracy.


title: Subcarrier-index modulation OFDM

abstract: A new transmission approach, referred to as subcarrier-index modulation (SIM) is proposed to be integrated with the orthogonal frequency division multiplexing (OFDM) systems. More specifically, it relates to adding an additional dimension to the conventional two-dimensional (2-D) amplitude/phase modulation (APM) techniques, i.e. amplitude shift keying (ASK) and quadrature amplitude modulation (QAM). The key idea of SIM is to employ the subcarrier-index to convey information to the receiver. Furthermore, a closed-form analytical bit error ratio (BER) of SIM OFDM in Rayleigh channel is derived. Analytical and simulation results show error probability performance gain of 4 dB over 4-QAM OFDM systems for both coded and uncoded data without power saving policy. Alternatively, power saving policy retains an average gain of 1 dB while using 3 dB less transmit power per OFDM symbol.


title: Genetic analysis of oriental fruit fly, Bactrocera dorsalis (Diptera: Tephritidae) populations based on mitochondrial cox1 and nad1 gene sequences from India and other Asian countries

abstract: The study examined the genetic diversity and demographic history of Bactrocera dorsalis, a destructive and polyphagous insect pest of fruit crops in diverse geographic regions of India. 19 widely dispersed populations of the fly from India and other Asian countries were analysed using partial sequences of mitochondrial cytochrome oxidase I (cox1) and NADH dehydrogenase 1 (nad1) genes to investigate genetic diversity, genetic structure, and demographic history in the region. Genetic diversity indices [number of haplotypes (H), haloptype diversity (Hd), nucleotide diversity (π) and average number of nucleotide difference (k)] of populations revealed that B. dorsalis maintains fairly high level of genetic diversity without isolation by distance among the geographic regions. Demographic analysis showed significant (negative) Tajimas' D and Fu's F S with non significant sum of squared deviations (SSD) values, which indicate the possibility of recent sudden expansion of species and is further supported through distinctively star-like distribution structure of haplotypes among populations. Thus, the results indicate that both ongoing and historical factors have played important role in determining the genetic structure and diversity of the species in India. Consequently, sterile insect technique (SIT) could be a possible management strategy of species in the regions.


title: Knowledge Graphs in the Libraries and Digital Humanities Domain

abstract: Knowledge graphs represent concepts (e.g., people, places, events) and their semantic relationships. As a data structure, they underpin a digital information system, support users in resource discovery and retrieval, and are useful for navigation and visualization purposes. Within the libaries and humanities domain, knowledge graphs are typically rooted in knowledge organization systems, which have a century-old tradition and have undergone their digital transformation with the advent of the Web and Linked Data. Being exposed to the Web, metadata and concept definitions are now forming an interconnected and decentralized global knowledge network that can be curated and enriched by community-driven editorial processes. In the future, knowledge graphs could be vehicles for formalizing and connecting findings and insights derived from the analysis of possibly large-scale corpora in the libraries and digital humanities domain.


title: Why confessions trump innocence.

abstract: As illustrated by the story of Amanda Knox and many others wrongfully convicted, false confessions often trump factual innocence. Focusing on consequences, recent research suggests that confessions are powerfully persuasive as a matter of logic and common sense; that many false confessions contain richly detailed narratives and accurate crime facts that appear to betray guilty knowledge; and that confessions in general can corrupt other evidence from lay witnesses and forensic experts-producing an illusion of false support. This latter phenomenon, termed "corroboration inflation," suggests that pretrial corroboration requirements as well as the concept of "harmless error" on appeal are based on an erroneous presumption of independence among items of evidence. In addition to previously suggested reforms to police practices that are designed to curb the risk of false confessions, measures should be taken as well to minimize the rippling consequences of those confessions.


title: Differential private random forest

abstract: Organizations be it private or public often collect ::: personal information about an individual who are their customers or clients. The personal information of an individual is private and sensitive which has to be secured from data mining algorithm which an adversary may apply to get access to the private information. In this paper we have consider the problem of securing these private and sensitive information when used in random forest classifier in the framework of differential privacy. We have incorporated the concept of differential privacy to the ::: classical random forest algorithm. Experimental results shows that quality functions such as information gain, max operator and gini index gives almost equal accuracy regardless of their sensitivity towards the noise. Also the accuracy of the classical random forest and the differential private random forest is almost equal for different size of datasets. The proposed algorithm works ::: for datasets with categorical as well as continuous attributes


title: Broadband air-filled SIW to waveguide transition for interconnect, instrumentation and measurement applications

abstract: This paper presents a broadband transition from a standard waveguide to an air-filled substrate integrate waveguide (AFSIW). The proposed transition can be used to interconnect low-cost and high performance AFSIW based microwave and millimeter-wave substrate integrated circuits with waveguide systems. For instrumentation and measurement purposes, a test-fixture based on the reported transition is introduced. A back to back experimental validation of the transition in the 16 to 22 GHz frequency range, shows a return loss of better than −25 dB and an insertion loss of 0.12 ±0.05 dB (0.06 ±0.05 dB per transition).


title: Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump

abstract: Measuring and forecasting opinion trends from real-time social media is a long-standing goal of big-data analytics. Despite the large amount of work addressing this question, there has been no clear validation of online social media opinion trend with traditional surveys. Here we develop a method to infer the opinion of Twitter users by using a combination of statistical physics of complex networks and machine learning based on hashtags co-occurrence to build an in-domain training set of the order of a million tweets. We validate our method in the context of 2016 US Presidential Election by comparing the Twitter opinion trend with the New York Times National Polling Average, representing an aggregate of hundreds of independent traditional polls. The Twitter opinion trend follows the aggregated NYT polls with remarkable accuracy. We investigate the dynamics of the social network formed by the interactions among millions of Twitter supporters and infer the support of each user to the presidential candidates. Our analytics unleash the power of Twitter to uncover social trends from elections, brands to political movements, and at a fraction of the cost of traditional surveys.


title: Phase Angle Control of High-Frequency Resonant Currents in a Multiple Inverter System for Zone-Control Induction Heating

abstract: This paper presents a phase angle control method of high-frequency resonant currents in a zone-control induction heating (ZCIH) system, which consists of split working coils and multiple inverters. The ZCIH system controls the amplitude of each coil current to make the temperature distribution on the workpiece uniform. The amplitude of the coil current can be controlled in a wide range when its phase angle is adjusted to be the same with other coil currents. This paper theoretically derives the phase-angle change of the coil current in transient states, and reveals that the phase-angle change can be considered as a first-order response. A phase-angle controller was designed and examined in experiments using a two-zone ZCIH system. It is clarified that the phase angle control makes it possible to adjust the current phase angle not only in steady states but also in transient states.


title: Application of TEXTIO in the Simulation of FPGA Image Processing Algorithm

abstract: In order to carry out high speed and effective digital image processing, the use of FPGA is a relatively reasonable choice due to the large amount of data in digital image. Thus we need a careful simulation test to verify the correctness of the VHDL code to achieve the corresponding algorithm. It is difficult to achieve the digital image processing algorithm simulation if we rely on the traditional input waveform method. And if we write a test file (Testbench) to generate a number of test vectors, it would be very low efficiency for the simulation test. If we put the real digital image data into a file as test input, not only can the correctness of the algorithm is verified, but also the processed data can be written into the image file. Thus the result can be viewed through image in this directly way which gives us great convenience for designing and debugging. The TEXTIO package of VHDL language provides exactly the function of interaction with the disk file. So we can use it to achieve the digital image processing algorithm simulation. This paper uses the algorithm of image banalization as an example to introduce this method.


title: Towards Secure Compressive Sampling Scheme

abstract: There have been some pioneering works concerning embedding cryptographic properties in Compressive Sampling (CS) but it turns out that the concise linear projection encoding process makes this approach ineffective. Here we introduce a bi-level protection (BLP) model for constructing secure compressive sampling scheme. Then we propose several techniques to establish secret key-related sparsifying basis and deploy them into our new CS model. It is demonstrated that the encoding process is simply a random linear projection, which is the same as the traditional model. However, decoding the measurements requires the knowledge of both the key-related sensing matrix and the key-related sparsifying basis. We apply the proposed model to construct digital image cipher under the parallel compressive sampling reconstruction framework. The main properties of this cipher, such as low computational complexity, compressibility, robustness and computational secrecy under known/chosen plaintext attacks, are thoroughly studied. It is shown that compressive sampling scheme based on our BLP model is robust under various attack scenarios although the encoding process is a simple linear projection.


title: FISCAL DEFICITS AND PRIVATE INVESTMENT: ECONOMETRIC EVIDENCE FROM NIGERIA

abstract: There has been considerable controversy about the possible crowding out effect of government expenditure in general and particularly deficits on private sector investments. This paper reexamines the controversial relationship within the Nigerian context, using data over 1970-2006. A modeling technique that incorporates co-integration and structural analysis was adopted. Evidence shows that there is a positive long run relationship between private investment and real growth of the national economy. This confirms the relevance of the accelerator principle to Nigeria, with contemporaneous accelerator parameter of 1.84. On aggregate, a 1% improvement in national income engenders 1.84% increase in private investment in Nigeria. In addition, the result indicates that fiscal deficits has had a depressive effect on private investment in the country. The estimation results suggest that a 1% increase in fiscal deficit leads to 0.267% decline in private investment. The results also indicate that Nigeria’s debt profile has had strong and negative impact on private investment in Nigeria.


title: Forecasting Transportation Network Speed Using Deep Capsule Networks with Nested LSTM Models

abstract: Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies of roadway links and the dynamic temporal patterns of traffic states make it particularly challenging. To address these challenges, we propose a new capsule network (CapsNet) to extract the spatial features of traffic networks and utilize a nested LSTM (NLSTM) structure to capture the hierarchical temporal dependencies in traffic sequence data. A framework for network-level traffic forecasting is also proposed by sequentially connecting CapsNet and NLSTM. On the basis of literature review, our study is the first to adopt CapsNet and NLSTM in the field of traffic forecasting. An experiment on a Beijing transportation network with 278 links shows that the proposed framework with the capability of capturing complicated spatiotemporal traffic patterns outperforms multiple state-of-the-art traffic forecasting baseline models. The superiority and feasibility of CapsNet and NLSTM are also demonstrated, respectively, by visualizing and quantitatively evaluating the experimental results.


title: Comparative Analysis and Simulation of Different CMOS Full Adders Using Cadence in 90 nm Technology

abstract: This paper analyzes & compares four adders with different logic styles (Conventional, transmission gate, 14 transistors & GDI based technique) for transistor count, power dissipation, delay and power delay product. It is performed in virtuoso platform, using Cadence tool with available GPDK - 90nm kit. The width of NMOS and PMOS is set at 120nm and 240nm respectively. Transmission gate full adder has sheer advantage of high speed but consumes more power. GDI full adder gives reduced voltage swing not being able to pass logic 1 and logic 0 completely showing degraded output. Transmission gate full adder shows better performance in terms of delay (0.417530 ns), whereas 14T full adder shows better performance in terms of all three aspects.


title: Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics.

abstract: Quantitative proteomics has traditionally been performed by two-dimensional gel electrophoresis, but recently, mass spectrometric methods based on stable isotope quantitation have shown great promise for the simultaneous and automated identification and quantitation of complex protein mixtures. Here we describe a method, termed SILAC, for stable isotope labeling by amino acids in cell culture, for the in vivo incorporation of specific amino acids into all mammalian proteins. Mammalian cell lines are grown in media lacking a standard essential amino acid but supplemented with a non-radioactive, isotopically labeled form of that amino acid, in this case deuterated leucine (Leu-d3). We find that growth of cells maintained in these media is no different from growth in normal media as evidenced by cell morphology, doubling time, and ability to differentiate. Complete incorporation of Leu-d3 occurred after five doublings in the cell lines and proteins studied. Protein populations from experimental and control samples are mixed directly after harvesting, and mass spectrometric identification is straightforward as every leucine-containing peptide incorporates either all normal leucine or all Leu-d3. We have applied this technique to the relative quantitation of changes in protein expression during the process of muscle cell differentiation. Proteins that were found to be up-regulated during this process include glyceraldehyde-3-phosphate dehydrogenase, fibronectin, and pyruvate kinase M2. SILAC is a simple, inexpensive, and accurate procedure that can be used as a quantitative proteomic approach in any cell culture system.