title: Design and simulation for a hydraulic actuated quadruped robot

abstract: This paper describes the mechanical configuration of a quadruped robot firstly. Each of the four legs consists of three rotary joints. All joints of the robot are actuated by linear hydraulic servo cylinders. Then it deduces the forward and inverse kinematic equations for four legs with D-H transformation matrices. Furthermore, it gives a composite foot trajectory composed of cubic curve and straight line, which greatly reduces the velocity and acceleration fluctuations of the torso along forward and vertical directions. Finally, dynamics cosimulation is given with MSC.ADAMS and MATLAB. The results of co-simulation provide important guidance to mechanism design and parameters preference for the linear hydraulic servo cylinders.


title: Exposing LTE Security Weaknesses at Protocol Inter-layer, and Inter-radio Interactions

abstract: Despite security shields to protect user communication with both the radio access network and the core infrastructure, 4G LTE is still susceptible to a number of security threats. The vulnerabilities mainly exist due to its protocol’s inter-layer communication, and the access technologies (2G/3G) inter-radio interaction. We categorize the uncovered vulnerabilities in three dimensions, i.e., authentication, security association and service availability, and verify these vulnerabilities in operational LTE networks. In order to assess practical impact from these security threats, we convert these threats into active attacks, where an adversary can (a) kick the victim device out of the network, (b) hijack the victim’s location, and (c) silently drain the victim’s battery power. Moreover, we have shown that the attacker does not need to communicate with the victim device or reside at the device to launch these attacks (i.e., no Trojan or malware is required). We further propose remedies for the identified attacks.


title: A Survey on Semantic Web Search Engine

abstract: The tremendous growth in the volume of data and with the terrific growth of number of web pages, traditional search engines now a days are not appropriate and not suitable anymore. Search engine is the most important tool to discover any information in World Wide Web. Semantic Search Engine is born of traditional search engine to overcome the above problem. The Semantic Web is an extension of the current web in which information is given well-defined meaning. Semantic web technologies are playing a crucial role in enhancing traditional web search, as it is working to create machine readable data. but it will not replace traditional search engine. In this paper we made a brief survey on various promising features of some of the best semantic search engines developed so far and we have discussed the various approaches to semantic search. We have summarized the techniques, advantages of some important semantic web search engines that are developed so far.The most prominent part is that how the semantic search engines differ from the traditional searches and their results are shown by giving a sample query as input.


title: A Version of Parallel Odd-Even Sorting Algorithm Implemented in CUDA Paradigm

abstract: Sorting data is an important problem for many applications. Parallel sorting is a way to improve sorting performance using more nodes or threads e.g. dividing data in more nodes and perform sorting in each node simultaneously or including more threads in process of sorting. It was experimented with one type of those sorting algorithms, namely the well-known sorting algorithms called Odd-Even sort. This paper describes a modification of the above mentioned algorithm. Namely, the algorithm modification consists in the ability to work with the blocks of elements instead of working with individual elements. This modification is done with the idea to make it in a closer form for use of the CUDA technology. Both theoretical and experimental analysis of Odd-Even sort algorithm together with its parallel implementation is done. For experimental purpose, a GeForce GT 645M with 2 GB memory is used. The programming language C++ with CUDA 7.0 paradigm is utilized to implement Odd-Even algorithm and the results indicated that sorting of integers in CUDA environment are dozens of times faster.


title: AccurateML: Information-aggregation-based approximate processing for fast and accurate machine learning on MapReduce

abstract: The growing demands of processing massive datasets have promoted irresistible trends of running machine learning applications on MapReduce. When processing large input data, it is often of greater values to produce fast and accurate enough approximate results than slow exact results. Existing techniques produce approximate results by processing parts of the input data, thus incurring large accuracy losses when using short job execution times, because all the skipped input data potentially contributes to result accuracy. We address this limitation by proposing AccurateML that aggregates information of input data in each map task to create small aggregated data points. These aggregated points enable all map tasks producing initial outputs quickly to save computation times and decrease the outputs' size to reduce communication times. Our approach further identifies the parts of input data most related to result accuracy, thus first using these parts to improve the produced outputs to minimize accuracy losses. We evaluated AccurateML using real machine learning applications and datasets. The results show: (i) it reduces execution times by 30 times with small accuracy losses compared to exact results; (ii) when using the same execution times, it achieves 2.71 times reductions in accuracy losses compared to existing approximate processing techniques.


title: A hybrid remote display scheme for interactive applications in band-limited environment

abstract: In Desktop as a Service (DaaS), the screen delivery is an essential part that influences the users' experience. In this paper, we propose a novel remote display scheme which makes use of both video and image encoder to improve the quality of service for interactive applications in a band-limited environment. The proposed scheme approximates the frame buffer update size and decides the encoder for every frame. The image encoder is used for frames with small changes to employ its low computation complexity. The video encoder with high compression ratio but higher complexity only activated for vastly changed frames to reduce the network transmission time thus maintains a low system response time. The experimental results show that applying our scheme improves the frame rate and decreases the response time while the CPU resources on both server and client are utilized effectively.


Automatically Tracking Metadata and Provenance of Machine Learning Experiments


title: Revisiting biomarker discovery by plasma proteomics

abstract: Clinical analysis of blood is the most widespread diagnostic procedure in medicine, and blood biomarkers are used to categorize patients and to support treatment decisions. However, existing biomarkers are far from comprehensive and often lack specificity and new ones are being developed at a very slow rate. As described in this review, mass spectrometry (MS)-based proteomics has become a powerful technology in biological research and it is now poised to allow the characterization of the plasma proteome in great depth. Previous "triangular strategies" aimed at discovering single biomarker candidates in small cohorts, followed by classical immunoassays in much larger validation cohorts. We propose a "rectangular" plasma proteome profiling strategy, in which the proteome patterns of large cohorts are correlated with their phenotypes in health and disease. Translating such concepts into clinical practice will require restructuring several aspects of diagnostic decision-making, and we discuss some first steps in this direction.


title: Balanced civilization map generation based on Open Data

abstract: This work investigates how to incorporate real-world data into game content so that the content is playable and enjoyable while not misrepresenting the data. We propose a method for generating balanced Civilization maps based on Open Data, describing how to acquire, transform and integrate information from different sources into a single content. Furthermore, we evolve players' initial positions in order to obtain balanced maps, while trying to minimize information accuracy loss. In addition, this paper describes a tool to assist users in this process. Maps generated using these method and tool are playable and balanced yet faithful to the original sources.


title: Sentiment Analysis and Text Mining for Social Media Microblogs using Open Source Tools: An Empirical Study

abstract: Social media has arisen not only as a personal communication media, but also, as a media to communicate opinions about products and services or even political and general events among its users. Due to its widespread and popularity, there is a massive amount of user reviews or opinions produced and shared daily. Twitter is one of the most widely used social media micro blogging sites. Mining user opinions from social media data is not a straight forward task; it can be accomplished in different ways. In this work, an open source approach is presented, throughout which, twitter Microblogs data has been collected, pre-processed, analyzed and visualized using open source tools to perform text mining and sentiment analysis for analyzing user contributed online reviews about two giant retail stores in the UK namely Tesco and Asda stores over Christmas period 2014. Collecting customer opinions can be expensive and time consuming task using conventional methods such as surveys. The sentiment analysis of the customer opinions makes it easier for businesses to understand their competitive value in a changing market and to understand their customer views about their products and services, which also provide an insight into future marketing strategies and decision making policies.


title: Emergence of Control in a Large-Scale Society of Economic Physical Agents

abstract: We present a formal foundation for distributed large-scale control, unifying agent-oriented microeconomic market theory with the standard mathematical theory of control engineering.


title: Triple-Mode Dielectric Resonator Diplexer for Base-Station Applications

abstract: This paper proposes a novel diplexer based on triple-mode dielectric-loaded cylindrical cavities. Such two metal cavities are designed to achieve two different frequency bands, while three resonant modes of a single cavity are classified as a TM mode and a pair of hybrid (HE) degeneration modes. An off-centered dielectric resonator instead of a traditional corner cuts perturbation or screws perturbation properly perturbs the two degenerate modes in the same cavity. Extensive study is then conducted to design this proposed diplexer. Finally, a diplexer prototype is fabricated using a brass cavity and it is tested for experimental verification of the predicted results. Good agreement between measurement and simulation is achieved.


2 Toward An Efficient Fingerprint Classification


title: Biopsy interpretation of colonic biopsies when inflammatory bowel disease is excluded

abstract: The interpretation of colonic biopsies related to inflammatory conditions can be challenging because the colorectal mucosa has a limited repertoire of morphologic responses to various injurious agents. Only few processes have specific diagnostic features, and many of the various histological patterns reflect severity and duration of the disease. Importantly the correlation with endoscopic and clinical information is often cardinal to arrive at a specific diagnosis in many cases.


Unsupervised Semantic Role Labeling


title: Nerve growth factor promotes reorganization of the axonal microtubule array at sites of axon collateral branching

abstract: The localized debundling of the axonal microtubule array and the entry of microtubules into axonal filopodia are two defining features of collateral branching. We report that nerve growth factor (NGF), a branch-inducing signal, increases the frequency of microtubule debundling along the axon shaft of chicken embryonic sensory neurons. Sites of debundling correlate strongly with the localized targeting of microtubules into filopodia. Platinum replica electron microscopy suggests physical interactions between debundled microtubules and axonal actin filaments. However, as evidenced by depolymerization of actin filaments and inhibition of myosin II, actomyosin force generation does not promote debundling. In contrast, loss of actin filaments or inhibition of myosin II activity promotes debundling, indicating that axonal actomyosin forces suppress debundling. MAP1B is a microtubule associated protein that represses axon branching. Following treatment with NGF, microtubules penetrating filopodia during the early stages of branching exhibited lower levels of associated MAP1B. NGF increased and decreased the levels of MAP1B phosphorylated at a GSK-3β site (pMAP1B) along the axon shaft and within axonal filopodia, respectively. The levels of MAP1B and pMAP1B were not altered at sites of debundling, relative to the rest of the axon. Unlike the previously determined effects of NGF on the axonal actin cytoskeleton, the effects of NGF on microtubule debundling were not affected by inhibition of protein synthesis. Collectively, these data indicate that NGF promotes localized axonal microtubule debundling, that actomyosin forces antagonize microtubule debundling, and that NGF regulates pMAP1B in axonal filopodia during the early stages of collateral branch formation. © 2015 Wiley Periodicals, Inc. Develop Neurobiol 75: 1441–1461, 2015


title: A Brain-Computer Interface for classifying EEG correlates of chronic mental stress

abstract: In this paper, a Brain-Computer Interface (BCI) for classifying EEG correlates of chronic mental stress is proposed. Data from 8 EEG channels are collected from 26 healthy right-handed students during university examination period and after the examination whereby the former is considered to be relatively more stressful to students than the latter. The mental stress level are measured using the Perceived Stress Scale 14 (PSS-14) and categorized into stressed and stress-free groups. The proposed BCI is then used to classify the subjects' mental stress level on EEG features extracted using the Higuchi's fractal dimension of EEG, Gaussian mixtures of EEG spectrogram, and Magnitude Square Coherence Estimation (MSCE) between the EEG channels. Classification on the EEG features are then performed using the K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM). The performance of the proposed BCI is then evaluated from the inter-subject classification accuracy using leave-one-out validation. The results showed that the proposed BCI using features extracted by MSCE yielded a promising inter-subject validation accuracy of over 90% in classifying the EEG correlates of chronic mental stress.


title: Consistent on-line classification of dbs workload events

abstract: An important goal of self-managing databases is the autonomic adaptation of the database configuration to evolving workloads. However, the diversity of SQL statements in real-world workloads typically causes the required analysis overhead to be prohibitive for a continuous workload analysis. The workload classification presented in this paper reduces the workload analysis overhead by grouping similar workload events into classes. Our approach employs clustering techniques based upon a general distance function for DBS workload events. To be applicable for a continuous workload analysis, our workload classification specifically addresses a stream-based, lightweight operation, a controllable loss of quality, and self-management.


title: People as Sensors and Collective Sensing-Contextual Observations Complementing Geo-Sensor Network Measurements

abstract: Ubiquitous sensor networks and Location-based Services can potentially assist in taking decisions in near real time in a variety of application areas such as public safety, traffic management, environmental monitoring or in public health. Yet, analysing our surroundings in real time is still a major challenge due to sparsely available data sources for real-time monitoring. The innovative concept of People as Sensors defines a measurement model, in which measurements are not only taken by calibrated hardware sensors, but in which also humans can contribute their individual ‘measurements’ such as their subjective sensations, current perceptions or personal observations. This chapter contains a disambiguation between the terms People as Sensors (people contributing subjective observations), Collective Sensing (analysing aggregated anonymised data coming from collective networks) and Citizen Science (exploiting and elevating expertise of citizens and their personal, local experiences). Then, the particular significance of integrating the People as Sensors concept with established LBS, data analysis and visualisation systems is elaborated. Finally, the paper discusses current challenges, points out possible solutions, and pin-points directions for future research areas.


title: A Driver Behavior Recognition Method Based on a Driver Model Framework

abstract: A method for detecting drivers’ intentions is essential to facilitate operating mode transitions between driver and driver assistance systems. We propose a driver behavior recognition method using Hidden Markov Models (HMMs) to characterize and detect driving maneuvers and place it in the framework of a cognitive model of human behavior. HMM-based steering behavior models for emergency and normal lane changes as well as for lane keeping were developed using a moving base driving simulator. Analysis of these models after training and recognition tests showed that driver behavior modeling and recognition of different types of lane changes is possible using HMMs.


title: Structural slotted waveguide antennas for multirotor UAV radio altimeter

abstract: Z-slots are presumed to offer a structural advantage over conventional inclined slots for narrow wall slotted waveguide antennas, in that the corners of the rectangular waveguide are not cut and consequently will not weaken a structural tube as much. Initial structural simulations are shown to confirm this assumption. This slot configuration was also further investigated as radiating slots in standing wave type slotted waveguide antennas. Fan-beam antennas having 10 to 30 slots were successfully designed for 10GHz using a commercially available antenna simulator, having directivities from 16 to 21dBi and aperture efficiency of 60%.


title: Compressed sensing

abstract: We study the notion of compressed sensing (CS) as put forward by Donoho, Candes, Tao and others. The notion proposes a signal or image, unknown but supposed to be compressible by a known transform, (e.g. wavelet or Fourier), can be subjected to fewer measurements than the nominal number of data points, and yet be accurately reconstructed. The samples are nonadaptive and measure 'random' linear combinations of the transform coefficients. Approximate reconstruction is obtained by solving for the transform coefficients consistent with measured data and having the smallest possible l1 norm.We present initial 'proof-of-concept' examples in the favorable case where the vast majority of the transform coefficients are zero. We continue with a series of numerical experiments, for the setting of lp-sparsity, in which the object has all coefficients nonzero, but the coefficients obey an lp bound, for some p ∈ (0, 1]. The reconstruction errors obey the inequalities paralleling the theory, seemingly with well-behaved constants.We report that several workable families of 'random' linear combinations all behave equivalently, including random spherical, random signs, partial Fourier and partial Hadamard.We next consider how these ideas can be used to model problems in spectroscopy and image processing, and in synthetic examples see that the reconstructions from CS are often visually "noisy". To suppress this noise we postprocess using translation-invariant denoising, and find the visual appearance considerably improved.We also consider a multiscale deployment of compressed sensing, in which various scales are segregated and CS applied separately to each; this gives much better quality reconstructions than a literal deployment of the CS methodology.These results show that, when appropriately deployed in a favorable setting, the CS framework is able to save significantly over traditional sampling, and there are many useful extensions of the basic idea.


title: On the Use of 2-D Coding Techniques for ECG Signals

abstract: This paper discusses the convenience of using two-dimensional (2-D) coding techniques for the compression of electrocardiogram (ECG) signals. These signals present a very clear periodicity that can be exploited by the use of a 2-D time/frequency transform to decorrelate it as much as possible. A brief theoretical approach is given to justify the use of this technique, and a comparison is made between a 2-D and a one-dimensional (1-D) uniform quantization scenarios. The influence of the error as well as the frame size on the estimation of the fundamental period is studied


title: MobSafe: cloud computing based forensic analysis for massive mobile applications using data mining

abstract: With the explosive increase in mobile apps, more and more threats migrate from traditional PC client to mobile device. Compared with traditional Win+Intel alliance in PC, Android+ARM alliance dominates in Mobile Internet, the apps replace the PC client software as the major target of malicious usage. In this paper, to improve the security status of current mobile apps, we propose a methodology to evaluate mobile apps based on cloud computing platform and data mining. We also present a prototype system named MobSafe to identify the mobile app's virulence or benignancy. Compared with traditional method, such as permission pattern based method, MobSafe combines the dynamic and static analysis methods to comprehensively evaluate an Android app. In the implementation, we adopt Android Security Evaluation Framework (ASEF) and Static Android Analysis Framework (SAAF), the two representative dynamic and static analysis methods, to evaluate the Android apps and estimate the total time needed to evaluate all the apps stored in one mobile app market. Based on the real trace from a commercial mobile app market called AppChina, we can collect the statistics of the number of active Android apps, the average number apps installed in one Android device, and the expanding ratio of mobile apps. As mobile app market serves as the main line of defence against mobile malwares, our evaluation results show that it is practical to use cloud computing platform and data mining to verify all stored apps routinely to filter out malware apps from mobile app markets. As the future work, MobSafe can extensively use machine learning to conduct automotive forensic analysis of mobile apps based on the generated multifaceted data in this stage.


title: A COMPREHENSIVE REVIEW OF THE ENTERPRISE SYSTEMS RESEARCH

abstract: Enterprise systems (ES) can be considered as a novel phenomenon for the information system research and other academic fields (e.g. operations and supply chain), which has opened an immense potential and opportunities for research. Although the interest of the scholars on ES is recent, the number of publications is continuously growing since 2000. The aim of this paper is to review a sample of important contributions of the ES works published to date. To do this, the selected works have been classified in four key topics: business implications, technical issues, managerial issues, and implementation issues.


title: A Dilemma in Assessing Stability of Feature Selection Algorithms

abstract: In realm, feature selection is an effective means for handling high-dimensional data that becomes increasingly abundant. The stability of a feature selection algorithm is becoming crucial for determining the fitness of the algorithm. Below, we review existing methods of stability assessment and analyse how they assess the stability of a feature selection algorithm. A common approach is to evaluate the similarity between the selected subsets of features produced by that algorithm over different training samples or over distributed datasets. We point out challenges facing the existing evaluation methods and suggest how to improve stability assessment of feature selection algorithms.


Zero-Inflated Exponential Family Embeddings


title: Anew feature selectionmethod for Gaussianmixture clustering

abstract: With the wide applications of Gaussian mixture clustering, e.g., in semantic video classification [H. Luo, J. Fan, J. Xiao, X. Zhu, Semantic principal video shot classification via mixture Gaussian, in: Proceedings of the 2003 International Conference on Multimedia and Expo, vol. 2, 2003, pp. 189-192], it is a nontrivial task to select the useful features in Gaussian mixture clustering without class labels. This paper, therefore, proposes a new feature selection method, through which not only the most relevant features are identified, but the redundant features are also eliminated so that the smallest relevant feature subset can be found. We integrate this method with our recently proposed Gaussian mixture clustering approach, namely rival penalized expectation-maximization (RPEM) algorithm [Y.M. Cheung, A rival penalized EM algorithm towards maximizing weighted likelihood for density mixture clustering with automatic model selection, in: Proceedings of the 17th International Conference on Pattern Recognition, 2004, pp. 633-636; Y.M. Cheung, Maximum weighted likelihood via rival penalized EM for density mixture clustering with automatic model selection, IEEE Trans. Knowl. Data Eng. 17(6) (2005) 750-761], which is able to determine the number of components (i.e., the model order selection) in a Gaussian mixture automatically. Subsequently, the data clustering, model selection, and the feature selection are all performed in a single learning process. Experimental results have shown the efficacy of the proposed approach.


title: A Data-driven Method for the Detection of Close Submitters in Online Learning Environments

abstract: Online learning has become very popular over the last decade. However, there are still many details that remain unknown about the strategies that students follow while studying online. In this study, we focus on the direction of detecting 'invisible' collaboration ties between students in online learning environments. Specifically, the paper presents a method developed to detect student ties based on temporal proximity of their assignment submissions. The paper reports on findings of a study that made use of the proposed method to investigate the presence of close submitters in two different massive open online courses. The results show that most of the students (i.e., student user accounts) were grouped as couples, though some bigger communities were also detected. The study also compared the population detected by the algorithm with the rest of user accounts and found that close submitters needed a statistically significant lower amount of activity with the platform to achieve a certificate of completion in a MOOC. These results confirm that the detected close submitters were performing some collaboration or even engaged in unethical behaviors, which facilitates their way into a certificate. However, more work is required in the future to specify various strategies adopted by close submitters and possible associations between the user accounts.


Identification of Dynamic Parameters of an Industrial Manipulator


Rogue’s Gallery – South African university website visibility


Blockchain Implementation Quality Challenges: A Literature Review


title: A low-power robust GFSK demodulation technique for WBAN applications

abstract: This paper presents a low-power Gaussian frequency shift keying (GFSK) demodulation technique for wireless body area network (WBAN) applications. The demodulator is composed of a digital pulse generator (DPG), a low-pass filter (LPF), a band-pass filter (BPF), and a hysteresis comparator. The robustness under frequency deviations is realized by a DPG, which outputs an optimized pulse width insensitive to variations of process, voltage, and temperature (PVT). The demodulator is designed with 0.18 μm CMOS technology, and the input testing GFSK signal is 200 kHz, with a data rate of 100 kb/s and a modulation index of 1.0. Simulation results show that the minimum input signal-to-noise ratio (SNR) is 18 dB for 0.1% bit error rate (BER), which falls in the same level with state-of-the-art. Nevertheless, the proposed demodulator can tolerate -15%~+10% frequency deviation, and consumes only 0.52 mA under 1.8 V supply.


title: Postmortem wounds caused by cookie-cutter sharks (Isistius species): an autopsy case of a drowning victim

abstract: A woman in her late 50s was found deceased and floating in an ocean bay in a temperate region (N31 3304800, E130 3305500) surrounded by wave dissipation blocks. The woman’s appearance was orderly, except for her sweater, which was turned up to her face. Her jacket, shoes, and bag were found on a nearby block. The police investigation ascertained that she suffered from depression and had previously contemplated suicide. No suicide note or farewell letters were found. She was last seen alive, by relatives, approximately 6 days earlier. Medico-legal autopsy revealed several antemortem abrasions and bruises on the head, one of which was accompanied by mild subarachnoid hemorrhages without cerebral contusions on the right parietal lobe. In addition, the victim showed the typical findings of drowning such as ballooning of the lungs (weight: left, 310 g; right, 380 g) with impressions created by the ribs and so-called ‘‘Paltauf spots’’ on the pleural surface, bilateral pleural effusions (left, 160 ml; right, 170 ml), and froth in the airway, despite moderately advanced putrefaction. In the diatom test, marine planktons were detected not only in the digested lungs, but also in the spleen and left kidney. Toxicological investigations were negative. From all of these findings, the cause of death was determined to be drowning. However, the most notable finding was that circular and elliptical wounds, ranging from soybean-size to 3.7 9 5.1 cm, were found on the exposed (undressed) parts of the body such as the anterior cervical region (Fig. 1) and bilateral antebrachial regions. The edges of these wounds were relatively sharp and were not associated with clear subcutaneous hemorrhages. Their spoon like concave bottom reached the fatty tissue or superficial layer of the muscle.


title: Social media and international advertising: theoretical challenges and future directions

abstract: Purpose – The primary aim of this article is to identify theoretical foundations that can be used in research on social media in the context of international advertising research. An additional objective is to identify future research directions for theory building in this research area.Design/methodology/approach – The article draws on the extant literature to identify three key theoretical foundations that can be used in research on advertising in media from an advertising perspective: networking capability; image transferability; and personal extensibility. For each of these perspectives, the current state of knowledge, theoretical challenges, and future research directions are summarized.Findings – The three key theoretical perspectives (networking capability, image transferability, and personal extensibility) provide strong potential for better understanding the advantages and disadvantages of social media use for advertisers. They are also useful for identifying important research gaps that need to ...


title: ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data

abstract: There are many different ways in which change point analysis can be performed, from purely parametric methods to those that are distribution free. The ecp package is designed to perform multiple change point analysis while making as few assumptions as possible. While many other change point methods are applicable only for univariate data, this R package is suitable for both univariate and multivariate observations. Hierarchical estimation can be based upon either a divisive or agglomerative algorithm. Divisive estimation sequentially identifies change points via a bisection algorithm. The agglomerative algorithm estimates change point locations by determining an optimal segmentation. Both approaches are able to detect any type of distributional change within the data. This provides an advantage over many existing change point algorithms which are only able to detect changes within the marginal distributions.


title: Sorting camera trap images

abstract: Camera trapping is used by conservation biologists to study snow leopards. In this research, we introduce techniques that sort camera trap images into sets with snow leopards and those without. We use Robust Principal Component Analysis, thresholding, and binary morphology to create motion templates. Cascade Object Detector finds spots in the camera trap images. The number of spots that overlap with the motion template and their density are among the features input to the Support Vector Machine classifier that sorts the images. Our results are very promising: we achieve an average classification accuracy of 93.74%.


title: The Subgoal Learning Model: Creating Better Examples So That Students Can Solve Novel Problems

abstract: Learners have great difficulty solving problems requiring changes to solutions demonstrated in examples. However, if the solution procedures learners form are organized by subgoals, then they are more successful. Subgoal learning is hypothesized to be aided by cues in example solutions that indicate that certain steps go together. These cues may induce a learner to attempt to self-explain the purpose of the steps, resulting in the formation of a subgoal. Across 4 experiments it was found that a label for a group of steps in examples helped participants form subgoals as assessed by measures such as problem-solving performance and talk aloud protocols. Abstract labels were more likely than superficial labels to lead participants to form subgoals with fewer ties to surface features. Subgoals guide problem solving by helping learners focus on the steps to modify in novel problems that involve the same subgoals but require new steps to achieve them.


title: Model-Based Recognition and Extraction of Information from Chart Images

abstract: Charts are widely used in technical and business documents as a graphical representation of numerical and qualitative data. We present a model-based method to automatically extract data carried by charts and convert them to XML format, thus making these data available for indexing, querying and analysis by common methods of textual data management. The proposed method includes several steps: 1) chart detection, 2) model-based classification by chart type and extraction of graphical components, 3) detection and recognition of textual components, 4) extraction of semantic relations between graphics and text. For testing purpose, a benchmark set was created with the XML/SWF Chart tool. By comparing the recovered data and the original data used for chart generation, we are able to evaluate our information extraction algorithm and confirm its validity. We also extensively tested each step of the proposed algorithm against existing approaches. Model- based classification showed high accuracy, comparable to those of the best supervised learning methods. The proposed text detection algorithm leads to significant improvement in text recognition rate (up to 20 times better).


title: Fully automated classification of mammograms using deep residual neural networks

abstract: In this paper, we propose a multi-view deep residual neural network (mResNet) for the fully automated classification of mammograms as either malignant or normal/benign. Specifically, our mResNet approach consists of an ensemble of deep residual networks (ResNet), which have six input images, including the unregistered craniocaudal (CC) and mediolateral oblique (MLO) mammogram views as well as the automatically produced binary segmentation maps of the masses and micro-calcifications in each view. We then form the mResNet by concatenating the outputs of each ResNet at the second to last layer, followed by a final, fully connected, layer. The resulting mResNet is trained in an end-to-end fashion to produce a case-based mammogram classifier that has the potential to be used in breast screening programs. We empirically show on the publicly available INbreast dataset, that the proposed mResNet classifies mammograms into malignant or normal/benign with an AUC of 0.8.


title: When Mobile Blockchain Meets Edge Computing: Challenges and Applications

abstract: Blockchain, as the backbone technology of the current popular Bitcoin digital currency, has become a promising decentralized approach for resource and transaction management. Although blockchain has been widely adopted in many applications, e.g., finance, healthcare, and logistics, its application in mobile environments is still limited. This is due to the fact that blockchain users need to solve preset proof-of-work puzzles to add new transactions to the blockchain. Solving the proof-of-work, however, consumes substantial resources in terms of CPU time and energy, which is not suitable for resource-limited mobile devices. To facilitate blockchain applications in future mobile Internet of Things systems, multiple access mobile edge computing appears to be an auspicious option to solve the proof-of-work puzzles for mobile users. We first introduce a novel concept of edge computing for mobile blockchain. Then, we introduce an economic approach for edge computing resource management. Moreover, a demonstrative prototype of mobile edge computing enabled blockchain systems is presented with experimental results to justify the proposed concept.


title: Evolutionary Reinforcement Learning

abstract: Deep Reinforcement Learning (DRL) algorithms have been successfully applied to a range of challenging control tasks. However, these methods typically suffer from three core difficulties: temporal credit assignment with sparse rewards, lack of effective exploration, and brittle convergence properties that are extremely sensitive to hyperparameters. Collectively, these challenges severely limit the applicability of these approaches to real world problems. Evolutionary Algorithms (EAs), a class of black box optimization techniques inspired by natural evolution, are well suited to address each of these three challenges. However, EAs typically suffer with high sample complexity and struggle to solve problems that require optimization of a large number of parameters. In this paper, we introduce Evolutionary Reinforcement Learning (ERL), a hybrid algorithm that leverages the population of an EA to provide diversified data to train an RL agent, and reinserts the RL agent into the EA population periodically to inject gradient information into the EA. ERL inherits EA's ability of temporal credit assignment with a fitness metric, effective exploration with a diverse set of policies, and stability of a population-based approach and complements it with off-policy DRL's ability to leverage gradients for higher sample efficiency and faster learning. Experiments in a range of challenging continuous control benchmark tasks demonstrate that ERL significantly outperforms prior DRL and EA methods, achieving state-of-the-art performances.


title: D-PCN: Parallel Convolutional Neural Networks for Image Recognition in Reverse Adversarial Style

abstract: In this paper, a recognition framework named D-PCN using a discriminator is proposed, which can intensify the feature extracting ability of convolutional neural networks. The framework contains two parallel convolutional neural networks, and a discriminator, which is introduced from the Generative Adversarial Nets and can improve the performance of parallel networks. The two nets are devised side by side, and the discriminator takes in the features from parallel networks as input, aiming to guide the two nets to learn features of different details in a reverse adversarial style. After that, the feature maps from two nets get aggregated, then an extra overall classifier is added and will output the final prediction employing the fused features. The training strategy of the D-PCN is also introduced which ensures the utilization of the discriminator. We experiment the D-PCN with several CNN models including NIN, ResNet, ResNeXt and DenseNet using single NVIDIA TITAN Xp, on the two benchmark datasets: CIFAR-100 and downsampled ImageNet-1k, the D-PCN enhances all models on CIFAR-100 and also reinforces the performance of ResNet on downsampled ImageNet-1k explicitly. In particular, it yields state-of-the-art classification performance on CIFAR-100 with compared to relative works.


title: Simulation and Visualization of Chaotic Systems

abstract: The objective of this paper is to present a suite of applications that allow the simulation and study of chaotic systems, as well as the estimation of the most important properties associated with them. These applications implement fundamental algorithms from the field of chaotic system dynamics, such as the reconstruction of the system trajectory in the appropriate embedding space, and the estimation of the Lyapunov exponents and the fractal dimension. Furthermore, they provide additional features such as the study of bifurcation diagrams and the detection of chaotic regions in the parameter space. The current version of the applications has been developed in the programming framework of Visual C++ 6.0 and they can be used under the operating system of Microsoft Windows.


title: Probase+: Inferring Missing Links in Conceptual Taxonomies

abstract: Much work has focused on automatically constructing conceptual taxonomies or semantic networks from large text corpora. In this paper, we use a state-of-the-art data-driven conceptual taxonomy, Probase, to show that missing links in taxonomies are the chief problem that hinders their adoption by many real life applications, for the missing links break the inferencing that the conceptual taxonomy claims to support. To solve this problem, we devise a collaborative filtering framework to infer missing links in taxonomies derived from text corpora. We implement our method mainly on Probase, creating a denser taxonomy containing 5.1 million (about 30 percent) more isA relationships, with an accuracy of above 90 percent. We conduct comprehensive experiments to demonstrate the quality of the revised conceptual taxonomies.


title: Rethinking the Data Center Networking: Architecture, Network Protocols, and Resource Sharing

abstract: Large-scale data centers enable the new era of cloud computing and provide the core infrastructure to meet the computing and storage requirements for both enterprise information technology needs and cloud-based services. To support the ever-growing cloud computing needs, the number of servers in today’s data centers are increasing exponentially, which in turn leads to enormous challenges in designing an efficient and cost-effective data center network. With data availability and security at stake, the issues with data center networks are more critical than ever. Motivated by these challenges and critical issues, many novel and creative research works have been proposed in recent years. In this paper, we investigate in data center networks and provide a general overview and analysis of the literature covering various research areas, including data center network interconnection architectures, network protocols for data center networks, and network resource sharing in multitenant cloud data centers. We start with an overview on data center networks and together with its requirements navigate the data center network designs. We then present the research literature related to the aforementioned research topics in the subsequent sections. Finally, we draw the conclusions.


title: An Investigation of the Impact of an Intervention to Reduce Academic Procrastination Using Short Message Service (SMS) Technology.

abstract: This mixed-method pilot study investigated the impact of a custom Short Message Service (SMS) reminder system developed to help students reduce procrastination and increase performance on weekly content-related quizzes in a high-enrollment hybrid online course. Text message reminders were sent to three students with high procrastination and low performance levels on a schedule based on free-operant avoidance principles, where messages would be terminated upon completion of the weekly quiz. The results suggest that there was sufficient evidence that the system had a positive effect on procrastination levels, but less evidence for an effect on performance. Subsequent interviews with the participants confirmed the utility and potential of the system, and revealed areas for improvement in the implementation of the SMS reminder system as well as an understanding of the students' response to the intervention. The phenomenon of procrastination is widespread in society, but it is particularly prevalent and potentially detrimental in the academic setting. It has been defined on several occasions (Lay, 1986; Milgram, Mey-Tal, & Levison, 1998; Solomon & Rothblum, 1984), but Schraw, Wadkins, and Olafson (2007) provided a useful working definition that captured the essence of the phenomenon. They defined procrastination as "intentionally deferring or delaying work that must be completed" (2007, p. 13). Within an academic institution, Steel (2007)


title: Towards Automated Related Work Summarization

abstract: We introduce the novel problem of automatic related work summarization. Given multiple articles (e.g., conference/journal papers) as input, a related work summarization system creates a topic-biased summary of related work specific to the target paper. Our prototype Related Work Summarization system, ReWoS, takes in set of keywords arranged in a hierarchical fashion that describes a target paper's topics, to drive the creation of an extractive summary using two different strategies for locating appropriate sentences for general topics as well as detailed ones. Our initial results show an improvement over generic multi-document summarization baselines in a human evaluation.


title: Rise of Mini-Drones: Applications and Issues

abstract: Miniature (mini) drones are enjoying increasing attention. They have a broad market and applications. However, a powerful technology often has two ethical sides. Miniature drones can be abused, rendering security and privacy concerns. The contribution of this paper is two-fold. First, we will perform a survey of mini-drones on market and compare their specifications such as flight time, maximum payload weight, and price, and regulations and issues of operating mini-drones. Second, we propose novel aerial localization strategies and compare six different localization strategies for a thorough study of aerial localization by a single drone.


title: Feature Weighting for Improved . . .

abstract: There are often discrepancies between the learning sample and the evaluation environment, be it natural or adversarial. It is therefore desirable that classifiers are robust, i.e., not very sensitive to changes in data distribution. In this paper, we introduce a new methodology to measure the lower bound of classifier robustness under adversarial attack and show that simple averaged classifiers can improve classifier robustness significantly. In addition, we propose a new feature reweighting technique that ameliorates the performance and robustness of standard classifiers at at most twice the computational cost. We verify our claims in content based email spam classification experiments on some public and private datasets.


title: Analysis of Extendible Hashing

abstract: Extendible hashing is an attractive direct-access technique which has been introduced recently. It is characterized by a combination of database-size flexibility and fast direct access. This paper derives performance measures for extendible hashing, and considers their implecations on the physical database design. A complete characterization of the probability distribution of the directory size and depth is derived, and its implications on the design of the directory are studied. The expected input/output costs of various operations are derived, and the effects of varying physical design parameters on the expected average operating cost and on the expected volume are studied.


title: Sentiment after Translation: A Case-Study on Arabic Social Media Posts

abstract: When text is translated from one language into another, sentiment is preserved to varying degrees. In this paper, we use Arabic social media posts as stand-in for source language text, and determine loss in sentiment predictability when they are translated into English, manually and automatically. As benchmarks, we use manually and automatically determined sentiment labels of the Arabic texts. We show that sentiment analysis of English translations of Arabic texts produces competitive results, w.r.t. Arabic sentiment analysis. We discover that even though translation significantly reduces the human ability to recover sentiment, automatic sentiment systems are still able to capture sentiment information from the translations.


title: Strengths and Weaknesses of Quantum Computing

abstract: Recently a great deal of attention has focused on quantum computation following a sequence of results suggesting that quantum computers are more powerful than classical probabilistic computers. Following Shor's result that factoring and the extraction of discrete logarithms are both solvable in quantum polynomial time, it is natural to ask whether all of NP can be efficiently solved in quantum polynomial time. In this paper, we address this question by proving that relative to an oracle chosen uniformly at random, with probability 1, the class NP cannot be solved on a quantum Turing machine in time $o(2^{n/2})$. We also show that relative to a permutation oracle chosen uniformly at random, with probability 1, the class $NP \cap coNP$ cannot be solved on a quantum Turing machine in time $o(2^{n/3})$. The former bound is tight since recent work of Grover shows how to accept the class NP relative to any oracle on a quantum computer in time $O(2^{n/2})$.


title: Encoding Syntactic Knowledge in Neural Networks for Sentiment Classification

abstract: Phrase/Sentence representation is one of the most important problems in natural language processing. Many neural network models such as Convolutional Neural Network (CNN), Recursive Neural Network (RNN), and Long Short-Term Memory (LSTM) have been proposed to learn representations of phrase/sentence, however, rich syntactic knowledge has not been fully explored when composing a longer text from its shorter constituent words. In most traditional models, only word embeddings are utilized to compose phrase/sentence representations, while the syntactic information of words is yet to be explored. In this article, we discover that encoding syntactic knowledge (part-of-speech tag) in neural networks can enhance sentence/phrase representation. Specifically, we propose to learn tag-specific composition functions and tag embeddings in recursive neural networks, and propose to utilize POS tags to control the gates of tree-structured LSTM networks. We evaluate these models on two benchmark datasets for sentiment classification, and demonstrate that improvements can be obtained with such syntactic knowledge encoded.


title: Sonography of the pediatric gastrointestinal system.

abstract: Sonography is a commonly used modality for the investigation of abdominal symptoms in the pediatric population. It is a highly sensitive, readily available imaging modality that does not require ionizing radiation, iodinated contrast material, or anesthesia and can be performed at the bedside if necessary. Abdominal ultrasound is therefore often the first examination performed. This article presents an overview of the ultrasound characteristics of some of the most frequently encountered pathologies as well as some more rarely encountered entities. Our aim was to present a series of characteristic images of a wide gamut of pediatric abdominal conditions. The goal was to familiarize the reader with key sonographic features of both congenital and acquired gastrointestinal pathologies in children, making them more easily recognizable.


abstract: Wireless sensor network is a self configured network being composed of a large number of sensors. Due to the fact that sensors in the wireless sensor network are powered with battery and it is difficult to replace and/or recharge their batteries, energy efficient routing is the major concern in the field of wireless sensor network to enhance the lifetime of the network. Consequently, Numbers of routing techniques have been proposed for wireless sensor network to make longer life time and low energy consumption. Mainly these are sorted into three categories such as Flat and data centric routing, Hierarchical routing, Location based routing. This paper presents a review of some major work in area of flat and data centric routing technique and hierarchical routing technique for WSNs. This article also compares the characteristics and performance issues of different routing protocols. General Terms Routing Techniques in Wireless Sensor Networks


title: Food volume computation for self dietary assessment applications

abstract: There is great demand for easily-accessible, user-friendly dietary self-management applications. Yet accurate, fully-automatic estimation of nutritional intake using computer vision methods remains an open research problem. One key element of this problem is the volume estimation, which can be computed from 3D models obtained using multi-view geometry. The paper presents a computational system for volume estimation based on the processing of two meal images. A 3D model of the served meal is reconstructed using the acquired images and the volume is computed from the shape. The algorithm was tested on food models (dummy foods) with known volume and on real served food. Volume accuracy was in the order of 90 %, while the total execution time was below 15 seconds per image pair. The proposed system combines simple and computational affordable methods for 3D reconstruction, remained stable throughout the experiments, operates in near real time, and places minimum constraints on users.


title: Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks

abstract: We introduce a new scene graph generation method called image-level attentional context modeling (ILAC). Our model includes an attentional graph network that effectively propagates contextual information across the graph using image-level features. Whereas previous works use an object-centric context, we build an image-level context agent to encode the scene properties. The proposed method comprises a single-stream network that iteratively refines the scene graph with a nested graph neural network. We demonstrate that our approach achieves competitive performance with the state-of-the-art for scene graph generation on the Visual Genome dataset, while requiring fewer parameters than other methods. We also show that ILAC can improve regular object detectors by incorporating relational image-level information.


title: Convolutional Simplex Projection Network (CSPN) for Weakly Supervised Semantic Segmentation

abstract: Weakly supervised semantic segmentation has been a subject of increased interest due to the scarcity of fully annotated images. We introduce a new approach for solving weakly supervised semantic segmentation with deep Convolutional Neural Networks (CNNs). The method introduces a novel layer which applies simplex projection on the output of a neural network using area constraints of class objects. The proposed method is general and can be seamlessly integrated into any CNN architecture. Moreover, the projection layer allows strongly supervised models to be adapted to weakly supervised models effortlessly by substituting ground truth labels. Our experiments have shown that applying such an operation on the output of a CNN improves the accuracy of semantic segmentation in a weakly supervised setting with image-level labels.


title: A Model and Architecture for Building a Sustainable National Open Government Data (OGD) Portal

abstract: Open Government Data (OGD), an extension of e-Government, so claimed, has become a major factor in the United Nations e-Government Development Index (EGDI) and to achieving the global sustainable development goals (SDGs). This is based on the argument that opening up government data has tremendous social, political and economic benefits. However, if data is open in a format that is not useful to data prospects, it is as good as data that is not open at all. Most governments around the world, especially in the developing countries, have concentrated their efforts around transparency in governance as a major reason for open data. They have only succeeded in opening and publishing data on government Web/open data Portals in unstructured formats. This has prevented innovative use and reuse of data by developers/entrepreneurs and the public for good governance, economic, environmental and social development. Also, access to quality and accurate data as well as how to address privacy issues are critical to achieving the promises of OGD. In addition, the institutional politics around data ownership among government agencies and their readiness to open actual data are critical to national OGD portal. This paper develops an integrated Open Government Data model & architecture based on literature reviews of secondary data to address issues around data format, quality, accuracy and ownership. While addressing these challenges, the goal of the architecture is to guide governments in building a sustainable OGD portal with linked data as the output and the highest level of open data format. The connection of related data makes data effectively reusable to create public value.


Modeling of Interior Permanent Magnet Synchronous Motor using Transient Simulation Techniques


title: Biofertilizers function as key player in sustainable agriculture by improving soil fertility, plant tolerance and crop productivity

abstract: Current soil management strategies are mainly dependent on inorganic chemical-based fertilizers, which caused a serious threat to human health and environment. The exploitation of beneficial microbes as a biofertilizer has become paramount importance in agriculture sector for their potential role in food safety and sustainable crop production. The eco-friendly approaches inspire a wide range of application of plant growth promoting rhizobacteria (PGPRs), endo- and ectomycorrhizal fungi, cyanobacteria and many other useful microscopic organisms led to improved nutrient uptake, plant growth and plant tolerance to abiotic and biotic stress. The present review highlighted biofertilizers mediated crops functional traits such as plant growth and productivity, nutrient profile, plant defense and protection with special emphasis to its function to trigger various growth- and defense-related genes in signaling network of cellular pathways to cause cellular response and thereby crop improvement. The knowledge gained from the literature appraised herein will help us to understand the physiological bases of biofertlizers towards sustainable agriculture in reducing problems associated with the use of chemicals fertilizers.


title: Transforming Ontology Representation from OWL to Relational Database

abstract: The current work has arisen with respect to the growing importance of ontology modelling in Informa- tion Systems development. Due to emerging technologies of Semantic Web, it is desirable to use for this purpose the Web Ontology Language OWL. From the other side, the relational database technology has ensured the best facilities for storing, updating and manipulating the information of problem domain. The algorithms for transformation of domain ontology, described in OWL, to relational database are proposed. The methodology is illustrated with an example.


title: Applying Cloud Computing Technology to BIM Visualization and Manipulation

abstract: A current trend in the construction sector is the application of Building Information Modeling (BIM) for the efficient integration and management of engineering information. Today, engineering consultant, construction and architectural companies conduct research into the development of BIM in order to integrate and visualize engineering information. The capabilities of BIM for information integration, visualization and parametric design, reduce duplication of work and interface integration complexity, significantly saving both time and cost. At present, BIM desktop applications such as Autodesk Architecture, Bentley Architecture and Graphsoft ArchiCAD, can only integrate and visualize information on-site. Difficulties remain in obtaining and updating information from other sites, which restricts communication and information distribution between different sites. As visualization efficiencies and hardware capabilities mature and continue to improve, the development framework of applications is now moving form “Client-Server” to “Host-based”, which is referred to as “Cloud Computing” technology. In this research, we utilize the concept of Software as a Service (SaaS) and Cloud Computing in order to develop a visual system for BIM visualization and manipulation. This system can not only visualize three-dimensional (3D) BIM models, it can also manipulate 3D BIM models through the web without the limitations of time or distance. This system can facilitate communication and distribution of information between related participants, such as construction companies, building owners and architectural companies in order to manage projects effectively and efficiently.


title: Evaluation of a threshold-based tri-axial accelerometer

abstract: Using simulated falls performed under supervised conditions and activities of daily living (ADL) performed by elderly subjects, the ability to discriminate between falls and ADL was investigated using tri-axial accelerometer sensors, mounted on the trunk and thigh. Data analysis was performed using MATLAB to determine the peak accelerations recorded during eight different types of falls. These included; forward falls, backward falls and lateral falls left and right, performed with legs straight and flexed. Falls detection algorithms were devised using thresholding techniques. Falls could be distinguished from ADL for a total data set from 480 movements. This was accomplished using a single threshold determined by the fall-event data-set, applied to the resultant-magnitude acceleration signal from a tri-axial accelerometer located at the trunk.


title: Batteries and battery management systems for electric vehicles

abstract: The battery is a fundamental component of electric vehicles, which represent a step forward towards sustainable mobility. Lithium chemistry is now acknowledged as the technology of choice for energy storage in electric vehicles. However, several research points are still open. They include the best choice of the cell materials and the development of electronic circuits and algorithms for a more effective battery utilization. This paper initially reviews the most interesting modeling approaches for predicting the battery performance and discusses the demanding requirements and standards that apply to ICs and systems for battery management. Then, a general and flexible architecture for battery management implementation and the main techniques for state-of-charge estimation and charge balancing are reported. Finally, we describe the design and implementation of an innovative BMS, which incorporates an almost fully-integrated active charge equalizer.


TwiSty: a multilingual Twitter Stylometry corpus for gender and personality profiling


title: Explosive-strength training improves 5-km running time by improving running economy and muscle power

abstract: To investigate the effects of simultaneous explosive-strength and endurance training on physical performance characteristics, 10 experimental (E) and 8 control (C) endurance athletes trained for 9 wk. The total training volume was kept the same in both groups, but 32% of training in E and 3% in C was replaced by explosive-type strength training. A 5-km time trial (5K), running economy (RE), maximal 20-m speed (V20 m), and 5-jump (5J) tests were measured on a track. Maximal anaerobic (MART) and aerobic treadmill running tests were used to determine maximal velocity in the MART (VMART) and maximal oxygen uptake (VO2 max). The 5K time, RE, and VMART improved (P < 0.05) in E, but no changes were observed in C. V20 m and 5J increased in E (P < 0.01) and decreased in C (P < 0.05). VO2 max increased in C (P < 0.05), but no changes were observed in E. In the pooled data, the changes in the 5K velocity during 9 wk of training correlated (P < 0.05) with the changes in RE [O2 uptake (r = -0.54)] and VMART (r = 0.55). In conclusion, the present simultaneous explosive-strength and endurance training improved the 5K time in well-trained endurance athletes without changes in their VO2 max. This improvement was due to improved neuromuscular characteristics that were transferred into improved VMART and running economy.


title: Addressing Behavioral Uncertainty in Security Games: An Efficient Robust Strategic Solution for Defender Patrols

abstract: Stackelberg Security Games (SSG) have been widely applied for solving real-world security problems -- with a significant research emphasis on modeling attackers' behaviors to handle their bounded rationality. However, access to real-world data (used for learning an accurate behavioral model) is often limited, leading to uncertainty in attacker's behaviors while modeling. This paper therefore focuses on addressing behavioral uncertainty in SSG with the following main contributions: 1) we present a new uncertainty game model that integrates uncertainty intervals into a behavioral model to capture behavioral uncertainty, and 2) based on this game model, we propose a novel robust algorithm that approximately computes the defender's optimal strategy in the worst-case scenario of uncertainty. We show that our algorithm guarantees an additive bound on its solution quality.


title: Tone Recognition in Mandarin using Focus

abstract: Native speakers of Mandarin produce and perceive tones in ways that depend on the focus with which each word is produced [1]. This paper gives one approach to improving tone recognition algorithms using focus, by training different support vector machines on syllables conditional on their position with respect to the focused word in a sentence. In a four-way tone classification task on focus-labelled laboratory Mandarin speech data collected by Xu [2], error rates improve from 15.2% without using focus to 8.7% when using focus. Using the fact that tones on syllables in focused words are especially easy to recognize, we propose a tone recognition algorithm that makes use of focus without requiring focus labels in either training or test set. The algorithm has an error rate of 9.8% on this data set.


title: Ameloblastic carcinoma of the mandible with metastasis to the skull and lung: advanced imaging appearance including computed tomography, magnetic resonance imaging and positron emission tomography computed tomography

abstract: Ameloblastic carcinoma is a very rare malignant odontogenic tumour with characteristic histopathological and clinical features, which requires aggressive surgical treatment and surveillance and, therefore, differs from ameloblastoma. Metastasis typically occurs in the lung. Only one patient with metastasis to the skull has previously been described and no prior case reports have presented MRI and positron emission tomography-CT (PET-CT) imaging findings. We describe a case of ameloblastic carcinoma with metastasis to the skull and lung with emphasis on imaging features including MRI and PET-CT.


title: A new compact folded patch GPS antenna using meander line

abstract: A novel miniature folded square patch antenna is proposed and developed for the Global Positioning System (GPS). By folding four different lengths of meander strips connected to the four edges of the square patch, the single coaxial-feed square patch antenna is a circularly polarized antenna. Proper positioning of the coaxial-feed on the square patch excites two orthogonal resonant modes with 900 phase difference, and the pure circular polarization is achieved. The prototype had been designed, fabricated and found to have an impedance bandwidth of 2.1% and a 3-dB axial-ratio (AR) bandwidth of about 0.76% at GPS frequency of 1575 MHz.


title: State-of-Charge Estimation with Open-Circuit-Voltage for Lead-Acid Batteries

abstract: A state-of-charge (SOC) estimation method is proposed based on the relationship between the state-of-discharge (SOD) and the dynamically changed open-circuit-voltage. The variation of the open-circuit-voltage is observed experimentally on the lead-acid batteries and, by considering the open-circuit time and the previous discharging current, an estimation equation based on the equivalent circuit of the battery is derived. With an accurate SOD estimation, a precise prediction of the SOC is achievable. The estimation accuracy is verified by coulomb counting on experiments with intermittent discharging test.


title: Dual polarized antenna decoupling for 60 GHz planar massive MIMO

abstract: Millimeter-wave (mm-Wave) Massive Multiple Input Multiple Output (MIMO) at 60 GHz is a candidate of advanced wireless communications for indoor environment. Mm-Wave massive MIMO antenna with lower frequency with sectoring technique has been proposed in previous research. Also dual polarized configuration for MIMO in lower frequency and lower number of antenna. Massive MIMO at 60 GHz mm-Wave is not evaluated yet in these previous research. In this paper, dual polarized mm-Wave massive MIMO has been proposed. Antennas are modeled in circular disk proximity coupled planar array. Bandwidth of dual polarized configuration is 4.349 GHz, wider than 4.303 GHz in single polarized configuration. Envelope Correlation Coefficient (ECC) of dual polarized configuration is 25 times lower compared to single polarized configuration. Dual polarized configuration shows better performance both in bandwidth and ECC compared to single polarized configuration.


title: In-Band Device-to-Device Communication in OFDMA Cellular Networks: A Survey and Challenges

abstract: Direct communication between two or more devices without the intervention of a base station, known as device-to-device (D2D) communication, is a promising way to improve performance of cellular networks in terms of spectral and energy efficiency. The D2D communication paradigm has been largely exploited in non-cellular technologies such as Bluetooth or Wi-Fi but it has not yet been fully incorporated into existing cellular networks. In this regard, a new proposal focusing on the integration of D2D communication into LTE-A has been recently approved by the 3GPP standardization community as discussed in this paper. In cellular networks, D2D communication introduces several critical issues, such as interference management and decisions on whether devices should communicate directly or not. In this survey, we provide a thorough overview of the state of the art focusing on D2D communication, especially within 3GPP LTE/LTE-A. First, we provide in-depth classification of papers looking at D2D from several perspectives. Then, papers addressing all major problems and areas related to D2D are presented and approaches proposed in the papers are compared according to selected criteria. On the basis of the surveyed papers, we highlight areas not satisfactorily addressed so far and outline major challenges for future work regarding efficient integration of D2D in cellular networks.


Summary of Mandelic Acid for the Improvement of Skin Conditions


title: A modular, open-source 3D printed underactuated hand

abstract: Commercially available robotic hands are often expensive, customized for specific platforms, and difficult to modify. In this paper, we present the design of an open-source, low-cost, single actuator underactuated hand that can be created through fast and commonly-accessible rapid-prototyping techniques and simple, off-the-shelf components. This project establishes the design of an adaptive, four-finger hand utilizing simple 3D-printed components, compliant flexure joints, and readily obtainable off-the-shelf parts. Modular and adjustable finger designs are provided, giving the user a range of options depending on the intended use of the hand. The design tradeoffs and decisions made to achieve the 3D-printable, compact and lightweight robotic gripper are discussed, as well as a preliminary discussion of the performance differences between the finger designs. The authors intend this work to be the first in a series of open-source designs to be released, and through the contributions of the open-source user community, result in a large number of design modifications and variations available to researchers.


title: Action Recommendation for Cyber Resilience

abstract: This paper presents an unifying graph-based model for representing the infrastructure, behavior and missions of an enterprise. We describe how the model can be used to achieve resiliency against a wide class of failures and attacks. We introduce an algorithm for recommending resilience establishing actions based on dynamic updates to the models. Without loss of generality, we show the effectiveness of the algorithm for preserving latency based quality of service (QoS). Our models and the recommendation algorithms are implemented in a software framework that we seek to release as an open source framework for simulating resilient cyber systems.


title: Estimation of Lightning Incidence to Overhead Transmission Lines

abstract: General expressions for the estimation of lightning incidence to overhead transmission lines on the basis of electrogeometric and generic models are introduced, which consider, besides transmission-line geometry, lightning crest current distribution and, based on the recently proposed statistical model, interception probability distribution. An application to typical 115 kV up to 765 kV and large scale 500 kV and UHV overhead transmission lines is performed and the computed results, varying significantly among lightning attachment models, are validated through comparisons with field data from literature; the IEEE Std 1243 overestimates significantly lightning incidence to shield wires of large scale transmission lines. The expected annual number of lightning strikes to shield wires of transmission lines depends on lightning crest current distribution; this dependence, easily quantified with the aid of the introduced general expressions, is not considered by the relevant IEEE standard. Lightning incidence results are discussed in the context of the backflashover rate of overhead transmission lines.


title: A survey of exemplar-based texture synthesis

abstract: Exemplar-based texture synthesis is the process of generating, from an input sample, new texture images of arbitrary size and which are perceptually equivalent to the sample. The two main approaches are statistics-based methods and patch re-arrangement methods. In the first class, a texture is characterized by a statistical signature; then, a random sampling conditioned to this signature produces genuinely different texture images. The second class boils down to a clever "copy-paste" procedure, which stitches together large regions of the sample. Hybrid methods try to combine ideas from both approaches to avoid their hurdles. The recent approaches using convolutional neural networks fit to this classification, some being statistical and others performing patch re-arrangement in the feature space. They produce impressive synthesis on various kinds of textures. Nevertheless, we found that most real textures are organized at multiple scales, with global structures revealed at coarse scales and highly varying details at finer ones. Thus, when confronted with large natural images of textures the results of state-of-the-art methods degrade rapidly, and the problem of modeling them remains wide open.


title: Forensics and the GSM Mobile Telephone System

abstract: The GSM system has become the most popular system for mobile communication in the world. Criminals commonly use GSM phones, and it is therefore a need for forensic investigators to understand which evidence can be obtained from the GSM system. This paper briefly explains the basics of the GSM system. Evidence items that can be obtained from the Mobile Equipment, the SIM and the core network are explored. Tools to extract such evidence from the components of the system exist, but there is a need to develop more sound forensic procedures and tools for extracting such evidence. The paper concludes with a short presentation on the future UMTS system, which largely builds on the design of GSM. All these factors make it important for forensic investigators to understand how the GSM system works, and how evidence can be extracted from it. Criminals took the step into the mobile age a long time ago, and information from the mobile system can give the investigator crucial information on the criminal's actions. It is however important that the information contained in the system is retrieved with a forensically sound method. It is equally important that the investigator understands the system in order to be able to explain to the courts how the system works. It is the aim of this paper to give forensic investigators an introduction to the current state of GSM forensics, and highlight some of the issues that will have to be solved in the future. 2.0 History of the GSM system In the beginning of the 1980s several different systems for mobile communications were developed in Europe. The need for a common system that allowed roaming between countries was early recognized. In 1982 a number of European countries created a new standardization organisation called "Groupe Speciale Mobile" (GSM). The mandate of this group was to develop a standard to be common for the countries that created it. In 1988 the GSM was included in the European Telecommunication Standards Institute (ETSI), and the standards developed by GSM thus became standards for all telecommunication administrations in Europe. The main work with the GSM took place from 1988 - 1990 and resulted in 12 series of specifications which in great detail specified the inner workings of GSM. In 1990, when phase 1 of the specifications was finished, there were three dominating automatic systems for mobile communications in the world:


title: Millimeter wave propagation through foliage: Comparison of models

abstract: Foliage attenuation is one of the key impairments in millimeter wave propagation. Existing literature shows that the foliage attenuation is a function of a multitude of parameters, including frequency, foliage depth, tree types, foliage thickness, leaf density, leaf size, branches, trunks, humidity, wind speed, height of the tree relative to the antenna heights, path length through foliage, etc. This paper surveys the current development in the foliage attenuation study in millimeter wave bands and reviews the established propagation models. Numerical simulations are presented to assess and compare the existing empirical models. The results show that the Weissberger model provides the most optimistic estimate (the lowest attenuation) while the FITU-R model delivers the most pessimistic estimate (the highest attenuation). The results also reveal that the ITU-R model has the highest rate of increase in foliage attenuation as a function of foliage depth.


title: Javert: fully automatic mining of general temporal properties from dynamic traces

abstract: Program specifications are important for many tasks during software design, development, and maintenance. Among these, temporal specifications are particularly useful. They express formal correctness requirements of an application's ordering of specific actions and events during execution, such as the strict alternation of acquisition and release of locks. Despite their importance, temporal specifications are often missing, incomplete, or described only informally. Many techniques have been proposed that mine such specifications from execution traces or program source code. However, existing techniques mine only simple patterns, or they mine a single complex pattern that is restricted to a particular set of manually selected events. There is no practical, automatic technique that can mine general temporal properties from execution traces. In this paper, we present Javert, the first general specification mining framework that can learn, fully automatically, complex temporal properties from execution traces. The key insight behind Javert is that real, complex specifications can be formed by composing instances of small generic patterns, such as the alternating pattern ((ab)) and the resource usage pattern ((ab c)). In particular, Javert learns simple generic patterns and composes them using sound rules to construct large, complex specifications. We have implemented the algorithm in a practical tool and conducted an extensive empirical evaluation on several open source software projects. Our results are promising; they show that Javert is scalable, general, and precise. It discovered many interesting, nontrivial specifications in real-world code that are beyond the reach of existing automatic techniques.


title: Fully homomorphic encryption using ideal lattices

abstract: We propose a fully homomorphic encryption scheme -- i.e., a scheme that allows one to evaluate circuits over encrypted data without being able to decrypt. Our solution comes in three steps. First, we provide a general result -- that, to construct an encryption scheme that permits evaluation of arbitrary circuits, it suffices to construct an encryption scheme that can evaluate (slightly augmented versions of) its own decryption circuit; we call a scheme that can evaluate its (augmented) decryption circuit bootstrappable. Next, we describe a public key encryption scheme using ideal lattices that is almost bootstrappable. Lattice-based cryptosystems typically have decryption algorithms with low circuit complexity, often dominated by an inner product computation that is in NC1. Also, ideal lattices provide both additive and multiplicative homomorphisms (modulo a public-key ideal in a polynomial ring that is represented as a lattice), as needed to evaluate general circuits. Unfortunately, our initial scheme is not quite bootstrappable -- i.e., the depth that the scheme can correctly evaluate can be logarithmic in the lattice dimension, just like the depth of the decryption circuit, but the latter is greater than the former. In the final step, we show how to modify the scheme to reduce the depth of the decryption circuit, and thereby obtain a bootstrappable encryption scheme, without reducing the depth that the scheme can evaluate. Abstractly, we accomplish this by enabling the encrypter to start the decryption process, leaving less work for the decrypter, much like the server leaves less work for the decrypter in a server-aided cryptosystem.


title: A two-step method for forecasting spare parts demand using information on component repairs

abstract: Forecasting spare parts demand is notoriously difficult, as demand is typically intermittent and lumpy. Specialized methods such as that by Croston are available, but these are not based on the repair operations that cause the intermittency and lumpiness of demand. In this paper, we do propose a method that, in addition to the demand for spare parts, considers the type of component repaired. This two-step forecasting method separately updates the average number of parts needed per repair and the number of repairs for each type of component. The method is tested in an empirical, comparative study for a service provider in the aviation industry. Our results show that the two-step method is one of the most accurate methods, and that it performs considerably better than Croston’s method. Moreover, contrary to other methods, the two-step method can use information on planned maintenance and repair operations to reduce forecasts errors by up to 20%. We derive further analytical and simulation results that help explain the empirical findings.


title: Experimental switching frequency limits of 15 kV SiC N-IGBT module

abstract: This paper presents extensive experimental switching characteristics of a state-of-the-art 15 kV SiC N-IGBT (0.32 cm ::: 2 ::: active area) up to 10 kV, 10 A and 175°C. The influence of the thermal resistance of the module package, cooling mechanism, and the increased energy loss with temperature are investigated for determining the switching frequency limits of the IGBT. Detailed FEM analysis is conducted for extracting the thermal resistance of each layer in the 15 kV module from the IGBT junction to the base plate, and then down to the ambient. Using this thermal information and the experimental switching data, the inductive switching frequency limits are analytically evaluated for liquid and air cooling cases with 660 W/cm ::: 2 ::: and 550 W/cm ::: 2 ::: power dissipation densities respectively, considering 150°C as maximum junction temperature. The air cooling power dissipation density of the 15 kV IGBT is experimentally validated using a dc-dc boost converter at 10 kV, 6.4 kW output and 550 W/cm ::: 2 ::: under steady state operating conditions. The gate resistances used for the entire experiments are R ::: G(ON) ::: = 20 Ω and R ::: G(OFF) ::: = 10 Ω.


title: A faster algorithm for constructing minimal perfect hash functions

abstract: Our previous research on one-probe access to large collections of data indexed by alphanumeric keys has produced the first practical minimal perfect hash functions for this problem. Here, a new algorithm is described for quickly finding minimal perfect hash functions whose specification space is very close to the theoretical lower bound, i.e., around 2 bits per key. The various stages of processing are detailed, along with analytical and empirical results, including timing for a set of over 3.8 million keys that was processed on a NeXTstation in about 6 hours.


title: A global averaging method for dynamic time warping, with applications to clustering

abstract: Mining sequential data is an old topic that has been revived in the last decade, due to the increasing availability of sequential datasets. Most works in this field are centred on the definition and use of a distance (or, at least, a similarity measure) between sequences of elements. A measure called dynamic time warping (DTW) seems to be currently the most relevant for a large panel of applications. This article is about the use of DTW in data mining algorithms, and focuses on the computation of an average of a set of sequences. Averaging is an essential tool for the analysis of data. For example, the K-means clustering algorithm repeatedly computes such an average, and needs to provide a description of the clusters it forms. Averaging is here a crucial step, which must be sound in order to make algorithms work accurately. When dealing with sequences, especially when sequences are compared with DTW, averaging is not a trivial task. Starting with existing techniques developed around DTW, the article suggests an analysis framework to classify averaging techniques. It then proceeds to study the two major questions lifted by the framework. First, we develop a global technique for averaging a set of sequences. This technique is original in that it avoids using iterative pairwise averaging. It is thus insensitive to ordering effects. Second, we describe a new strategy to reduce the length of the resulting average sequence. This has a favourable impact on performance, but also on the relevance of the results. Both aspects are evaluated on standard datasets, and the evaluation shows that they compare favourably with existing methods. The article ends by describing the use of averaging in clustering. The last section also introduces a new application domain, namely the analysis of satellite image time series, where data mining techniques provide an original approach.


title: Survey and Taxonomy of Self-Aware and Self-Adaptive Autoscaling Systems in the Cloud

abstract: Autoscaling system can reconfigure cloud-based services and applications, through various configurations of cloud software and provisions of hardware resources, to adapt to the changing environment at runtime. Such a behavior offers the foundation for achieving elasticity in a modern cloud computing paradigm. Given the dynamic and uncertain nature of the shared cloud infrastructure, the cloud autoscaling system has been engineered as one of the most complex, sophisticated, and intelligent artifacts created by humans, aiming to achieve self-aware, self-adaptive, and dependable runtime scaling. Yet the existing Self-aware and Self-adaptive Cloud Autoscaling System (SSCAS) is not at a state where it can be reliably exploited in the cloud. In this article, we survey the state-of-the-art research studies on SSCAS and provide a comprehensive taxonomy for this field. We present detailed analysis of the results and provide insights on open challenges, as well as the promising directions that are worth investigated in the future work of this area of research. Our survey and taxonomy contribute to the fundamentals of engineering more intelligent autoscaling systems in the cloud.


title: Light-Weight RefineNet for Real-Time Semantic Segmentation

abstract: We consider an important task of effective and efficient semantic image segmentation. In particular, we adapt a powerful semantic segmentation architecture, called RefineNet, into the more compact one, suitable even for tasks requiring real-time performance on high-resolution inputs. To this end, we identify computationally expensive blocks in the original setup, and propose two modifications aimed to decrease the number of parameters and floating point operations. By doing that, we achieve more than twofold model reduction, while keeping the performance levels almost intact. Our fastest model undergoes a significant speed-up boost from 20 FPS to 55 FPS on a generic GPU card on 512x512 inputs with solid 81.1% mean iou performance on the test set of PASCAL VOC, while our slowest model with 32 FPS (from original 17 FPS) shows 82.7% mean iou on the same dataset. Alternatively, we showcase that our approach is easily mixable with light-weight classification networks: we attain 79.2% mean iou on PASCAL VOC using a model that contains only 3.3M parameters and performs only 9.3B floating point operations.


title: Let the state follow its flows: An SDN-based flow handover protocol to support state migration

abstract: Dynamically steering flows through virtualized network function instances is a key enabler for elastic, on-demand deployments of virtualized network functions. This becomes particular challenging when stateful functions are involved, necessitating state management. The problem with existing solutions is that they typically embrace state migration and flow rerouting jointly, imposing a huge set of requirements on the on-boarded VNFs, e.g., solution-specific state management interfaces. In this paper, we introduce the seamless handover protocol (SHarP). It provides an easy-to-use, loss-less, and order-preserving flow rerouting mechanism that is not fixed to a single state management approach. This allows VNF vendors to implement or use the state management solution of their choice. SHarP supports these solutions with additional information when flows are migrated. Further, we show how SHarP significantly reduces the buffer usage at a central (SDN) controller, which is a typical bottleneck in existing solutions. Our experiments show that SHarP uses a constant amount of controller buffer, irrespective of the time taken to migrate the VNF state.


title: Measurement of bitumen coverage of stones for road building, based on digital image analysis

abstract: The top layer of a road is made up of a mixture of stones and bitumen and the durability is dependent on how well the bitumen adheres to the stones. The standard way of determining the bitumen coverage in the industry is the so called rolling bottle method, where a number of stones covered with bitumen are put in a rolling bottle and the bitumen coverage is estimated after different times. This paper describes a novel method for measuring the bitumen coverage of the stones by using advanced segmentation methods instead of manual inspection. The stones are put on a table and a number of images with different exposure times are taken. The images are normalized and the stones are segmented from the background based on a threshold obtained from an optimality criterion. Then the bitumen covered parts of the stones are segmented based on a graph-cut method. The results are compared to manual inspection and are well in agreement with these.


title: Multiview random forest of local experts combining RGB and LIDAR data for pedestrian detection

abstract: Despite recent significant advances, pedestrian detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities and a strong multi-view classifier that accounts for different pedestrian views and poses. In this paper we provide an extensive evaluation that gives insight into how each of these aspects (multi-cue, multi-modality and strong multi-view classifier) affect performance both individually and when integrated together. In the multi-modality component we explore the fusion of RGB and depth maps obtained by high-definition LIDAR, a type of modality that is only recently starting to receive attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the performance, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. These simple blocks can be easily replaced with more sophisticated ones recently proposed, such as the use of convolutional neural networks for feature representation, to further improve the accuracy.


title: On the influence of social bots in online protests. Preliminary findings of a Mexican case study

abstract: Social bots can affect online communication among humans. We study this phenomenon by focusing on #YaMeCanse, the most active protest hashtag in the history of Twitter in Mexico. Accounts using the hashtag are classified using the BotOrNot bot detection tool. Our preliminary analysis suggests that bots played a critical role in disrupting online communication about the protest movement.


title: A New Development in Online Marketing: Introducing Digital Inbound Marketing

abstract: Particularly in online settings, marketing is undergoing a transformation. Online business can no longer rely on traditional marketing tactics and campaigns to attract, retain and expand consumers because there is a transformation in how people interact with brands and companies, how they shop and buy in online and offline settings. Traditional marketing is no longer a viable option because it focuses on pushing a message out. In online business framework, a new marketing development has arisen, namely digital inbound marketing. This new marketing type is focused on attracting valuable consumers (potential, existing, or aspirational) that choose to interact with a particular company that provides them with something useful. In this paper, we propose a definition for digital inbound marketing and examine the online strategies associated with this concept: brand-focused marketing communications, content marketing, social media marketing, and search engine optimization.


title: Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data

abstract: In the past few years, Convolutional Neural Networks (CNNs) have been achieving state-of-the-art performance on a variety of problems. Many companies employ resources and money to generate these models and provide them as an API, therefore it is in their best interest to protect them, i.e., to avoid that someone else copies them. Recent studies revealed that state-of-the-art CNNs are vulnerable to adversarial examples attacks, and this weakness indicates that CNNs do not need to operate in the problem domain (PD). Therefore, we hypothesize that they also do not need to be trained with examples of the PD in order to operate in it. Given these facts, in this paper, we investigate if a target black-box CNN can be copied by persuading it to confess its knowledge through random non-labeled data. The copy is two-fold: i) the target network is queried with random data and its predictions are used to create a fake dataset with the knowledge of the network; and ii) a copycat network is trained with the fake dataset and should be able to achieve similar performance as the target network. This hypothesis was evaluated locally in three problems (facial expression, object, and crosswalk classification) and against a cloud-based API. In the copy attacks, images from both non-problem domain and PD were used. All copycat networks achieved at least 93.7% of the performance of the original models with non-problem domain data, and at least 98.6% using additional data from the PD. Additionally, the copycat CNN successfully copied at least 97.3% of the performance of the Microsoft Azure Emotion API. Our results show that it is possible to create a copycat CNN by simply querying a target network as black-box with random non-labeled data.