title: Towards the automatic detection and identification of English puns

abstract: Lexical polysemy, a fundamental characteristic of all human languages, has long been regarded as a major challenge to machine translation, human–computer interaction, and other applications of computational natural language processing (NLP). Traditional approaches to automatic word sense disambiguation (WSD) rest on the assumption that there exists a single, unambiguous communicative intention underlying every word in a document. However, writers sometimes intend for a word to be interpreted as simultaneously carrying multiple distinct meanings. This deliberate use of lexical ambiguity — i.e. punning — is a particularly common source of humour, and therefore has important implications for how NLP systems process documents and interact with users. In this paper we make a case for research into computational methods for the detection of puns in running text and for the isolation of the intended meanings. We discuss the challenges involved in adapting principles and techniques from WSD to humorously ambiguous text, and outline our plans for evaluating WSD-inspired systems in a dedicated pun identification task. We describe the compilation of a large manually annotated corpus of puns and present an analysis of its properties. While our work is principally concerned with simple puns which are monolexemic and homographic (i.e. exploiting single words which have different meanings but are spelled identically), we touch on the challenges involved in processing other types .


title: An artificial intelligence system for predicting customer default in e-commerce

abstract: Abstract The growing number of e-commerce orders is leading to increased risk management to prevent default in payment. Default in payment is the failure of a customer to settle a bill within 90 days upon receipt. Frequently, credit scoring (CS) is employed to identify customers’ default probability. CS has been widely studied, and many computational methods have been proposed. The primary aim of this work is to develop a CS model to replace the pre-risk check of the e-commerce risk management system Risk Solution Services (RSS), which is currently one of the most used systems to estimate customers’ default probability. The pre-risk check uses data from the order process and includes exclusion rules and a generic CS model. The new model is supposed to replace the whole pre-risk check and has to work both in isolation and in integration with the RSS main risk check. An application of genetic programming (GP) to CS is presented in this paper. The model was developed on a real-world dataset provided by a well-known German financial solutions company. The dataset contains order requests processed by RSS. The results show that GP outperforms the generic CS model of the pre-risk check in both classification accuracy and profit. GP achieved competitive classificatory accuracy with several state-of-the-art machine learning methods, such as logistic regression, support vector machines and boosted trees. Furthermore, the GP model can be used in combination with the RSS main risk check to create a model with even higher discriminatory power.


title: Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus

abstract: Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances. However, to this date, there are no large-scale question-answer corpora available. In this paper we present the 30M Factoid Question-Answer Corpus, an enormous question answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions. The produced question answer pairs are evaluated both by human evaluators and using automatic evaluation metrics, including well-established machine translation and sentence similarity metrics. Across all evaluation criteria the question-generation model outperforms the competing template-based baseline. Furthermore, when presented to human evaluators, the generated questions appear comparable in quality to real human-generated questions.


title: Bar Section Image Enhancement and Positioning Method in On-Line Steel Bar Counting and Automatic Separating System

abstract: At the separating and inspecting section of steel bar plant, steel bars have to be counted and separated by given number, then to be baled in the next procedure. Nowadays, most plants at home and abroad still count steel bars manually, which can't meet the requirement of roboticized production. This paper proposes a non-line steel bar counting and automatic separating system based on computer vision. The system gives out the amount of steel bars moving through the counting area, and separates bars by driving separating machine to tacked bar. Further more, this paper discusses some key problems in credible detection such as section oxidation, inter-cover and body disturbance. By template matching and mutative threshold segmentation, we improve the dependability of steel bars' recognition rate. Long time used in steel bar plant, this method is proved to be authentic and applied, the rate of misdetection has been curtailed to lower than 0.01 percent.


title: Truth and consequences: Using the bogus pipeline to examine sex differences in self‐reported sexuality

abstract: Men report more permissive sexual attitudes and behavior than do women. This experiment tested whether these differences might result from false accommodation to gender norms (distorted reporting consistent with gender stereotypes). Participants completed questionnaires under three conditions. Sex differences in self‐reported sexual behavior were negligible in a bogus pipeline condition in which participants believed lying could be detected, moderate in an anonymous condition, and greatest in an exposure threat condition in which the experimenter could potentially view participants' responses. This pattern was clearest for behaviors considered less acceptable for women than men (e.g., masturbation, exposure to hardcore & softcore erotica). Results suggest that some sex differences in self‐reported sexual behavior reflect responses influenced by normative expectations for men and women.


title: Revisiting the Master-Slave Architecture in Multi-Agent Deep Reinforcement Learning

abstract: Many tasks in artificial intelligence require the collaboration of multiple agents. We exam deep reinforcement learning for multi-agent domains. Recent research efforts often take the form of two seemingly conflicting perspectives, the decentralized perspective, where each agent is supposed to have its own controller; and the centralized perspective, where one assumes there is a larger model controlling all agents. In this regard, we revisit the idea of the master-slave architecture by incorporating both perspectives within one framework. Such a hierarchical structure naturally leverages advantages from one another. The idea of combining both perspective is intuitive and can be well motivated from many real world systems, however, out of a variety of possible realizations, we highlights three key ingredients, i.e. composed action representation, learnable communication and independent reasoning. With network designs to facilitate these explicitly, our proposal consistently outperforms latest competing methods both in synthetics experiments and when applied to challenging StarCraft micromanagement tasks.


title: CatBoost: gradient boosting with categorical features support

abstract: In this paper we present CatBoost, a new open-sourced gradient boosting library that successfully handles categorical features and outperforms existing publicly available implementations of gradient boosting in terms of quality on a set of popular publicly available datasets. The library has a GPU implementation of learning algorithm and a CPU implementation of scoring algorithm, which are significantly faster than other gradient boosting libraries on ensembles of similar sizes.


title: A new performance measure taking into account the mental load in mobile text entry tasks

abstract: Text entry research has received a lot of attention in recent years because of the need for more effective and usable entry methods on mobile devices. Technical limitations such as screen size have led to the design of entry interfaces that mentally load the user in order to obtain better performances. Current evaluation methodologies of these interfaces focus on text entry speed and error rate but don't pay enough attention to the mental load. In this paper, we concentrate on the evaluation of the load's effect on text entry process and we present a comparative evaluation of three mobile text entry methods with and without the application of a secondary task. We also define a performance measure that takes into account the mental load characteristic for a given text entry interface.


title: Eye Tracking Experiments on Process Model Comprehension: Lessons Learned

abstract: For documenting business processes, there exists a plethora of process modeling languages. In this context, graphical process models are used to enhance the process comprehensibility of the stakeholders involved. The large number of available modeling languages, however, aggravates process model comprehension and increases the knowledge gap between domain and modeling experts. Upon this, one major challenge is to identify factors fostering the comprehension of process models. This paper discusses the experiences we gathered with the use of eye tracking in experiments on process model comprehension and the lessons learned in this context. The objective of the experiments was to study the comprehension of process models expressed in terms of four different modeling languages (i.e., BPMN, eGantt, EPC, and Petri Net). This paper further provides recommendations along nine identified categories that can foster related experiments on process model comprehension.


title: How the brain uses time to represent and process visual information

abstract: Information theory provides a theoretical framework for addressing fundamental questions concerning the nature of neural codes. Harnessing its power is not straightforward, because of the differences between mathematical abstractions and laboratory reality. We describe an approach to the analysis of neural codes that seeks to identify the informative features of neural responses, rather than to estimate the information content of neural responses per se. Our analysis, applied to neurons in primary visual cortex (V1), demonstrates that the informative precision of spike times varies with the stimulus modality being represented. Contrast is represented by spike times on the shortest time scale, and different kinds of pattern information are represented on longer time scales. The interspike interval distribution has a structure that is unanticipated from the firing rate. The significance of this structure is not that it contains additional information, but rather, that it may provide a means for simple synaptic mechanisms to decode the information that is multiplexed within a spike train. Extensions of this analysis to the simultaneous responses of pairs of neurons indicate that neighboring neurons convey largely independent information, if the decoding process is sensitive to the neuron of origin and not just the average firing rate. In summary, stimulus-related information is encoded into the precise times of spikes fired by V1 neurons. Much of this information would be obscured if individual spikes were merely taken to be estimators of the firing rate. Additional information would be lost by averaging across the responses of neurons in a local population. We propose that synaptic mechanisms sensitive to interspike intervals and dendritic processing beyond simple summation exist at least in part to enable the brain to take advantage of this extra information.


title: A Feature Selection Method Based on Information Gain and Genetic Algorithm

abstract: with the rapid development of the Computer Science and Technology, It has become a major problem for the users that how to quickly find useful or needed information. Text categorization can help people to solve this question. The feature selection method has become one of the most critical techniques in the field of the text automatic categorization. A new method of the text feature selection based on Information Gain and Genetic Algorithm is proposed in this paper. This method chooses the feature based on information gain with the frequency of items. Meanwhile, for the information filtering systems, this method has been improved fitness function to fully consider the characteristics of weight, text and vector similarity dimension, etc. The experiment has proved that the method can reduce the dimension of text vector and improve the precision of text classification.


title: Comparative study of pressure distribution at the user-cushion interface with different cushions in a population with spinal cord injury ☆

abstract: Abstract Background Few studies have offered comparative information on the mechanical characteristics of different wheelchair seat cushions. The objective of the present study was to compare the benefits of the wheelchair seat cushions most frequently used in a population of patients with spinal cord injury in terms of pressure distribution and contact surface at the user-cushion interface. Methods Each one of 48 patients with spinal cord injury was seated in his or her own wheelchair on the four models of cushions analyzed (low-profile air, high-profile air, dual-compartment air, and gel and firm foam), which were presented in randomized order. The pressure distribution readings and support surface area of the user-cushion interface were obtained with a matrix of piezocapacitive sensors. Findings The dual-compartment air cushion yielded lower readings for all pressure parameters analyzed ( P max , P mean , P sd , and P isch ) than the other three cushion models ( P S tot , S > 60 and % S > 60) also were obtained with the dual-compartment air cushion ( P Interpretation In the sample analyzed, the dual-compartment air cushion was the cushion with the best pressure distribution and largest contact surface of the user-cushion interface compared to the other three cushions studied.


title: Applying computational tools to predict gaze direction in interactive visual environments

abstract: Future interactive virtual environments will be “attention-aware,” capable of predicting, reacting to, and ultimately influencing the visual attention of their human operators. Before such environments can be realized, it is necessary to operationalize our understanding of the relevant aspects of visual perception, in the form of fully automated computational heuristics that can efficiently identify locations that would attract human gaze in complex dynamic environments. One promising approach to designing such heuristics draws on ideas from computational neuroscience. We compared several neurobiologically inspired heuristics with eye-movement recordings from five observers playing video games, and found that human gaze was better predicted by heuristics that detect outliers from the global distribution of visual features than by purely local heuristics. Heuristics sensitive to dynamic events performed best overall. Further, heuristic prediction power differed more between games than between different human observers. While other factors clearly also influence eye position, our findings suggest that simple neurally inspired algorithmic methods can account for a significant portion of human gaze behavior in a naturalistic, interactive setting. These algorithms may be useful in the implementation of interactive virtual environments, both to predict the cognitive state of human operators, as well as to effectively endow virtual agents in the system with humanlike visual behavior.


title: On the Complexity of Neural Network Classifiers: A Comparison Between Shallow and Deep Architectures

abstract: Recently, researchers in the artificial neural net- work field have focused their attention on connectionist models composed by several hidden layers. In fact, experimental results and heuristic considerations suggest that deep architectures are more suitable than shallow ones for modern applications, facing very complex problems, e.g., vision and human language understanding. However, the actual theoretical results supporting such a claim are still few and incomplete. In this paper, we propose a new approach to study how the depth of feedforward neural networks impacts on their ability in implementing high complexity functions. First, a new measure based on topological concepts is introduced, aimed at evaluating the complexity of the function implemented by a neural network, used for classification purposes. Then, deep and shallow neural architectures with com- mon sigmoidal activation functions are compared, by deriving upper and lower bounds on their complexity, and studying how the complexity depends on the number of hidden units and the used activation function. The obtained results seem to support the idea that deep networks actually implements functions of higher complexity, so that they are able, with the same number of resources, to address more difficult problems.


The Influence of Emotion on Trust 1


title: Reducing Buyer Search Costs: Implications for Electronic Marketplaces

abstract: Information systems can serve as intermediaries between the buyers and the sellers in a market creating an "electronic marketplace" that lowers the buyers' cost to acquire information about seller prices and product offerings. As a result, electronic marketplaces reduce the inefficiencies caused by buyer search costs, in the process reducing the ability of sellers to extract monopolistic profits while increasing the ability of markets to optimally allocate productive resources. This article models the role of buyer search costs in markets with differentiated product offerings. The impact of reducing these search costs is analyzed in the context of an electronic marketplace, and the allocational efficiencies such a reduction can bring to a differentiated market are formalized. The resulting implications for the incentives of buyers, sellers, and independent intermediaries to invest in electronic marketplaces are explored. Finally, the possibility to separate price information from product attribute information is introduced, and the implications of designing markets promoting competition along each of these dimensions are discussed.


title: Looking for Hyponyms in Vector Space

abstract: The task of detecting and generating hyponyms is at the core of semantic understanding of language, and has numerous practical applications. We investigate how neural network embeddings perform on this task, compared to dependency-based vector space models, and evaluate a range of similarity measures on hyponym generation. A new asymmetric similarity measure and a combination approach are described, both of which significantly improve precision. We release three new datasets of lexical vector representations trained on the BNC and our evaluation dataset for hyponym generation.


title: A novel approach for tuberculosis screening based on deep convolutional neural networks

abstract: Tuberculosis (TB) is one of the major global health threats especially in developing countries. Although newly diagnosed TB patients can be recovered with high cure rate, many curable TB patients in the developing countries are obliged to die because of delayed diagnosis, partly by the lack of radiography and radiologists. Therefore, developing a computer-aided diagnosis (CAD) system for TB screening can contribute to early diagnosis of TB, which results in prevention of deaths from TB. Currently, most CAD algorithms adopt carefully designed morphological features distinguishing different lesion types to improve screening performances. However, such engineered features cannot be guaranteed to be the best descriptors for TB screening. Deep learning has become a majority in machine learning society. Especially in computer vision fields, it has been verified that deep convolutional neural networks (CNN) is a very promising algorithm for various visual tasks. Since deep CNN enables end-to-end training from feature extraction to classification, it does not require objective-specific manual feature engineering. In this work, we designed CAD system based on deep CNN for automatic TB screening. Based on large-scale chest X-rays (CXRs), we achieved viable TB screening performance of 0.96, 0.93 and 0.88 in terms of AUC for three real field datasets, respectively, by exploiting the effect of transfer learning.


title: Physiological properties of planktic species of Anabaena (Cyanobacteria) and their taxonomic value at species level

abstract: Fifty axenic strains of planktic Anabaena were determined for their physiological growth characteristics, including growth temperature range, salinity tolerance and heterotrophic ability. The taxonomic value of these physiological properties was evaluated by combining with their morphological characteristics. Except for A. affinis LEMMERMANN, all species showed good correlation between the morphological and growth temperature characteristics. A. affinis could be distinguished into two groups by temperature characteristics. An examination of salinity tolerance showed that most species could grow in salinity up to 1/5 (0.78% of salinity) or 1/3 sea-water medium (1.28% of salinity). Two species (A. solitaria and A. circinalis) grew only up to 1/10 sea-water medium (0.43% of salinity). None of planktic species ofAnabaena grew either in dark with addition of organic substances such as organic acids and sugars, or in the light with 3'-(3-4-dichlorophenyl)1', 1'-dimethyl urea (DCMU) and the above organic substances. The newly established species A. eucompacta Li et WATANABE was shown to have distinctly different temperature and salinity properties from the morphologically similar species, A. compacta (NYGAARD) HICKEL.


title: Development of an Emergency Department Violence Intervention Program for Youth: An Integrated Knowledge Translation Approach

abstract: BACKGROUND: Violent interpersonal injury is a common presentation to emergency departments (EDs) and is increasingly being treated as a preventable condition. Given the complexity of the issue, it is key to ensure interventions are feasible and acceptable within the communities that are affected by violence. Our team consists of ED staff, community members who work with youth affected by violence, people who were affected by violence in their youth, and researchers. OBJECTIVES: We describe how an integrated knowledge translation (KT) process was used to develop an ED violence intervention program (EDVIP) for youth affected by violence. METHODS: We used the Canadian Institutes of Health Research Guidelines for integrated KT (iKT) to develop an EDVIP. Specifically, we report the Knowledge to Action process which involves both knowledge creation and an action cycle. RESULTS: Our team determined the research question, the research approach, assessed feasibility and determined outcomes for our study. Using the iKT approach facilitated initiation of a funded trial that is now active. CONCLUSIONS: This paper highlights the benefit of including community experts at the beginning of and throughout the research process. Language: en


title: Aflatoxin Detoxification in Rice using Citric Acid

abstract: Aflatoxins cause health hazards to human and animals and has also economical problems. Therefore, the de- toxification effect of citric acid was investigated in rice as the main food of Iranian people. Methods: Initially 275 samples of rice were examined for aflatoxins by HPLC. The aflatoxins contaminated samples were later treated by aqueous citric acid and detoxification of aflatoxins were quantified using HPLC. Results: Among the 275 samples analyzed, aflatoxin B1 and aflatoxin B2 were detected in 211(76.72% of total) samples. Aflatoxin B1 was detected in 203(73.82% of total) samples with a mean and standard deviation of 2.3±10.21ppb. Aflatoxin B2 together with aflatoxin B1 were detected in only 8(2.91% of total) samples with a mean and standard deviation of 1.38±2.7ppb of aflatoxin B2 and 2.99±1.56 of aflatoxin B1 respectively. Aflatoxin B1 level in 5 samples (1.82%) was above the maximum tolerated level of aflatoxin B1 in Iran (5ppb). However considering the Iranian maximum tolerated level for aflatoxins in rice (30ppb), only 3(1.09%) samples were above the 30ppb and also in regard to the European maximum toler- ated level for aflatoxins in rice (4ppb), only 9(3.27%) samples were considered as higher than 4ppb. Conclusion: The HPLC assay showed that although aflatoxins with a concentration of <30 and <4 ppb in the rice samples were completely degraded, but 97.22% degradation occurred in rice contaminated with �30 and �4ppb when treated with 1N citric acid. These results revealed the efficacy of 1N citric acid in reducing aflatoxins levels in rice.


title: Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks

abstract: Effective training of deep neural networks suffers from two main issues. The first is that the parameter spaces of these models exhibit pathological curvature. Recent methods address this problem by using adaptive preconditioning for Stochastic Gradient Descent (SGD). These methods improve convergence by adapting to the local geometry of parameter space. A second issue is overfitting, which is typically addressed by early stopping. However, recent work has demonstrated that Bayesian model averaging mitigates this problem. The posterior can be sampled by using Stochastic Gradient Langevin Dynamics (SGLD). However, the rapidly changing curvature renders default SGLD methods inefficient. Here, we propose combining adaptive preconditioners with SGLD. In support of this idea, we give theoretical properties on asymptotic convergence and predictive risk. We also provide empirical results for Logistic Regression, Feedforward Neural Nets, and Convolutional Neural Nets, demonstrating that our preconditioned SGLD method gives state-of-the-art performance on these models.


Fructose: A Dietary Sugar in Crosstalk with Microbiota Contributing to the Development and Progression of Non-Alcoholic Liver Disease


title: Building robust geospatial web services for agricultural information extraction and sharing

abstract: Geospatial web service of agricultural information has a wide variety of consumers. An operational agricultural service will receive considerable requests and process a huge amount of datasets each day. To ensure the service quality, many strategies have to be taken during developing and deploying agricultural information services. This paper presents a set of methods to build robust geospatial web service for agricultural information extraction and sharing. The service is designed to serve the public and handle heavy-load requests for a long-lasting term with least maintenance. We have developed a web service to validate our approach. The service is used to serve more than 10 TB data product of agricultural drought. The performance is tested. The result shows that the service has an excellent response time and the use of system resources is stable. We have plugged the service into an operational system for global drought monitoring. The statistics and feedbacks show our approach is feasible and efficient in operational web systems.


title: Leveraging Forensic Tools for Virtual Machine Introspection

abstract: Virtual machine introspection (VMI) has formed the basis of a number of novel approaches to security in recent years. Although the isolation provided by a virtualized environment provides improved security, software that makes use of VMI must overcome the semantic gap, reconstructing high-level state information from low-level data sources such as physical memory. The digital forensics community has likewise grappled with semantic gap problems in the field of forensic memory analysis (FMA), which seeks to extract forensically relevant information from dumps of physical memory. In this paper, we will show that work done by the forensic community is directly applicable to the VMI problem, and that by providing an interface between the two worlds, the difficulty of developing new virtualization security solutions can be significantly reduced.


title: Small wind turbine operating points and their effect on the DC-link control for frequency support on low power microgrids with high wind penetration

abstract: When the wind power accounts for a large portion of the islanded microgrid power, it may need to help the ac bus frequency regulation. The increasing penetration of variable speed wind turbines (WT) in microgrids leads to a lower inertia, as the rotational speed of the turbine and the grid are decoupled by power electronic converters. Lower system inertia results in a larger and faster frequency deviation after occurrence of abrupt variations on generation and load. It is possible to implement control loops in the WT converters to provide a virtual inertia and support frequency regulation in the microgrid. This paper investigates the variables related to the frequency and active power compensation capability of WT, such as kinetic energy, DC-link capacitance, turbine size, speed and operation region. The analysis is done in closed loop considering an optimal control technique to provide fair comparison among the variables and provide the optimum control trajectory, limiting the mechanical losses.


title: On robust truth discovery in sparse social media sensing

abstract: In the big data era, it's important to identify trustworthy information from an influx of noisy data contributed by unvetted sources from online social media (e.g., Twitter, Instagram, Flickr). This task is referred to as truth discovery which aims at identifying the reliability of the sources and the truthfulness of claims they make without knowing either of them a priori. There are two important challenges that have not been well addressed in current truth discovery solutions. The first one is “misinformation spread” where a majority of sources are contributing to false claims, making the identification of truthful claims difficult. The second challenge is “data sparsity” where sources contribute a small number of claims, providing insufficient evidence to accomplish the truth discovery task. In this paper, we developed a Robust Truth Discovery (RTD) scheme to address the above two challenges. In particular, the RTD scheme explicitly quantifies different degrees of attitude that a source may express on a claim and incorporates the historical contributions of a source using a principled approach. The evaluation results on two real world datasetsshow that the RTD scheme significantly outperforms the state-of-the-art truth discovery methods.


title: An experimental study of speech emotion recognition based on deep convolutional neural networks

abstract: Speech emotion recognition (SER) is a challenging task since it is unclear what kind of features are able to reflect the characteristics of human emotion from speech. However, traditional feature extractions perform inconsistently for different emotion recognition tasks. Obviously, different spectrogram provides information reflecting difference emotion. This paper proposes a systematical approach to implement an effectively emotion recognition system based on deep convolution neural networks (DCNNs) using labeled training audio data. Specifically, the log-spectrogram is computed and the principle component analysis (PCA) technique is used to reduce the dimensionality and suppress the interferences. Then the PCA whitened spectrogram is split into non-overlapping segments. The DCNN is constructed to learn the representation of the emotion from the segments with labeled training speech data. Our preliminary experiments show the proposed emotion recognition system based on DCNNs (containing 2 convolution and 2 pooling layers) achieves about 40% classification accuracy. Moreover, it also outperforms the SVM based classification using the hand-crafted acoustic features.


title: Forward Error Correction for DNA Data Storage

abstract: Abstract We report on a strong capacity boost in storing digital data in synthetic DNA. In principle, synthetic DNA is an ideal media to archive digital data for very long times because the achievable data density and longevity outperforms today's digital data storage media by far. On the other hand, neither the synthesis, nor the amplification and the sequencing of DNA strands can be performed error-free today and in the foreseeable future. In order to make synthetic DNA available as digital data storage media, specifically tailored forward error correction schemes have to be applied. For the purpose of realizing a DNA data storage, we have developed an efficient and robust forwarderror-correcting scheme adapted to the DNA channel. We based the design of the needed DNA channel model on data from a proof-of-concept conducted 2012 by a team from the Harvard Medical School [1]. Our forward error correction scheme is able to cope with all error types of today's DNA synthesis, amplification and sequencing processes, e.g. insertion, deletion, and swap errors. In a successful experiment, we were able to store and retrieve error-free 22 MByte of digital data in synthetic DNA recently. The found residual error probability is already in the same order as it is in hard disk drives and can be easily improved further. This proves the feasibility to use synthetic DNA as longterm digital data storage media.


title: Simple online and realtime tracking with spherical panoramic camera

abstract: In this paper, a simple yet effective method for online and real-time multi-object tracking (MOT) in 360-degree equi-rectangular panoramic videos is proposed. Based on the current state-of-the-art tracking-by-detection paradigm, several improvements have been made to overcome the challenges of full field-of-view (FOV) of Spherical Panoramic Camera (SPC). In addition, two datasets are presented for evaluation. It is shown that the proposed method outperforms the baseline by 28.6% and 27.8% in terms of average Multiple Object Tracking Accuracy (MOTA) on each dataset.


title: A fast and adaptive method for image contrast enhancement

abstract: In this paper we describe a fast approach for image contrast enhancement, based on localized contrast manipulation. Our approach is not only last and easy to implement, but also has several other promising properties (adaptive, multiscale, weighted localization, etc.). We will also discuss in this paper an anisotropic version of our approach. Several examples of medical images, including brain MR images, chest CT images and mammography images, will be provided to demonstrate the performance of our approach.


title: Teenager’s Addiction to Smart Phones and Its Integrated Therapy Method

abstract: The fundamental cause of teenagers’ addiction to smart phone is due to stress arising from family and school. Smartphone can cause many problems including musculoskeletal symptom which can develop due to staying in one position for a long time. Furthermore, starring at a small screen for an extended time can cause decreased eye sight, along with amblyopia. Smart phone addiction causes depression and anxiety which will lead to severe mental problems. This will lead to problems associated to one’s social life and human network. Fundamental treatment of cognitive and emotional elements needed to become harmonized as a whole in order to increase the effectiveness of the treatment results. The therapy has been verified to cure the behavioral and emotional aspect related to depression, sense of control, and assimilation in school. From these integrated treatment approaches, this report compared the treatment’s upsides and downsides and also, assessed the possibility the integrated treatment process. We can presume that this report provided the opportunity and support for the creation of a fundamental treatment method that can help teenagers live a healthy life by overcoming addiction to smart phones.


title: Three-Dimensional Super-Resolution Imaging by Stochastic Optical Reconstruction Microscopy

abstract: Recent advances in far-field fluorescence microscopy have led to substantial improvements in image resolution, achieving a near-molecular resolution of 20 to 30 nanometers in the two lateral dimensions. Three-dimensional (3D) nanoscale-resolution imaging, however, remains a challenge. We demonstrated 3D stochastic optical reconstruction microscopy (STORM) by using optical astigmatism to determine both axial and lateral positions of individual fluorophores with nanometer accuracy. Iterative, stochastic activation of photoswitchable probes enables high-precision 3D localization of each probe, and thus the construction of a 3D image, without scanning the sample. Using this approach, we achieved an image resolution of 20 to 30 nanometers in the lateral dimensions and 50 to 60 nanometers in the axial dimension. This development allowed us to resolve the 3D morphology of nanoscopic cellular structures.


title: Binary object extraction using bi-directional self-organizing neural network (BDSONN) architecture with fuzzy context sensitive thresholding

abstract: A novel neural network architecture suitable for image processing applications and comprising three interconnected fuzzy layers of neurons and devoid of any back-propagation algorithm for weight adjustment is proposed in this article. The fuzzy layers of neurons represent the fuzzy membership information of the image scene to be processed. One of the fuzzy layers of neurons acts as an input layer of the network. The two remaining layers viz. the intermediate layer and the output layer are counter-propagating fuzzy layers of neurons. These layers are meant for processing the input image information available from the input layer. The constituent neurons within each layer of the network architecture are fully connected to each other. The intermediate layer neurons are also connected to the corresponding neurons and to a set of neighbors in the input layer. The neurons at the intermediate layer and the output layer are also connected to each other and to the respective neighbors of the corresponding other layer following a neighborhood based connectivity. The proposed architecture uses fuzzy membership based weight assignment and subsequent updating procedure. Some fuzzy cardinality based image context sensitive information are used for deciding the thresholding capabilities of the network. The network self organizes the input image information by counter-propagation of the fuzzy network states between the intermediate and the output layers of the network. The attainment of stability of the fuzzy neighborhood hostility measures at the output layer of the network or the corresponding fuzzy entropy measures determine the convergence of the network operation. An application of the proposed architecture for the extraction of binary objects from various degrees of noisy backgrounds is demonstrated using a synthetic and a real life image.


Drawing boundaries of social interaction


title: A Survey of Deep Learning Techniques for Mobile Robot Applications

abstract: Advancements in deep learning over the years have attracted research into how deep artificial neural networks can be used in robotic systems. This research survey will present a summarization of the current research with a specific focus on the gains and obstacles for deep learning to be applied to mobile robotics.


title: A new approach to deanonymization of unreachable bitcoin nodes

abstract: Mounting deanonymization attacks on the unreachable Bitcoin nodes – these nodes do not accept incoming connections – residing behind the NAT is a challenging task. Such an attack was first given by Biryukov, Khovratovich and Pustogarov based on their observation that a node can be uniquely identified in a single session by their directly-connected neighbouring nodes (ACM CCS’15). However, the BKP15 attack is less effective across multiple sessions. To address this issue, Biryukov and Pustogarov later on devised a new strategy exploiting certain properties of address-cookies (IEEE S&P’15). Unfortunately, the BP15 attack is also rendered ineffective by the present modification to the Bitcoin client.


title: Fully automatic image colorization based on Convolutional Neural Network

abstract: This paper deals with automatic image colorization. This is a very difficult task, since it is an ill-posed problem that usually requires user intervention to achieve high quality. A fully automatic approach is proposed that is able to produce realistic colorization of an input grayscale image. Motivated by the recent success of deep learning techniques in image processing, we propose a feed-forward, two-stage architecture based on Convolutional Neural Network that predicts the U and V color channels. Unlike most of the previous works, this paper presents a fully automatic colorization which is able to produce high-quality and realistic colorization even of complex scenes. Comprehensive experiments and qualitative and quantitative evaluations were conducted on the images of SUN database and on other images. We have found that Quaternion Structural Similarity (QSSIM) gives in some degree a good base for quantitative evaluation, that is why we chose QSSIM as an index-number for the quality of colorization.


title: LCR-Net: Localization-Classification-Regression for Human Pose

abstract: We propose an end-to-end architecture for joint 2D and 3D human pose estimation in natural images. Key to our approach is the generation and scoring of a number of pose proposals per image, which allows us to predict 2D and 3D pose of multiple people simultaneously. Hence, our approach does not require an approximate localization of the humans for initialization. Our architecture, named LCR-Net, contains 3 main components: 1) the pose proposal generator that suggests potential poses at different locations in the image, 2) a classifier that scores the different pose proposals, and 3) a regressor that refines pose proposals both in 2D and 3D. All three stages share the convolutional feature layers and are trained jointly. The final pose estimation is obtained by integrating over neighboring pose hypotheses, which is shown to improve over a standard non maximum suppression algorithm. Our approach significantly outperforms the state of the art in 3D pose estimation on Human3.6M, a controlled environment. Moreover, it shows promising results on real images for both single and multi-person subsets of the MPII 2D pose benchmark.


title: Compact Wideband Open-End Slot Antenna With Inherent Matching

abstract: A compact wideband open-end slot antenna is proposed for DCS, PCS, UMTS, WLAN, and WiMAX band applications. The antenna is composed of an open-ended slot and a central line within the slot. The open-end slot decreases the antenna size in half, compared to a conventional slot-ring antenna. Furthermore, the narrowed slot and central line miniaturize the antenna and obtain wideband performance using the inherent impedance-matching network of the central line. The proposed antenna obtained an omnidirectional radiation pattern with an antenna gain of larger than 1 dBi and an antenna efficiency of greater than 50% over 1710-3660 MHz.


title: Fair Adaptive Data Rate Allocation and Power Control in LoRaWAN

abstract: In this paper, we present results of a study of the data rate fairness among nodes within a LoRaWAN cell. Since LoRa/LoRaWAN supports various data rates, we firstly derive the fairest ratios of deploying each data rate within a cell for a fair collision probability. LoRa/LoRaWan, like other frequency modulation based radio interfaces, exhibits the capture effect in which only the stronger signal of colliding signals will be extracted. This leads to unfairness, where far nodes or nodes experiencing higher attenuation are less likely to see their packets received correctly. Therefore, we secondly develop a transmission power control algorithm to balance the received signal powers from all nodes regardless of their distances from the gateway for a fair data extraction. Simulations show that our approach achieves higher fairness in data rate than the state-of-art in almost all network configurations.


title: Topological pattern discovery and feature extraction for fraudulent financial reporting

abstract: Fraudulent financial reporting (FFR) involves conscious efforts to mislead others regarding the financial condition of a business. It usually consists of deliberate actions to deceive regulators, investors or the general public that also hinder systematic approaches from effective detection. The challenge comes from distinguishing dichotomous samples that have their major attributes falling in the same distribution. This study pioneers a novel dual GHSOM (Growing Hierarchical Self-Organizing Map) approach to discover the topological patterns of FFR, achieving effective FFR detection and feature extraction. Specifically, the proposed approach uses fraudulent samples and non-fraudulent samples to train a pair of dual GHSOMs under the same training parameters and examines the hypotheses for counterpart relationships among their subgroups taking advantage of unsupervised learning nature and growing hierarchical structures from GHSOMs. This study further presents (1) an effective classification rule to detect FFR based on the topological patterns and (2) an expert-competitive feature extraction mechanism to capture the salient characteristics of fraud behaviors. The experimental results against 762 annual financial statements from 144 public-traded companies in Taiwan (out of which 72 are fraudulent and 72 are non-fraudulent) reveal that the topological pattern of FFR follows the non-fraud-central spatial relationship, as well as shows the promise of using the topological patterns for FFR detection and feature extraction.


title: Seeing Invisible Poses: Estimating 3D Body Pose from Egocentric Video

abstract: Understanding the camera wearer's activity is central to egocentric vision, yet one key facet of that activity is inherently invisible to the camera--the wearer's body pose. Prior work focuses on estimating the pose of hands and arms when they come into view, but this 1) gives an incomplete view of the full body posture, and 2) prevents any pose estimate at all in many frames, since the hands are only visible in a fraction of daily life activities. We propose to infer the "invisible pose" of a person behind the egocentric camera. Given a single video, our efficient learning-based approach returns the full body 3D joint positions for each frame. Our method exploits cues from the dynamic motion signatures of the surrounding scene--which changes predictably as a function of body pose--as well as static scene structures that reveal the viewpoint (e.g., sitting vs. standing). We further introduce a novel energy minimization scheme to infer the pose sequence. It uses soft predictions of the poses per time instant together with a non-parametric model of human pose dynamics over longer windows. Our method outperforms an array of possible alternatives, including deep learning approaches for direct pose regression from images.


title: Firewall and load balancing as an application of SDN

abstract: It is needed to restructure the static internetwork to more flexible networking environment. Today's infrastructure is not able to change according to fast changing requirements of the users. Due to this there is an introduction of Software Defined Networking (SDN) and OpenFlow (OF) architecture in the year 2005 to modify today's static environment to dynamic one. This concept helps to have programmability by decoupling the data plane and control plane, fast transformation and centralized controller. This paper focuses on the replacement of physical switches by the virtual networking domain and also development of firewall and load balancing application that resides on the OpenFlow based SDN controller. It also demonstrates that most of the committed hardware firewall devices can be replaced by the software firewall as it can be built by software. There are numerous OpenFlow based controllers available, among which the selected controller is floodlight as it displays the SDN network in GUI. OpenvSwitch is chosen to develop the SDN network.


A New Social Media Security Model (SMSM)


title: Predicting Parameters Importance in Higher Education Using Data Analysis: A Regression Model

abstract: Data Analysis is key to understand the importance of accumulated data over a period of time. The importance of the accumulated data is understood by the data analyst over period of time. The authors had shown the importance of the data collected of the higher education by finding the new and un-identified facts using the statistically techniques. The authors found that the regression techniques could be a boom for the analysis of the data of the higher education and is being analysed to understand the significance and relationship between key parameters of the higher education setup.


The Space Usage Problem: An Evaluation Kit for Graph-Reduction Semantics


title: Optimal Capacitor Placement to Distribution Transformers for Power Loss Reduction in Radial Distribution Systems

abstract: Deploying shunt capacitor banks in distribution systems can effectively reduce the power loss and provide additional benefits for system operation. In practice, the power loss on distribution transformers can account for a considerable portion of the overall loss. This paper proposes a method for optimal placement of capacitor banks to the distribution transformers to reduce power loss. The capacitor bank locations are considered at the low-side of transformers. The net present value (NPV) criterion is adopted to evaluate the cost benefit of the capacitor installation project. First, an explicit formula for directly calculating the power loss of radial distribution systems is derived. Then, the optimal capacitor bank placement is formulated as a mixed-integer programming (MIP) model maximizing the NPV of the project subject to certain constraints. The model is suitable for being solved by commercial MIP packages, and the operational control of the capacitor banks to maximize the power loss reduction can be simply achieved by local automatic switching according to VAR measurements. The proposed method has been practically applied in the Macau distribution system, and the simulation results show that the proposed method is computationally efficient, and a considerable positive NPV can be obtained from the optimal capacitor bank placement.


Extreme lateral lumbar disc herniation.


title: Intention-aware online POMDP planning for autonomous driving in a crowd

abstract: This paper presents an intention-aware online planning approach for autonomous driving amid many pedestrians. To drive near pedestrians safely, efficiently, and smoothly, autonomous vehicles must estimate unknown pedestrian intentions and hedge against the uncertainty in intention estimates in order to choose actions that are effective and robust. A key feature of our approach is to use the partially observable Markov decision process (POMDP) for systematic, robust decision making under uncertainty. Although there are concerns about the potentially high computational complexity of POMDP planning, experiments show that our POMDP-based planner runs in near real time, at 3 Hz, on a robot golf cart in a complex, dynamic environment. This indicates that POMDP planning is improving fast in computational efficiency and becoming increasingly practical as a tool for robot planning under uncertainty. © 2015 IEEE


title: Design of Permanent Multipole Magnets with Oriented Rare Earth Cobalt Material

abstract: Abstract By taking advantage of both the magnetic strength and the astounding simplicity of the magnetic properties of oriented rare earth cobalt material, new designs have been developed for a number of devices. In this article on multipole magnets, special emphasis is put on quadrupoles because of their frequent use and because the aperture fields achievable (1.2–1.4 T) are rather large. This paper also lays the foundation for future papers on: (a) linear arrays for use as “plasma buckets” or undulators for the production of synchrotron radiation; (b) structures for the production of solenoidal fields; and (c) three-dimensional structures such as helical undulators or multipoles.


title: Resolving Complex Cases of Definite Pronouns: The Winograd Schema Challenge

abstract: We examine the task of resolving complex cases of definite pronouns, specifically those for which traditional linguistic constraints on coreference (e.g., Binding Constraints, gender and number agreement) as well as commonly-used resolution heuristics (e.g., string-matching facilities, syntactic salience) are not useful. Being able to solve this task has broader implications in artificial intelligence: a restricted version of it, sometimes referred to as the Winograd Schema Challenge, has been suggested as a conceptually and practically appealing alternative to the Turing Test. We employ a knowledge-rich approach to this task, which yields a pronoun resolver that outperforms state-of-the-art resolvers by nearly 18 points in accuracy on our dataset.


title: Enabling ∼100fps detection on a landing unmanned aircraft for its on-ground vision-based recovery

abstract: In this paper, a deep learning inspired solution is proposed and developed to enable timeliness and practicality of the pre-existing ground stereo vision guidance system for flxed-wing UAVs' safe landing. Since the ground guidance prototype was restricted within applications due to its untimeliness, eventually the vision-based detection less than 15 fps (frame per second). Under such circumstances, we employ a regression based deep learning algorithm into automatic detection on the flying aircraft in the landing sequential images. The system architecture is upgraded so as to be compatible with the novel deep learning requests, and furthermore, annotated datasets are conducted to support training and testing of the regression-based learning detection algorithm. Experimental results validate that the detection attaches 100 fps or more while the localization accuracy is kept in the same level.


Social Network Hate Speech Detection for Amharic Language


title: At-speed I/O Test for Fast Vref Optimization in High Speed Single-ended Memory Systems

abstract: Single-ended memory interfaces have gone through a remarkable increase in data rates over the last decade increasing the challenges faced by system designers and OEMs. One of the major challenges in single-ended system design is optimizing the reference voltage level (Vref) in the receiver. The traditional method to choose Vref is to sweep the reference voltage level while performing a link Bit Error Rate (BER) test. This existing method has serious disadvantages like additional circuitry, system overhead and a very long time to completion. In this paper, a fast technique to obtain optimum value of Vref is proposed. It uses a simple density test at the receiver to perform the operation. This technique has been demonstrated in a POD (Pseudo Open-Drain) signaling based single-ended transceiver designed in TSMC 40nm process. System level measurements in the lab at 6 Gb/s data rate prove that this technique is as accurate as the traditional technique in choosing Vref while being several thousand times faster.


Extractive summarization of meeting recordings


title: Global Agile Team Configuration

abstract: Drawing upon extant literature, we first construct a framework consisting of three major dimensions (agility, virtualness, and structure) to address the question of how productive agile teams can be configured in globally distributed environments. We then propose a configurational pattern for global agile teams by taking each of the twelve principles of the Agile Manifesto and describe its intersection with each of the dimensions of the framework. Our examination indicates that eight principles are especially relevant for the configuration of global agile teams, while the four remaining principles are either unsupported by previous literature or do not constitute unique considerations.


The effect of social media on brand loyalty


Verifiable Computation in Smart Contracts


title: Review on Participatory Bread Wheat Breeding Program in Kermanshah, Iran under Rainfed Condition: Importance, Opportunities and Challenges

abstract: Farmers and researchers have been isolated from each other for decades. But, now both groups realize the limits of conventional varietal selection in research stations, the quality of products grown, and their adaptability to target areas and to agricultural systems. Conventional plant breeding has mostly benefited farmers in high potential environments and those who can afford inputs such as fertilizers and pesticides and have access to water. The poorest farmers who cannot afford to change their fields through the application of additional inputs get left behind. As a consequence, low yields, crop failures, malnutrition, poverty, and eventually famine are still affecting a large proportion of humanity. Working in partnership with farmers to develop varieties adapted to the conditions they face in participatory plant breeding (PPB) programs can help to overcome these problems. This paper describes the main aspects of a PPB program, the related aspects of variety release, variety adoption and seed production, and the results obtained with a PPB program applied to rainfed bread wheat in Kermanshah province, Iran.


title: Covariance discriminative learning: A natural and efficient approach to image set classification

abstract: We propose a novel discriminative learning approach to image set classification by modeling the image set with its natural second-order statistic, i.e. covariance matrix. Since nonsingular covariance matrices, a.k.a. symmetric positive definite (SPD) matrices, lie on a Riemannian manifold, classical learning algorithms cannot be directly utilized to classify points on the manifold. By exploring an efficient metric for the SPD matrices, i.e., Log-Euclidean Distance (LED), we derive a kernel function that explicitly maps the covariance matrix from the Riemannian manifold to a Euclidean space. With this explicit mapping, any learning method devoted to vector space can be exploited in either its linear or kernel formulation. Linear Discriminant Analysis (LDA) and Partial Least Squares (PLS) are considered in this paper for their feasibility for our specific problem. We further investigate the conventional linear subspace based set modeling technique and cast it in a unified framework with our covariance matrix based modeling. The proposed method is evaluated on two tasks: face recognition and object categorization. Extensive experimental results show not only the superiority of our method over state-of-the-art ones in both accuracy and efficiency, but also its stability to two real challenges: noisy set data and varying set size.


title: Joint modeling of multiple time series via the beta process with application to motion capture segmentation

abstract: We propose a Bayesian nonparametric approach to the problem of jointly modeling multiple related time series. Our model discovers a latent set of dynamical behaviors shared among the sequences, and segments each time series into regions defined by a subset of these behaviors. Using a beta process prior, the size of the behavior set and the sharing pattern are both inferred from data. We develop Markov chain Monte Carlo (MCMC) methods based on the Indian buffet process representation of the predictive distribution of the beta process. Our MCMC inference algorithm efficiently adds and removes behaviors via novel split-merge moves as well as data-driven birth and death proposals, avoiding the need to consider a truncated model. We demonstrate promising results on unsupervised segmentation of human motion capture data. 1. Introduction. Classical time series analysis has generally focused on the study of a single (potentially multivariate) time series. Instead, we consider analyzing collections of related time series, motivated by the increasing abundance of such data in many domains. In this work we explore this problem by considering time series produced by motion capture sensors on the joints of people performing exercise routines. An individual recording provides a multivariate time series that can be segmented into types of exercises (e.g., jumping jacks, arm-circles, and twists). Each exercise type describes locally coherent and simple dynamics that persist over a segment of time. We have such motion capture recordings from multiple individuals, each of whom performs some subset of a global set of exercises, as shown in Figure1. Our goal is to discover the set of global exercise types (“behaviors”) and their occurrences in each individual’s data stream. We would like to take advantage of the overlap between individuals: if a jumping-jack behavior is discovered in one sequence, then it can be used to model data for other individuals.


Beyond Bitcoin - Part II: Blockchain-based systems without mining


title: Scoping rules on a platter: a framework for understanding and specifying name binding

abstract: We present a simple and generic way to reason about name binding. Name binding is an essential component of every nontrivial programming language, matching uses of names, references, with the things that they name, declarations, based on scoping rules defined by the language. The definition of name binding is often entangled with the language-specific details, which makes abstract and comparative analysis of competing designs challenging. We present a framework that allows to abstract the fundamental notions of references, declarations, and scopes, and to express scoping rules in terms of four scope combinators and three properties of a specific programming language encapsulated in a concept named Language. Using this framework, we clarify complex scoping rules like argument-dependent lookup in C++, investigate the implications of the concepts feature for C++, and introduce a novel scoping rule named weak hiding. In an ideal world, specifications could be formulated based on our framework, and compilers could use such formulation to unambiguously implement name binding. While our examples are primarily centered around C++ and lexical scoping, our framework has applications in other languages and dynamic scoping.


Exploring family violence Links between child maltreatment and domestic violence


title: M.Smaili, “Wireless attendance management system based on iris recognition

abstract: Iris recognition verification is one of the most reliable personal identification methods in Biometrics. With the rapid development of iris recognition verification, a number of its applications have been proposed until now including time attendance system etc. In this paper, a wireless iris recognition attendance management system is designed and implemented using Daugman’s algorithm (Daugman, 2003). This system based biometrics and wireless technique solves the problem of spurious attendance and the trouble of laying the corresponding network. It can make the users’ attendances more easily and effectively. ::: ::: ::: ::: Key words: Iris recognition verification, personal identification, biometrics, attendance management, wireless.


title: An iterative design methodology for user-friendly natural language office information applications

abstract: A six-step, iterative, empirical human factors design methodology was used to develop CAL, a natural language computer application to help computer-naive business professionals manage their personal calenders. Input language is processed by a simple, nonparsing algorithm with limited storage requirements and a quick response time. CAL allows unconstrained English inputs from users with no training (except for a five minute introduction to the keyboard and display) and no manual (except for a two-page overview of the system). In a controlled test of performance, CAL correctly responded to between 86 percent and 97 percent of the storage and retrieval requests it received, according to various criteria. This level of performance could never have been achieved with such a simple processing model were it not for the empirical approach used in the development of the program and its dictionaries. The tools of the engineering psychologist are clearly invaluable in the development of user-friendly software, if that software is to accommodate the unruly language of computer-naive, first-time users. The key is elicit the cooperation of such users as partners in an iterative, empirical development process. 15 references.


title: Donor Retention in Online Crowdfunding Communities: A Case Study of DonorsChoose.org

abstract: Online crowdfunding platforms like DonorsChoose.org and Kickstarter allow specific projects to get funded by targeted contributions from a large number of people. Critical for the success of crowdfunding communities is recruitment and continued engagement of donors. With donor attrition rates above 70%, a significant challenge for online crowdfunding platforms as well as traditional offline non-profit organizations is the problem of donor retention. We present a large-scale study of millions of donors and donations on DonorsChoose.org, a crowdfunding platform for education projects. Studying an online crowdfunding platform allows for an unprecedented detailed view of how people direct their donations. We explore various factors impacting donor retention which allows us to identify different groups of donors and quantify their propensity to return for subsequent donations. We find that donors are more likely to return if they had a positive interaction with the receiver of the donation. We also show that this includes appropriate and timely recognition of their support as well as detailed communication of their impact. Finally, we discuss how our findings could inform steps to improve donor retention in crowdfunding communities and non-profit organizations.


title: Toward Superfluid Deployment of Virtual Functions: Exploiting Mobile Edge Computing for Video Streaming

abstract: The Network Function Virtualization (NFV) technologies are fundamental enablers to meet the objectives of 5G networks. In this work, we first introduce the architecture for dynamic deployment and composition of virtual functions proposed by the Superfluidity project. Then we consider a case study based on a typical 5G scenario. In particular, we detail the design and implementation of a Video Streaming service exploiting Mobile Edge Computing (MEC) functionalities. The analysis of the case study provide an assessment on what can be achieved with current technologies and gives a first confirmation of the validity of the proposed approach. Finally, we identify future directions of work towards the realization of a superfluid softwarized network.


title: Unsupervised real-time anomaly detection for streaming data

abstract: Abstract We are seeing an enormous increase in the availability of streaming, time-series data. Largely driven by the rise of connected real-time data sources, this data presents technical challenges and opportunities. One fundamental capability for streaming analytics is to model each stream in an unsupervised fashion and detect unusual, anomalous behaviors in real-time. Early anomaly detection is valuable, yet it can be difficult to execute reliably in practice. Application constraints require systems to process data in real-time, not batches. Streaming data inherently exhibits concept drift, favoring algorithms that learn continuously. Furthermore, the massive number of independent streams in practice requires that anomaly detectors be fully automated. In this paper we propose a novel anomaly detection algorithm that meets these constraints. The technique is based on an online sequence memory algorithm called Hierarchical Temporal Memory (HTM). We also present results using the Numenta Anomaly Benchmark (NAB), a benchmark containing real-world data streams with labeled anomalies. The benchmark, the first of its kind, provides a controlled open-source environment for testing anomaly detection algorithms on streaming data. We present results and analysis for a wide range of algorithms on this benchmark, and discuss future challenges for the emerging field of streaming analytics.


title: Segmentation-Based Projected Clustering of Hyperspectral Images Using Mutual Nearest Neighbour

abstract: In this study, a simple yet effective projected clustering method is proposed for hyperspectral images. The proposed method assimilates segmentation, clustering, and local band selection within its framework. Initially, a segmented map of the hyperspectral image is obtained by using $k$ –means algorithm. In the subsequent stage, the obtained segments are considered as clusters and are merged by utilizing the mutual nearest neighbour information. In the last stage, the method identifies the $k$ significant clusters using a criterion based on entropy. The final cluster map is obtained by assigning all the remaining clusters to these $k$ significant clusters. Additionally, the clustering stages make use of multiple subspaces, obtained by deploying a local band selection approach. To test the performance of the proposed method, experiments are conducted over five hyperspectral images and is further compared with other clustering frameworks. The efficacy of the proposed method is confirmed by the experiments conducted over these images.


title: A Proposed Hybrid Model For Adopting Cloud Computing In E-Government

abstract: Abstract Many developing countries are now experiencing revolution in e-government to deliver fluent and simple services for their citizens. However, governmental sectors face many challenges in using its e-governments’ services and its infrastructure, improving current services or developing new services; as data and applications increasingly inflating, IT budget costs, software licensing and support and difficulties in migration, integration and management for software and hardware. These challenges may lead to failure of e-governments’ projects. Therefore, there is a need for a solution to overcome these challenges. Cloud Computing plays a vital role to solve these problems. This paper demonstrates e-government's obstacles and cloud computing features. Also, it proposes an abstract hybrid model for adapting cloud computing in e-government that overcomes the e-government's challenges. This hybrid proposed model identifies three different patterns of cloud computing which are Local Governmental Cloud “LGC”, Regional Governmental Cloud “RGC” and Wide Governmental Cloud “WGC”. The proposed model determines how the entity connects to each of three clouds and what the relation between them is. In addition, readiness assessment of the services need to migrate into cloud. Finally, a set of recommended cloud aspects and their values for each of three clouds are suggested that ensure implementation of the sorted services.


title: Terrain generation using procedural models based on hydrology

abstract: We present a framework that allows quick and intuitive modeling of terrains using concepts inspired by hydrology. The terrain is generated from a simple initial sketch, and its generation is controlled by a few parameters. Our terrain representation is both analytic and continuous and can be rendered by using varying levels of detail. The terrain data are stored in a novel data structure: a construction tree whose internal nodes define a combination of operations, and whose leaves represent terrain features. The framework uses rivers as modeling elements, and it first creates a hierarchical drainage network that is represented as a geometric graph over a given input domain. The network is then analyzed to construct watersheds and to characterize the different types and trajectories of rivers. The terrain is finally generated by combining procedural terrain and river patches with blending and carving operators.


title: Increasing the radiation efficiency and matching stability of in-body capsule antennas

abstract: We propose an approach to increase the operating range and matching stability of in-body ingestible or implantable capsule antennas. It involves using high permittivity biocompatible superstrate on a high-Q narrowband microstrip antenna. In this way, the antenna-body coupling can be significantly reduced, resulting in improved detuning immunity, increased radiation efficiency, and reduced power absorption inside the body. To demonstrate the feasibility of the proposed approach, we designed a mid-ISM band (433 to 434.8) MHz low-profile (85 µm) conformal microstrip antenna loaded with high permittivity ceramic superstrate. We achieved a simulated gain of −20.4 dBi. The radiation efficiency reaches 0.6%, which exceeds the counterparts operating within the same band by at least 3.5 times. The bandwidth is 26 MHz (6%), while the antenna remains matched (S11 < −10 dB) at 434 MHz within a wide range of tissue-equivalent phantoms. To validate the numerical results, we manufactured an antenna prototype and characterized it in terms of reflection coefficient S11.


Learning to Detect Rooftops in Aerial Images


title: Issues,Challenges and Tools of Clustering Algorithms

abstract: Clustering is an unsupervised technique of Data Mining. It means grouping similar objects together and separating the dissimilar ones. Each object in the data set is assigned a class label in the clustering process using a distance measure. This paper has captured the problems that are faced in real when clustering algorithms are implemented .It also considers the most extensively used tools which are readily available and support functions which ease the programming. Once algorithms have been implemented, they also need to be tested for its validity. There exist several validation indexes for testing the performance and accuracy which have also been discussed here.


title: Fear and anxiety: animal models and human cognitive psychophysiology.

abstract: The aim of this paper is to explicate what is special about emotional information processing, emphasizing the neural foundations that underlie the experience and expression of fear. A functional, anatomical model of defense behavior in animals is presented and applications are described in cognitive and physiological studies of human affect. It is proposed that unpleasant emotions depend on the activation of an evolutionarily primitive subcortical circuit, including the amygdala and the neural structures to which it projects. This motivational system mediates specific autonomic (e.g., heart rate change) and somatic reflexes (e.g., startle change) that originally promoted survival in dangerous conditions. These same response patterns are illustrated in humans, as they process objective, memorial, and media stimuli. Furthermore, it is shown how variations in the neural circuit and its outputs may separately characterize cue-specific fear (as in specific phobia) and more generalized anxiety. Finally, again emphasizing links between the animal and human data, we focus on special, attentional features of emotional processing: The automaticity of fear reactions, hyper-reactivity to minimal threat-cues, and evidence that the physiological responses in fear may be independent of slower, language-based appraisal processes.


Input/Output Devices and Interaction Techniques


title: Egocentric Daily Activity Recognition via Multitask Clustering

abstract: Recognizing human activities from videos is a fundamental research problem in computer vision. Recently, there has been a growing interest in analyzing human behavior from data collected with wearable cameras. First-person cameras continuously record several hours of their wearers' life. To cope with this vast amount of unlabeled and heterogeneous data, novel algorithmic solutions are required. In this paper, we propose a multitask clustering framework for activity of daily living analysis from visual data gathered from wearable cameras. Our intuition is that, even if the data are not annotated, it is possible to exploit the fact that the tasks of recognizing everyday activities of multiple individuals are related, since typically people perform the same actions in similar environments, e.g., people working in an office often read and write documents). In our framework, rather than clustering data from different users separately, we propose to look for clustering partitions which are coherent among related tasks. In particular, two novel multitask clustering algorithms, derived from a common optimization problem, are introduced. Our experimental evaluation, conducted both on synthetic data and on publicly available first-person vision data sets, shows that the proposed approach outperforms several single-task and multitask learning methods.


title: Push vs. pull: data movement for linked data structures

abstract: As the performance gap between the CPU and main memory continues to grow, techniques to hide memory latency are essential to deliver a high performance computer system. Prefetching can often overlap memory latency with computation for array-based numeric applications. However, prefetching for pointer-intensive applications still remains a challenging problem. Prefetching linked data structures (LDS) is difficult because the address sequence of LDS traversal does not present the same arithmetic regularity as array-based applications and the data dependence of pointer dereferences can serialize the address generation process. In this paper, we propose a cooperative hardware/software mechanism to reduce memory access latencies for linked data structures. Instead of relying on the past address history to predict future accesses, we identify the load instructions that traverse the LDS, and execute them ahead of the actual computation. To overcome the serial nature of the LDS address generation, we attach a prefetch controller to each level of the memory hierarchy and push, rather than pull, data to the CPU. Our simulations, using four pointer-intensive applications, show that the push model can achieve between 4% and 30% larger reductions in execution time compared to the pull model.


title: Characterizing the capacity region in multi-radio multi-channel wireless mesh networks

abstract: Next generation fixed wireless broadband networks are being increasingly deployed as mesh networks in order to provide and extend access to the internet. These networks are characterized by the use of multiple orthogonal channels and nodes with the ability to simultaneously communicate with many neighbors using multiple radios (interfaces) over orthogonal channels. Networks based on the IEEE 802.11a/b/g and 802.16 standards are examples of these systems. However, due to the limited number of available orthogonal channels, interference is still a factor in such networks. In this paper, we propose a network model that captures the key practical aspects of such systems and characterize the constraints binding their behavior. We provide necessary conditions to verify the feasibility of rate vectors in these networks, and use them to derive upper bounds on the capacity in terms of achievable throughput, using a fast primal-dual algorithm. We then develop two link channel assignment schemes, one static and the other dynamic, in order to derive lower bounds on the achievable throughput. We demonstrate through simulations that the dynamic link channel assignment scheme performs close to optimal on the average, while the static link channel assignment algorithm also performs very well. The methods proposed in this paper can be a valuable tool for network designers in planning network deployment and for optimizing different performance objectives.


title: Missing value estimation methods for DNA microarrays

abstract: Motivation: Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data. Results: We present a comparative study of several methods for the estimation of missing values in gene microarray data. We implemented and evaluated three methods: a Singular Value Decomposition (SVD) based method (SVDimpute), weighted K-nearest neighbors (KNNimpute), and row average. We evaluated the methods using a variety of parameter settings and over different real data sets, and assessed the robustness of the imputation methods to the amount of missing data over the range of 1–20% missing values. We show that KNNimpute appears to provide a more robust and sensitive method for missing value estimation than SVDimpute, and both SVDimpute and KNNimpute surpass the commonly used row average method (as well as filling missing values with zeros). We report results of the comparative experiments and provide recommendations and tools for accurate estimation of missing microarray data under a variety of conditions. Availability: The software is available at http://smi-web.


The Nazi hypothermia experiments: forbidden data?


title: The Word Tree, an Interactive Visual Concordance

abstract: We introduce the Word Tree, a new visualization and information-retrieval technique aimed at text documents. A Word Tree is a graphical version of the traditional "keyword-in-context" method, and enables rapid querying and exploration of bodies of text. In this paper we describe the design of the technique, along with some of the technical issues that arise in its implementation. In addition, we discuss the results of several months of public deployment of word trees on Many Eyes, which provides a window onto the ways in which users obtain value from the visualization.


title: 2003b. Automatic shoreline extraction from highresolution IKONOS satellite imagery

abstract: ABSTRACT Shoreline mapping and shoreline change detection are critical for safe navigation, coastal resource management, coastal environmental protection, and sustainable coastal development and planning. This paper reports the results of IKONOS geopositioning accuracy improvement, showing that a 1-2 meter accuracy can be achieved from 1m-resolution IKONOS Geo stereo images. The experiment indicated that a simple adjustment model, e.g., either the Affine or the Scale &Offset model, is effective both to eliminate the systematic errors found in the vendor-provided Rational Function (RF) coefficients and to significantly improve the accuracy of 3D geopositioning. Aiming to automate shoreline mapping, we investigated a novel approach for automatic extraction of shorelines from high-resolution IKONOS imagery. In the first step of the proposed approach, the image is segmented into homogeneous regions by mean shift segmentation. Then, the major water body is identified and an initial shoreline is generated. The final shoreline is obtained by local refinement within the boundaries of the candidate regions adjacent to the initial shoreline. We test the approach using 4m- and 1m-resolution IKONOS images in a pilot project area along the Lake Erie shore. Test results show that the proposed approach is capable of extracting shorelines from IKONOS images with little human interaction. A method for 3D-shoreline generation is also discussed. The accuracies of the extracted shorelines from 4m XS and 1m Pan stereo images are estimated to be 8.5m and 2-3m respectively.


title: Using argumentation logic for firewall configuration management

abstract: Firewalls remain the main perimeter security protection for corporate networks. However, network size and complexity make firewall configuration and maintenance notoriously difficult. Tools are needed to analyse firewall configurations for errors, to verify that they correctly implement security requirements and to generate configurations from higher-level requirements. In this paper we extend our previous work on the use of formal argumentation and preference reasoning for firewall policy analysis and develop means to automatically generate firewall policies from higher-level requirements. This permits both analysis and generation to be done within the same framework, thus accommodating a wide variety of scenarios for authoring and maintaining firewall configurations. We validate our approach by applying it to both examples from the literature and real firewall configurations of moderate size (≈ 150 rules).


title: Problematic use of the mobile phone : a literature review and a pathways model

abstract: Despite its unambiguous advantages, cellular phone use has been associated with harmful or potentially disturbing behaviors. Problematic use of the mobile phone is considered as an inability to regulate one's use of the mobile phone, which eventually involves negative consequences in daily life (e.g., financial problems). The current article describes what can be considered dysfunctional use of the mobile phone and emphasizes its multifactorial nature. Validated assessment instruments to measure problematic use of the mobile phone are described. The available literature on risk factors for dysfunctional mobile phone use is then reviewed, and a pathways model that integrates the existing literature is proposed. Finally, the assumption is made that dysfunctional use of the mobile phone is part of a spectrum of cyber addictions that encompasses a variety of dysfunctional behaviors and implies involvement in specific online activities (e.g., video games, gambling, social networks, sex-related websites).


title: Microstrip-Ridge Gap Waveguide–Study of Losses, Bends, and Transition to WR-15

abstract: This paper presents the design of microstrip-ridge gap waveguide using via-holes in printed circuit boards, a solution for high-frequency circuits. The study includes how to define the numerical ports, pin sensitivity, losses, and also a comparison with performance of normal microstrip lines and inverted microstrip lines. The results are produced using commercially available electromagnetic simulators. A WR-15 to microstrip-ridge gap waveguide transition was also designed. The results are verified with measurements on microstrip-ridge gap waveguides with WR15 transitions at both ends.


title: Empirical comparison of algorithms for network community detection

abstract: Detecting clusters or communities in large real-world graphs such as large social or information networks is a problem of considerable interest. In practice, one typically chooses an objective function that captures the intuition of a network cluster as set of nodes with better internal connectivity than external connectivity, and then one applies approximation algorithms or heuristics to extract sets of nodes that are related to the objective function and that "look like" good communities for the application of interest. In this paper, we explore a range of network community detection methods in order to compare them and to understand their relative performance and the systematic biases in the clusters they identify. We evaluate several common objective functions that are used to formalize the notion of a network community, and we examine several different classes of approximation algorithms that aim to optimize such objective functions. In addition, rather than simply fixing an objective and asking for an approximation to the best cluster of any size, we consider a size-resolved version of the optimization problem. Considering community quality as a function of its size provides a much finer lens with which to examine community detection algorithms, since objective functions and approximation algorithms often have non-obvious size-dependent behavior.


title: Architecture of a Network Monitoring Element

abstract: The scalability of a network monitoring system, a vital component of a grid is challenging. We propose a solution based on demand driven monitoring sessions that use passive network monitoring techniques on a domain oriented overlay network. Related aspects of security and group membership maintenance are also considered


title: IT for Peace? Fighting Against Terrorism in Social Media – An Explorative Twitter Study

abstract: The Internet and especially social media are not only used for supposedly good purposes. For example, the recruitment of new members and the dissemination of ideologies of terrorism also takes place in the media. However, the fight against terrorism also makes use of the same tools. The type of these countermeasures, as well as the methods, are covered in this work. In the first part, the state of the art is summarized. The second part presents an explorative empirical study of the fight against terrorism in social media, especially on Twitter. Different, preferably characteristic forms are structured within the scope with the example of Twitter. The aim of this work is to approach this highly relevant subject with the goal of peace, safety and safety from the perspective of information systems. Moreover, it should serve following researches in this field as basis and starting point.


title: 0.293-mm2 Fast Transient Response Hysteretic Quasi- $V^{2}$ DC–DC Converter With Area-Efficient Time-Domain-Based Controller in 0.35- $\mu$ m CMOS

abstract: A time-domain-control-based quasi- $V^{2}$ buck converter with small area, fast transient response, wide load current range, and constant switching frequency is proposed in this paper. The proposed time-domain-based controller achieves compact core area and simplified constant switching frequency control by replacing the conventional voltage-domain comparator with a phase detector having an adaptive detection window. Moreover, a coupling-based inductor current emulator is also proposed to support the discontinuous conduction mode operation in the light load. The proposed buck converter is implemented using 0.35- $\mu \text{m}$ CMOS process. The core area of the proposed buck converter is only 0.293 mm2. The measured peak efficiency in the continuous conduction mode is 92% and the end-to-end efficiency in the load current range of 25–700 mA is 73%. The undershoot/overshoot and recovery time with the up/down load current steps of 510 mA are 38/20 mV and 2.5/2.6 $\mu \text{s}$ , respectively.


title: Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges

abstract: Abstract For various reasons, the cloud computing paradigm is unable to meet certain requirements (e.g. low latency and jitter, context awareness, mobility support) that are crucial for several applications (e.g. vehicular networks, augmented reality). To fulfill these requirements, various paradigms, such as fog computing, mobile edge computing, and mobile cloud computing, have emerged in recent years. While these edge paradigms share several features, most of the existing research is compartmentalized; no synergies have been explored. This is especially true in the field of security, where most analyses focus only on one edge paradigm, while ignoring the others. The main goal of this study is to holistically analyze the security threats, challenges, and mechanisms inherent in all edge paradigms, while highlighting potential synergies and venues of collaboration. In our results, we will show that all edge paradigms should consider the advances in other paradigms.


title: Iatrogenic occlusion of the ophthalmic artery after cosmetic facial filler injections: a national survey by the Korean Retina Society.

abstract: IMPORTANCE ::: Iatrogenic occlusion of the ophthalmic artery and its branches is a rare but devastating complication of cosmetic facial filler injections. ::: ::: ::: OBJECTIVE ::: To investigate clinical and angiographic features of iatrogenic occlusion of the ophthalmic artery and its branches caused by cosmetic facial filler injections. ::: ::: ::: DESIGN, SETTING, AND PARTICIPANTS ::: Data from 44 patients with occlusion of the ophthalmic artery and its branches after cosmetic facial filler injections were obtained retrospectively from a national survey completed by members of the Korean Retina Society from 27 retinal centers. Clinical features were compared between patients grouped by angiographic findings and injected filler material. ::: ::: ::: MAIN OUTCOMES AND MEASURES ::: Visual prognosis and its relationship to angiographic findings and injected filler material. ::: ::: ::: RESULTS ::: Ophthalmic artery occlusion was classified into 6 types according to angiographic findings. Twenty-eight patients had diffuse retinal and choroidal artery occlusions (ophthalmic artery occlusion, generalized posterior ciliary artery occlusion, and central retinal artery occlusion). Sixteen patients had localized occlusions (localized posterior ciliary artery occlusion, branch retinal artery occlusion, and posterior ischemic optic neuropathy). Patients with diffuse occlusions showed worse initial and final visual acuity and less visual gain compared with those having localized occlusions. Patients receiving autologous fat injections (n = 22) had diffuse ophthalmic artery occlusions, worse visual prognosis, and a higher incidence of combined brain infarction compared with patients having hyaluronic acid injections (n = 13). ::: ::: ::: CONCLUSIONS AND RELEVANCE ::: Clinical features of iatrogenic occlusion of the ophthalmic artery and its branches following cosmetic facial filler injections were diverse according to the location and extent of obstruction and the injected filler material. Autologous fat injections were associated with a worse visual prognosis and a higher incidence of combined cerebral infarction. Extreme caution and care should be taken during these injections, and physicians should be aware of a diverse spectrum of complications following cosmetic facial filler injections.


title: Complementary Aspect-Based Opinion Mining

abstract: Aspect-based opinion mining is finding elaborate opinions towards a subject such as a product or an event. With explosive growth of opinionated texts on the Web, mining aspect-level opinions has become a promising means for online public opinion analysis. In particular, the boom of various types of online media provides diverse yet complementary information, bringing unprecedented opportunities for cross media aspect-opinion mining. Along this line, we propose CAMEL, a novel topic model for complementary aspect-based opinion mining across asymmetric collections. CAMEL gains information complementarity by modeling both common and specific aspects across collections, while keeping all the corresponding opinions for contrastive study. An auto-labeling scheme called AME is also proposed to help discriminate between aspect and opinion words without elaborative human labeling, which is further enhanced by adding word embedding-based similarity as a new feature. Moreover, CAMEL-DP, a nonparametric alternative to CAMEL is also proposed based on coupled Dirichlet Processes. Extensive experiments on real-world multi-collection reviews data demonstrate the superiority of our methods to competitive baselines. This is particularly true when the information shared by different collections becomes seriously fragmented. Finally, a case study on the public event “2014 Shanghai Stampede” demonstrates the practical value of CAMEL for real-world applications.


title: Movement visualisation in virtual reality rehabilitation of the lower limb: a systematic review

abstract: BACKGROUND ::: Virtual reality (VR) based applications play an increasing role in motor rehabilitation. They provide an interactive and individualized environment in addition to increased motivation during motor tasks as well as facilitating motor learning through multimodal sensory information. Several previous studies have shown positive effect of VR-based treatments for lower extremity motor rehabilitation in neurological conditions, but the characteristics of these VR applications have not been systematically investigated. The visual information on the user's movement in the virtual environment, also called movement visualisation (MV), is a key element of VR-based rehabilitation interventions. The present review proposes categorization of Movement Visualisations of VR-based rehabilitation therapy for neurological conditions and also summarises current research in lower limb application. ::: ::: ::: METHODS ::: A systematic search of literature on VR-based intervention for gait and balance rehabilitation in neurological conditions was performed in the databases namely; MEDLINE (Ovid), AMED, EMBASE, CINAHL, and PsycInfo. Studies using non-virtual environments or applications to improve cognitive function, activities of daily living, or psychotherapy were excluded. The VR interventions of the included studies were analysed on their MV. ::: ::: ::: RESULTS ::: In total 43 publications were selected based on the inclusion criteria. Seven distinct MV groups could be differentiated: indirect MV (N = 13), abstract MV (N = 11), augmented reality MV (N = 9), avatar MV (N = 5), tracking MV (N = 4), combined MV (N = 1), and no MV (N = 2). In two included articles the visualisation conditions included different MV groups within the same study. Additionally, differences in motor performance could not be analysed because of the differences in the study design. Three studies investigated different visualisations within the same MV group and hence limited information can be extracted from one study. ::: ::: ::: CONCLUSIONS ::: The review demonstrates that individuals' movements during VR-based motor training can be displayed in different ways. Future studies are necessary to fundamentally explore the nature of this VR information and its effect on motor outcome.