title: Deep Learning Based MIMO Communications

abstract: We introduce a novel physical layer scheme for single user Multiple-Input Multiple-Output (MIMO) communications based on unsupervised deep learning using an autoencoder. This method extends prior work on the joint optimization of physical layer representation and encoding and decoding processes as a single end-to-end task by expanding transmitter and receivers to the multi-antenna case. We introduce a widely used domain appropriate wireless channel impairment model (Rayleigh fading channel), into the autoencoder optimization problem in order to directly learn a system which optimizes for it. We considered both spatial diversity and spatial multiplexing techniques in our implementation. Our deep learning-based approach demonstrates significant potential for learning schemes which approach and exceed the performance of the methods which are widely used in existing wireless MIMO systems. We discuss how the proposed scheme can be easily adapted for open-loop and closed-loop operation in spatial diversity and multiplexing modes and extended use with only compact binary channel state information (CSI) as feedback.


Show Me the Money! Finding Flawed Implementations of Third-party In-app Payment in Android Apps


title: Oscillator-based assistance of cyclical movements: model-based and model-free approaches

abstract: In this article, we propose a new method for providing assistance during cyclical movements. This method is trajectory-free, in the sense that it provides user assistance irrespective of the performed movement, and requires no other sensing than the assisting robot’s own encoders. The approach is based on adaptive oscillators, i.e., mathematical tools that are capable of learning the high level features (frequency, envelope, etc.) of a periodic input signal. Here we present two experiments that we recently conducted to validate our approach: a simple sinusoidal movement of the elbow, that we designed as a proof-of-concept, and a walking experiment. In both cases, we collected evidence illustrating that our approach indeed assisted healthy subjects during movement execution. Owing to the intrinsic periodicity of daily life movements involving the lower-limbs, we postulate that our approach holds promise for the design of innovative rehabilitation and assistance protocols for the lower-limb, requiring little to no user-specific calibration.


A conditional neural fields model for protein threading


title: Analysis and Synthesis of Double-Sided Parallel-Strip Transitions

abstract: Antenna feeders, mixers, and filters made in double-sided parallel-strip technology usually must be adapted to unbalanced lines like the microstrip structure, needing transitions from asymmetric to symmetric waveguides (baluns). In this paper, we propose a new method for the evaluation of a generic tapered balun based on a conformal-mapping technique and an integral equation. This method, along with the use of an optimization technique such as genetic algorithms, allows for quick evaluation of the return losses of any tapered balun and the synthesizing of specific shapes to achieve desired responses in terms of return losses or impedance values.


title: Friendly Artificial Intelligence: the Physics Challenge

abstract: Relentless progress in artificial intelligence (AI) is increasingly raising concerns that machines will replace humans on the job market, and perhaps altogether. Eliezer Yudkowski and others have explored the possibility that a promising future for humankind could be guaranteed by a superintelligent "Friendly AI", designed to safeguard humanity and its values. I argue that, from a physics perspective where everything is simply an arrangement of elementary particles, this might be even harder than it appears. Indeed, it may require thinking rigorously about the meaning of life: What is "meaning" in a particle arrangement? What is "life"? What is the ultimate ethical imperative, i.e., how should we strive to rearrange the particles of our Universe and shape its future? If we fail to answer the last question rigorously, this future is unlikely to contain humans.


title: Core Vector Machines : Fast SVM Training on Very Large Data Sets

abstract: Standard SVM training has O(m3) time and O(m2) space complexities, where m is the training set size. It is thus computationally infeasible on very large data sets. By observing that practical SVM implementations only approximate the optimal solution by an iterative strategy, we scale up kernel methods by exploiting such "approximateness" in this paper. We first show that many kernel methods can be equivalently formulated as minimum enclosing ball (MEB) problems in computational geometry. Then, by adopting an efficient approximate MEB algorithm, we obtain provably approximately optimal solutions with the idea of core sets. Our proposed Core Vector Machine (CVM) algorithm can be used with nonlinear kernels and has a time complexity that is linear in m and a space complexity that is independent of m. Experiments on large toy and real-world data sets demonstrate that the CVM is as accurate as existing SVM implementations, but is much faster and can handle much larger data sets than existing scale-up methods. For example, CVM with the Gaussian kernel produces superior results on the KDDCUP-99 intrusion detection data, which has about five million training patterns, in only 1.4 seconds on a 3.2GHz Pentium--4 PC.


title: Low Resolution Face Recognition Across Variations in Pose and Illumination

abstract: We propose a completely automatic approach for recognizing low resolution face images captured in uncontrolled environment. The approach uses multidimensional scaling to learn a common transformation matrix for the entire face which simultaneously transforms the facial features of the low resolution and the high resolution training images such that the distance between them approximates the distance had both the images been captured under the same controlled imaging conditions. Stereo matching cost is used to obtain the similarity of two images in the transformed space. Though this gives very good recognition performance, the time taken for computing the stereo matching cost is significant. To overcome this limitation, we propose a reference-based approach in which each face image is represented by its stereo matching cost from a few reference images. Experimental evaluation on the real world challenging databases and comparison with the state-of-the-art super-resolution, classifier based and cross modal synthesis techniques show the effectiveness of the proposed algorithm.


title: Effects of Humic Acid and Chitosan under Different Levels of Nitrogen and Potassium fertilizers on Growth and Yield potential of Potato plants (Solanum tuberosum, L.)

abstract: Two field experiments were carried out during the fall seasons of 2014 and 2015 to investigate the main effect ofhumic acid (0 and 1000 ppm), chitosan (0 and 250 ppm) and NK (0, 25, 50, 75, and 100%) of the recommended rate forthe commercial production of potato as well as their various interactions on some vegetative growth characters, tubersyield and its components. The obtained results indicated that increasing the applied level of NK up to 100% constantlyincreased most of vegetative growth characters (foliage fresh weight, leaf area, plant height, number of main stems andleaf chlorophyll content) and tubers yield and its components (total tuber yield plant-1 and fed-1 and tubers yield of large,medium and small sized plant-1). The highest two NK levels (75 and 100 %) didn’t, significantly, differ in their effects onmost of vegetative traits. Application of humic acid or chitosan produced, significantly, increased growth parameters aswell as tubers yield and its components. The most favorable combination treatments that produced the highest meanvalues of growth and tubers yield as well as yield components were 75 or 100 % NK + humic acid or chitosan, humic acid+ chitosan and 75 or 100% NK+ humic acid + chitosan . Therefore, fertilizing potato plants with 75 or 100 % of therecommended dose of NK combined with foliar spray of humic acid and chitosan could be recommended to gain the bestresults.


title: Applying Variable-Switching-Frequency Variable-Phase-Shift Control and E-Mode GaN HEMTs to an Indirect Matrix Converter-Based EV Battery Charger

abstract: An indirect matrix converter is employed directly converting the grid ac to the battery voltage, with the dual-active-bridge taking care of the power factor correction and power delivery simultaneously. Such circuit is regarded as one candidate of the high-efficiency and high-power-density electric vehicle on-board chargers, if the double-frequency current ripple to the battery is tolerated. Instead of optimizing the overall charger, this paper is focused on adopting variable switching frequency with multiple phase shifts to accommodate the wide input range (80–260 $V_{\mathrm{ ac}}$ ) and output range (200 V–450 $V_{\mathrm{ dc}}$ ). In addition to the phase shift between the transformer primary-side and secondary-side voltage, one extra phase shift is added to the primary-side H-bridge when the instantaneous input voltage is higher than the reflected output, otherwise, to the secondary side. The goal is to secure zero-voltage-switching for all switches at all voltage range. Such control strategy is further optimized incorporating with the switch parasitic capacitance and dead-band settings. To further enhance the charger performance, GaN HEMTs are equipped to the on-board charger aiming at higher efficiency and higher power density than Si devices. Experimental results indicated that such charger with proposed control strategy embraces the peak efficiency of >97% at 7.2 kW and a power density of ~4 kW/L.


title: Childhood Pesticide Exposures on the Texas–Mexico Border: Clinical Manifestations and Poison Center Use

abstract: Objectives. The purpose of this study was to describe differences in childhood pesticide exposures between counties on the Texas–Mexico border and nonborder counties. Method. The authors reviewed all pesticide exposures among children younger than 6 years reported to the South Texas Poison Center during 1997 through 2000. Results. Nonborder counties had twice the reported exposure rate of border counties. Parents of border children were significantly less likely to contact the poison center after an exposure and more likely to have their children evaluated in a health care facility. Conclusions. Increasing residents’ awareness of the poison center and identifying potential barriers to its use among residents of Texas–Mexico border communities may prevent unnecessary visits to health care facilities.


title: Watching social interactions produces dorsomedial prefrontal and medial parietal BOLD fMRI signal increases compared to a resting baseline

abstract: Abstract Some human brain areas are tonically active in a resting state when subjects are not engaged in any overt task. The activity of these areas decreases when subjects are engaged in a wide variety of laboratory tasks designed to study cognitive operations. It has been suggested that these areas, among them the medial parietal (precyneus) and the dorsomedial prefrontal cortices, may support a “default state” of the human brain. Passive visual observation of laboratory stimuli typically yields no change in activity in these default areas compared to rest. Here we report functional magnetic resonance imaging (fMRI) data on normal subjects watching realistic movie clips depicting everyday social interactions. In contrast with previous findings on default state brain areas, the observation of the relational segment of the movie clip, during which two persons interact, yielded increased activity in the medial parietal (precuneus) and dorsomedial prefrontal cortices, compared to rest and to observation of the segment of the movie clip depicting a single individual engaged in everyday activities. To the best of our knowledge, this is the first report of joint increased activity in medial parietal and dorsomedial prefrontal cortices. We suggest that the default state areas may participate in the processing of social relations in concert with regions previously identified as critical for social cognition that were also activated by our stimuli, including the inferior frontal cortex, the superior temporal cortex, and the fusiform gyrus.


title: The Effect of Thin Ideal Media Images on Women’s Self-Objectification, Mood, and Body Image

abstract: Objectification theory (Fredrickson and Roberts, Psychology of Women Quarterly, 21, 173–206, 1997) contends that experiences of sexual objectification socialize women to engage in self-objectification. The present study used an experimental design to examine the effects of media images on self-objectification. A total of 90 Australian undergraduate women aged 18 to 35 were randomly allocated to view magazine advertisements featuring a thin woman, advertisements featuring a thin woman with at least one attractive man, or advertisements in which no people were featured. Participants who viewed advertisements featuring a thin-idealized woman reported greater state self-objectification, weight-related appearance anxiety, negative mood, and body dissatisfaction than participants who viewed product control advertisements. The results demonstrate that self-objectification can be stimulated in women without explicitly focusing attention on their own bodies.


title: Phase conversion of chemically exfoliated molybdenum disulfide

abstract: Abstract Multilayer MoS2 is exfoliated by Li treatment. A thin film of Li-treated MoS2 contains a large portion of 1T phases. This is attributed to the atomic structural change caused by Li insertion, which is investigated using X-ray photoelectron spectroscopy (XPS). In a phase recovery via thermal annealing at 523 K in air, we observe another phase, referred to as the relaxed 1T phase, with a slightly larger binding energy than the 1T phase. After annealing at 523 K in air, the peak intensity of the relaxed 1T phase is reduced, accompanying a strong MoOx peak and weak S 2s peak, according to XPS. This indicates that the annealing of Li-treated MoS2 in air yields sulfur vacancies that induce the oxidation of Mo. However, after annealing at 523 K in vacuum, no MoOx is observed, and the considerable peak intensity of the relaxed 1T phase remains, which starkly contrasts Raman-spectroscopy results supporting a full recovery from the 1T phase to the 2H phase. The absence of a gating effect of the Li-treated MoS2 device supports the possibility of an incomplete phase change of Li-treated MoS2 annealed in a vacuum.


title: Implementation of dynamic charging and V2G using Chademo and CCS/Combo DC charging standard

abstract: Dynamic charging of electric vehicles (EV) refers to charging the EV using variable charging power. This is important for applications where the EV is charged from intermittent renewable energy sources like wind or solar. Dynamic charging can be implemented using three standards - AC charging, DC charging via Chademo and CCS/COMBO. This paper compares the charging standards in their implementation of dynamic charging and vehicle to grid (V2X) and brings out its influence on the charging system design, response time, flexibility in charging from renewable sources and buffer capacity that is required. Experimental results of dynamic charging using Chademo and CCS/COMBO are presented for different compatible EV that shows the fundamental difference between the two standards.


Chaos Game and Fractal Dimension in Image Processing to Detect Rice Leaf Blast Disease


title: Learning my music how I learn my medicine

abstract: I have a secret ambition. One day, I’d like to be the soloist in William Walton’s Viola Concerto . Perhaps it’s more a fantasy, than an ambition, as I’m not a good enough viola player to manage it. But if I were to have a go, my main experience of learning things has been in medicine. Is learning music the same? ::: ::: Perhaps I should start by writing some competency statements. Let’s assume, that in order to start the module ‘Walton’s Viola Concerto ,’ I have met the course prerequisites, such as being able to read the viola clef, and knowing what Andante comodo means (a comfortable walking pace). Then, I need to be competent in playing an A, a longer C, B, C … …


Fake News , Real Consequences : Recruiting Neural Networks for the Fight Against Fake News


title: Efficient perovskite solar cells based on low-temperature solution-processed (CH3NH3)PbI3 perovskite/CuInS2 planar heterojunctions

abstract: In this work, the solution-processed CH3NH3PbI3 perovskite/copper indium disulfide (CuInS2) planar heterojunction solar cells with Al2O3 as a scaffold were fabricated at a temperature as low as 250°C for the first time, in which the indium tin oxide (ITO)-coated glass instead of the fluorine-doped tin oxide (FTO)-coated glass was used as the light-incidence electrode and the solution-processed CuInS2 layer was prepared to replace the commonly used TiO2 layer in previously reported perovskite-based solar cells. The influence of the thickness of the as-prepared CuInS2 film on the performance of the ITO/CuInS2(n)/Al2O3/(CH3NH3)PbI3/Ag cells was investigated. The ITO/CuInS2(2)/Al2O3/(CH3NH3)PbI3/Ag cell showed the best performance and achieved power conversion efficiency up to 5.30%.


title: Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems

abstract: Abstract Much research in artificial intelligence is concerned with the development of autonomous agents that can interact effectively with other agents. An important aspect of such agents is the ability to reason about the behaviours of other agents, by constructing models which make predictions about various properties of interest (such as actions, goals, beliefs) of the modelled agents. A variety of modelling approaches now exist which vary widely in their methodology and underlying assumptions, catering to the needs of the different sub-communities within which they were developed and reflecting the different practical uses for which they are intended. The purpose of the present article is to provide a comprehensive survey of the salient modelling methods which can be found in the literature. The article concludes with a discussion of open problems which may form the basis for fruitful future research.


title: Context-aware Sensor Search, Selection and Ranking Model for Internet of Things Middleware

abstract: As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a substantial acceleration of the growth rate in the future. It is also evident that the increasing number of IoT middleware solutions are developed in both research and commercial environments. However, sensor search and selection remain a critical requirement and a challenge. In this paper, we present CASSARAM, a context-aware sensor search, selection, and ranking model for Internet of Things to address the research challenges of selecting sensors when large numbers of sensors with overlapping and sometimes redundant functionality are available. CASSARAM proposes the search and selection of sensors based on user priorities. CASSARAM considers a broad range of characteristics of sensors for search such as reliability, accuracy, battery life just to name a few. Our approach utilises both semantic querying and quantitative reasoning techniques. User priority based weighted Euclidean distance comparison in multidimensional space technique is used to index and rank sensors. Our objectives are to highlight the importance of sensor search in IoT paradigm, identify important characteristics of both sensors and data acquisition processes which help to select sensors, understand how semantic and statistical reasoning can be combined together to address this problem in an efficient manner. We developed a tool called CASSARA to evaluate the proposed model in terms of resource consumption and response time.


title: Shadow detection for moving humans using gradient-based background subtraction

abstract: Cast shadows cause serious problems in the functionality of vision-based applications, such as video surveillance, traffic monitoring and various other applications. Accurate detection and removal of cast shadows is a challenging task. Common shadow detection techniques normally use color information, which is not a reliable base in every scenario. This paper presents a novel scheme for real time detection of cast shadows using contour like structures of objects, which are obtained by gradient-based background subtraction. The scheme does not use any color information. Two basic rules are followed for shadow detection. The first rule is that shadows do not change the texture of the background. The second rule is a cast shadow lies outside the boundary of an object and has a relatively small common boundary with the object. Experimental results show the performance of the proposed scheme. Objective evaluation shows that the algorithm classifies 90 percent of the pixels of the objects and their shadow correctly.


title: EMG-based hands-free wheelchair control with EOG attention shift detection

abstract: This paper presents a novel hands-free control system for an electric-powered wheelchair, which is based on EMG (Electromyography) signals recorded from eyebrow muscle activity. By using a simple CyberLink device, one-dimensional continuous EMG signals are obtained, analysed, and then translated into multi-directional control commands (forward, left, right, etc.) for the wheelchair that supports multi-directional control. At the same time, EOG (Electrooculography) signals detected from eye movements are used to adjust wheelchair speed. The system also allows a user to choose either control state or non-control state so that any non-intended muscle activity can be ignored during the non-control state. The system is reliable, easy to set up, and easy to use.


title: Tagging Accuracy Analysis on Part-of-Speech Taggers

abstract: Part of Speech (POS) Tagging can be applied ::: by several tools and several programming languages. This work focuses on the ::: Natural Language Toolkit (NLTK) library in the Python environment and the gold ::: standard corpora installable. The corpora and tagging methods are analyzed and ::: com- pared by using the Python language. Different taggers are analyzed ::: according to their tagging ac- curacies with data from three different corpora. ::: In this study, we have analyzed Brown, Penn Treebank and NPS Chat corpuses. The ::: taggers we have used for the analysis are; default tagger, regex tagger, n-gram ::: taggers. We have applied all taggers to these three corpuses, resultantly we ::: have shown that whereas Unigram tagger does the best tagging in all corpora, ::: the combination of taggers does better if it is correctly ordered. Additionally, ::: we have seen that NPS Chat Corpus gives different accuracy results than the ::: other two corpuses.


title: Unsupervised color image segmentation using a dynamic color gradient thresholding algorithm

abstract: We propose a novel algorithm for unsupervised segmentation of color images. The proposed approach utilizes a dynamic ::: color gradient thresholding scheme that guides the region growing process. Given a color image, a weighted vectorbased ::: color gradient map is generated. Seeds are identified and a dynamic threshold is then used to perform reliable ::: growing of regions on the weighted gradient map. Over-segmentation, if any, is addressed by a Similarity Measurebased ::: region merging stage to produce the final segmented image. Comparative results demonstrate the effectiveness of ::: this algorithm for color image segmentation.


title: The impact of staff turnover on software projects: the importance of understanding what makes software practitioners tick

abstract: In this paper we investigate the impact of staff turnover on software projects. In particular we investigate whether high staff turnover damages project success. We analyse data from an empirical study of 89 software practitioners to show that projects with high staff turnover are less successful. Furthermore our empirical data suggests a relationship between high staff turnover on projects and low staff motivation levels. We discuss factors which have been previously found to improve motivation levels and conclude that improving motivation levels can reduce staff turnover, which in turn increases project success.


title: Applications of error-control coding

abstract: An overview of the many practical applications of channel coding theory in the past 50 years is presented. The following application areas are included: deep space communication, satellite communication, data transmission, data storage, mobile communication, file transfer, and digital audio/video transmission. Examples, both historical and current, are given that typify the different approaches used in each application area. Although no attempt is made to be comprehensive in the coverage, the examples chosen clearly illustrate the richness, variety, and importance of error-control coding methods in modern digital applications.


title: Adaptive video-based algorithm for accident detection on highways

abstract: For the past few decades, automatic accident detection, especially using video analysis, has become a very important subject. It is important not only for traffic management but also, for Intelligent Transportation Systems (ITS) through its contribution to avoid the escalation of accidents especially on highways. In this paper a novel vision-based road accident detection algorithm on highways and expressways is proposed. This algorithm is based on an adaptive traffic motion flow modeling technique, using Farneback Optical Flow for motions detection and a statistic heuristic method for accident detection. The algorithm was applied on a set of collected videos of traffic and accidents on highways. The results prove the efficiency and practicability of the proposed algorithm using only 240 frames for traffic motion modeling. This method avoids to utilization of a large database while adequate and common accidents videos benchmarks do not exist.


title: Real-time processing of IoT events with historic data using Apache Kafka and Apache Spark with dashing framework

abstract: IoT (Internet of Things) is a concept that broadens the idea of connecting multiple devices to each other over the Internet and enabling communication between these devices. Traditionally, the packets are sent over the network for communication only if both, the sender as well as the receiver, are online. This forces the sender and the receiver to be online 24×7; which is not achievable in each and every environment the devices communicates in. Considering the humongous data generated in the communication, it is necessary to store and process this data so that data insights can be identified to improve the organizational benefits. This generated data can be in two forms, real-time as well as existing or historical data. When this data is obtained in real-time and it is processed, even traditional big data technologies do not perform up to the mark. Hence to process this real-time data, streaming of this data is required; which is not a feature of traditional big data technologies. To achieve these objectives, the proposed architecture uses open source technologies such as Apache Kafka, for online and offline consumption of messages, and Apache Spark, to stream, process and provide a structure to the real-time and existing data. A framework known as Dashing is used to present the processed data in a more attractive and readable manner.


title: Robust $L_{2}E$ Estimation of Transformation for Non-Rigid Registration

abstract: We introduce a new transformation estimation algorithm using the $L_{2}E$ estimator and apply it to non-rigid registration for building robust sparse and dense correspondences. In the sparse point case, our method iteratively recovers the point correspondence and estimates the transformation between two point sets. Feature descriptors such as shape context are used to establish rough correspondence. We then estimate the transformation using our robust algorithm. This enables us to deal with the noise and outliers which arise in the correspondence step. The transformation is specified in a functional space, more specifically a reproducing kernel Hilbert space. In the dense point case for nonrigid image registration, our approach consists of matching both sparsely and densely sampled SIFT features, and it has particular advantages in handling significant scale changes and rotations. The experimental results show that our approach greatly outperforms state-of-the-art methods, particularly when the data contains severe outliers.


Solving Probability Problems in Natural Language


title: Automatic Bengali number plate reader

abstract: In this paper, we propose connected component analysis based algorithm to automatically detect and read Bengali number plates used in the metropolitan cities of Bangladesh. Our proposed automatic number plate recognition (ANPR) system involves image pre-processing and morphological operation followed by edge detection, regional localization and character segmentation to identify the Bengali characters in the number plate efficiently as well as with less computational complexity. For various weather conditions, our proposed algorithm shows a detection accuracy rate of ∼95% with an average processing time of 0.75 seconds. Our system might be highly effective in real time traffic control, security enhancement as well in electronic toll collection.


title: Matrix Sketching Over Sliding Windows

abstract: Large-scale matrix computation becomes essential for many data data applications, and hence the problem of sketching matrix with small space and high precision has received extensive study for the past few years. This problem is often considered in the row-update streaming model, where the data set is a matrix A -- Rn x d, and the processor receives a row (1 x d) of A at each timestamp. The goal is to maintain a smaller matrix (termed approximation matrix, or simply approximation) B -- Rl x d as an approximation to A, such that the covariance error |AT A - BTB| is small and l ll n. This paper studies continuous tracking approximations to the matrix defined by a sliding window of most recent rows. We consider both sequence-based and time-based window. We show that maintaining ATA exactly requires linear space in the sliding window model, as opposed to O(d2) space in the streaming model. With this observation, we present three general frameworks for matrix sketching on sliding windows. The sampling techniques give random samples of the rows in the window according to their squared norms. The Logarithmic Method converts a mergeable streaming matrix sketch into a matrix sketch on time-based sliding windows. The Dyadic Interval framework converts arbitrary streaming matrix sketch into a matrix sketch on sequence-based sliding windows. In addition to proving all algorithmic properties theoretically, we also conduct extensive empirical study with real data sets to demonstrate the efficiency of these algorithms.


title: The NAIP-NLRC4 inflammasome in innate immune detection of bacterial flagellin and type III secretion apparatus

abstract: Summary ::: Bacterial flagella and type III secretion system (T3SS) are evolutionarily related molecular transport machineries. Flagella mediate bacterial motility; the T3SS delivers virulence effectors to block host defenses. The inflammasome is a cytosolic multi-protein complex that activates caspase-1. Active caspase-1 triggers interleukin-1β (IL-1β)/IL-18 maturation and macrophage pyroptotic death to mount an inflammatory response. Central to the inflammasome is a pattern recognition receptor that activates caspase-1 either directly or through an adapter protein. Studies in the past 10 years have established a NAIP–NLRC4 inflammasome, in which NAIPs are cytosolic receptors for bacterial flagellin and T3SS rod/needle proteins, while NLRC4 acts as an adapter for caspase-1 activation. Given the wide presence of flagella and the T3SS in bacteria, the NAIP–NLRC4 inflammasome plays a critical role in anti-bacteria defenses. Here, we review the discovery of the NAIP–NLRC4 inflammasome and further discuss recent advances related to its biochemical mechanism and biological function as well as its connection to human autoinflammatory disease.


title: Concept-based interactive query expansion

abstract: Despite the recent advances in search quality, the fast increase in the size of the Web collection has introduced new challenges for Web ranking algorithms. In fact, there are still many situations in which the users are presented with imprecise or very poor results. One of the key difficulties is the fact that users usually submit very short and ambiguous queries, and they do not fully specify their information needs. That is, it is necessary to improve the query formation process if better answers are to be provided. In this work we propose a novel concept-based query expansion technique, which allows disambiguating queries submitted to search engines. The concepts are extracted by analyzing and locating cycles in a special type of query relations graph. This is a directed graph built from query relations mined using association rules. The concepts related to the current query are then shown to the user who selects the one concept that he interprets is most related to his query. This concept is used to expand the original query and the expanded query is processed instead. Using a Web test collection, we show that our approach leads to gains in average precision figures of roughly 32%. Further, if the user also provides information on the type of relation between his query and the selected concept, the gains in average precision go up to roughly 52%.


title: VectorDefense: Vectorization as a Defense to Adversarial Examples

abstract: Training deep neural networks on images represented as grids of pixels has brought to light an interesting phenomenon known as adversarial examples. Inspired by how humans reconstruct abstract concepts, we attempt to codify the input bitmap image into a set of compact, interpretable elements to avoid being fooled by the adversarial structures. We take the first step in this direction by experimenting with image vectorization as an input transformation step to map the adversarial examples back into the natural manifold of MNIST handwritten digits. We compare our method vs. state-of-the-art input transformations and further discuss the trade-offs between a hand-designed and a learned transformation defense.


Knowledge discovery from patients’ behavior via clustering-classification algorithms based on weighted eRFM and CLV model: An empirical study in public health care services


title: On a Linear Cryptanalysis of a Family of Modified DES Ciphers with Even Weight S-Boxes

abstract: Abstract We investigate the effect of inserting extra linearity in the Data Encryption Standard DES through appropriate nonsingular linear encodings of the output of the individual S-boxes. More specifically, we examine the general situation when the output of each S-box of the DESis precoded separately intoaproperly constructed copy of the inherent even-weight code of length 4. The study is focused on finding multi-round linear characteristics for thus modified DESciphers having maximal effectiveness. Depending on the particular encodings, it turns out that the effectiveness of interest may be larger but in most cases is smaller than that one for the original DESwith the same number of rounds. The latter means that the complexity of successful linear cryptanalysis against these ciphers will mainly increase comparing to the DESitself. The present research extends inanatural way our previous work[Linear Cryptanalysis and Modified DESwith Parity Check in the S-boxes, LNCS 9540 2016, pp. 60-78].


title: Learning Mutual Visibility Relationship for Pedestrian Detection with a Deep Model

abstract: Detecting pedestrians in cluttered scenes is a challenging problem in computer vision. The difficulty is added when several pedestrians overlap in images and occlude each other. We observe, however, that the occlusion/visibility statuses of overlapping pedestrians provide useful mutual relationship for visibility estimation--the visibility estimation of one pedestrian facilitates the visibility estimation of another. In this paper, we propose a mutual visibility deep model that jointly estimates the visibility statuses of overlapping pedestrians. The visibility relationship among pedestrians is learned from the deep model for recognizing co-existing pedestrians. Then the evidence of co-existing pedestrians is used for improving the single pedestrian detection results. Compared with existing image-based pedestrian detection approaches, our approach has the lowest average miss rate on the Caltech-Train dataset and the ETH dataset. Experimental results show that the mutual visibility deep model effectively improves the pedestrian detection results. The mutual visibility deep model leads to 6---15 % improvements on multiple benchmark datasets.


title: Named Entity Recognition Using An HMM-Based Chunk Tagger

abstract: This paper proposes a Hidden Markov Model (HMM) and an HMM-based chunk tagger, from which a named entity (NE) recognition (NER) system is built to recognize and classify names, times and numerical quantities. Through the HMM, our system is able to apply and integrate four types of internal and external evidences: 1) simple deterministic internal feature of the words, such as capitalization and digitalization; 2) internal semantic feature of important triggers; 3) internal gazetteer feature; 4) external macro context feature. In this way, the NER problem can be resolved effectively. Evaluation of our system on MUC-6 and MUC-7 English NE tasks achieves F-measures of 96.6% and 94.1% respectively. It shows that the performance is significantly better than reported by any other machine-learning system. Moreover, the performance is even consistently better than those based on handcrafted rules.


title: Augmented borders: Big Data and the ethics of immigration control

abstract: Purpose – Investments in the technologies of borders and their securitisation continue to be a focal point for many governments across the globe. This paper is concerned with a particular example of such technologies, namely, “Big Data” analytics. In the past two years, the technology of Big Data has gained a remarkable popularity within a variety of sectors, ranging from business and government to scientific and research fields. While Big Data techniques are often extolled as the next frontier for innovation and productivity, they are also raising many ethical and political issues. The aim of this paper is to consider some of these issues and provide a critical reflection on the implications of using Big Data for the governance of borders. Design/methodology/approach – The author draws on the example of the new Big Data solution recently developed by IBM for the Australian Customs and Border Protection Service. The system, which relies on data collected from Passenger Name Records, aims to facilitate and...


Values-based Service for Sustainable Business: Lessons from the retailers IKEA; Starbucks; H&M and Body Shop


title: Food-Bridging: A New Network Construction to Unveil the Principles of Cooking

abstract: In this manuscript we propose, analyse, and discuss a possible new principle behind traditional cuisine: the Food-bridging hypothesis and its comparison with the food-pairing hypothesis using the same dataset and graphical models employed in the food-pairing study by Ahn et al. [Scientific Reports,1:196,2011]. The Food-bridging hypothesis assumes that if two ingredients do not share a strong molecular or empirical affinity, they may become affine through a chain of pairwise affinities. That is, in a graphical model as employed by Ahn et al., a chain represents a path that joints the two ingredients, the shortest path represents the strongest pairwise chain of affinities between the two ingredients. Food-pairing and Food-bridging are different hypotheses that may describe possible mechanisms behind the recipes of traditional cuisines. Food-pairing intensifies flavour by mixing ingredients in a recipe with similar chemical compounds, and food-bridging smoothes contrast between ingredients. Both food-pairing and food-bridging are observed in traditional cuisines, as shown in this work. We observed four classes of cuisines according to food-pairing and food-bridging: (1) East Asian cuisines, at one extreme, tend to avoid food-pairing as well as food-bridging; and (4) Latin American cuisines, at the other extreme, follow both principles. For the two middle classes: (2) Southeastern Asian cuisines, avoid food-pairing and follow food-bridging; and (3) Western cuisines, follow food-pairing and avoid food-bridging.


title: Soil beneficial bacteria and their role in plant growth promotion: a review

abstract: Soil bacteria are very important in biogeochemical cycles and have been used for crop production for decades. Plant–bacterial interactions in the rhizosphere are the determinants of plant health and soil fertility. Free-living soil bacteria beneficial to plant growth, usually referred to as plant growth promoting rhizobacteria (PGPR), are capable of promoting plant growth by colonizing the plant root. PGPR are also termed plant health promoting rhizobacteria (PHPR) or nodule promoting rhizobacteria (NPR). These are associated with the rhizosphere, which is an important soil ecological environment for plant–microbe interactions. Symbiotic nitrogen-fixing bacteria include the cyanobacteria of the genera Rhizobium, Bradyrhizobium, Azorhizobium, Allorhizobium, Sinorhizobium and Mesorhizobium. Free-living nitrogen-fixing bacteria or associative nitrogen fixers, for example bacteria belonging to the species Azospirillum, Enterobacter, Klebsiella and Pseudomonas, have been shown to attach to the root and efficiently colonize root surfaces. PGPR have the potential to contribute to sustainable plant growth promotion. Generally, PGPR function in three different ways: synthesizing particular compounds for the plants, facilitating the uptake of certain nutrients from the soil, and lessening or preventing the plants from diseases. Plant growth promotion and development can be facilitated both directly and indirectly. Indirect plant growth promotion includes the prevention of the deleterious effects of phytopathogenic organisms. This can be achieved by the production of siderophores, i.e. small metal-binding molecules. Biological control of soil-borne plant pathogens and the synthesis of antibiotics have also been reported in several bacterial species. Another mechanism by which PGPR can inhibit phytopathogens is the production of hydrogen cyanide (HCN) and/or fungal cell wall degrading enzymes, e.g., chitinase and s-1,3-glucanase. Direct plant growth promotion includes symbiotic and non-symbiotic PGPR which function through production of plant hormones such as auxins, cytokinins, gibberellins, ethylene and abscisic acid. Production of indole-3-ethanol or indole-3-acetic acid (IAA), the compounds belonging to auxins, have been reported for several bacterial genera. Some PGPR function as a sink for 1-aminocyclopropane-1-carboxylate (ACC), the immediate precursor of ethylene in higher plants, by hydrolyzing it into α-ketobutyrate and ammonia, and in this way promote root growth by lowering indigenous ethylene levels in the micro-rhizo environment. PGPR also help in solubilization of mineral phosphates and other nutrients, enhance resistance to stress, stabilize soil aggregates, and improve soil structure and organic matter content. PGPR retain more soil organic N, and other nutrients in the plant–soil system, thus reducing the need for fertilizer N and P and enhancing release of the nutrients.


title: Parallelizing a Simple Chess Program

abstract: In a closely watched match in 1997, the parallel chess supercomputer Deep Blue defeated then world champion Garry Kasparov3 1 to 2 1 . The fruits of Moore’s law combined with steadily improving chess algorithms now allow programs such asDeep Fritz to challenge human grandmasters while running on nothing more than a commodity 4-way SMP workstation. This paper provides a short overview of the parallel chess algorithms that help achieve these feats and details the implementation of a simple parallel chess program. It also muses about possible methods for extracting additional parallelism using reconfigurable hardware.


title: Automatic 3D reconstruction for face recognition

abstract: An analysis-by-synthesis framework for face recognition with variant pose, illumination and expression (PIE) is proposed in this paper. First, an efficient 2D-to-3D integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with neutral expression and normal illumination. Then, realistic virtual faces with different PIE are synthesized based on the personalized 3D face to characterize the face subspace. Finally, face recognition is conducted based on these representative virtual faces. Compared with other related works, this framework has the following advantages: 1) only one single frontal face is required for face recognition, which avoids the burdensome enrollment work; 2) the synthesized face samples provide the capability to conduct recognition under difficult conditions like complex PIE; and 3) the proposed 2D-to-3D integrated face reconstruction approach is fully automatic and more efficient. The extensive experimental results show that the synthesized virtual faces significantly improve the accuracy of face recognition with variant PIE.


title: Anytime path planning and replanning in dynamic environments

abstract: We present an efficient, anytime method for path planning in dynamic environments. Current approaches to planning in such domains either assume that the environment is static and replan when changes are observed, or assume that the dynamics of the environment are perfectly known a priori. Our approach takes into account all prior information about both the static and dynamic elements of the environment, and efficiently updates the solution when changes to either are observed. As a result, it is well suited to robotic path planning in known or unknown environments in which there are mobile objects, agents or adversaries


title: Hybrid Decision Tree

abstract: In this paper, a hybrid learning approach named hybrid decision tree (HDT) is proposed. HDT simulates human reasoning by using symbolic learning to do qualitative analysis and using neural learning to do subsequent quantitative analysis. It generates the trunk of a binary HDT according to the binary information gain ratio criterion in an instance space defined by only original unordered attributes. If unordered attributes cannot further distinguish training examples falling into a leaf node whose diversity is beyond the diversity-threshold, then the node is marked as a dummy node. After all those dummy nodes are marked, a specific feedforward neural network named Fannc that is trained in an instance space defined by only original ordered attributes is exploited to accomplish the learning task. Moreover, this paper distinguishes three kinds of incremental learning tasks. Two incremental learning procedures designed for example-incremental learning with different storage requirements are provided, which enables HDT to deal gracefully with data sets where new data are frequently appended. Also a hypothesis-driven constructive induction mechanism is provided, which enables HDT to generate compact concept descriptions.


title: A Database for Person Re-Identification in Multi-Camera Surveillance Networks

abstract: Person re-identification involves recognising individuals in different locations across a network of cameras and is a challenging task due to a large number of varying factors such as pose (both subject and camera) and ambient lighting conditions. Existing databases do not adequately capture these variations, making evaluations of proposed techniques difficult. In this paper, we present a new challenging multi-camera surveillance database designed for the task of person re-identification. This database consists of 150 unscripted sequences of subjects travelling in a building environment though up to eight camera views, appearing from various angles and in varying illumination conditions. A flexible XML-based evaluation protocol is provided to allow a highly configurable evaluation setup, enabling a variety of scenarios relating to pose and lighting conditions to be evaluated. A baseline person re-identification system consisting of colour, height and texture models is demonstrated on this database.


title: Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

abstract: We present a statistical model to estimate the accuracy of peptide assignments to tandem mass (MS/MS) spectra made by database search applications such as SEQUEST. Employing the expectation maximization algorithm, the analysis learns to distinguish correct from incorrect database search results, computing probabilities that peptide assignments to spectra are correct based upon database search scores and the number of tryptic termini of peptides. Using SEQUEST search results for spectra generated from a sample of known protein components, we demonstrate that the computed probabilities are accurate and have high power to discriminate between correctly and incorrectly assigned peptides. This analysis makes it possible to filter large volumes of MS/MS database search results with predictable false identification error rates and can serve as a common standard by which the results of different research groups are compared.


title: Robot Representation and Reasoning with Knowledge from Reinforcement Learning

abstract: Reinforcement learning (RL) agents aim at learning by interacting with an environment, and are not designed for representing or reasoning with declarative knowledge. Knowledge representation and reasoning (KRR) paradigms are strong in declarative KRR tasks, but are ill-equipped to learn from such experiences. In this work, we integrate logical-probabilistic KRR with model-based RL, enabling agents to simultaneously reason with declarative knowledge and learn from interaction experiences. The knowledge from humans and RL is unified and used for dynamically computing task-specific planning models under potentially new environments. Experiments were conducted using a mobile robot working on dialog, navigation, and delivery tasks. Results show significant improvements, in comparison to existing model-based RL methods.


Counseling for Wellness With Older Adults


title: Application and Research of Chinese Cultural Elements on Concept Car Design

abstract: The aim of this research is to investigate the status of concept car design in china and abroad, and analysis the application of Chinese culture elements in concept car design. Based on the ergonomics principles, we refined Chinese culture elements and design a concept car which is environmental, fashionable and efficient. And we try to provide some suggestions to the Chinese elements concept car design in the future.


title: Integrated Object Detection and Tracking with Tracklet-Conditioned Detection

abstract: Accurate detection and tracking of objects is vital for effective video understanding. In previous work, the two tasks have been combined in a way that tracking is based heavily on detection, but the detection benefits marginally from the tracking. To increase synergy, we propose to more tightly integrate the tasks by conditioning the object detection in the current frame on tracklets computed in prior frames. With this approach, the object detection results not only have high detection responses, but also improved coherence with the existing tracklets. This greater coherence leads to estimated object trajectories that are smoother and more stable than the jittered paths obtained without tracklet-conditioned detection. Over extensive experiments, this approach is shown to achieve state-of-the-art performance in terms of both detection and tracking accuracy, as well as noticeable improvements in tracking stability.


Artificial Intelligence and Cognitive Systems


Multi-Thresholding Image Segmentation Using Genetic Algorithm


title: A controller design framework for a NextGen circulation control based UAV

abstract: Circulation Control (CC) is an active flow blowing technique that can be used on small scale fixed-wing Unmanned Aerial Vehicles (UAVs) to enhance lift and aerodynamic efficiency, resulting in enhanced payload capabilities. This paper presents a novel, newly introduced controller design for what could be the next generation UAV, the Unmanned Circulation Control Aerial Vehicle (UC2AV). Controller design for the UC2AV is a challenging task mainly due to the unknown effect of the CC system on the aircraft flight dynamics. This challenge is tackled by using and implementing μ-Analysis and additive uncertainty weighting functions. The general control system architecture of the UC2AV consists of two parts: a dynamic inversion inner-loop control law and a μ-synthesis outer-loop controller. Preliminary results for the proposed control scheme show that the technique can be used for efficient tracking of desired reference signals.


title: PSYCHOMETRIC PROPERTIES OF TRAVELERS NEEDS QUESTIONNAIRE (TNQ)

abstract: The purpose of this research was to develop the Travelers Needs Questionnaire (TNQ), which measures travelers’ needs, and to determine its psychometric properties. The TNQ was developed for travelers with native Russian language from different countries: Russia, Belarus, Ukraine, the Baltic States. The sample consisted of 237 participants aged from 17 to 68 years (26.2 % male, 73.8 % female, mean age M = 30.49, SD = 9.21). The factorial validity of the TNQ was established using principal components analysis with varimax rotation; this yielded seven factors: Professional Realization, Pilgrimage, Personal Development, Cultural Development, Sport, Physical Hedonism and Communication. All the TNQ scales had high internal consistency. The reaction and discrimination indices satisfied the accepted psychometric criteria. The further stage of the TNQ development would be the confirmatory factor analysis in broader international sample, the concurrent and convergent validity establishing, and test-retest reliability examination.


title: MIT App Inventor: Enabling Personal Mobile Computing

abstract: MIT App Inventor is a drag-and-drop visual programming tool for designing and building fully functional mobile apps for Android. App Inventor promotes a new era of personal mobile computing in which people are empowered to design, create, and use personally meaningful mobile technology solutions for their daily lives, in endlessly unique situations. App Inventor's intuitive programming metaphor and incremental development capabilities allow the developer to focus on the logic for programming an app rather than the syntax of the coding language, fostering digital literacy for all. Since it was moved from Google to MIT, a number of improvements have been added, and research projects are underway.


title: Weak Signals as Predictors of Real-World Phenomena in Social Media

abstract: Global and national events in recent years have shown that online social media can be a force for good (e.g., Arab Spring) and harm (e.g., the London riots). In both of these examples, social media played a key role in group formation and organization, and in the coordination of the group's subsequent collective actions (i.e., the move from rhetoric to action). Surprisingly, despite its clear importance, little is understood about the factors that lead to this kind of group development and the transition to collective action. This paper focuses on an approach to the analysis of data from social media to detect weak signals, i.e., indicators that initially appear at the fringes, but are, in fact, early indicators of such large-scale real-world phenomena. Our approach is in contrast to existing research which focuses on analysing major themes, i.e., the strong signals, prevalent in a social network at a particular point in time. Analysis of weak signals can provide interesting possibilities for forecasting, with online user-generated content being used to identify and anticipate possible offline future events. We demonstrate our approach through analysis of tweets collected during the London riots in 2011 and use of our weak signals to predict tipping points in that context.


Development of Online Student Course Registration System


title: End-to-End Multi-View Networks for Text Classification

abstract: We propose a multi-view network for text classification. Our method automatically creates various views of its input text, each taking the form of soft attention weights that distribute the classifier's focus among a set of base features. For a bag-of-words representation, each view focuses on a different subset of the text's words. Aggregating many such views results in a more discriminative and robust representation. Through a novel architecture that both stacks and concatenates views, we produce a network that emphasizes both depth and width, allowing training to converge quickly. Using our multi-view architecture, we establish new state-of-the-art accuracies on two benchmark tasks.


title: Advanced analytics: opportunities and challenges

abstract: Purpose – Advanced analytics‐driven data analyses allow enterprises to have a complete or “360 degrees” view of their operations and customers. The insight that they gain from such analyses is then used to direct, optimize, and automate their decision making to successfully achieve their organizational goals. Data, text, and web mining technologies are some of the key contributors to making advanced analytics possible. This paper aims to investigate these three mining technologies in terms of how they are used and the issues that are related to their effective implementation and management within the broader context of predictive or advanced analytics.Design/methodology/approach – A range of recently published research literature on business intelligence (BI); predictive analytics; and data, text and web mining is reviewed to explore their current state, issues and challenges learned from their practice.Findings – The findings are reported in two parts. The first part discusses a framework for BI using th...


title: NAOMI: Non-Autoregressive Multiresolution Sequence Imputation

abstract: Missing value imputation is a fundamental problem in spatiotemporal modeling, from motion tracking to the dynamics of physical systems. Deep autoregressive models suffer from error propagation which becomes catastrophic for imputing long-range sequences. In this paper, we take a non-autoregressive approach and propose a novel deep generative model: Non-AutOregressive Multiresolution Imputation (NAOMI) to impute long-range sequences given arbitrary missing patterns. NAOMI exploits the multiresolution structure of spatiotemporal data and decodes recursively from coarse to fine-grained resolutions using a divide-and-conquer strategy. We further enhance our model with adversarial training. When evaluated extensively on benchmark datasets from systems of both deterministic and stochastic dynamics. NAOMI demonstrates significant improvement in imputation accuracy (reducing average prediction error by 60% compared to autoregressive counterparts) and generalization for long range sequences.


title: Affine Registration with Feature Space Mutual Information

abstract: This paper introduces two important issues of image registration. At first we want to recall the very general definition of mutual information that allows the choice of various feature spaces to perform image registration. Second we discuss the problem of finding the global maximum in an arbitrary feature space. We used a very general parallel, distributed memory, genetic optimization which turned out to be very robust. We restrict the examples to the context of multi-modal medical image registration but we want to point out that the approach is very general and therefore applicable to a wide range of other applications. The registration algorithm was analysed on a LINUX cluster.


title: Cloud computing security challenges & solutions-A survey

abstract: Cloud Computing and its’ related security issues as well as countermeasures are one of the highly debated topics in today's research field. Though, various surveys regarding Cloud security are already prevalent, there remains a certain gap between the proper mapping of these issues to their corresponding solutions. Some surveys present the Virtualization issues and solutions while other deal with the access control mechanisms, but what lacks is a common framework that would at the same time generalize the concept of cloud security as well as intricately analyze its’ specific requirements. Moreover, countermeasures that are provided in a survey must clearly depict the issue that it is handling. Keeping all these factors in mind this survey paper has been designed so as to cover the necessary areas with a proper interconnection between them and lastly discuss a set of open problems in this domain.


title: Rural Building Detection in High-Resolution Imagery Based on a Two-Stage CNN Model

abstract: High-level feature extraction and hierarchical feature representation of image objects with a convolutional neural network (CNN) can overcome the limitations of the traditional building detection models using middle/low-level features extracted from a complex background. Aiming at the drawbacks of manual village location, high cost, and the limited accuracy of building detection in the existing rural building detection models, a two-stage CNN model is proposed in this letter to detect rural buildings in high-resolution imagery. Simulating the hierarchical processing mechanism of human vision, the proposed model is constructed with two CNNs, whose architectures can automatically locate villages and efficiently detect buildings, respectively. This two-stage CNN model effectively reduces the complexity of the background and improves the efficiency of rural building detection. The experiments showed that the proposed model could automatically locate all the villages in the two study areas, achieving a building detection accuracy of 88%. Compared with the existing models, the proposed model was proved to be effective in detecting buildings in rural areas with a complex background.


title: A Computationally Efficient Method for Large-Scale Concurrent Mapping and Localization

abstract: Decoupled stochastic mapping (DSM) is a computationally efficient approach to large-scale concurrent mapping and localization. DSM reduces the computational burden of conventional stochastic mapping by dividing the environment into multiple overlapping submap regions, each with its own stochastic map. Two new approximation techniques are utilized for transferring vehicle state information from one submap to another, yielding a constant-time algorithm whose memory requirements scale linearly with the size of the operating area. The performance of two different variations of the algorithm is demonstrated through simulations of environments with 110 and 1200 features. Experimental results are presented for an environment with 93 features using sonar data obtained in a 3 by 9 by 1 meter testing tank.


title: Optimal Threshold Policy for In-Home Smart Grid with Renewable Generation Integration

abstract: In-home Smart Grid (SG), the integration of Renewable Power Systems (RPSs) with Conventional Power Systems (CPSs), calls for cost-effective management for the electricity usages of end users’ household appliances. In this paper, by taking the charging process of RPSs and multiple types of household appliances in to consideration, we have developed analytical models to characterize the electricity cost in the in-home smart grid. Based on these models, we formulate the electricity cost minimization problem as a finite-horizon continuous-time Markov decision process (CTMDP), from which we obtain a threshold policy to minimize the cost. Numerical results show that the threshold policy can manage the electricity usage very effectively.


title: Neural Text Generation: Past, Present and Beyond

abstract: This paper presents a systematic survey on recent development of neural text generation models. Specifically, we start from recurrent neural network language models with the traditional maximum likelihood estimation training scheme and point out its shortcoming for text generation. We thus introduce the recently proposed methods for text generation based on reinforcement learning, re-parametrization tricks and generative adversarial nets (GAN) techniques. We compare different properties of these models and the corresponding techniques to handle their common problems such as gradient vanishing and generation diversity. Finally, we conduct a benchmarking experiment with different types of neural text generation models on two well-known datasets and discuss the empirical results along with the aforementioned model properties.


title: Regularised CSP for sensor selection in BCI

abstract: The Common Spatial Pattern (CSP) algorithm is a highly successful method for efficiently calculating spatial filters for brain signal classification. Spatial filtering can improve classification performance considerably, but demands that a large number of electrodes be mounted, which is inconvenient in day-to-day BCI usage. The CSP algorithm is also known for its tendency to overfit, i.e. to learn the noise in the training set rather than the signal. Both problems motivate an approach in which spatial filters are sparsified. We briefly sketch a reformulation of the problem which allows us to do this, using 1-norm regularisation. Focusing on the electrode selection issue, we present preliminary results on EEG data sets that suggest that effective spatial filters may be computed with as few as 10– 20 electrodes, hence offering the potential to simplify the practical realisation of BCI systems significantly.


title: Transforming teaching and learning: embedding ICT into everyday classroom practices

abstract: Drawing on socio-cultural theory, this paper describes how teams of teachers and researchers have developed ways of embedding information and communications technology (ICT) into everyday classroom practices to enhance learning. The focus is on teaching and learning across a range of subjects: English, history, geography, mathematics, modern foreign languages, music and science. The influence of young people's out-of-school uses of ICT on in-school learning is discussed. The creative tension between idiosyncratic and institutional knowledge construction is emphasised and we argue that this is exacerbated by the use of ICT in the classroom.


Impact of Work Environment on Performance of Employees in Manufacturing Sector in India: Literature Review


title: Numerical estimation of characteristics of voltage-current sensor of resin molded type for 22kV power distribution systems

abstract: The electric power is indispensable and its stable supply is always expected. A stable supply needs the real-time observation of load conditions by measurement of power factor. In addition, the various faults have been caused from the harmonic voltage and current. In the authors' laboratory, the sensor, which simultaneously measures both the waveforms of the voltage and current without contacting the conductors in the three-phase power distribution system directly, has been designed and implemented. Using the sensor, we will observe the power factor and harmonics from the measured waveforms of voltage and current in power distribution line. However, the experimental sensor was designed to be applied to the 6.6 kV power distribution system in the dielectric strength. The authors will propose the sensor for the 22kV power distribution system and investigate the output characteristics of this new sensor from numerical results based on the finite element method.


title: Avatar: Mobile Distributed Computing in the Cloud

abstract: Avatar is a system that leverages cloud resources to support fast, scalable, reliable, and energy efficient distributed computing over mobile devices. An avatar is a per-user software entity in the cloud that runs apps on behalf of the user's mobile devices. The avatars are instantiated as virtual machines in the cloud that run the same operating system with the mobile devices. In this way, avatars provide resource isolation and execute unmodified app components, which simplifies technology adoption. Avatar apps execute over distributed and synchronized (mobile device, avatar) pairs to achieve a global goal. The three main challenges that must be overcome by the Avatar system are: creating a high-level programming model and a middleware that enable effective execution of distributed applications on a combination of mobile devices and avatars, re-designing the cloud architecture and protocols to support billions of mobile users and mobile apps with very different characteristics from the current cloud workloads, and explore new approaches that balance privacy guarantees with app efficiency/usability. We have built a basic Avatar prototype on Android devices and Android x86 virtual machines. An application that searches for a lost child by analyzing the photos taken by people at a crowded public event runs on top of this prototype.


title: Migrating to Ipv6 — The role of basic coordination

abstract: The need for a larger Internet address space was acknowledged early on, and a solution (IPv6) standardized years ago. Its adoption has, however, been anything but easy and still faces significant challenges. The situation begs the questions of “why has it been so difficult?” and “what could have been (or still be) done to facilitate this migration?” There has been significant recent interest in those questions, and the paper builds on a line of work based on technology adoption models to explore them. The results confirm the impact of several known factors, but also provide new insight. In particular, they highlight the destabilizing effect of Internet Service Providers (ISPs) offering competing alternatives (to IPv6), and demonstrate the benefits of even minimum coordination among them in offering IPv6 as an option. The findings afford additional visibility into what affects technology transition in large systems with complex dependencies such as the Internet.


title: A printed 16 ports massive MIMO antenna system with directive port beams

abstract: The design of a massive multiple-input-multiple-output (mMIMO) antenna system based on patch antennas is presented in this paper. The array consists of 16 ports, each port consists of a 2×2 patch antenna array with different phase excitation at each element to tilt the beam toward different directions to provide lower correlation coefficient values. A fixed progressive phase feed network is designed to provide the beam tilts. The proposed antenna system is designed using a 3-layer FR4 substrate with total size of 33.33×33.33×0.16 cm3.


title: Efficient Sparse-Winograd Convolutional Neural Networks

abstract: Convolutional Neural Networks (CNNs) are computationally intensive, which limits their application on mobile devices. Their energy is dominated by the number of multiplies needed to perform the convolutions. Winograd's minimal filtering algorithm (Lavin, 2015) and network pruning (Han et al., 2015) can reduce the operation count, but these two methods cannot be directly combined $-$ applying the Winograd transform fills in the sparsity in both the weights and the activations. We propose two modifications to Winograd-based CNNs to enable these methods to exploit sparsity. First, we move the ReLU operation into the Winograd domain to increase the sparsity of the transformed activations. Second, we prune the weights in the Winograd domain to exploit static weight sparsity. For models on CIFAR-10, CIFAR-100 and ImageNet datasets, our method reduces the number of multiplications by $10.4\times$, $6.8\times$ and $10.8\times$ respectively with loss of accuracy less than $0.1\%$, outperforming previous baselines by $2.0\times$-$3.0\times$. We also show that moving ReLU to the Winograd domain allows more aggressive pruning.


title: Learning, Attentional Control, and Action Video Games

abstract: While humans have an incredible capacity to acquire new skills and alter their behavior as a result of experience, enhancements in performance are typically narrowly restricted to the parameters of the training environment, with little evidence of generalization to different, even seemingly highly related, tasks. Such specificity is a major obstacle for the development of many real-world training or rehabilitation paradigms, which necessarily seek to promote more general learning. In contrast to these typical findings, research over the past decade has shown that training on ‘action video games' produces learning that transfers well beyond the training task. This has led to substantial interest among those interested in rehabilitation, for instance, after stroke or to treat amblyopia, or training for various precision-demanding jobs, for instance, endoscopic surgery or piloting unmanned aerial drones. Although the predominant focus of the field has been on outlining the breadth of possible action-game-related enhancements, recent work has concentrated on uncovering the mechanisms that underlie these changes, an important first step towards the goal of designing and using video games for more definite purposes. Game playing may not convey an immediate advantage on new tasks (increased performance from the very first trial), but rather the true effect of action video game playing may be to enhance the ability to learn new tasks. Such a mechanism may serve as a signature of training regimens that are likely to produce transfer of learning.


title: Unsupervised pattern discovery in electronic health care data using probabilistic clustering models

abstract: Bedside clinicians routinely identify temporal patterns in physiologic data in the process of choosing and administering treatments intended to alter the course of critical illness for individual patients. Our primary interest is the study of unsupervised learning techniques for automatically uncovering such patterns from the physiologic time series data contained in electronic health care records. This data is sparse, high-dimensional and often both uncertain and incomplete. In this paper, we develop and study a probabilistic clustering model designed to mitigate the effects of temporal sparsity inherent in electronic health care records data. We evaluate the model qualitatively by visualizing the learned cluster parameters and quantitatively in terms of its ability to predict mortality outcomes associated with patient episodes. Our results indicate that the model can discover distinct, recognizable physiologic patterns with prognostic significance.


title: Interactive Evolutionary Computation : Fusion of the Capabilities of EC Optimization and Human Evaluation

abstract: We survey the research on interactive evolutionary computation (IEC). The IEC is an EC that optimizes systems based on subjective human evaluation. The definition and features of the IEC are first described and then followed by an overview of the IEC research. The overview primarily consists of application research and interface research. In this survey the IEC application fields include graphic arts and animation, 3D computer graphics lighting, music, editorial design, industrial design, facial image generation, speed processing and synthesis, hearing aid fitting, virtual reality, media database retrieval, data mining, image processing, control and robotics, food industry, geophysics, education, entertainment, social system, and so on. The interface research to reduce human fatigue is also included. Finally, we discuss the IEC from the point of the future research direction of computational intelligence. This paper features a survey of about 250 IEC research papers.


title: Cutaneous manifestations of antiphospholipid antibody syndrome.

abstract: Many different cutaneous lesions or cutaneous-systemic syndromes can be the presenting sign of antiphospholipid antibody syndrome (APS), or can develop during the course of disease. None of these conditions are specific for APS. Livedo reticularis or racemosa is commonly seen in APS, but it is one of the least specific findings. Other diseases are less commonly seen, in either their idiopathic or APS-associated form, but are more suggestive of APS. APS should be considered in patients who may appear to have idiopathic livedo reticularis with cerebrovascular accidents (Sneddon's syndrome), atrophie blanche, livedoid vasculitis, malignant atrophic papulosis, or anetoderma. Finally, retiform (branching, stellate) purpura or necrosis is perhaps the most characteristic cutaneous lesion of many different cutaneous microvascular occlusion syndromes, including APS.


title: Designing service-dominant business models

abstract: The emergence of service-dominant logic has influenced business in many domains. It emphasizes the interaction of the producer, consumer, and other value-network partners as they co-create value through collaborative processes. These processes can be seen as service-for-service exchanges of these actors, which contrasts with the output orientation of the goods-dominant-logic that emphasizes how actors exchange output units. This paradigm transition has significant implications on doing business: the business requirements to services will change faster, and the complexity of value-networks required to meet these requirements will increase further. This requires new approaches to business engineering that are grounded in the premises of service-dominant logic. This paper introduces the service-dominant business model radar (SDBM/R) as an integral component of the business engineering framework that we have developed for engineering network-based, service-dominant business. Existing approaches to business model design follow an organization-centric view rooted in goods-dominant-logic, which does not allow adequately modeling the character of service-dominant business. The SDBM/R was developed with close collaboration with industry experts and empirically validated through a series of hands-on workshops with industry professionals from several domains. Thereby, this paper contributes a novel business design approach that has proper academic background and relevant practical embedding.


title: Fast and effective text mining using linear-time document clustering

abstract: Clustering is a powerful technique for large-scale topic discovery from text. It involves two phases: first, feature extraction maps each document or record to a point in high-dimensional space, then clustering algorithms automatically group the points into a hierarchy of clusters. We describe an unsupervised, near-linear time text clustering system that offers a number of algorithm choices for each phase. We introduce a methodology for measuring the quality of a cluster hierarchy in terms of FMeasure, and present the results of experiments comparing different algorithms. The evaluation considers some feature selection parameters (tfidfand feature vector length) but focuses on the clustering algorithms, namely techniques from Scatter/Gather (buckshot, fractionation, and split/join) and kmeans. Our experiments suggest that continuous center adjustment contributes more to cluster quality than seed selection does. It follows that using a simpler seed selection algorithm gives a better time/quality tradeoff. We describe a refinement to center adjustment, “vector average damping,” that further improves cluster quality. We also compare the near-linear time algorithms to a group average greedy agglomerative clustering algorithm to demonstrate the time/quality tradeoff quantitatively.


title: Image Search Reranking With Query-Dependent Click-Based Relevance Feedback

abstract: Our goal is to boost text-based image search results via image reranking. There are diverse modalities (features) of images that we can leverage for reranking, however, the effects of different modalities are query-dependent. The primary challenge we face is how to fuse multiple modalities adaptively for different queries, which has often been overlooked in previous reranking research. Moreover, multimodality fusion without an understanding of the query is risky, and may lead to incorrect judgment in reranking. Therefore, to obtain the best fusion weights for the query, in this paper, we leverage click-through data, which can be viewed as an “implicit” user feedback and an effective means of understanding the query. A novel reranking algorithm, called click-based relevance feedback, is proposed. This algorithm emphasizes the successful use of click-through data for identifying user search intention, while leveraging multiple kernel learning algorithm to adaptively learn the query-dependent fusion weights for multiple modalities. We conduct experiments on a real-world data set collected from a commercial search engine with click-through data. Encouraging experimental results demonstrate that our proposed reranking approach can significantly improve the NDCG@10 of the initial search results by 11.62%, and can outperform several existing approaches for most kinds of queries, such as tail, middle, and top queries.


abstract: Prompting Cognitive and Metacognitive Processing in Writing-to-Learn Enhances Learning Outcomes Sandra Hubner (huebner@psychologie.uni-freiburg.de) Matthias Nuckles (nueckles@psychologie.uni-freiburg.de) Alexander Renkl (renkl@psychologie.uni-freiburg.de) University of Freiburg, Educational Psychologie, Engelbergerstr. 41 79085 Freiburg, Germany instructional support procedure for writing learning proto- cols. We present an experiment that analyzed the effects that various types of instructions for writing a learning protocol had on understanding and retention. Furthermore, we exam- ined how the specific instructions are reflected in the learn- ing protocols. Abstract Learning protocols are a promising follow-up course work. A learning protocol is a written explication of one’s learning processes and outcomes. According to the self-regulation view of writing-to-learn, such writing assignments have to be supported. In this study, learning protocols were structured by prompts to elicit important learning activities as they are pos- tulated in a cyclical model of self-regulated learning. An ex- periment (N = 103) was conducted in which students received either (a) no prompts, (b) cognitive prompts, (c) metacogni- tive prompts, (d) mixed prompts without planning-of- regulation prompts or, (e) mixed prompts including planning- of-regulation prompts. The groups with prompts outper- formed the control group on comprehension and retention measures. Furthermore, prompting all essential sub-processes of self-regulated learning (mixed prompts including planning- of-regulation prompts) was most effective. Theoretical Approaches to Writing-to-Learn Learning by writing can be considered from different theo- retical perspectives (Bangert-Drowns, Hurley, & Wilkinson, 2004). According to the strong text view of writing-to-learn (e.g., Emig, 1977), the processes involved in writing share intrinsic similarities with thinking and learning processes. It is assumed that writing inherently fosters thinking and learning. In line with this assumption, empirical studies generally showed a superiority of learning journal groups over non-writing groups (Connor-Greene, 2000). However, following the meta-analysis of Bangert-Drowns et al., most writing-to-learn assignments yielded rather small effects, typically showing an effect size of .20 on average. Hence, writing per se does not necessarily foster learning to a prac- tically relevant degree. Rather, following Bangert-Drowns et al. conclusions, it is the specific type of writing assign- ment that strongly influences the learning processes and outcomes. In their meta-analysis, the most important mod- erator variable was the presence of prompts that stimulated metacognitive processing such as monitoring and regulation of one’s own learning processes. Bangert-Drowns et al. con- cluded that the available evidence clearly supports a self- regulation view of writing-to-learn rather than the strong text view. According to the self-regulation view, writing as such does not produce learning. Nevertheless, writing may serve as medium that facilitates the application of beneficial cognitive and metacognitive learning activities. However, as argued by Bangert-Drowns et al. (2004), students should explicitly be prompted to elicit the desirable learning activi- ties to a satisfactory degree. For example, Nuckles et al. (2004) analysed “naive” learning protocols of students who had only received brief and informal advice on how to write their protocols. They found that cognitive and metacognitive learning activities did not occur frequently. These results underscore the necessity to support the writing of learning protocols. How should the writing of learning protocols be sup- ported? Following current models of self-regulated learning (Winne, 1996; Zimmerman, 1999), students should be as- Keywords: writing-to-learn; self-regulated learning; prompts; cognitive and metacognitive learning processes; learning journals Introduction Typically, lesson or lecture contents “evaporate” rather quickly, for after the students have left the classroom, only a few continue to reflect on the learning contents. The stu- dents rarely elaborate and organize learning contents in a meaningful and coherent fashion. For example, they seldom come up with examples to put abstract concepts into effect. Also, students neither routinely monitor their understanding nor employ corresponding remedial activities. The writing of learning protocols is a method that helps to overcome these shortcomings (McCrindle & Christensen, 1995). A learning protocol represents a written explication of one’s own learning processes and outcomes. When such protocols are written in an extended period of time (e.g., a whole term or school year) we call it a “learning journal” (cf. McCrindle & Christensen). Learning protocols are especially appropriate for follow-up course work. They help students apply the previously mentioned cognitive and metacognitive activities. The writing of learning protocols has been shown to be effective in improving students’ learning across vari- ous educational settings and subjects (Cantrell, Fusaro, & Doughtery, 2000; McCrindle & Christensen, 1995). How- ever, there is also evidence that without appropriate instruc- tional support, students do not apply the learning protocol method in an optimal way (Nuckles, Schwonke, Berthold, & Renkl, 2004). To bridge this gap, we have developed an


title: An optimized NLMS algorithm for system identification

abstract: The normalized least-mean-square (NLMS) adaptive filter is widely used in system identification. In this paper, we develop an optimized NLMS algorithm, in the context of a state variable model. The proposed algorithm follows a joint-optimization problem on both the normalized step-size and regularization parameters, in order to minimize the system misalignment. Consequently, it achieves a proper compromise between the performance criteria, i.e., fast convergence/tracking and low misadjustment. Simulations performed in the context of acoustic echo cancellation indicate the good features of the proposed algorithm. HighlightsAn optimized normalized least-mean-square (NLMS) algorithm is developed for system identification, in the context of a state variable model.The proposed algorithm is based on a joint-optimization on both the normalized step-size and regularization parameters, in order to minimize the system misalignment.The performance of the proposed joint-optimized NLMS (JO-NLMS) algorithm is evaluated in the framework of acoustic echo cancellation.The JO-NLMS algorithm achieves both fast convergence and tracking, but also low misadjustment.


title: OOFM - A feature modeling approach to implement MPLs and DSPLs

abstract: This paper uses the Object Oriented Feature Modeling (OOFM) technique to develop Multiple Product Lines (MPLs) and Dynamic SPLs (DSPLs). The idea is combine defined OOFM resources in a Model-View-Controller (MVC) architectural style. As result, a pattern-based Product Line Architecture (PLA) is provided, a kind of middleware that performs an advanced usage of multiple and dynamic OOFM features able to generate desired MPLs and DSPLs.


title: Evaluating Effects of User Experience and System Transparency on Trust in Automation

abstract: Existing research assessing human operators' trust in automation and robots has primarily examined trust as a steady-state variable, with little emphasis on the evolution of trust over time. With the goal of addressing this research gap, we present a study exploring the dynamic nature of trust. We defined trust of entirety as a measure that accounts for trust across a human's entire interactive experience with automation, and first identified alternatives to quantify it using real-time measurements of trust. Second, we provided a novel model that attempts to explain how trust of entirety evolves as a user interacts repeatedly with automation. Lastly, we investigated the effects of automation transparency on momentary changes of trust. Our results indicated that trust of entirety is better quantified by the average measure of "area under the trust curve" than the traditional post-experiment trust measure. In addition, we found that trust of entirety evolves and eventually stabilizes as an operator repeatedly interacts with a technology. Finally, we observed that a higher level of automation transparency may mitigate the "cry wolf" effect -- wherein human operators begin to reject an automated system due to repeated false alarms.


title: Security analysis of modern mission critical android mobile applications

abstract: Mobile devices have become an indispensable component of our daily life. New applications published by developers help users to do their daily activities easier and faster. As the market leader of mobile OS, Android provides numerous applications in official and other application markets. However the simplified access model to mobile applications makes malicious applications more accessible to sensitive data that users store on their mobile devices. For instance, mobile banking applications are lucrative targets of the hackers to access user data without authorization. Current security structure of the Android OS makes trivial for hackers to acquire source codes of legitimate applications and republish them after injecting malicious codes into the original source codes. This process of acquiring legitimate application codes, modifying them with malicious intents and then republishing on available application stores is often known as Repackaging attack. The main focus of this study is to analyze popular security attacks to mobile applications, conduct preliminary experiments to evaluate the feasibility and difficulty in implementing security attacks to a mission critical mobile application, identify existing solutions and research gaps, and propose research directions. We successfully conduct three repackaging attacks to access victim's data by by using different hacking tools and techniques. By analyzing these scenarios, we evaluate their level of risks and propose technical mitigation.


title: Creating and benchmarking a new dataset for physical activity monitoring

abstract: Physical activity monitoring has recently become an important field in wearable computing research. However, there is a lack of a commonly used, standard dataset and established benchmarking problems. In this work, a new dataset for physical activity monitoring --- recorded from 9 subjects, wearing 3 inertial measurement units and a heart rate monitor, and performing 18 different activities --- is created and made publicly available. Moreover, 4 classification problems are benchmarked on the dataset, using a standard data processing chain and 5 different classifiers. The benchmark shows the difficulty of the classification tasks and exposes some challenges, defined by e.g. a high number of activities and personalization.


Thermal cycle testing of titanium superhydrophobic surfaces for a spacecraft jumping droplet thermal diode


Organization design for new product development


title: Robust reconstruction of 2D curves from scattered noisy point data

abstract: In this paper, a robust algorithm is proposed for reconstructing 2D curve from unorganized point data with a high level of noise and outliers. By constructing the quadtree of the input point data, we extract the ''grid-like'' boundaries of the quadtree, and smooth the boundaries using a modified Laplacian method. The skeleton of the smoothed boundaries is computed and thereby the initial curve is generated by circular neighboring projection. Subsequently, a normal-based processing method is applied to the initial curve to smooth jagged features at low curvatures areas, and recover sharp features at high curvature areas. As a result, the curve is reconstructed accurately with small details and sharp features well preserved. A variety of experimental results demonstrate the effectiveness and robustness of our method.


title: Building Nutch: Open Source Search

abstract: Search engines are as critical to Internet use as any other part of the network infrastructure, but they differ from other components in two important ways. First, their internal workings are secret, unlike, say, the workings of the DNS (domain name system). Second, they hold political and cultural power, as users increasingly rely on them to navigate online content.


title: Quantitative Relationship Between Kv4.2 mRNA and A-Type K+ Current in Rat Striatal Cholinergic Interneurons During Development

abstract: Channel density is a fundamental factor in determining neuronal firing and is primarily regulated during development through transcriptional and translational regulation. In adult rats, striatal cholinergic interneurons have a prominent A-type current and co-express Kv4.1 and Kv4.2 mRNAs. There is evidence that Kv4.2 plays a primary role in producing the current in adult neurons. The contribution of Kv4.2 and Kv4.1 to the A-type current in cholinergic interneurons during development, however, is not known. Here, using patch-clamp recording and semi-quantitative single-cell reverse transcription-polymerase chain reaction (RT-PCR) techniques, we have examined the postnatal development of A-type current and the expression of Kv4.2 and Kv4.1 in rat striatal cholinergic interneurons. A-type current was detectable at birth, and its amplitude was up-regulated with age, reaching a plateau at about 3 wk after birth. At all ages, the current inactivated with two time constants: one ranging from 15 to 27 ms and the other ranging from 99 to 142 ms. Kv4.2 mRNA was detectable at birth, and the expression level increased exponentially with age, reaching a plateau by 3 wk postnatal. In contrast, Kv4.1 mRNA was not detectable during the first week after birth, and the expression level did not show a clear tendency with age. Taken together, our results suggest that Kv4.2 plays an essential role in producing the A-type current in striatal cholinergic interneurons during the entire course of postnatal development.


title: Cadaver decomposition in terrestrial ecosystems

abstract: A dead mammal (i.e. cadaver) is a high quality resource (narrow carbon:nitrogen ratio, high water content) that releases an intense, localised pulse of carbon and nutrients into the soil upon decomposition. Despite the fact that as much as 5,000 kg of cadaver can be introduced to a square kilometre of terrestrial ecosystem each year, cadaver decomposition remains a neglected microsere. Here we review the processes associated with the introduction of cadaver-derived carbon and nutrients into soil from forensic and ecological settings to show that cadaver decomposition can have a greater, albeit localised, effect on belowground ecology than plant and faecal resources. Cadaveric materials are rapidly introduced to belowground floral and faunal communities, which results in the formation of a highly concentrated island of fertility, or cadaver decomposition island (CDI). CDIs are associated with increased soil microbial biomass, microbial activity (C mineralisation) and nematode abundance. Each CDI is an ephemeral natural disturbance that, in addition to releasing energy and nutrients to the wider ecosystem, acts as a hub by receiving these materials in the form of dead insects, exuvia and puparia, faecal matter (from scavengers, grazers and predators) and feathers (from avian scavengers and predators). As such, CDIs contribute to landscape heterogeneity. Furthermore, CDIs are a specialised habitat for a number of flies, beetles and pioneer vegetation, which enhances biodiversity in terrestrial ecosystems.


title: Anomaly detection in sea traffic - A comparison of the Gaussian Mixture Model and the Kernel Density Estimator

abstract: This paper presents a first attempt to evaluate two previously proposed methods for statistical anomaly detection in sea traffic, namely the Gaussian Mixture Model (GMM) and the adaptive Kernel Density Estimator (KDE). A novel performance measure related to anomaly detection, together with an intermediate performance measure related to normalcy modeling, are proposed and evaluated using recorded AIS data of vessel traffic and simulated anomalous trajectories. The normalcy modeling evaluation indicates that KDE more accurately captures finer details of normal data. Yet, results from anomaly detection show no significant difference between the two techniques and the performance of both is considered suboptimal. Part of the explanation is that the methods are based on a rather artificial division of data into geographical cells. The paper therefore discusses other clustering approaches based on more informed features of data and more background knowledge regarding the structure and natural classes of the data.