title: Evaluating run-time techniques for leakage power reduction

abstract: While some leakage power reduction techniques require modification of process technology achieving savings at the fabrication stage, others are based on circuit-level optimizations and are applied at run-time. We focus our study on the latter kind and compare three techniques: input vector control, body bias control and power supply gating. We determine their limits and benefits, in terms of the potential leakage reduction, performance penalty and area and power overhead. The importance of the 'minimum idle time' parameter, as an additional evaluation tool, is emphasized, as well as the feasibility of achieving power supply gating at low levels of granularity. The obtained data supports the formulation of a comprehensive leakage reduction scheme, in which each technique is targeted for certain types of functional units and a given level of granularity depending on the incurred overhead cost and the obtainable savings.


title: Deep Spectral Descriptors: Learning the point-wise correspondence metric via Siamese deep neural networks

abstract: A robust and informative local shape descriptor plays an important role in mesh registration. In this regard, spectral descriptors that are based on the spectrum of the Laplace-Beltrami operator have gained a spotlight among the researchers for the last decade due to their desirable properties, such as isometry invariance. Despite such, however, spectral descriptors often fail to give a correct similarity measure for non-isometric cases where the metric distortion between the models is large. Hence, they are in general not suitable for the registration problems, except for the special cases when the models are near-isometry. In this paper, we investigate a way to develop shape descriptors for non-isometric registration tasks by embedding the spectral shape descriptors into a different metric space where the Euclidean distance between the elements directly indicates the geometric dissimilarity. We design and train a Siamese deep neural network to find such an embedding, where the embedded descriptors are promoted to rearrange based on the geometric similarity. We found our approach can significantly enhance the performance of the conventional spectral descriptors for the non-isometric registration tasks, and outperforms recent state-of-the-art method reported in literature.


title: Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks

abstract: Early detection of the motor faults is essential and artificial neural networks are widely used for this purpose. The typical systems usually encapsulate two distinct blocks: feature extraction and classification. Such fixed and hand-crafted features may be a suboptimal choice and require a significant computational cost that will prevent their usage for real-time applications. In this paper, we propose a fast and accurate motor condition monitoring and early fault-detection system using 1-D convolutional neural networks that has an inherent adaptive design to fuse the feature extraction and classification phases of the motor fault detection into a single learning body. The proposed approach is directly applicable to the raw data (signal), and, thus, eliminates the need for a separate feature extraction algorithm resulting in more efficient systems in terms of both speed and hardware. Experimental results obtained using real motor data demonstrate the effectiveness of the proposed method for real-time motor condition monitoring.


title: Semantic Modelling with Long-Short-Term Memory for Information Retrieval

abstract: In this paper we address the following problem in web document and information retrieval (IR): How can we use long-term context information to gain better IR performance? Unlike common IR methods that use bag of words representation for queries and documents, we treat them as a sequence of words and use long short term memory (LSTM) to capture contextual dependencies. To the best of our knowledge, this is the first time that LSTM is applied to information retrieval tasks. Unlike training traditional LSTMs, the training strategy is different due to the special nature of information retrieval problem. Experimental evaluation on an IR task derived from the Bing web search demonstrates the ability of the proposed method in addressing both lexical mismatch and long-term context modelling issues, thereby, significantly outperforming existing state of the art methods for web document retrieval task.


title: Users Tagging Visual Moments: Timed Tags in Social Video

abstract: A timed tag is a tag that a user has assigned to a specific time point in a video. Although timed tags are supported by an increasing number of social video platforms on the Internet, multimedia research remains focused on conventional tags, here called "timeless tags", which users assign to the video as a whole, rather than to a specific moment. This paper presents a video data set consisting of social videos and user-contributed timed tags. A large crowdsourcing experiment was used to annotate this data set. The annotations allow us to better understand the phenomenon of timed tagging. We describe the design of the crowdsourcing experiment, and how it was executed. Then we present results of our analysis, which reveal the properties of timed tags, and their differences from timeless tags. The results suggest that the two differ with respect to what the user is attempting to express about the video. We close with an outlook that lays the groundwork for further study of timed tags in social video within the research community.


title: 4W1H in mobile crowd sensing

abstract: With the rapid proliferation of sensor-rich smartphones, mobile crowd sensing has become a popular research field. In this article, we propose a four-stage life cycle (i.e., task creation, task assignment, individual task execution, and crowd data integration) to characterize the mobile crowd sensing process, and use 4W1H (i.e., what, when, where, who, and how) to sort out the research problems in the mobile crowd sensing domain. Furthermore, we attempt to foresee some new research directions in future mobile crowd sensing research.


title: Critical review on video game evaluation heuristics: social games perspective

abstract: This paper presents the first step in creating design and evaluation heuristics for social games which emerge from the domain of social media. Initial high level heuristics for social games are offered by reviewing four existing video game heuristic models and analyzing two social games design frameworks.


title: Mining mobility data to minimise travellers' spending on public transport

abstract: As the public transport infrastructure of large cities expands, transport operators are diversifying the range and prices of tickets that can be purchased for travel. However, selecting the best fare for each individual traveller's needs is a complex process that is left almost completely unaided. By examining the relation between urban mobility and fare purchasing habits in large datasets from London, England's public transport network, we estimate that travellers in the city cumulatively spend, per year, up to approximately GBP 200 million more than they need to, as a result of purchasing the incorrect fares. We propose to address these incorrect purchases by leveraging the huge volumes of data that travellers create as they move about the city, by providing, to each of them, personalised ticket recommendations based on their estimated future travel patterns. In this work, we explore the viability of building a fare-recommendation system for public transport networks by (a) formalising the problem as two separate prediction problems and (b) evaluating a number of algorithms that aim to match travellers to the best fare. We find that applying data mining techniques to public transport data has the potential to provide travellers with substantial savings.


Security , Privacy & Trust in Internet of Things : the road ahead


title: Advertising keyword suggestion based on concept hierarchy

abstract: The increasing growth of the World Wide Web constantly enlarges the revenue generated by search engine advertising. Advertisers bid on keywords associated with their products to display their ads on the search result pages. Keyword suggestion methods are proposed to fill the gap between the keywords chosen by advertisers and the popular queries, through finding new relevant keywords according to some statistical information (for example, the keyword co-occurrence). However, there is little effort taking semantic information, such as concept hierarchy, into account. In this paper, we propose a novel keyword suggestion method that fully exploits the semantic knowledge among concept hierarchy. Given a keyword, we first match it with some relevant concepts. Then the relevant concepts are used with their hierarchy to fertilize the meanings of the keywords. Finally new keywords are suggested according to the concept information rather than the statistical co-occurrence of the keyword itself. Experimental results show that our proposed method can successfully provide suggestion that meets the accuracy and coverage requirements


title: Theory and Simulation in Neuroscience

abstract: Modeling work in neuroscience can be classified using two different criteria. The first one is the complexity of the model, ranging from simplified conceptual models that are amenable to mathematical analysis to detailed models that require simulations in order to understand their properties. The second criterion is that of direction of workflow, which can be from microscopic to macroscopic scales (bottom-up) or from behavioral target functions to properties of components (top-down). We review the interaction of theory and simulation using examples of top-down and bottom-up studies and point to some current developments in the fields of computational and theoretical neuroscience.


GenomeCompress : A Novel Algorithm for DNA Compression


title: Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications

abstract: In this paper, the problem of joint power and resource allocation (JPRA) for ultra-reliable low-latency communication (URLLC) in vehicular networks is studied. Therein, the network-wide power consumption of vehicular users (VUEs) is minimized subject to high reliability in terms of probabilistic queuing delays. Using extreme value theory (EVT), a new reliability measure is defined to characterize extreme events pertaining to vehicles’ queue lengths exceeding a predefined threshold. To learn these extreme events, assuming they are independently and identically distributed over VUEs, a novel distributed approach based on federated learning (FL) is proposed to estimate the tail distribution of the queue lengths. Considering the communication delays incurred by FL over wireless links, Lyapunov optimization is used to derive the JPRA policies enabling URLLC for each VUE in a distributed manner. The proposed solution is then validated via extensive simulations using a Manhattan mobility model. Simulation results show that FL enables the proposed method to estimate the tail distribution of queues with an accuracy that is close to a centralized solution with up to 79% reductions in the amount of exchanged data. Furthermore, the proposed method yields up to 60% reductions of VUEs with large queue lengths, while reducing the average power consumption by two folds, compared to an average queue-based baseline.


title: 2P-DNN : Privacy-Preserving Deep Neural Networks Based on Homomorphic Cryptosystem

abstract: Machine Learning as a Service (MLaaS), such as Microsoft Azure, Amazon AWS, offers an effective DNN model to complete the machine learning task for small businesses and individuals who are restricted to the lacking data and computing power. However, here comes an issue that user privacy is ex-posed to the MLaaS server, since users need to upload their sensitive data to the MLaaS server. In order to preserve their privacy, users can encrypt their data before uploading it. This makes it difficult to run the DNN model because it is not designed for running in ciphertext domain. In this paper, using the Paillier homomorphic cryptosystem we present a new Privacy-Preserving Deep Neural Network model that we called 2P-DNN. This model can fulfill the machine leaning task in ciphertext domain. By using 2P-DNN, MLaaS is able to provide a Privacy-Preserving machine learning ser-vice for users. We build our 2P-DNN model based on LeNet-5, and test it with the encrypted MNIST dataset. The classification accuracy is more than 97%, which is close to the accuracy of LeNet-5 running with the MNIST dataset and higher than that of other existing Privacy-Preserving machine learning models


title: An empirical Bayes approach to detect anomalies in dynamic multidimensional arrays

abstract: We consider the problem of detecting anomalies in data that arise as multidimensional arrays with each dimension corresponding to the levels of a categorical variable. In typical data mining applications, the number of cells in such arrays is usually large. Our primary focus is detecting anomalies by comparing information at the current time to historical data. Naive approaches advocated in the process control literature do not work well in this scenario due to the multiple testing problems - performing multiple statistical tests on the same data produce excessive number of false positives. We use an empirical Bayes method which works by fitting a two component Gaussian mixture to deviations at current time. The approach is scalable to problems that involve monitoring massive number of cells and fast enough to be potentially useful in many streaming scenarios. We show the superiority of the method relative to a naive "per component error rate" procedure through simulation. A novel feature of our technique is the ability to suppress deviations that are merely the consequence of sharp changes in the marginal distributions. This research was motivated by the need to extract critical application information and business intelligence from the daily logs that accompany large-scale spoken dialog systems deployed by AT&T. We illustrate our method on one such system.


title: Kernel-Based Object Tracking

abstract: A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects, is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples, the new method successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data association techniques is also discussed. We describe only a few of the potential applications: exploitation of background information, Kalman tracking using motion models, and face tracking.


title: Discrepancy and Error in Radiology: Concepts, Causes and Consequences

abstract: “All men are liable to error; and most men are, in many points, by passion or interest, under temptation to it”. Locke, John, An Essay concerning Human Understanding (1690), bk. 4, ch. 20, sect. 17. ::: ::: In all branches of medicine, there is an inevitable element of patient exposure to problems arising from human error, and this is increasingly the subject of bad publicity, often skewed towards an assumption that perfection is achievable, and that any error or discrepancy represents a wrong that must be punished1. Radiology involves decision-making under conditions of uncertainty2, and therefore cannot always produce infallible interpretations or reports. The interpretation of a radiologic study is not a binary process; the “answer” is not always normal or abnormal, cancer or not. The final report issued by a radiologist is influenced by many variables, not least among them the information available at the time of reporting. In some circumstances, radiologists are asked specific questions (in requests for studies) which they endeavour to answer; in many cases, no obvious specific question arises from the provided clinical details (e.g. “chest pain”, “abdominal pain”), and the reporting radiologist must strive to interpret what may be the concerns of the referring doctor. (A friend of one of the authors, while a resident in a North American radiology department, observed a staff radiologist dictate a chest x-ray reporting stating “No evidence of leprosy”. When subsequently confronted by an irate respiratory physician asking for an explanation of the seemingly-perverse report, he explained that he had no idea what the clinical concerns were, as the clinical details section of the request form had been left blank). ::: ::: Notwithstanding these complexities, the public frequently expects that a medical investigation will produce “the correct answer”, all the time. This unfortunate over-simplification of a multi-factorial process is often informed by representations on TV dramas, media reports describing every discrepancy or dispute over interpretation as a scandal, and the political imperative to divert anger over perceived failings on to others, preferably easy targets, often portrayed and perceived as privileged. ::: ::: Amid many possibilities of error, it would be strange indeed to be always in the right. Peter Mere Latham (1789-1875), General remarks on the Practice of Medicine, The Heart and its Affections Ch. IV ::: ::: With respect to radiological investigations, the use of the term “error” is often unsuitable; it is more appropriate to concentrate on “discrepancies” between a report and a retrospective review of a film or outcome1. Professional body guidelines recommend that all imaging procedures should include an expert opinion from a radiologist, given by means of a written report or comment3. “Opinion” may be defined as “a conclusion arrived at after some weighing of evidence, but open to debate or suggestion”, and thus an expert’s opinion should not be expected to be incontrovertible4. Error implies a mistake (an incorrect interpretation of an imaging study, in this context). In order for a report to be erroneous, it follows that a correct report must also be possible. Because of the subjectivity of image interpretation, the definition of error depends on “expert opinion”. An observer makes an error if he or she fails to reach the same conclusion that would be reached by a group of expert observers. Errors can only arise in cases where the correct interpretation is not in dispute. Somewhere between the clear-cut error and the inevitable difference of opinion in interpretation is an arbitrary division defining the limit of professional acceptability4. ::: ::: Errors in judgement must occur in the practice of an art which consists largely in balancing probabilities. Sir William Osler (1849-1919), Aequanimitas, with Other Addresses, Teacher and Student. ::: ::: Unlike physical examination of patients, or findings at surgery or endoscopy, evidence of a radiologic examination remains available for subsequent scrutiny, and can be used for study of observer variation. A 20-year literature review in 2001 suggested the level of error for clinically significant or major error in radiology is in the range 2-20% and varies depending on the radiological investigation5. ::: ::: The issue of error in radiology has been recognised for many years. Studies in the 1940s found that CXRs of patients with suspected tuberculosis were read differently by different observers in 10-20% of cases. In the 1970s, it was found that 71% of lung cancers detected on screening radiographs were visible in retrospect on previous films4,6. The “average” observer has been found to miss 30% of visible lesions on barium enemas4. A 1999 study found that 19% of lung cancers presenting as a nodular lesion on chest x-rays were missed7. Another study identified major disagreement between 2 observers in interpreting x-rays of patients in an emergency department in 5-9% of cases, with an estimated incidence of errors per observer of 3-6%8. A 1997 study using experienced radiologists reporting a collection of normal and abnormal x-rays found an overall 23% error rate when no clinical information was supplied, falling to 20% when clinical details were available9. A recent report suggests a significant major discrepancy rate (13%) between specialist neuroradiology second opinion and primary general radiology opinion10. ::: ::: A recent review found a “real-time” error rate among radiologists in their day-to-day practices averages 3-5%, but also quoted previous research showing that in patients subsequently diagnosed with lung or breast cancer with previous “normal” relevant radiologic studies, retrospective review of the chest radiographs (in the case of lung cancer) or mammogram (in breast cancer cases) identified the lung cancer in as many as 90% and the breast cancer in as many as 75% of cases11. Prolonged attention to a specific area on a radiograph (“visual dwell”) increases both false negative and false positive errors. Reducing the viewing time for CXRs to less than 4 seconds also increases the miss rate4. ::: ::: Comparative studies of other medical non-radiologic fields have found a similar prevalence of inaccuracy in clinical assessment and examination. A Mayo Clinic study of autopsies published in 2000, which compared clinical diagnoses with post-mortem diagnoses, found that in 26% of cases, a major diagnosis was missed clinically11. ::: ::: Common experience in radiology suggests that many errors are of little or no significance to the patient, and some significant errors remain undiscovered. Errors are inevitable, and the concept of necessary fallibility must be accepted. Equally a threshold of competency is required of all professionals involved in the delivery of radiology services.


title: Transfer Learning in Multi-agent Reinforcement Learning Domains

abstract: In the context of reinforcement learning, transfer learning refers to the concept of reusing knowledge acquired in past tasks to speed up the learning procedure in new tasks. Transfer learning methods have been succesfully applied in single-agent reinforcement learning algorithms, but no prior work has focused on applying them in a multi-agent environment. We propose a novel method for transfer learning in multi-agent reinforcement learning domains. We proceed to test the proposed approach in a multi-agent domain under various configurations. The results demonstrate that the method can reduce the learning time and increase the asymptotic performance of the learning algorithm.


title: Automatic White Balance algorithm through the average equalization and threshold

abstract: Automatic White Balance (AWB) is one of the important functions for digital cameras. The purpose is removing the color cast caused by the un-canonical illuminants where the image captured at. Images with color cast always lose the color contrast and variations, especially those with heavy color cast. For traditional white balance algorithms, the lost color contrast and variations would probably bring the incorrect color temperature predictions and the lack of contrast in the output images. In this paper, a new automatic white balance algorithm through the average equalization and threshold (AWBAAET) is proposed. The proposed approach keeps the assumption of gray world method (GW) and the wide color range of color histogram stretching algorithm (CHS) at the same time. In the simulation results, the proposed algorithm can meet the theory of GW, with similar averages on the R, G, and B channels. Also, it can enhance the color contrast and variances as CHS algorithm efficiently, without over stretching the color range and changing the character of the original image.


title: Improved extraction of vegetable oils under high-intensity ultrasound and/or microwaves.

abstract: Ultrasound-assisted extraction (UAE) and microwave-assisted extraction (MAE) techniques have been employed as complementary techniques to extract oils from vegetable sources, viz, soybean germ and a cultivated marine microalga rich in docosahexaenoic acid (DHA). Ultrasound (US) devices developed by ourselves, working at several frequencies (19, 25, 40 and 300 kHz), were used for US-based protocols, while a multimode microwave (MW) oven (operating with both open and closed vessels) was used for MAE. Combined treatments were also studied, such as simultaneous double sonication (at 19 and 25 kHz) and simultaneous US/MW irradiation, achieved by inserting a non-metallic horn in a MW oven. Extraction times and yields were compared with those resulting from conventional procedures. With soybean germ the best yield was obtained with a 'cavitating tube' prototype (19 kHz, 80 W), featuring a thin titanium cylinder instead of a conventional horn. Double sonication, carried out by inserting an immersion horn (25 kHz) in the same tube, improved the yield only slightly but halved the extraction time. Almost comparable yields were achieved by closed-vessel MAE and simultaneous US/MW irradiation. Compared with conventional methods, extraction times were reduced by up to 10-fold and yields increased by 50-500%. In the case of marine microalgae, UAE worked best, as the disruption by US of the tough algal cell wall considerably improved the extraction yield from 4.8% in soxhlet to 25.9%. Our results indicate that US and MW, either alone or combined, can greatly improve the extraction of bioactive substances, achieving higher efficiency and shorter reaction times at low or moderate costs, with minimal added toxicity.


title: Automatic Detection of Nonverbal Behavior Predicts Learning in Dyadic Interactions

abstract: Nonverbal behavior can reveal the psychological states of those engaged in interpersonal interaction. Previous research has highlighted the relationship between gesture and learning during instruction. In the current study we applied readily available computer vision hardware and machine learning algorithms to the gestures of teacher/student dyads (N = 106) during a learning session to automatically distinguish between high and low success learning interactions, operationalized by recall for information presented during that learning session. Models predicted learning performance of the dyad with accuracies as high as 85.7 percent when tested on dyads not included in the training set. In addition, correlations were found between summed measures of body movement and learning score. We discuss theoretical and applied implications for learning.


title: Organizational Structure in IT Governance: A Case Study of an IT Governance Implementation Project

abstract: The organizations today need to apply proper IT governance for beneficial IT investments.. This research focuses on how the organization structure should be done through the IT governance mechanism ...


title: An AMR parser for English, French, German, Spanish and Japanese and a new AMR-annotated corpus

abstract: In this demonstration, we will present our online parser that allows users to submit any sentence and obtain an analysis following the specification of AMR (Banarescu et al., 2014) to a large extent. This AMR analysis is generated by a small set of rules that convert a native Logical Form analysis provided by a preexisting parser (see Vanderwende, 2015) into the AMR format. While we demonstrate the performance of our AMR parser on data sets annotated by the LDC, we will focus attention in the demo on the following two areas: 1) we will make available AMR annotations for the data sets that were used to develop our parser, to serve as a supplement to the LDC data sets, and 2) we will demonstrate AMR parsers for German, French, Spanish and Japanese that make use of the same small set of LF-to-AMR conversion rules.


Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning


title: Deep packet inspection using parallel bloom filters

abstract: There is a class of packet processing applications that inspect packets deeper than the protocol headers to analyze content. For instance, network security applications must drop packets containing certain malicious Internet worms or computer viruses carried in a packet payload. Content forwarding applications look at the hypertext transport protocol headers and distribute the requests among the servers for load balancing. Packet inspection applications, when deployed at router ports, must operate at wire speeds. With networking speeds doubling every year, it is becoming increasingly difficult for software-based packet monitors to keep up with the line rates. We describe a hardware-based technique using Bloom filters, which can detect strings in streaming data without degrading network throughput. A Bloom filter is a data structure that stores a set of signatures compactly by computing multiple hash functions on each member of the set. This technique queries a database of strings to check for the membership of a particular string. The answer to this query can be false positive but never a false negative. An important property of this data structure is that the computation time involved in performing the query is independent of the number of strings in the database provided the memory used by the data structure scales linearly with the number of strings stored in it. Furthermore, the amount of storage required by the Bloom filter for each string is independent of its length.


title: Arrested development? Reconsidering dual-systems models of brain function in adolescence and disorders

abstract: The dual-systems model of a ventral affective system, whose reactivity confers risks and liabilities, and a prefrontal control system, whose regulatory capacities buffer against these vulnerabilities, is an intuitive account that pervades many fields in the cognitive neurosciences – especially in the study of populations that differ from neurotypical adults, such as adolescents or individuals with affective or impulse regulation disorders. However, recent evidence that is inconsistent with dual-systems models illustrates the complexity of developmental and clinical variations in brain function. Building new models to account for this complexity is critical to progress in these fields, and will be facilitated by research that emphasizes network-based approaches and maps relationships between structure and function, as well as brain and behavior, over time.


title: Achieving Flatness: Selecting the Honeywords from Existing User Passwords

abstract: Recently, Juels and Rivest proposed honeywords (decoy passwords) to detect attacks against hashed password databases. For each user account, the legitimate password is stored with several honeywords in order to sense impersonation. If honeywords are selected properly, a cyber-attacker who steals a file of hashed passwords cannot be sure if it is the real password or a honeyword for any account. Moreover, entering with a honeyword to login will trigger an alarm notifying the administrator about a password file breach. At the expense of increasing the storage requirement by 20 times, the authors introduce a simple and effective solution to the detection of password file disclosure events. In this study, we scrutinize the honeyword system and present some remarks to highlight possible weak points. Also, we suggest an alternative approach that selects the honeywords from existing user passwords in the system in order to provide realistic honeywords—a perfectly flat honeyword generation method—and also to reduce storage cost of the honeyword scheme.


title: Adaptive Traffic Lights Using Car-to-Car Communication

abstract: Traffic coordination in intersections is a very studied and challenging topic. This paper presents an adaptive traffic light system based on wireless communication between vehicles and fixed controller nodes deployed in intersections. We present the integrated simulation environment we have developed in order to study the system. We argue that our system can significantly improve traffic fluency in intersections, and has clear advantages over other architectures regarding both cost and performance.


title: Skin lesion segmentation in clinical images using deep learning

abstract: Melanoma is the most aggressive form of skin cancer and is on rise. There exists a research trend for computerized analysis of suspicious skin lesions for malignancy using images captured by digital cameras. Analysis of these images is usually challenging due to existence of disturbing factors such as illumination variations and light reflections from skin surface. One important stage in diagnosis of melanoma is segmentation of lesion region from normal skin. In this paper, a method for accurate extraction of lesion region is proposed that is based on deep learning approaches. The input image, after being preprocessed to reduce noisy artifacts, is applied to a deep convolutional neural network (CNN). The CNN combines local and global contextual information and outputs a label for each pixel, producing a segmentation mask that shows the lesion region. This mask will be further refined by some post processing operations. The experimental results show that our proposed method can outperform the existing state-of-the-art algorithms in terms of segmentation accuracy.


title: Combining feature ranking algorithms through rank aggregation

abstract: The problem of combining multiple feature rankings into a more robust ranking is investigated. A general framework for ensemble feature ranking is proposed, alongside four instantiations of this framework using different ranking aggregation methods. An empirical evaluation using 39 UCI datasets, three different learning algorithms and three different performance measures enable us to reach a compelling conclusion: ensemble feature ranking do improve the quality of feature rankings. Furthermore, one of the proposed methods was able to achieve results statistically significantly better than the others.


title: Review of hybrid exoskeletons to restore gait following spinal cord injury

abstract: Different approaches are available to compensate gait in persons with spinal cord injury, including passive orthoses, functional electrical stimulation (FES), and robotic exoskeletons. However, several drawbacks arise from each specific approach. Orthotic gait is energy-demanding for the user and functionally ineffective. FES uses the muscles as natural actuators to generate gait, providing not only functional but also psychological benefits to the users. However, disadvantages are also related to the early appearance of muscle fatigue and the control of joint trajectories. Robotic exoskeletons that provide joint moment compensation or substitution to the body during walking have been developed in recent years. Significant advances have been achieved, but the technology itself is not mature yet because of many limitations related to both physical and cognitive interaction as well as portability and energy-management issues. Meanwhile, the combination of FES technology and exoskeletons has emerged as a promising approach to both gait compensation and rehabilitation, bringing together technologies, methods, and rehabilitation principles that can overcome the drawbacks of each individual approach. This article presents an overview of hybrid lower-limb exoskeletons, related technologies, and advances in actuation and control systems. Also, we highlight the functional assessment of individuals with spinal cord injury.


title: A practical method for documenting software architectures

abstract: A practical approach for documenting software architectures is presented. The approach is based on the well-known architectural concept of views, and holds that documentation consists of documenting the relevant views and then documenting the information that applies to more than one view. Views can be usefully grouped into viewtypes, corresponding to the three broad ways an architect must think about a system: as a set of implementation units, as a set of runtime elements interacting to carry out the system’s work, and as a set of elements existing in and relating to external structures in its environment. A simple three-step procedure for choosing the relevant views to document is given, and applied to the problem of documentation for a large, complex NASA system.


title: An overview of time-based and condition-based maintenance in industrial application

abstract: This paper presents an overview of two maintenance techniques widely discussed in the literature: time-based maintenance (TBM) and condition-based maintenance (CBM). The paper discusses how the TBM and CBM techniques work toward maintenance decision making. Recent research articles covering the application of each technique are reviewed. The paper then compares the challenges of implementing each technique from a practical point of view, focusing on the issues of required data determination and collection, data analysis/modelling, and decision making. The paper concludes with significant considerations for future research. Each of the techniques was found to have unique concepts/principles, procedures, and challenges for real industrial practise. It can be concluded that the application of the CBM technique is more realistic, and thus more worthwhile to apply, than the TBM one. However, further research on CBM must be carried out in order to make it more realistic for making maintenance decisions. The paper provides useful information regarding the application of the TBM and CBM techniques in maintenance decision making and explores the challenges in implementing each technique from a practical perspective.


title: Factors Influencing Adult Learners' Decision to Drop Out or Persist in Online Learning

abstract: The number of adult learners who participate in online learning has rapidly grown in the last two decades due to online learning’s many advantages. In spite of the growth, the high dropout rate in online learning has been of concern to many higher education institutions and organizations. The purpose of this study was to determine whether persistent learners and dropouts are different in individual characteristics (i.e., age, gender, and educational level), external factors (i.e., family and organizational supports), and internal factors (i.e., satisfaction and relevance as sub-dimensions of motivation). Quantitative data were collected from 147 learners who had dropped out of or finished one of the online courses offered from a large Midwestern university. Dropouts and persistent learners showed statistical differences in perceptions of family and organizational support, and satisfaction and relevance. It was also shown that the theoretical framework, which includes family support, organizational support, satisfaction, and relevance in addition to individual characteristics, is able to predict learners’ decision to drop out or persist. Organizational support and relevance were shown to be particularly predictive. The results imply that lower dropout rates can be achieved if online program developers or instructors find ways to enhance the relevance of the course. It also implies that adult learners need to be supported by their organizations in order for them to finish online courses that they register for.


title: Autonomous UAV Surveillance in Complex Urban Environments

abstract: We address the problem of multi-UAV-based surveillance in complex urban environments with occlusions. The problem consists of controlling the flight of UAVs with on-board cameras so that the coverage and recency of the information about the designated area is maximized. In contrast to the existing work, sensing constraints due to occlusions and UAV motion constraints are modeled realistically and taken into account. We propose a novel \emph{occlusion-aware} surveillance algorithm based on a decomposition of the surveillance problem into a variant of the 3D art gallery problem and an instance of traveling salesman problem for Dubins vehicles. The algorithm is evaluated on the high-fidelity \textsc{AgentFly} UAV simulation testbed which accurately models all constraints and effects involved. The results confirm the importance of occlusion-aware flight path planning, in particular in the case of narrow-street areas and low UAV flight altitudes.


title: “Socializing” Achievement Goal Theory: The Need for Social Goals

abstract: Cultures shape the types of goals that students pursue in the classroom. However, research in achievement goal theory seems to have neglected this cultural aspect with its exclusive focus on individualistically-based goals such as mastery and performance goals. This emphasis on mastery and performance goals may reflect Western individualist psychological thinking. Thus, social goals, which may be more salient in collectivist cultures, are relatively neglected. There is a dearth of studies investigating the role of social goals in academic motivation, and the few studies that did explore them are somewhat problematic. This paper reviews research done within the achievement goal theory, considers the need for more studies on social goals, and concludes with the argument that social goals are important in understanding student motivation especially in collectivist cultures.


title: Female Slave Narratives and Appearance: Assimilation, Experience, and Escape

abstract: The purpose of this study was to explore how dress and appearance functioned for African American female slaves in published narratives. Research relevant to Black feminism, usefulness of slave narratives, and literature on African American slave appearance provided a foundation for this research. Data collection was conducted by using a hybrid narrative inquiry method consisting of thematic analysis and categorical-content analysis, to interpret 708 verbatim references to appearance. Analysis exposed that the data were partitioned into two discrete themes: (a) components reflective of Control and (b) components that revealed physical and psychological Emancipation. The stories of the women in these narratives provided descriptive information and an intersectional understanding of appearance in relation to social interactions in chattel environments. Appearance enabled telling these women's life stories as slaves through the processes of assimilating, experiencing and escaping. A matrix of female slave na...


title: Bandwidth Enhancement of Printed E-Shaped Slot Antennas Fed by CPW and Microstrip Line

abstract: Two printed wide-slot antennas with E-shaped patches and slots, for broadband applications, are proposed. They are fed by a coplanar waveguide (CPW) and a microstrip line with almost the same performances. Detailed simulation and experimental investigations are conducted to understand their behaviors and optimize for broadband operation. Good agreement between the measurement and simulation has been achieved. The impedance bandwidths, determined by 10-dB reflection coefficient, of the proposed slot antennas fed by microstrip line and CPW, from both measurement and simulation, are about 136% (2.85 to 15.12 GHz) and 146% (2.83 to 18.2 GHz), respectively. This large operating bandwidth is obtained by choosing suitable combinations of feed and slot shapes. In order to achieve wider operation bandwidth both of the designed antennas have round corners on the wide slot and patch. Meanwhile, the proposed antennas exhibit almost omnidirectional radiation patterns, relatively high gain, and low cross polarization. A comprehensive numerical sensitivity analysis has been done to understand the effects of various dimensional parameters and to optimize the performance of the designed antennas. Results for reflection coefficient, far-field E and H-plane radiation patterns, and gain of the designed antennas are presented and discussed.


title: Intelligent UAV-assisted routing protocol for urban VANETs

abstract: The process of routing in Vehicular Ad hoc Networks (VANET) is a challenging task in city environments. Finding the shortest end-to-end connected path satisfying delay restriction and minimal overhead is confronted with many constraints and difficulties. Such difficulties are due to the high mobility of vehicles, the frequent path failures, and the various obstructions, which may affect the reliability of the data transmission and routing. Commercial Unmanned Aerial Vehicles (UAVs) or what are commonly referred to as drones can come in handy in dealing with these constraints. In this paper, we study how UAVs operating in ad hoc mode can cooperate with VANET on the ground so as to assist in the routing process and improve the reliability of the data delivery by bridging the communication gap whenever it is possible. In a previous work, we have proposed UVAR a UAV-Assisted VANETs routing protocol, which improves data routing and connectivity of the vehicles on the ground through the use of UAVs. However, UVAR does not fully exploit UAVs in the sky for data forwarding because it uses UAVs only when the network is poorly dense. In this paper, we propose an extension of this protocol by supporting two different ways of routing data: (i) delivering data packets exclusively on the ground using UVAR-G; and (ii) transmitting data packets in the sky using a reactive routing based on UVAR-S. Simulation results demonstrate that the hybrid communication between vehicles and UAVs is ideally suited for VANETs compared to traditional vehicle-to-vehicle (V2V) communications.


title: An Open Source HMM-based Text-to-Speech System for Brazilian Portuguese

abstract: Text-to-speech (TTS) is currently a mature tech- nology that is used in many applications. Some modules of a TTS depend on the language and, while there are many public resources for English, the resources for some under- represented languages are still limited. This work describes the development of a complete TTS system for Brazilian Portuguese which expands the already available resources. The system uses the MARY framework and is based on the hidden Markov model (HMM) speech synthesis approach. Some of the contributions of this work consist in implementing syllab- ification, determination of stressed syllable and grapheme-to- phoneme (G2P) conversion. This work also describes the steps for organizing the developed resources and implementing a Brazilian Portuguese voice within the MARY. These resources are made available and facilitate the research in text analysis and HMM-based synthesis for Brazilian Portuguese. Keywords— Text-to-speech systems, HMM-based speech syn- thesis, text analysis.


title: Social media analytics - Challenges in topic discovery, data collection, and data preparation

abstract: Abstract Since an ever-increasing part of the population makes use of social media in their day-to-day lives, social media data is being analysed in many different disciplines. The social media analytics process involves four distinct steps, data discovery, collection, preparation, and analysis. While there is a great deal of literature on the challenges and difficulties involving specific data analysis methods, there hardly exists research on the stages of data discovery, collection, and preparation. To address this gap, we conducted an extended and structured literature analysis through which we identified challenges addressed and solutions proposed. The literature search revealed that the volume of data was most often cited as a challenge by researchers. In contrast, other categories have received less attention. Based on the results of the literature search, we discuss the most important challenges for researchers and present potential solutions. The findings are used to extend an existing framework on social media analytics. The article provides benefits for researchers and practitioners who wish to collect and analyse social media data.


title: Water from Two Rocks: Maximizing the Mutual Information

abstract: We build a natural connection between the learning problem, co-training, and forecast elicitation without verification (related to peer-prediction) and address them simultaneously using the same information theoretic approach. ::: In co-training/multiview learning, the goal is to aggregate two views of data into a prediction for a latent label. We show how to optimally combine two views of data by reducing the problem to an optimization problem. Our work gives a unified and rigorous approach to the general setting. ::: In forecast elicitation without verification we seek to design a mechanism that elicits high quality forecasts from agents in the setting where the mechanism does not have access to the ground truth. By assuming the agents' information is independent conditioning on the outcome, we propose mechanisms where truth-telling is a strict equilibrium for both the single-task and multi-task settings. Our multi-task mechanism additionally has the property that the truth-telling equilibrium pays better than any other strategy profile and strictly better than any other "non-permutation" strategy profile when the prior satisfies some mild conditions.


title: Anticipatory driving competence: motivation, definition & modeling

abstract: Anticipation of future events is recognized to be a significant element of driver competence. Surely, guiding one's behavior through the anticipation of future traffic states provides potential gains in recognition and reaction times. However, the role of anticipation in driving and ways to support it have not been systematically studied. In this paper, we identify the characteristics of anticipatory driving and provide a working definition. In particular, we distinguish it from overall driving goals such as eco or defensive driving, but rather present it as a high-level competence for efficient positioning of the vehicle to ultimately facilitate these goals. We also argue that anticipation occurs within the context of stereotypical scenarios and provide an initial taxonomy for the identification of such scenarios. We suggest the Decision Ladder as a useful way of modeling anticipatory driving and finally discuss a potential approach for the facilitation of anticipatory driving through skill- and rule-based behavior, which can allow for shortcuts on the Decision Ladder.


title: Short-term electricity demand forecasting using double seasonal exponential smoothing

abstract: This paper considers univariate online electricity demand forecasting for lead times from a half-hour-ahead to a day-ahead. A time series of demand recorded at half-hourly intervals contains more than one seasonal pattern. A within-day seasonal cycle is apparent from the similarity of the demand profile from one day to the next, and a within-week seasonal cycle is evident when one compares the demand on the corresponding day of adjacent weeks. There is strong appeal in using a forecasting method that is able to capture both seasonalities. The multiplicative seasonal ARIMA model has been adapted for this purpose. In this paper, we adapt the Holt-Winters exponential smoothing formulation so that it can accommodate two seasonalities. We correct for residual autocorrelation using a simple autoregressive model. The forecasts produced by the new double seasonal Holt-Winters method outperform those from traditional Holt-Winters and from a well-specified multiplicative double seasonal ARIMA model.


title: N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning

abstract: While bigger and deeper neural network architectures continue to advance the state-of-the-art for many computer vision tasks, real-world adoption of these networks is impeded by hardware and speed constraints. Conventional model compression methods attempt to address this problem by modifying the architecture manually or using pre-defined heuristics. Since the space of all reduced architectures is very large, modifying the architecture of a deep neural network in this way is a difficult task. In this paper, we tackle this issue by introducing a principled method for learning reduced network architectures in a data-driven way using reinforcement learning. Our approach takes a larger `teacher' network as input and outputs a compressed `student' network derived from the `teacher' network. In the first stage of our method, a recurrent policy network aggressively removes layers from the large `teacher' model. In the second stage, another recurrent policy network carefully reduces the size of each remaining layer. The resulting network is then evaluated to obtain a reward -- a score based on the accuracy and compression of the network. Our approach uses this reward signal with policy gradients to train the policies to find a locally optimal student network. Our experiments show that we can achieve compression rates of more than 10x for models such as ResNet-34 while maintaining similar performance to the input `teacher' network. We also present a valuable transfer learning result which shows that policies which are pre-trained on smaller `teacher' networks can be used to rapidly speed up training on larger `teacher' networks.


title: Efficient pulse-Doppler processing and ambiguity functions of nonuniform coherent pulse trains

abstract: We propose a DFT based pulse Doppler processing receiver for staggered pulse trains. The proposed receiver is a simple extension of traditional DFT based coherent pulse train processing. We show that P DFT processors are required to process the staggered train of pulses as a coherent signal, where P is the number of available pulse positions in each pulse repetition interval (PRI). Thus the complexity of the processing hardware only increases linearly with the number of available positions. We also look at the distribution of ambiguity volume around the delay-Doppler map by varying the pulse positions and the selection of pulse shapes.


title: A Developmental Taxonomy of Juvenile Sex Offenders for Theory, Research, and Prevention

abstract: The current study investigates the offending trajectories of juvenile sex offenders (JSOs) across and beyond adolescence. In doing so, the study examines the number, the rate, and the shape of nonsexual and sexual offending trajectories in a sample of JSOs followed retrospectively and prospectively from late childhood to adulthood. Using semiparametric group-based modeling, the study reveals the presence of five distinctive nonsexual offending and two sexual offending trajectories: adolescent-limited and high-rate slow desisters. The study does not find strong evidence of synchronicity between nonsexual and sexual trajectories, suggesting that the current taxonomy of antisocial behaviors may offer a limited perspective on sex offender types. Furthermore, sexual trajectories do not differ much across sex offender types, suggesting that the findings might be generalized to child and peer abusers. The study findings offer supporting evidence for the presence of two distinct JSO types with important implications for theory, research, and interventions. Language: en


title: Learning to classify short and sparse text & web with hidden topics from large-scale data collections

abstract: This paper presents a general framework for building classifiers that deal with short and sparse text & Web segments by making the most of hidden topics discovered from large-scale data collections. The main motivation of this work is that many classification tasks working with short segments of text & Web, such as search snippets, forum & chat messages, blog & news feeds, product reviews, and book & movie summaries, fail to achieve high accuracy due to the data sparseness. We, therefore, come up with an idea of gaining external knowledge to make the data more related as well as expand the coverage of classifiers to handle future data better. The underlying idea of the framework is that for each classification task, we collect a large-scale external data collection called "universal dataset", and then build a classifier on both a (small) set of labeled training data and a rich set of hidden topics discovered from that data collection. The framework is general enough to be applied to different data domains and genres ranging from Web search results to medical text. We did a careful evaluation on several hundred megabytes of Wikipedia (30M words) and MEDLINE (18M words) with two tasks: "Web search domain disambiguation" and "disease categorization for medical text", and achieved significant quality enhancement.


title: Tripartite model of anxiety and depression: Psychometric evidence and taxonomic implications.

abstract: We review psychometric and other evidence relevant to mixed anxiety-depression. Properties of anxiety and depression measures, including the convergent and discriminant validity of self- and clinical ratings, and interrater reliability, are examined in patient and normal samples. Results suggest that anxiety and depression can be reliably and validly assessed; moreover, although these disorders share a substantial component of general affective distress, they can be differentiated on the basis of factors specific to each syndrome. We also review evidence for these specific factors, examining the influence of context and scale content on ratings, factor analytic studies, and the role of low positive affect in depression. With these data, we argue for a tripartite structure consisting of general distress, physiological hyperarousal (specific anxiety), and anhedonia (specific depression), and we propose a diagnosis of mixed anxiety-depression.


title: Video Text extraction and recognition: A survey

abstract: A means to naturally recognizing and fetching out the content of video description would possibly make them indexed in considerable and appropriate way for later reference, and would facilitate actions viz. automatic notification and dissemination, to be triggered in real time by the contents of streaming video. Video text recognition, or video OCR, is a constructive tool to characterize the contents of video containing overlay text (text captions superimposed over the video imagery, such as in broadcast news programs) and scene text (text that appears in the real scene of the video, such as text on street signs, nameplates, and billboards). In this paper exhaustive survey is done for text detecting, extraction and recognizing in complex images and video frames. Digital Videos are widely used both professionally and domestically because of the easy availability of camcorders to mobile phones. People are increasingly making videos may be for commercial use or personal use, this is leading to growing content of Video. While we can capture, compress, store, transmit and display video with great facility, editing videos and manipulating them based on their content is still a non-trivial activity. This paper concentrates on extracting text out of the video frames, taken out of the video.


Natural Language Communication with Robots


title: Design Issues, Characteristics and Challenges in Routing Protocols for Wireless Sensor Networks

abstract: The modern research has found a variety of applications and systems with vastly varying requirements and characteristics in Wireless Sensor Networks (WSNs). The research has led to materialization of many application specific routing protocols which must be energy-efficient. As a consequence, it is becoming increasingly difficult to discuss the design issues requirements regarding hardware and software support. Implementation of efficient system in a multidisciplinary research such as WSNs is becoming very difficult. In this paper we discuss the design issues in routing protocols for WSNs by considering its various dimensions and metrics such as QoS requirement, path redundancy etc. The paper concludes by presenting the suitable applications and QoS of routing protocols for WSNs.


Personal information management: From information consumption to curation


title: Healthcare Blockchain System Using Smart Contracts for Secure Automated Remote Patient Monitoring

abstract: As Internet of Things (IoT) devices and other remote patient monitoring systems increase in popularity, security concerns about the transfer and logging of data transactions arise. In order to handle the protected health information (PHI) generated by these devices, we propose utilizing blockchain-based smart contracts to facilitate secure analysis and management of medical sensors. Using a private blockchain based on the Ethereum protocol, we created a system where the sensors communicate with a smart device that calls smart contracts and writes records of all events on the blockchain. This smart contract system would support real-time patient monitoring and medical interventions by sending notifications to patients and medical professionals, while also maintaining a secure record of who has initiated these activities. This would resolve many security vulnerabilities associated with remote patient monitoring and automate the delivery of notifications to all involved parties in a HIPAA compliant manner.


title: Short circuit current comparison of DFIG during symmetrical faults with different wind speeds

abstract: The short-circuit current calculation of any equipment in the power system is very important for selection of appropriate relay characteristics and circuit breaker for the protection of the system. The power system is undergoing changes because of large scale penetration of renewable energy sources in the conventional system. Major renewable sources which are included in the power system are wind energy and solar energy sources. The wind energy is supplied by wind turbine generators of various types. Type III generators i.e. Doubly Fed Induction Generator (DFIG) is the most common types of generator employed offering different behavior compared to conventionally employed synchronous generators. In this paper; the short circuit current contribution of DFIG is calculated analytically and the same is validated by PSCAD/EMTDC software under various wind speeds and by considering certain voltage drops of the generator output.


title: Advice drainage plan Farm 70 : Salamá, Puntarenas, Costa Rica

abstract: Dit rapport geeft een second opinion van een drainage plan dat is opgesteld voor de teak plantage Farm 70, in Costa Rica. Het rapport beschrijft de mogelijkheden om het risico op hoge grondwaterstanden in deze teak (Tectona grandis) te verminderenThis report presents a “second” opinion of the drainage plan for the teak plantation Farm 70, in Costa Rica. The Dutch Foundation Terra Vitalis has requested this second opinion on the drainage plan prepared by the BARCA Company for Farm 70, Puntarenas, Costa Rica. This teak farm is located in the southwest region of Costa Rica, a region characterized by high rainfall, especially in the period May to November. The BARCA Company is developing the area as a teak reforestation plantation. Because of the heavy rainfall intensity, flat topography, soils with low hydraulic conductivity and impermeable layers close to the soil surface, an intensive drainage system is required. The report discusses options to reduce the risk of high water tables in this teak plantation (71.56 ha)


title: Standardized Transplantation Referral Ratio to Assess Performance of Transplant Referral among Dialysis Facilities.

abstract: BACKGROUND AND OBJECTIVES ::: For patients with ESRD, referral from a dialysis facility to a transplant center for evaluation is an important step toward kidney transplantation. However, a standardized measure for assessing clinical performance of dialysis facilities transplant access is lacking. We describe methodology for a new dialysis facility measure: the Standardized Transplantation Referral Ratio. ::: ::: ::: DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS ::: Transplant referral data from 8308 patients with incident ESRD within 249 dialysis facilities in the United States state of Georgia were linked with US Renal Data System data from January of 2008 to December of 2011, with follow-up through December of 2012. Facility-level expected referrals were computed from a two-stage Cox proportional hazards model after patient case mix risk adjustment including demographics and comorbidities. The Standardized Transplantation Referral Ratio (95% confidence interval) was calculated as a ratio of observed to expected referrals. Measure validity and reliability were assessed. ::: ::: ::: RESULTS ::: Over 2008-2011, facility Standardized Transplantation Referral Ratios in Georgia ranged from 0 to 4.87 (mean =1.16, SD=0.76). Most (77%) facilities had observed referrals as expected, whereas 11% and 12% had Standardized Transplantation Referral Ratios significantly greater than and less than expected, respectively. Age, race, sex, and comorbid conditions were significantly associated with the likelihood of referral, and they were included in risk adjustment for Standardized Transplantation Referral Ratio calculations. The Standardized Transplantation Referral Ratios were positively associated with evaluation, waitlisting, and transplantation (r=0.46, 0.35, and 0.20, respectively; P<0.01). On average, approximately 33% of the variability in Standardized Transplantation Referral Ratios was attributed to between-facility variation, and 67% of the variability in Standardized Transplantation Referral Ratios was attributed to within-facility variation. ::: ::: ::: CONCLUSIONS ::: The majority of observed variation in dialysis facility referral performance was due to characteristics within a dialysis facility rather than patient factors included in risk adjustment models. Our study shows a method for computing a facility-level standardized measure for transplant referral on the basis of a pilot sample of Georgia dialysis facilities that could be used to monitor transplant referral performance of dialysis facilities.


title: Ondemand language model interpolation for mobile speech input

abstract: Google offers several speech features on the Android mobile operating system: search by voice, voice input to any text field, and an API for application developers. As a result, our speech recognition service must support a wide range of usage scenarios and speaking styles: relatively short search queries, addresses, business names, dictated SMS and e-mail messages, and a long tail of spoken input to any of the applications users may install. We present a method of on-demand language model interpolation in which contextual information about each utterance determines interpolation weights among a number of n-gram language models. On-demand interpolation results in an 11.2% relative reduction in WER compared to using a single language model to handle all traffic. Index Terms: language modeling, interpolation, mobile


title: HEPATOZOON CANIS AND HEPATOZOONOSIS IN THE DOG

abstract: This review summarizes data from the numerous investigations from the beginning of the last century to the present. The studies concerned the main issues of the morphology, the life cycle, hosts and localization of Hepatozoon canis (phylum Apicomplexa, suborder Adeleorina, family Hepatozoidae). The characteristic features of hepatozoonosis, caused by Hepatozoon canis in the dog, are evaluated. A survey of clinical signs, gross pathological changes, epidemiology, diagnosis and treatment of the disease was made. The measures for prevention of Hepatozoon canis infection in animals are listed. The importance of hepatozoonosis with regard to public health was evaluated. The studies on the subject, performed in Bulgaria, are discussed.


title: Somatic gain-of-function mutations in PIK3CA in patients with macrodactyly

abstract: Macrodactyly is a discrete congenital anomaly consisting of enlargement of all tissues localized to the terminal portions of a limb, typically within a ‘nerve territory’. The classic terminology for this condition is ‘lipofibromatous hamartoma of nerve’ or Type I macrodactyly. The peripheral nerve, itself, is enlarged both in circumference and in length. It is not related to neurofibromatosis (NF1), nor is it associated with vascular malformations, such as in the recently reported CLOVES syndrome. The specific nerve pathophysiology in this form of macrodactyly has not been well described and a genetic etiology for this specific form of enlargement is unknown. To identify the genetic cause of macrodactyly, we used whole-exome sequencing to identify somatic mutations present in the affected nerve of a single patient. We confirmed a novel mutation in PIK3CA (R115P) present in the patient's affected nerve tissue but not in blood DNA. Sequencing PIK3CA exons identified gain-of-function mutations (E542K, H1047L or H1047R) in the affected tissue of five additional unrelated patients; mutations were absent in blood DNA available from three patients. Immunocytochemistry confirmed AKT activation in cultured cells from the nerve of a macrodactyly patient. Additionally, we found that the most abnormal structure within the involved nerve in a macrodactylous digit is the perineurium, with additional secondary effects on the axon number and size. Thus, isolated congenital macrodactyly is caused by somatic activation of the PI3K/AKT cell-signaling pathway and is genetically and biochemically related to other overgrowth syndromes.


title: What (and why) is positive psychology

abstract: Positive psychology is the study of the conditions and processes that contribute to the flourishing or optimal functioning of people, groups, and institutions. In this brief introduction, the autho...


title: From data to knowledge: city-wide traffic flows analysis and prediction using bing maps

abstract: Traffic jam is a common contemporary society issue in urban areas. City-wide traffic modeling, visualization, analysis, and prediction are still challenges in this context. Based on Bing Maps information, this work aims to acquire, aggregate, analyze, visualize, and predict traffic jam. Chicago area was evaluated as case study. The flow intensity (free or congested) was analyzed to allow the identification of phase transitions (shocks in the system). Also, a prediction model was developed based on logistic regression to correct discovery future flow intensities for a target street.


title: 3D Reconstruction of Temples in the Special Region of Yogyakarta By Using Close-Range Photogrammetry

abstract: Object reconstruction is one of the main problems in cultural heritage preservation. This problem is due to lack of data in documentation. Thus in this research we presented a method of 3D reconstruction using close-range photogrammetry. We collected 1319 photos from five temples in Yogyakarta. Using A-KAZE algorithm, keypoints of each image were obtained. Then we employed LIOP to create feature descriptor from it. After performing feature matching, L1RA was utilized to create sparse point clouds. In order to generate the geometry shape, MVS was used. Finally, FSSR and Large Scale Texturing were employed to deal with the surface and texture of the object. The quality of the reconstructed 3D model was measured by comparing the 3D images of the model with the original photos utilizing SSIM. The results showed that in terms of quality, our method was on par with other commercial method such as PhotoModeler and PhotoScan.


title: How do habits guide behavior? Perceived and actual triggers of habits in daily life

abstract: Abstract What are the psychological mechanisms that trigger habits in daily life? Two studies reveal that strong habits are influenced by context cues associated with past performance (e.g., locations) but are relatively unaffected by current goals. Specifically, performance contexts—but not goals—automatically triggered strongly habitual behaviors in memory (Experiment 1) and triggered overt habit performance (Experiment 2). Nonetheless, habits sometimes appear to be linked to goals because people self-perceive their habits to be guided by goals. Furthermore, habits of moderate strength are automatically influenced by goals, yielding a curvilinear, U-shaped relation between habit strength and actual goal influence. Thus, research that taps self-perceptions or moderately strong habits may find habits to be linked to goals.


Contending with group image: The psychology of stereotype and social identity threat


title: Your Attention Please Designing security-decision UIs to make genuine risks harder to ignore

abstract: We designed and tested attractors for computer security dialogs: user-interface modifications used to draw users' attention to the most important information for making decisions. Some of these modifications were purely visual, while others temporarily inhibited potentially-dangerous behaviors to redirect users' attention to salient information. We conducted three between-subjects experiments to test the effectiveness of the attractors. In the first two experiments, we sent participants to perform a task on what appeared to be a third-party site that required installation of a browser plugin. We presented them with what appeared to be an installation dialog from their operating system. Participants who saw dialogs that employed inhibitive attractors were significantly less likely than those in the control group to ignore clues that installing this software might be harmful. In the third experiment, we attempted to habituate participants to dialogs that they knew were part of the experiment. We used attractors to highlight a field that was of no value during habituation trials and contained critical information after the habituation period. Participants exposed to inhibitive attractors were two to three times more likely to make an informed decision than those in the control condition.


title: Contrastive Hebbian Learning with Random Feedback Weights

abstract: Abstract Neural networks are commonly trained to make predictions through learning algorithms. Contrastive Hebbian learning, which is a powerful rule inspired by gradient backpropagation, is based on Hebb’s rule and the contrastive divergence algorithm. It operates in two phases, the free phase, where the data are fed to the network, and a clamped phase, where the target signals are clamped to the output layer of the network and the feedback signals are transformed through the transpose synaptic weight matrices. This implies symmetries at the synaptic level, for which there is no evidence in the brain so far. In this work, we propose a new variant of the algorithm, called random contrastive Hebbian learning, which does not rely on any synaptic weights symmetries. Instead, it uses random matrices to transform the feedback signals during the clamped phase, and the neural dynamics are described by first order non-linear differential equations. The algorithm is experimentally verified by solving a Boolean logic task, classification tasks (handwritten digits and letters), and an autoencoding task. This article also shows how the parameters affect learning, especially the random matrices. We use the pseudospectra analysis to investigate further how random matrices impact the learning process. Finally, we discuss the biological plausibility of the proposed algorithm, and how it can give rise to better computational models for learning.


Augmented-Virtual Reality: How to improve education systems


title: Long short term memory based hardware prefetcher: a case study

abstract: Hardware prefetching is an efficient mechanism to hide cache miss penalties. Accuracy, coverage, and timeliness are three primary metrics in evaluating prefetcher performance. Highly accurate hardware prefetches are desired to predict complex memory access patterns in multicore systems. In this paper, we propose a long short term memory (LSTM) prefetcher---a neural network based hardware prefetcher. Offline experiment shows that the proposed LSTM prefetcher achieves higher accuracy and better coverage on a set of evaluated traces.


Towards Privacy-Preserving Medical Cloud Computing Using Homomorphic Encryption


Family Adaptation, Coping and Resources: Parents Of Children With Developmental Disabilities and Behaviour Problems


title: Essential Oils, A New Horizon in Combating Bacterial Antibiotic Resistance

abstract: For many years, the battle between humans and the multitudes of infection and disease causing pathogens continues. Emerging at the battlefield as some of the most significant challenges to human health are bacterial resistance and its rapid rise. These have become a major concern in global public health invigorating the need for new antimicrobial compounds. A rational approach to deal with antibiotic resistance problems requires detailed knowledge of the different biological and non-biological factors that affect the rate and extent of resistance development. Combination therapy combining conventional antibiotics and essential oils is currently blooming and represents a potential area for future investigations. This new generation of phytopharmaceuticals may shed light on the development of new pharmacological regimes in combating antibiotic resistance. This review consolidated and described the observed synergistic outcome between essential oils and antibiotics, and highlighted the possibilities of essential oils as the potential resistance modifying agent.


title: A fast stereo matching algorithm suitable for embedded real-time systems

abstract: In this paper, the challenge of fast stereo matching for embedded systems is tackled. Limited resources, e.g. memory and processing power, and most importantly real-time capability on embedded systems for robotic applications, do not permit the use of most sophisticated stereo matching approaches. The strengths and weaknesses of different matching approaches have been analyzed and a well-suited solution has been found in a Census-based stereo matching algorithm. The novelty of the algorithm used is the explicit adaption and optimization of the well-known Census transform in respect to embedded real-time systems in software. The most important change in comparison with the classic Census transform is the usage of a sparse Census mask which halves the processing time with nearly unchanged matching quality. This is due the fact that large sparse Census masks perform better than small dense masks with the same processing effort. The evidence of this assumption is given by the results of experiments with different mask sizes. Another contribution of this work is the presentation of a complete stereo matching system with its correlation-based core algorithm, the detailed analysis and evaluation of the results, and the optimized high speed realization on different embedded and PC platforms. The algorithm handles difficult areas for stereo matching, such as areas with low texture, very well in comparison to state-of-the-art real-time methods. It can successfully eliminate false positives to provide reliable 3D data. The system is robust, easy to parameterize and offers high flexibility. It also achieves high performance on several, including resource-limited, systems without losing the good quality of stereo matching. A detailed performance analysis of the algorithm is given for optimized reference implementations on various commercial of the shelf (COTS) platforms, e.g. a PC, a DSP and a GPU, reaching a frame rate of up to 75 fps for 640x480 images and 50 disparities. The matching quality and processing time is compared to other algorithms on the Middlebury stereo evaluation website reaching a middle quality and top performance rank. Additional evaluation is done by comparing the results with a very fast and well-known sum of absolute differences algorithm using several Middlebury datasets and real-world scenarios.


title: Georeferenced Point Clouds: A Survey of Features and Point Cloud Management

abstract: This paper presents a survey of georeferenced point clouds. Concentration is, on the one hand, put on features, which originate in the measurement process themselves, and features derived by processing the point cloud. On the other hand, approaches for the processing of georeferenced point clouds are reviewed. This includes the data structures, but also spatial processing concepts. We suggest a categorization of features into levels that reflect the amount of processing. Point clouds are found across many disciplines, which is reflected in the versatility of the literature suggesting specific features.


title: Image and Video Abstraction by Coherence-Enhancing Filtering*

abstract: In this work, we present a non-photorealistic rendering technique to create stylized abstractions from color images and videos. Our approach is based on adaptive line integral convolution in combination with directional shock filtering. The smoothing process regularizes directional image features while the shock filter provides a sharpening effect. Both operations are guided by a flow field derived from the structure tensor. To obtain a high-quality flow field, we present a novel smoothing scheme for the structure tensor based on Poisson's equation. Our approach effectively regularizes anisotropic image regions while preserving the overall image structure and achieving a consistent level of abstraction. Moreover, it is suitable for per-frame filtering of video and can be efficiently implemented to process content in real-time.


title: Real-time pedestrian detection with deformable part models

abstract: We describe a real-time pedestrian detection system intended for use in automotive applications. Our system demonstrates superior detection performance when compared to many state-of-the-art detectors and is able to run at a speed of 14 fps on an Intel Core i7 computer when applied to 640×480 images. Our approach uses an analysis of geometric constraints to efficiently search feature pyramids and increases detection accuracy by using a multiresolution representation of a pedestrian model to detect small pixel-sized pedestrians normally missed by a single representation approach. We have evaluated our system on the Caltech Pedestrian benchmark which is currently the largest publicly available pedestrian dataset at the time of this publication. Our system shows a detection rate of 61% with 1 false positive per image (FPPI) whereas recent other state-of-the-art detectors show a detection rate of 50% ∼ 61% under the ‘reasonable’ test scenario (explained later). Furthermore, we also demonstrate the practicality of our system by conducting a series of use case experiments on selected videos of Caltech dataset.


title: New Digital Forensics Investigation Procedure Model

abstract: In this paper, we presented a new digital forensics investigation procedure model which is as follows: investigation preparation, classifying cyber crime and deciding investigation priority, investigating damaged (victim) digital crime scene, criminal profiling consultant and analysis, tracking suspects, investigating injurer digital crime scene, summoning suspect, additional investigation, writing criminal profiling, writing report.


title: CartoonGAN: Generative Adversarial Networks for Photo Cartoonization

abstract: In this paper, we propose a solution to transforming photos of real-world scenes into cartoon style images, which is valuable and challenging in computer vision and computer graphics. Our solution belongs to learning based methods, which have recently become popular to stylize images in artistic forms such as painting. However, existing methods do not produce satisfactory results for cartoonization, due to the fact that (1) cartoon styles have unique characteristics with high level simplification and abstraction, and (2) cartoon images tend to have clear edges, smooth color shading and relatively simple textures, which exhibit significant challenges for texture-descriptor-based loss functions used in existing methods. In this paper, we propose CartoonGAN, a generative adversarial network (GAN) framework for cartoon stylization. Our method takes unpaired photos and cartoon images for training, which is easy to use. Two novel losses suitable for cartoonization are proposed: (1) a semantic content loss, which is formulated as a sparse regularization in the high-level feature maps of the VGG network to cope with substantial style variation between photos and cartoons, and (2) an edge-promoting adversarial loss for preserving clear edges. We further introduce an initialization phase, to improve the convergence of the network to the target manifold. Our method is also much more efficient to train than existing methods. Experimental results show that our method is able to generate high-quality cartoon images from real-world photos (i.e., following specific artists' styles and with clear edges and smooth shading) and outperforms state-of-the-art methods.


ANALYSIS OF VOICE STRESS IN CALL CENTERS


Added Value of Coreference Annotation for Character Analysis in Narratives


title: Securing Virtual Machines from Anomalies Using Program-Behavior Analysis in Cloud Environment

abstract: Cloud Computing is the key technology of today's cyber world which provides online provisioning of resources on demand and pay per use basis. Malware attacks such as virus, worm and rootkits etc. are some threats to virtual machines (VMs) in cloud environment. In this paper, we present a system call analysis approach to detect malware attacks which maliciously affect the legitimate programs running in Virtual Machines (VMs) and modify their behavior. Our approach is named as 'Malicious System Call Sequence Detection (MSCSD)' which is based on analysis of short sequence of system calls (n-grams). MSCSD employs an efficient feature representation method for system call patterns to improve the accuracy of attack detection and reduce the cost of storage with reduced false positives. MSCSD applies Machine Learning (Decision Tree C 4.5) over the collected n-gram patterns for learning the behavior of monitored programs and detecting malicious system call patterns in future. We have analyzed the performance of some other classifiers and compared our work with the existing work for securing virtual machine in cloud. A prototype implementation of the approach is carried out over UNM dataset and results seem to be promising.


title: Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification

abstract: In the literature of remote sensing, deep models with multiple layers have demonstrated their potentials in learning the abstract and invariant features for better representation and classification of hyperspectral images. The usual supervised deep models, such as convolutional neural networks, need a large number of labeled training samples to learn their model parameters. However, the real-world hyperspectral image classification task provides only a limited number of training samples. This paper adopts another popular deep model, i.e., deep belief networks (DBNs), to deal with this problem. The DBNs allow unsupervised pretraining over unlabeled samples at first and then a supervised fine-tuning over labeled samples. But the usual pretraining and fine-tuning method would make many hidden units in the learned DBNs tend to behave very similarly or perform as “dead” (never responding) or “potential over-tolerant” (always responding) latent factors. These results could negatively affect description ability and thus classification performance of DBNs. To further improve DBN’s performance, this paper develops a new diversified DBN through regularizing pretraining and fine-tuning procedures by a diversity promoting prior over latent factors. Moreover, the regularized pretraining and fine-tuning can be efficiently implemented through usual recursive greedy and back-propagation learning framework. The experiments over real-world hyperspectral images demonstrated that the diversity promoting prior in both pretraining and fine-tuning procedure lead to the learned DBNs with more diverse latent factors, which directly make the diversified DBNs obtain much better results than original DBNs and comparable or even better performances compared with other recent hyperspectral image classification methods.


title: Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches

abstract: To effectively predict stock price for investors is a very important research problem. In literature, data mining techniques have been applied to stock (market) prediction. Feature selection, a pre-processing step of data mining, aims at filtering out unrepresentative variables from a given dataset for effective prediction. As using different feature selection methods will lead to different features selected and thus affect the prediction performance, the purpose of this paper is to combine multiple feature selection methods to identify more representative variables for better prediction. In particular, three well-known feature selection methods, which are Principal Component Analysis (PCA), Genetic Algorithms (GA) and decision trees (CART), are used. The combination methods to filter out unrepresentative variables are based on union, intersection, and multi-intersection strategies. For the prediction model, the back-propagation neural network is developed. Experimental results show that the intersection between PCA and GA and the multi-intersection of PCA, GA, and CART perform the best, which are of 79% and 78.98% accuracy respectively. In addition, these two combined feature selection methods filter out near 80% unrepresentative features from 85 original variables, resulting in 14 and 17 important features respectively. These variables are the important factors for stock prediction and can be used for future investment decisions.


title: Human Activity Learning using Object Affordances from RGB-D Videos

abstract: Human activities comprise several sub-activities performed in a sequence and involve interactions with various objects. This makes reasoning about the object affordances a central task for activity recognition. In this work, we consider the problem of jointly labeling the object affordances and human activities from RGB-D videos. We frame the problem as a Markov Random Field where the nodes represent objects and sub-activities, and the edges represent the relationships between object affordances, their relations with sub-activities, and their evolution over time. We formulate the learning problem using a structural SVM approach, where labeling over various alternate temporal segmentations are considered as latent variables. We tested our method on a dataset comprising 120 activity videos collected from four subjects, and obtained an end-to-end precision of 81.8% and recall of 80.0% for labeling the activities.


title: Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

abstract: Data center power consumption is among the largest commodity expenditures for many organizations. Reduction of power used in cloud data centres with heterogeneous physical resources can be achieved through Virtual-Machine (VM) consolidation which reduces the number of Physical Machines (PMs) used, subject to Quality of Service (QoS) constraints. This paper provides an in-depth survey of the most recent techniques and algorithms used in proactive dynamic VM consolidation focused on energy consumption. We present a general framework that can be used on multiple phases of a complete consolidation process.


title: Genetic and Genomic Analysis of Rhizoctonia solani Interactions with Arabidopsis; Evidence of Resistance Mediated through NADPH Oxidases

abstract: Rhizoctonia solani is an important soil-borne necrotrophic fungal pathogen, with a broad host range and little effective resistance in crop plants. Arabidopsis is resistant to R. solani AG8 but susceptible to R. solani AG2-1. A screen of 36 Arabidopsis ecotypes and mutants affected in the auxin, camalexin, salicylic acid, abscisic acid and ethylene/jasmonic acid pathways did not reveal any variation in response to R. solani and demonstrated that resistance to AG8 was independent of these defense pathways. The Arabidopsis Affymetrix ATH1 Genome array was used to assess global gene expression changes in plants infected with AG8 and AG2-1 at seven days post-infection. While there was considerable overlap in the response, some gene families were differentially affected by AG8 or AG2-1 and included those involved in oxidative stress, cell wall associated proteins, transcription factors and heat shock protein genes. Since a substantial proportion of the gene expression changes were associated with oxidative stress responses, we analysed the role of NADPH oxidases in resistance. While single NADPH oxidase mutants had no effect, a NADPH oxidase double mutant atrbohf atrbohd resulted in an almost complete loss of resistance to AG8, suggesting that reactive oxidative species play an important role in Arabidopsis's resistance to R. solani.


title: Scene text detection and recognition: recent advances and future trends

abstract: Text, as one of the most influential inventions of humanity, has played an important role in human life, so far from ancient times. The rich and precise information embodied in text is very useful in a wide range of vision-based applications, therefore text detection and recognition in natural scenes have become important and active research topics in computer vision and document analysis. Especially in recent years, the community has seen a surge of research efforts and substantial progresses in these fields, though a variety of challenges (e.g. noise, blur, distortion, occlusion and variation) still remain. The purposes of this survey are three-fold: 1) introduce up-to-date works, 2) identify state-of-the-art algorithms, and 3) predict potential research directions in the future. Moreover, this paper provides comprehensive links to publicly available resources, including benchmark datasets, source codes, and online demos. In summary, this literature review can serve as a good reference for researchers in the areas of scene text detection and recognition.


title: Chimpanzees are more skilful in competitive than in cooperative cognitive tasks

abstract: In a series of four experiments, chimpanzees, Pan troglodytes, were given two cognitive tasks, an object choice task and a discrimination task (based on location), each in the context of either cooperation or competition. In both tasks chimpanzees performed more skilfully when competing than when cooperating, with some evidence that competition with conspecifics was especially facilitatory in the discrimination location task. This is the first study to demonstrate a facilitative cognitive effect for competition in a single experimental paradigm. We suggest that chimpanzee cognitive evolution is best understood in its socioecological context.


title: Location Models With Groups Of Demand Points

abstract: AbstractIn this paper we investigate the location of a new facility in an area where demand is generated by groups of demand points. The group-distance between the facility and a group of demand points is determined in three ways: the closest point in the group, the farthest one, and the average distance to all members in the group. Three objectives are considered: minisum (minimize the sum of weighted group-distances to the group), minimax, and maximin. Two of the nine resulting possible models (maximal group-distance using minisum objective, and average group-distance using minimax objective) are analyzed in the paper. These problems can be solved by standard mathematical programming methods. Special algorithms designed for large problems are proposed and computational experience reported.


title: An Efficient Motion-Resistant Method for Wearable Pulse Oximeter

abstract: Reduction of motion artifact and power saving are crucial in designing a wearable pulse oximeter for long-term telemedicine application. In this paper, a novel algorithm, minimum correlation discrete saturation transform (MCDST) has been developed for the estimation of arterial oxygen saturation (SnO2), based on an optical model derived from photon diffusion analysis. The simulation shows that the new algorithm MCDST is more robust under low SNRs than the clinically verified motion-resistant algorithm discrete saturation transform (DST). Further, the experiment with different severity of motions demonstrates that MCDST has a slightly better performance than DST algorithm. Moreover, MCDST is more computationally efficient than DST because the former uses linear algebra instead of the time-consuming adaptive filter used by latter, which indicates that MCDST can reduce the required power consumption and circuit complexity of the implementation. This is vital for wearable devices, where the physical size and long battery life are crucial.


title: Discovering key concepts in verbose queries

abstract: Current search engines do not, in general, perform well with longer, more verbose queries. One of the main issues in processing these queries is identifying the key concepts that will have the most impact on effectiveness. In this paper, we develop and evaluate a technique that uses query-dependent, corpus-dependent, and corpus-independent features for automatic extraction of key concepts from verbose queries. We show that our method achieves higher accuracy in the identification of key concepts than standard weighting methods such as inverse document frequency. Finally, we propose a probabilistic model for integrating the weighted key concepts identified by our method into a query, and demonstrate that this integration significantly improves retrieval effectiveness for a large set of natural language description queries derived from TREC topics on several newswire and web collections.


title: Advanced sonar technologies for autonomous mine countermeasures

abstract: The U.S. Office of Naval Research is developing synthetic aperture sonars for the detection, localization, and classification of mines, for protection of sea lines of communication and Naval operating areas, and for support of amphibious operations. This paper reviews several evolving mine countermeasures sonars deployed on autonomous undersea vehicles that operate stand-alone and in coordination with complementary sensors, including the Small Synthetic Aperture Minehunter (SSAM), the Autonomous Topographic & Large Area Survey (ATLAS) sonar, and the Buried Object Scanning Sonar (BOSS).