title: A framework of traveling companion discovery on trajectory data streams

text: The advance of mobile technologies leads to huge volumes of spatio-temporal data collected in the form of trajectory data streams. In this study, we investigate the problem of discovering object groups that travel together (i.e., traveling companions) from trajectory data streams. Such technique has broad applications in the areas of scientific study, transportation management, and military surveillance. To discover traveling companions, the monitoring system should cluster the objects of each snapshot and intersect the clustering results to retrieve moving-together objects. Since both clustering and intersection steps involve high computational overhead, the key issue of companion discovery is to improve the efficiency of algorithms. We propose the models of closed companion candidates and smart intersection to accelerate data processing. A data structure termed traveling buddy is designed to facilitate scalable and flexible companion discovery from trajectory streams. The traveling buddies are microgroups of objects that are tightly bound together. By only storing the object relationships rather than their spatial coordinates, the buddies can be dynamically maintained along the trajectory stream with low cost. Based on traveling buddies, the system can discover companions without accessing the object details. In addition, we extend the proposed framework to discover companions on more complicated scenarios with spatial and temporal constraints, such as on the road network and battlefield. The proposed methods are evaluated with extensive experiments on both real and synthetic datasets. Experimental results show that our proposed buddy-based approach is an order of magnitude faster than the baselines and achieves higher accuracy in companion discovery.


title: Deep Learning for Multi-task Medical Image Segmentation in Multiple Modalities

text: Automatic segmentation of medical images is an important task for many clinical applications. In practice, a wide range of anatomical structures are visualised using different imaging modalities. In this paper, we investigate whether a single convolutional neural network (CNN) can be trained to perform different segmentation tasks. A single CNN is trained to segment six tissues in MR brain images, the pectoral muscle in MR breast images, and the coronary arteries in cardiac CTA. The CNN therefore learns to identify the imaging modality, the visualised anatomical structures, and the tissue classes. For each of the three tasks (brain MRI, breast MRI and cardiac CTA), this combined training procedure resulted in a segmentation performance equivalent to that of a CNN trained specifically for that task, demonstrating the high capacity of CNN architectures. Hence, a single system could be used in clinical practice to automatically perform diverse segmentation tasks without task-specific training.


title: Coded exposure deblurring: Optimized codes for PSF estimation and invertibility

text: We consider the problem of single image object motion deblurring from a static camera. It is well-known that deblurring of moving objects using a traditional camera is ill-posed, due to the loss of high spatial frequencies in the captured blurred image. A coded exposure camera modulates the integration pattern of light by opening and closing the shutter within the exposure time using a binary code. The code is chosen to make the resulting point spread function (PSF) invertible, for best deconvolution performance. However, for a successful deconvolution algorithm, PSF estimation is as important as PSF invertibility. We show that PSF estimation is easier if the resulting motion blur is smooth and the optimal code for PSF invertibility could worsen PSF estimation, since it leads to non-smooth blur. We show that both criterions of PSF invertibility and PSF estimation can be simultaneously met, albeit with a slight increase in the deconvolution noise. We propose design rules for a code to have good PSF estimation capability and outline two search criteria for finding the optimal code for a given length. We present theoretical analysis comparing the performance of the proposed code with the code optimized solely for PSF invertibility. We also show how to easily implement coded exposure on a consumer grade machine vision camera with no additional hardware. Real experimental results demonstrate the effectiveness of the proposed codes for motion deblurring.


title: Natural image statistics and neural representation.

text: It has long been assumed that sensory neurons are adapted, through both evolutionary and developmental processes, to the statistical properties of the signals to which they are exposed. Attneave (1954)Barlow (1961) proposed that information theory could provide a link between environmental statistics and neural responses through the concept of coding efficiency. Recent developments in statistical modeling, along with powerful computational tools, have enabled researchers to study more sophisticated statistical models for visual images, to validate these models empirically against large sets of data, and to begin experimentally testing the efficient coding hypothesis for both individual neurons and populations of neurons.


title: Blue Screen Matting

text: A classical problem of imaging—the matting problem—is separation of a non-rectangular foreground image from a (usually) rectangular background image—for example, in a film frame, extraction of an actor from a background scene to allow substitu tion of a different background. Of the several attacks on this diff cult and persistent problem, we discuss here only the special ca of separating a desired foreground image from a background of constant, or almost constant, backing color. This backing color has often been blue, so the problem, and its solution, have bee called blue screen matting . However, other backing colors, such as yellow or (increasingly) green, have also been used, so we o ten generalize to constant color matting . The mathematics of constant color matting is presented and proven to be unsolvable as generally practiced. This, of course, flies in the face of the fact that the technique is commonly used in film and video, so we demonstrate constraints on the general problem that lead to so tions, or at least significantly prune the search space of solution We shall also demonstrate that an algorithmic solution is possib by allowing the foreground object to be shot against two constant backing colors—in fact, against two completely arbitrary backin so long as they differ everywhere.


title: Image statistics and the perception of surface qualities

text: The world is full of surfaces, and by looking at them we can judge their material qualities. Properties such as colour or glossiness can help us decide whether a pancake is cooked, or a patch of pavement is icy. Most studies of surface appearance have emphasized textureless matte surfaces, but real-world surfaces, which may have gloss and complex mesostructure, are now receiving increased attention. Their appearance results from a complex interplay of illumination, reflectance and surface geometry, which are difficult to tease apart given an image. If there were simple image statistics that were diagnostic of surface properties it would be sensible to use them. Here we show that the skewness of the luminance histogram and the skewness of sub-band filter outputs are correlated with surface gloss and inversely correlated with surface albedo (diffuse reflectance). We find evidence that human observers use skewness, or a similar measure of histogram asymmetry, in making judgements about surfaces. When the image of a surface has positively skewed statistics, it tends to appear darker and glossier than a similar surface with lower skewness, and this is true whether the skewness is inherent to the original image or is introduced by digital manipulation. We also find a visual after-effect based on skewness: adaptation to patterns with skewed statistics can alter the apparent lightness and glossiness of surfaces that are subsequently viewed. We suggest that there are neural mechanisms sensitive to skewed statistics, and that their outputs can be used in estimating surface properties.


title: Non-local sparse models for image restoration

text: We propose in this paper to unify two different approaches to image restoration: On the one hand, learning a basis set (dictionary) adapted to sparse signal descriptions has proven to be very effective in image reconstruction and classification tasks. On the other hand, explicitly exploiting the self-similarities of natural images has led to the successful non-local means approach to image restoration. We propose simultaneous sparse coding as a framework for combining these two approaches in a natural manner. This is achieved by jointly decomposing groups of similar signals on subsets of the learned dictionary. Experimental results in image denoising and demosaicking tasks with synthetic and real noise show that the proposed method outperforms the state of the art, making it possible to effectively restore raw images from digital cameras at a reasonable speed and memory cost.


title: Hidden Markov model-based speech emotion recognition

text: In this contribution we introduce speech emotion recognition by use of continuous hidden Markov models. Two methods are propagated and compared throughout the paper. Within the first method a global statistics framework of an utterance is classified by Gaussian mixture models using derived features of the raw pitch and energy contour of the speech signal. A second method introduces increased temporal complexity applying continuous hidden Markov models considering several states using low-level instantaneous features instead of global statistics. The paper addresses the design of working recognition engines and results achieved with respect to the alluded alternatives. A speech corpus consisting of acted and spontaneous emotion samples in German and English language is described in detail. Both engines have been tested and trained using this equivalent speech corpus. Results in recognition of seven discrete emotions exceeded 86% recognition rate. As a basis of comparison the similar judgment of human deciders classifying the same corpus at 79.8% recognition rate was analyzed.


title: Fundamentals for Future Mobile-Health (mHealth): A Systematic Review of Mobile Phone and Web-Based Text Messaging in Mental Health

text:
BACKGROUND
Mobile phone text messages (short message service, SMS) are used pervasively as a form of communication. Almost 100% of the population uses text messaging worldwide and this technology is being suggested as a promising tool in psychiatry. Text messages can be sent either from a classic mobile phone or a web-based application. Reviews are needed to better understand how text messaging can be used in mental health care and other fields of medicine.

OBJECTIVE
The objective of the study was to review the literature regarding the use of mobile phone text messaging in mental health care.

METHODS
We conducted a thorough literature review of studies involving text messaging in health care management. Searches included PubMed, PsycINFO, Cochrane, Scopus, Embase and Web of Science databases on May 25, 2015. Studies reporting the use of text messaging as a tool in managing patients with mental health disorders were included. Given the heterogeneity of studies, this review was summarized using a descriptive approach.

RESULTS
From 677 initial citations, 36 studies were included in the review. Text messaging was used in a wide range of mental health situations, notably substance abuse (31%), schizophrenia (22%), and affective disorders (17%). We identified four ways in which text messages were used: reminders (14%), information (17%), supportive messages (42%), and self-monitoring procedures (42%). Applications were sometimes combined.

CONCLUSIONS
We report growing interest in text messaging since 2006. Text messages have been proposed as a health care tool in a wide spectrum of psychiatric disorders including substance abuse, schizophrenia, affective disorders, and suicide prevention. Most papers described pilot studies, while some randomized clinical trials (RCTs) were also reported. Overall, a positive attitude toward text messages was reported. RCTs reported improved treatment adherence and symptom surveillance. Other positive points included an increase in appointment attendance and in satisfaction with management and health care services. Insight into message content, preventative strategies, and innovative approaches derived from the mental health field may be applicable in other medical specialties.


title: Remote Sensing for Crop Management

text: Scientists with the Agricultural Research Service (ARS) and various government agencies and private institutions have provided a great deal of fundamental information relating spectral reflectance and thermal emittance properties of soils and crops to their agronomic and biophysical characteristics. This knowledge has facilitated the development and use of various remote sensing methods for non-destructive monitoring of plant growth and development and for the detection of many environmental stresses which limit plant productivity. Coupled with rapid advances in computing and positionlocating technologies, remote sensing from ground-, air-, and space-based platforms is now capable of providing detailed spatial and temporal information on plant response to their local environment that is needed for site specific agricultural management approaches. This manuscript, which emphasizes contributions by ARS researchers, reviews the biophysical basis of remote sensing; examines approaches that have been developed, refined, and tested for management of water, nutrients, and pests in agricultural crops; and assesses the role of remote sensing in yield prediction. It concludes with a discussion of challenges facing remote sensing in the future. Introduction Agricultural production strategies have changed dramatically over the past decade. Many of these changes have been driven by economic decisions to reduce inputs and maximize profits and by environmental guidelines mandating more efficient and safer use of agricultural chemicals. However, growers now have a heightened sensitivity to concerns over the quality, nutritional value, and safety of agricultural products. They are selecting cultivars and adjusting planting dates to accommodate anticipated patterns in weather, e.g., El Niño or La Niña events (Jones et al., 2000). They are also relying on biotechnological innovations for suppressing pests, e.g., insect protected (Bt) and Roundup® ready crops (Monsanto Company, 2003). The possibility for selling carbon credits to industry is breathing new life into on-farm conservation tillage practices that enhance carbon sequestration (Robert, 2001). Perhaps the most significant change in agriculture during the past ten years is the shift towards precision, or sitespecific, crop management (National Research Council, 1997). Growers have long recognized within-field variability in potential productivity. Now, at the beginning of the 21st Century, they are seeking new ways to exploit that variability. In the process, they are discovering they need more information on soil and plant conditions than was required a decade ago. Not only does this information need to be accurate and consistent across their farm and from year to year, it must also be available at temporal and spatial scales that match rapidly evolving capabilities to vary cultural procedures, irrigations, and agrochemical inputs. A very large body of research spanning almost four decades has demonstrated that much of this required information is available remotely, via aircraftand satellitebased sensor systems. When combined with remarkable advances in Global Positioning System (GPS) receivers, microcomputers, geographic information systems (GIS), yield monitors, and enhanced crop simulation models, remote sensing technology has the potential to transform the ways that growers manage their lands and implement precision farming techniques. The objective of this paper is to review progress that has been made in remote sensing applications for crop management and, in particular, highlight the role that the USDA and its primary research agency, the Agricultural Research Service (ARS), has had in the movement. Of course, these advances have not been a singular effort by ARS (Pinter et al., 2003; p. 615 this issue). They have resulted from long-standing cooperation among a number of different agencies and institutions, all in pursuit of expanding remote sensing’s role in providing information for crop management. We will begin with some fundamental relationships between the electromagnetic spectrum and basic agronomic conditions and biophysical plant processes, and then present specific examples of remote sensing P H O T O G R A M M E T R I C E N G I N E E R I N G & R E M O T E S E N S I N G Photogrammetric Engineering & Remote Sensing Vol. 69, No. 6, June 2003, pp. 647–664. 0099-1112/03/6906–647$3.00/0 © 2003 American Society for Photogrammetry and Remote Sensing P.J. Pinter, Jr., is with the USDA, ARS, U.S. Water Conservation Laboratory, 4331 E. Broadway Rd., Phoenix, AZ 85040-8807 (ppinter@uswcl.ars.ag.gov). J.L. Hatfield is with the USDA, ARS, National Soil Tilth Research Laboratory, 2150 Pammel Dr., Ames/Ankeny, IA 50011-3120 (hatfield@nstl.gov). J.S. Schepers is with the USDA, ARS, Soil and Water Conservation Research Laboratory, 120 Keim Hall on the University of Nebraska E. Campus, Lincoln, NE 68583-0934 (jschepers1@unl.edu). E. M. Barnes was with the USDA, ARS, U.S. Water Conservation Laboratory, 4331 E. Broadway Rd., Phoenix, AZ 85040-8807; he is currently with Cotton Inc., 6399 Weston Parkway, Cary, NC 27513 (Ebarnes@cottoninc.com). M.S. Moran is with the USDA, ARS, Southwest Watershed Research Center, 200 E. Allen Rd., Tucson, AZ 85719-1596 (smoran@tucson.ars.ag.gov). C.S.T. Daughtry is with the USDA, ARS, Hydrology and Remote Sensing Laboratory, Bldg. 007, BARC-West, 10300 Baltimore Blvd., Beltsville, MD 20705-2350 (cdaughtry@ hydrolab.arsusda.gov). D.R. Upchurch is with the USDA, ARS, Cropping Systems Research Laboratory, 3810 4th Street, Lubbock, TX 79415 (dupchurch@lbk.ars.usda.gov). IPC_Grams_03-904 4/12/03 7:40 AM Page 1


title: Special Perforated Steel Plate Shear Walls with Reduced Beam Section Anchor Beams . I : Experimental Investigation

text: This paper presents results of an experimental investigation of specially detailed ductile perforated steel plate shear walls SPSWs designed to accommodate utility passage, and having anchor beams with reduced beam sections connections. Single-story, single-bay SPSW frames are subjected to quasi-static cyclic loading up to their maximum strength and displacement capacity. The tested specimens also had low yield strength steel infill panels. Two specimens make allowances for penetration of the panel by utilities. The first, having multiple holes specially laid out in the steel panel, also has the characteristic of reduced panel strength and stiffness compared to the corresponding SPSW having a solid panel. The second such specimen utilizes quarter-circle cutouts in the panel corners, which are reinforced to transfer the panel forces to the adjacent framing. A SPSW with solid panel is also tested for reference purposes. All specimens resisted an imposed input history of increasing displacements to a minimum drift of 3%. The perforated panel reduced the elastic stiffness and overall strength of the specimen by 15% as compared with the solid panel specimen. DOI: 10.1061/ ASCE 0733-9445 2009 135:3 211 CE Database subject headings: Steel plates; Beams; Anchors; Experimentation; Shear walls.


title: Automatic Building of Synthetic Voices from Audio Books

text: Current state-of-the-art text-to-speech systems produce intelligible speech but lack the prosody of natural utterances. Building better models of prosody involves development of prosodically rich speech databases. However, development of such speech databases requires a large amount of effort and time. An alternative is to exploit story style monologues (long speech files) in audio books. These monologues already encapsulate rich prosody including varied intonation contours, pitch accents and phrasing patterns. Thus, audio books act as excellent candidates for building prosodic models and natural sounding synthetic voices. The processing of such audio books poses several challenges including segmentation of long speech files, detection of mispronunciations, extraction and evaluation of representations of prosody. In this thesis, we address the issues of segmentation of long speech files, capturing prosodic phrasing patterns of a speaker, and conversion of speaker characteristics. Techniques developed to address these issues include – text-driven and speech-driven methods for segmentation of long speech files; an unsupervised algorithm for learning speaker-specific phrasing patterns and a voice conversion method by modeling target speaker characteristics. The major conclusions of this thesis are – • Audio books can be used for building synthetic voices. Segmentation of such long speech files can be accomplished without the need for a speech recognition system. • The prosodic phrasing patterns are specific to a speaker. These can be learnt and incorporated to improve the quality of synthetic voices. • Conversion of speaker characteristics can be achieved by modeling speaker-specific features of a target speaker. Finally, the techniques developed in this thesis enable prosody research by leveraging a large number of audio books available in the public domain.


title: System in wafer-level package technology with RDL-first process

text: We have developed a new system-in-package (SiP) called a “System in Wafer-Level Package” (SiWLP). It is fabricated using “RDL-first” technology for fan-out wafer-level-packages (FO-WLPs) and provides high chip-I/O density, design flexibility, and package miniaturization. We developed this SiWLP by using multilayer RDLs and evaluated its unique packaging processes. We achieved high-throughput fabrication by using die-to-wafer (D2W) bonding with fine-pitch reflow soldering and simultaneous molding/underfilling at the wafer level.


title: DropConnected neural network trained with diverse features for classifying heart sounds

text: A fully-connected, two-hidden-layer neural network trained by error backpropagation, and regularized with DropConnect is used to classify heart sounds as normal or abnormal. The heart sounds are segmented using an open-source algorithm based on a hidden semi-Markov model. Features are extracted from the heart sounds using a wavelet transform, mel-frequency cepstral coefficients, inter-beat properties, and signal complexity. Features are normalized by subtracting by their means and dividing by their standard deviations across the whole training set. Any feature which is not significantly different between normal and abnormal recordings in the training data is removed, as are highly-correlated features. The dimensionality of the features vector is reduced by projecting it onto its first 70 principal components. A 10 fold cross-validation study gives a mean classification score of 84.1% with a variance of 2.9%. The final score on the test data was 85.2%.


title: Review: IT-Dependent Strategic Initiatives and Sustained Competitive Advantage: A Review and Synthesis of the Literature

text: The role of information systems in the creation and appropriation of economic value has a long tradition of research, within which falls the literature on the sustainability of IT-dependent competitive advantage. In this article, we formally define the notion of IT-dependent strategic initiative and use Jane Webster was the accepting senior editor for this paper. Anitesh Barua was the associate editor. Jeanne Ross, Anandhi Bharadwaj, and Paul Pavlou served as reviewers. it to frame a review of the literature on the sustainability of competitive advantage rooted in information systems use. We offer a framework that articulates both the dynamic approach to ITdependent strategic advantage currently receiving attention in the literature and the underlying drivers of sustainability. This framework models how and why the characteristics of the IT-dependent strategic initiative enable sustained competitive advantage, and how the determinants of sustainability are developed and strengthened over time. Such explanation facilitates the pre-implementation analysis of planned initiatives by innovators, as well as the post-implementation evaluation of existing initiatives so as to identify the basis of their sustainability. In carrying out this study, we examined the interdisciplinary literature on strategic information systems. Using a structured methodology, we reviewed the titles and abstracts of 648 articles drawn from information systems, strategic management, and marketing literature. We then examined and individually coded a relevant subset of 117 articles. The literature has identified four barriers to erosion of competitive advantage for ITdependent strategic initiatives and has surfaced the structural determinants of their magnitude. Previous work has also begun to theorize about the process by which these barriers to erosion evolve over time. Our review reveals that signifiPiccoli & Ives/IT-Dependent Strategic Initiatives 748 MIS Quarterly Vol. 29 No. 4/December 2005 cant exploratory research and theoretical development have occurred in this area, but there is a paucity of research providing rigorous tests of theoretical propositions. Our work makes three principal contributions. First, it formalizes the definition of IT-dependent strategic initiative. Second, it organizes the extant interdisciplinary research around an integrative framework that should prove useful to both research and practice. This framework offers an explanation of how and why IT-dependent strategic initiatives contribute to sustained competitive advantage, and explains the process by which they evolve over time. Finally, our review and analysis of the literature offers the basis for future research directions.


title: Selecting Valuable Stock Using Genetic Algorithm

text: In this study, we utilize the genetic algorithm (GA) to select high quality stocks with investment value. Given the fundamental financial and price information of stocks trading, we attempt to use GA to identify stocks that are likely to outperform the market by having excess returns. To evaluate the efficiency of the GA for stock selection, the return of equally weighted portfolio formed by the stocks selected by GA is used as evaluation criterion. Experiment results reveal that the proposed GA for stock selection provides a very flexible and useful tool to assist the investors in selecting valuable stocks.


title: Model-Driven Artificial Intelligence for Online Network Optimization

text: Future 5G wireless networks will rely on agile and automated network management, where the usage of diverse resources must be jointly optimized with surgical accuracy. A number of key wireless network functionalities (e.g., traffic steering, energy savings) give rise to hard optimization problems. What is more, high spatio-temporal traffic variability coupled with the need to satisfy strict per slice/service SLAs in modern networks, suggest that these problems must be constantly (re-)solved, to maintain close-to-optimal performance. To this end, in this paper we propose the framework of Online Network Optimization (ONO), which seeks to maintain both agile and efficient control over time, using an arsenal of data-driven, adaptive, and AI-based techniques. Since the mathematical tools and the studied regimes vary widely among these methodologies, a theoretical comparison is often out of reach. Therefore, the important question “what is the right ONO technique?” remains open to date. In this paper, we discuss the pros and cons of each technique and further attempt a direct quantitative comparison for a specific use case, using real data. Our results suggest that carefully combining the insights of problem modeling with state-of-the-art AI techniques provides significant advantages at reasonable complexity.


title: Analytic Performance Model of a Main-Memory Index Structure

text: Efficient evaluation of multi-dimensional range queries in a main-memory database is an important, but difficult task. State-of-the-art techniques rely on optimised sequential scans or tree-based structures. For range queries with small result sets, sequential scans exhibit poor asymptotic performance. Also, as the dimensionality of the data set increases, the performance of tree-based structures degenerates due to the curse of dimensionality. Recent literature proposed the Elf, a main-memory structure that is optimised for the case of such multi-dimensional low-selectivity queries. The Elf outperforms other state-ofthe-art methods in manually tuned scenarios. However, choosing an optimal parameter configuration for the Elf is vital, since for poor configurations, the search performance degrades rapidly. Consequently, further knowledge about the behaviour of the Elf in different configurations is required to achieve robust performance. In this thesis, we therefore propose a numerical cost model for the Elf. Like all main-memory index structures, the Elf response time is not dominated by disk accesses, refusing a straightforward analysis. Our model predicts the size and shape of the Elf region that is examined during search. We propose that the response time of a search is linear to the size of this region. Furthermore, we study the impact of skewed data distributions and correlations on the shape of the Elf. We find that they lead to behaviour that is accurately describable through simple reductions in attribute cardinality. Our experimental results indicate that for data sets of up to 15 dimensions, our cost model predicts the size of the examined Elf region with relative errors below 5%. Furthermore, we find that the size of the Elf region examined during search predicts the response time with an accuracy of 80 %.


title: Beyond Cyberbullying: Self-Disclosure, Harm and Social Support on ASKfm

text: ASKfm is a social media platform popular among teens and young adults where users can interact anonymously or semi-anonymously. In this paper, we identify the modes of disclosure and interaction that occur on the site, and evaluate why users are motivated to post and interact on the site, despite its reputation for facilitating cyberbullying. Through topic modeling - supplemented with manual annotation - of a large dataset of ASKfm posts, we identify and classify the rich variety of discourse posted on ASKfm, including both positive and negative forms, providing insights into the why individuals continue to engage with the site. These findings are complemented by a survey of young adults (aged 18-20) ASKfm users, which provides additional insights into users' motivations and interaction patterns. We discuss how the affordances specific to platforms like ASKfm, including anonymity and visibility, might enable users to respond to cyberbullying in novel ways, engage in positive forms of self-disclosure, and gain social support on sensitive topics. We conclude with design recommendations that would highlight the positive interactions on the website and help diminish the repurcussions of the negative interactions.


title: Cognitive behavioural therapy for major psychiatric disorder: does it really work? A meta-analytical review of well-controlled trials.

text:
BACKGROUND
Although cognitive behavioural therapy (CBT) is claimed to be effective in schizophrenia, major depression and bipolar disorder, there have been negative findings in well-conducted studies and meta-analyses have not fully considered the potential influence of blindness or the use of control interventions.

METHOD
We pooled data from published trials of CBT in schizophrenia, major depression and bipolar disorder that used controls for non-specific effects of intervention. Trials of effectiveness against relapse were also pooled, including those that compared CBT to treatment as usual (TAU). Blinding was examined as a moderating factor.

RESULTS
CBT was not effective in reducing symptoms in schizophrenia or in preventing relapse. CBT was effective in reducing symptoms in major depression, although the effect size was small, and in reducing relapse. CBT was ineffective in reducing relapse in bipolar disorder.

CONCLUSIONS
CBT is no better than non-specific control interventions in the treatment of schizophrenia and does not reduce relapse rates. It is effective in major depression but the size of the effect is small in treatment studies. On present evidence CBT is not an effective treatment strategy for prevention of relapse in bipolar disorder.


title: Attention-based Pyramid Aggregation Network for Visual Place Recognition

text: Visual place recognition is challenging in the urban environment and is usually viewed as a large scale image retrieval task. The intrinsic challenges in place recognition exist that the confusing objects such as cars and trees frequently occur in the complex urban scene, and buildings with repetitive structures may cause over-counting and the burstiness problem degrading the image representations. To address these problems, we present an Attention-based Pyramid Aggregation Network (APANet), which is trained in an end-to-end manner for place recognition. One main component of APANet, the spatial pyramid pooling, can effectively encode the multi-size buildings containing geo-information. The other one, the attention block, is adopted as a region evaluator for suppressing the confusing regional features while highlighting the discriminative ones. When testing, we further propose a simple yet effective PCA power whitening strategy, which significantly improves the widely used PCA whitening by reasonably limiting the impact of over-counting. Experimental evaluations demonstrate that the proposed APANet outperforms the state-of-the-art methods on two place recognition benchmarks, and generalizes well on standard image retrieval datasets.


title: Paving green passage for emergency vehicle in heavy traffic: Real-time motion planning under the connected and automated vehicles environment

text: This paper describes a real-time multi-vehicle motion planning (MVMP) algorithm for the emergency vehicle clearance task. To address the inherent limitations of human drivers in perception, communication, and cooperation, we require that the emergency vehicle and the surrounding normal vehicles are connected and automated vehicles (CAVs). The concerned MVMP task is to find cooperative trajectories such that the emergency vehicle can efficiently pass through the normal vehicles ahead. We use an optimal-control based formulation to describe the MVMP problem, which is centralized, straightforward, and complete. For the online solutions, the centralized MVMP formulation is converted into a multi-period and multi-stage version. Concretely, each period consists of two stages: the emergency vehicle and several normal CAVs ahead try to form a regularized platoon via acceleration or deceleration (stage 1); when a regularized platoon is formed, these vehicles act cooperatively to make way for the emergency vehicle until the emergency vehicle becomes the leader in this local platoon (stage 2). When one period finishes, the subsequent period begins immediately. This sequential process continues until the emergency vehicle finally passes through all the normal CAVs. The subproblem at stage 1 is extremely easy because nearly all the challenging nonlinearity gathers only in stage 2; typical solutions to the subproblem at stage 2 can be prepared offline, and then implemented online directly. Through this, our proposed MVMP algorithm avoids heavy online computations and thus runs in real time.


title: Simulating LTE Cellular Systems: An Open-Source Framework

text: Long-term evolution (LTE) represents an emerging and promising technology for providing broadband ubiquitous Internet access. For this reason, several research groups are trying to optimize its performance. Unfortunately, at present, to the best of our knowledge, no open-source simulation platforms, which the scientific community can use to evaluate the performance of the entire LTE system, are freely available. The lack of a common reference simulator does not help the work of researchers and poses limitations on the comparison of results claimed by different research groups. To bridge this gap, herein, the open-source framework LTE-Sim is presented to provide a complete performance verification of LTE networks. LTE-Sim has been conceived to simulate uplink and downlink scheduling strategies in multicell/multiuser environments, taking into account user mobility, radio resource optimization, frequency reuse techniques, the adaptive modulation and coding module, and other aspects that are very relevant to the industrial and scientific communities. The effectiveness of the proposed simulator has been tested and verified considering 1) the software scalability test, which analyzes both memory and simulation time requirements; and 2) the performance evaluation of a realistic LTE network providing a comparison among well-known scheduling strategies.


title: 2395-1621 Hand Gesture Recognition Using Flex Sensors

text: ARTICLE INFO In this system an electro-mechanical robot is designed and controlled using hand gesture in real time. The system is designed on microcontroller platform using Keil and MPLAB tools. Hand gesture recognition is done on the principle of resistance change sensed through flex sensor. These sensors are integrated in a hand gloves from which input to the system is given. The designed system is divided into two sections as transmitter and receiver. The transmitter section will be in hand gloves from which the data is sensed and processed through PIC16F7487 and send serially to the receiver section. RF technology is used to transmit the data at the receiver section at the frequency of 2.4 GHz. ARM 7 (LPC2148) processor is used to receive the data. Here from the received data, the character is predicted and matched with the closet character from which the character is identified and displayed on LCD. The various case studies is prepared for the designed system and tested in real time. The proposed system can be used for the various applications such as in unmanned machines, industries, handicapped personnel etc. Keywords— a Sensor gloves, flex sensors, PIC controller, ARM processor etc Article History Received:20 th September 2015 Received in revised form :


title: Wide-band microstrip-to-coplanar stripline/slotline transitions

text: Wide-band microstrip line to coplanar stripline (CPS) transitions are proposed. The transition consists of a multisection matching transformer and a quarter-wavelength radial stub for the impedance matching and field matching between the microstrip line and CPS, respectively. The proposed planar transition has the advantages of compact size, wide bandwidth, and straightforward design procedure. Several parameters are studied through simulations and experiments to derive some design guidelines. With the return loss of better than 14 dB, the 1- and 3-dB back-to-back insertion loss bandwidth can cover from 1.4 to 6.6 GHz (1 : 4.7) and from 1.1 to 10.5 GHz (1 : 9.6), respectively. In addition, the microstrip-to-CPS transition is extended to design a microstrip-to-slotline transition by tapering the CPS into a slotline. From 2.7 to 10.4 GHz (1 : 3.85), the back-to-back return loss is better than 15 dB and the insertion loss is less than 3 dB.


title: A survey of sparse matrix-vector multiplication performance on large matrices

text: One of the main sources of sparse matrices is the discretization of partial differential equations that govern continuumphysics phenomena such as fluid flow and transport, phase separation, mechanical deformation, electromagnetic wave propagation, and others. Recent advances in high-performance computing area have been enabling researchers to tackle increasingly larger problems leading to sparse linear systems with hundreds of millions to a few tens of billions of unknowns, e.g., [5, 6]. Iterative linear solvers are popular in large-scale computing as they consume less memory than direct solvers. Contrary to direct linear solvers, iterative solvers approach the solution gradually requiring the computation of sparse matrixvector (SpMV) products. The evaluation of SpMV products can emerge as a bottleneck for computational performance within the context of the simulation of large problems. In this work, we focus on a linear system arising from the discretization of the Cahn–Hilliard equation, which is a fourth order nonlinear parabolic partial differential equation that governs the separation of a two-component mixture into phases [3]. The underlying spatial discretization is performed using the discontinuous Galerkin method (e.g. [10]) and Newton’s method. A number of parallel algorithms and strategies have been evaluated in this work to accelerate the evaluation of SpMV products.


title: Scale Space Technique for Word Segmentation in Handwritten Documents

text: Indexing large archives of historical manuscripts, like the papers of George Washington, is required to allow rapid perusal by scholars and researchers who wish to consult the original manuscripts. Presently, such large archives are indexed manually. Since optical character recognition (OCR) works poorly with handwriting, a scheme based on matching word images called word spotting has been suggested previously for indexing such documents. The important steps in this scheme are segmentation of a document page into words and creation of lists containing instances of the same word by word image matching. We have developed a novel methodology for segmenting handwritten document images by analyzing the extent of “blobs” in a scale space representationof the image. We believe this is the first application of scale space to this problem. The algorithm has been applied to around 30 grey level images randomly picked from different sections of the George Washington corpus of 6,400 handwritten document images. An accuracy of 77 − 96 percent was observed with an average accuracy of around 87 percent. The algorithm works well in the presence of noise, shine through and other artifacts which may arise due aging and degradation of the page over a couple of centuries or through the man made processes of photocopying and scanning.


title: Mining and analyzing the enterprise knowledge graph

text: Today's enterprises hold ever-growing amounts of public data, stemming from different organizational systems, such as development environments, CRM systems, business intelligence systems, and enterprise social media. This data unlocks rich and diverse information about entities, people, terms, and the relationships among them. A lot of insight can be gained through analyzing this knowledge graph, both by individual employees and by the organization as a whole. In this talk, I will review recent work done by the Social Technologies & Analytics group at IBM Research-Haifa to mine these relationships, represent them in a generalized model, and use the model for different aims within the enterprise, including social search [5], expertise location [1], social recommendation [2, 3], and network analysis [4].


title: Exploring IOT Application Using Raspberry Pi

text: There are thousands of sensors in an industry with different usage, such as, pressure transmitters, flow meter, temperature transmitters, level transmitters, and so on. Wired networks are mainly used to transfer data to base station by connecting sensor. It brings advantage as it provides reliable and stable communication system for instruments and controls. However, the cost of cables necessary is very costly. Therefore, recently low cost wireless networks are strongly required by customers, for example, temporary instrument networks and/or some non-critical permanent sites which require low data rate and longer battery life. In client/server model, file server act as a parent’s node which allow multiple child node to connect with it. It is responsible for central storage and data management so that other computers enable to access the file under the same network. This article explores the use of Raspberry Pi to function as a server in which several laptops are connected to it to copy, store and delete the file over network. IT requires authentication for user login before granting access to the file to ensure data integrity and security. File server is widely used in many areas, for example in education for uploading study note into the serve and student immediate downloading it into their computer. Moreover this work also explores the use of Raspberry Pi B+ model and XBee (ZigBee module) to demonstrate wireless communication data transmission, proving the validity of usage as a mobile low-power wireless network communication. The main goal of the research is to explore the use of Raspberry Pi for client-server communication using various wireless communication scenario such as Wi-Fi and ZigBee. Index Terms – client/server model, Raspberry Pi, XBee.


title: Unsupervised Learning of Video Representations using LSTMs

text: We use multilayer Long Short Term Memory (LSTM) networks to learn representations of video sequences. Our model uses an encoder LSTM to map an input sequence into a fixed length representation. This representation is decoded using single or multiple decoder LSTMs to perform different tasks, such as reconstructing the input sequence, or predicting the future sequence. We experiment with two kinds of input sequences – patches of image pixels and high-level representations (“percepts”) of video frames extracted using a pretrained convolutional net. We explore different design choices such as whether the decoder LSTMs should condition on the generated output. We analyze the outputs of the model qualitatively to see how well the model can extrapolate the learned video representation into the future and into the past. We try to visualize and interpret the learned features. We stress test the model by running it on longer time scales and on out-of-domain data. We further evaluate the representations by finetuning them for a supervised learning problem – human action recognition on the UCF-101 and HMDB-51 datasets. We show that the representations help improve classification accuracy, especially when there are only a few training examples. Even models pretrained on unrelated datasets (300 hours of YouTube videos) can help action recognition performance.


title: Spatial Transformer Networks

text: Convolutional Neural Networks define an exceptionally powerful class of models, but are still limited by the lack of ability to be spatially invariant to the input data in a computationally and parameter efficient manner. In this work we introduce a new learnable module, the Spatial Transformer, which explicitly allows the spatial manipulation of data within the network. This differentiable module can be inserted into existing convolutional architectures, giving neural networks the ability to actively spatially transform feature maps, conditional on the feature map itself, without any extra training supervision or modification to the optimisation process. We show that the use of spatial transformers results in models which learn invariance to translation, scale, rotation and more generic warping, resulting in state-of-the-art performance on several benchmarks, and for a number of classes of transformations.


title: Describing Videos by Exploiting Temporal Structure

text: Recent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are static, working with videos requires modeling their dynamic temporal structure and then properly integrating that information into a natural language description model. In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions. First, our approach incorporates a spatial temporal 3-D convolutional neural network (3-D CNN) representation of the short temporal dynamics. The 3-D CNN representation is trained on video action recognition tasks, so as to produce a representation that is tuned to human motion and behavior. Second we propose a temporal attention mechanism that allows to go beyond local temporal modeling and learns to automatically select the most relevant temporal segments given the text-generating RNN. Our approach exceeds the current state-of-art for both BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on a new, larger and more challenging dataset of paired video and natural language descriptions.


title: How to Construct Deep Recurrent Neural Networks

text: In this paper, we explore different ways to extend a recurrent neural network (RNN) to a deep RNN. We start by arguing that the concept of depth in an RNN is not as clear as it is in feedforward neural networks. By carefully analyzing and understanding the architecture of an RNN, however, we find three points of an RNN which may be made deeper; (1) input-to-hidden function, (2) hidden-tohidden transition and (3) hidden-to-output function. Based on this observation, we propose two novel architectures of a deep RNN which are orthogonal to an earlier attempt of stacking multiple recurrent layers to build a deep RNN (Schmidhuber, 1992; El Hihi and Bengio, 1996). We provide an alternative interpretation of these deep RNNs using a novel framework based on neural operators. The proposed deep RNNs are empirically evaluated on the tasks of polyphonic music prediction and language modeling. The experimental result supports our claim that the proposed deep RNNs benefit from the depth and outperform the conventional, shallow RNNs.


title: A new approach to developing and implementing eager database replication protocols

text: Database replication is traditionally seen as a way to increase the availability and performance of distributed databases. Although a large number of protocols providing data consistency and fault-tolerance have been proposed, few of these ideas have ever been used in commercial products due to their complexity and performance implications. Instead, current products allow inconsistencies and often resort to centralized approaches which eliminates some of the advantages of replication. As an alternative, we propose a suite of replication protocols that addresses the main problems related to database replication. On the one hand, our protocols maintain data consistency and the same transactional semantics found in centralized systems. On the other hand, they provide flexibility and reasonable performance. To do so, our protocols take advantage of the rich semantics of group communication primitives and the relaxed isolation guarantees provided by most databases. This allows us to eliminate the possibility of deadlocks, reduce the message overhead and increase performance. A detailed simulation study shows the feasibility of the approach and the flexibility with which different types of bottlenecks can be circumvented.


title: Job performance prediction in a call center using a naive Bayes classifier

text: This study presents an approach to predict the performance of sales agents of a call center dedicated exclusively to sales and telemarketing activities. This approach is based on a naive Bayesian classifier. The objective is to know what levels of the attributes are indicative of individuals who perform well. A sample of 1037 sales agents was taken during the period between March and September of 2009 on campaigns related to insurance sales and service pre-paid phone services, to build the naive Bayes network. It has been shown that, socio-demographic attributes are not suitable for predicting performance. Alternatively, operational records were used to predict production of sales agents, achieving satisfactory results. In this case, the classifier training and testing is done through a stratified tenfold cross-validation. It classified the instances correctly 80.60% of times, with the proportion of false positives of 18.1% for class no (does not achieve minimum) and 20.8% for the class yes (achieves equal or above minimum acceptable). These results suggest that socio-demographic attributes has no predictive power on performance, while the operational information of the activities of the sale agent can predict the future performance of the


title: TUCH: Turning Cross-view Hashing into Single-view Hashing via Generative Adversarial Nets

text: Cross-view retrieval, which focuses on searching images as response to text queries or vice versa, has received increasing attention recently. Crossview hashing is to efficiently solve the cross-view retrieval problem with binary hash codes. Most existing works on cross-view hashing exploit multiview embedding method to tackle this problem, which inevitably causes the information loss in both image and text domains. Inspired by the Generative Adversarial Nets (GANs), this paper presents a new model that is able to Turn Crossview Hashing into single-view hashing (TUCH), thus enabling the information of image to be preserved as much as possible. TUCH is a novel deep architecture that integrates a language model network T for text feature extraction, a generator network G to generate fake images from text feature and a hashing network H for learning hashing functions to generate compact binary codes. Our architecture effectively unifies joint generative adversarial learning and cross-view hashing. Extensive empirical evidence shows that our TUCH approach achieves state-of-the-art results, especially on text to image retrieval, based on imagesentences datasets, i.e. standard IAPRTC-12 and large-scale Microsoft COCO.


title: Application of Machine Learning Algorithms to an online Recruitment System

text: In this work, we present a novel approach for evaluating job applicants in online recruitment systems, leveraging machine learning algorithms to solve the candidate ranking problem. An application of our approach is implemented in the form of a prototype system, whose functionality is showcased and evaluated in a real-world recruitment scenario. The proposed system extracts a set of objective criteria from the applicants’ LinkedIn profile, and infers their personality characteristics using linguistic analysis on their blog posts. Our system was found to perform consistently compared to human recruiters; thus, it can be trusted for the automation of applicant ranking and


title: Aggression in the Laboratory : Problems with the Validity of the Modified Taylor Competitive Reaction Time Test as a Measure of Aggression in Media Violence Studies

text: Many laboratory studies of aggression use a measure known as the modified Taylor Competitive Reaction Time Test (TCRTT), for which validation studies are lacking. Using sound blasts administered by the participant against a fictional human opponent, the TCRTT also allows for multiple methods of measuring aggression. The validity of the TCRTT was tested in 53 college student participants. Participants took a self-report measure of aggressiveness as well as neuropsychological measures of frontal lobe functioning predictive of aggression. Results were not supportive of the TCRTT’s validity and indicated concerns regarding the use of the TCRTT as a measure of aggression. Results suggest that labaratory studies of media violence using the TCRTT are of questionable validity.


title: Understanding consumers' continuance intention towards mobile purchase: A theoretical framework and empirical study - A case of China

text: Although mobile purchase is convenient in the age of mobile commerce, many consumers still do not utilize mobile purchase to its full potential. From the mobile vendor’s perspective, retaining current customers and facilitating their continued purchase are crucial to create profitability and achieve a sustainable development. An understanding of the continuance intention towards mobile purchase can provide insights into mobile vendors’ marketing campaigns. Therefore, it is important to examine the determinants that impact continuance intentions of consumers for mobile purchase. Drawing upon information success model, flow theory and trust, this study proposed and empirically tested an integrated model to better understand the determinants of consumers’ continued intention to purchase on mobile sites. Empirical data from 462 users who had experience with mobile purchase were tested against the proposed research model by using structural equation modelling (SEM). The results indicated that information quality, and privacy and security concerns are the main factors affecting trust, whereas service quality is the main factor affecting flow. System quality, and privacy and security concerns affect satisfaction. Trust affects flow, which in turn affects satisfaction. These three factors together affect continued intention towards mobile purchase. The findings of this study provide several important implications for mobile commerce research and practice. 2015 Elsevier Ltd. All rights reserved.


title: A Hybrid Word-Character Model for Abstractive Summarization

text: Automatic abstractive text summarization is an important and challenging research topic of natural language processing. Among many widely used languages, the Chinese language has a special property that a Chinese character contains rich information comparable to a word. Existing Chinese text summarization methods, either adopt totally character-based or word-based representations, fail to fully exploit the information carried by both representations. To accurately capture the essence of articles, we propose a hybrid word-character approach (HWC) which preserves the advantages of both wordbased and character-based representations. We evaluate the advantage of the proposed HWC approach by applying it to two existing methods, and discover that it generates state-of-the-art performance with a margin of 24 ROUGE points on a widely used dataset LCSTS. In addition, we find an issue contained in the LCSTS dataset and offer a script to remove overlapping pairs (a summary and a short text) to create a clean dataset for the community. The proposed HWC approach also generates the best performance on the new, clean LCSTS dataset.


title: Open Information Extraction with Meta-pattern Discovery in Biomedical Literature

text: Biomedical open information extraction (BioOpenIE) is a novel paradigm to automatically extract structured information from unstructured text with no or little supervision. It does not require any pre-specified relation types but aims to extract all the relation tuples from the corpus. A major challenge for open information extraction (OpenIE) is that it produces massive surface-name formed relation tuples that cannot be directly used for downstream applications. We propose a novel framework CPIE (Clause+Pattern-guided Information Extraction) that incorporates clause extraction and meta-pattern discovery to extract structured relation tuples with little supervision. Compared with previous OpenIE methods, CPIE produces massive but more structured output that can be directly used for downstream applications. We first detect short clauses from input sentences. Then we extract quality textual patterns and perform synonymous pattern grouping to identify relation types. Last, we obtain the corresponding relation tuples by matching each quality pattern in the text. Experiments show that CPIE achieves the highest precision in comparison with state-of-the-art OpenIE baselines, and also keeps the distinctiveness and simplicity of the extracted relation tuples. CPIE shows great potential in effectively dealing with real-world biomedical literature with complicated sentence structures and rich information.


title: Tensegrity II. How structural networks influence cellular information processing networks.

text: The major challenge in biology today is biocomplexity: the need to explain how cell and tissue behaviors emerge from collective interactions within complex molecular networks. Part I of this two-part article, described a mechanical model of cell structure based on tensegrity architecture that explains how the mechanical behavior of the cell emerges from physical interactions among the different molecular filament systems that form the cytoskeleton. Recent work shows that the cytoskeleton also orients much of the cell's metabolic and signal transduction machinery and that mechanical distortion of cells and the cytoskeleton through cell surface integrin receptors can profoundly affect cell behavior. In particular, gradual variations in this single physical control parameter (cell shape distortion) can switch cells between distinct gene programs (e.g. growth, differentiation and apoptosis), and this process can be viewed as a biological phase transition. Part II of this article covers how combined use of tensegrity and solid-state mechanochemistry by cells may mediate mechanotransduction and facilitate integration of chemical and physical signals that are responsible for control of cell behavior. In addition, it examines how cell structural networks affect gene and protein signaling networks to produce characteristic phenotypes and cell fate transitions during tissue development.


title: Alternating Directions Dual Decomposition

text: We propose AD, a new algorithm for approximate maximum a posteriori (MAP) inference on factor graphs based on the alternating directions method of multipliers. Like dual decomposition algorithms, AD uses worker nodes to iteratively solve local subproblems and a controller node to combine these local solutions into a global update. The key characteristic of AD is that each local subproblem has a quadratic regularizer, leading to a faster consensus than subgradient-based dual decomposition, both theoretically and in practice. We provide closed-form solutions for these AD subproblems for binary pairwise factors and factors imposing first-order logic constraints. For arbitrary factors (large or combinatorial), we introduce an active set method which requires only an oracle for computing a local MAP configuration, making AD applicable to a wide range of problems. Experiments on synthetic and realworld problems show that AD compares favorably with the state-of-the-art.


title: Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach

text:
OBJECTIVE
In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This framework serves two potential functions: (1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and (2) the basis for clinical artificial intelligence - an AI that can "think like a doctor".

METHODS
This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans as actions are performed and new observations are obtained. This framework was evaluated using real patient data from an electronic health record.

RESULTS
The results demonstrate the feasibility of this approach; such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate/fee-for-service models of healthcare. The cost per unit of outcome change (CPUC) was $189 vs. $497 for AI vs. TAU (where lower is considered optimal) - while at the same time the AI approach could obtain a 30-35% increase in patient outcomes. Tweaking certain AI model parameters could further enhance this advantage, obtaining approximately 50% more improvement (outcome change) for roughly half the costs.

CONCLUSION
Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments. Future work is described that outlines potential lines of research and integration of machine learning algorithms for personalized medicine.


title: Seq2seq Fingerprint: An Unsupervised Deep Molecular Embedding for Drug Discovery

text: Many of today's drug discoveries require expertise knowledge and insanely expensive biological experiments for identifying the chemical molecular properties. However, despite the growing interests of using supervised machine learning algorithms to automatically identify those chemical molecular properties, there is little advancement of the performance and accuracy due to the limited amount of training data. In this paper, we propose a novel unsupervised molecular embedding method, providing a continuous feature vector for each molecule to perform further tasks, e.g., solubility classification. In the proposed method, a multi-layered Gated Recurrent Unit (GRU) network is used to map the input molecule into a continuous feature vector of fixed dimensionality, and then another deep GRU network is employed to decode the continuous vector back to the original molecule. As a result, the continuous encoding vector is expected to contain rigorous and enough information to recover the original molecule and predict its chemical properties. The proposed embedding method could utilize almost unlimited molecule data for the training phase. With sufficient information encoded in the vector, the proposed method is also robust and task-insensitive. The performance and robustness are confirmed and interpreted in our extensive experiments.


title: Social disconnectedness, perceived isolation, and health among older adults.

text: Previous research has identified a wide range of indicators of social isolation that pose health risks, including living alone, having a small social network, infrequent participation in social activities, and feelings of loneliness. However multiple forms of isolation are rarely studied together making it difficult to determine which aspects of isolation are most deleterious for health. Using population-based data from the National Social Life, Health, and Aging Project, we combine multiple indicators of social isolation into scales assessing social disconnectedness (e.g., small social network, infrequent participation in social activities) and perceived isolation (e.g., loneliness, perceived lack of social support). We examine the extent to which social disconnectedness and perceived isolation have distinct associations with physical and mental health among older adults. Results indicate that social disconnectedness and perceived isolation are independently associated with lower levels of self-rated physical health. However, the association between disconnectedness and mental health may operate through the strong relationship between perceived isolation and mental health. We conclude that health researchers need to consider social disconnectedness and perceived isolation simultaneously.


title: FC^4: Fully Convolutional Color Constancy with Confidence-Weighted Pooling

text: Improvements in color constancy have arisen from the use of convolutional neural networks (CNNs). However, the patch-based CNNs that exist for this problem are faced with the issue of estimation ambiguity, where a patch may contain insufficient information to establish a unique or even a limited possible range of illumination colors. Image patches with estimation ambiguity not only appear with great frequency in photographs, but also significantly degrade the quality of network training and inference. To overcome this problem, we present a fully convolutional network architecture in which patches throughout an image can carry different confidence weights according to the value they provide for color constancy estimation. These confidence weights are learned and applied within a novel pooling layer where the local estimates are merged into a global solution. With this formulation, the network is able to determine what to learn and how to pool automatically from color constancy datasets without additional supervision. The proposed network also allows for end-to-end training, and achieves higher efficiency and accuracy. On standard benchmarks, our network outperforms the previous state-of-the-art while achieving 120x greater efficiency.


title: Internet addiction among Norwegian adults: a stratified probability sample study.

text: Most Norwegians are Internet users. We conducted a stratified probability sample study (Norway, 2007, age-group 16-74 years, N= 3,399, response rate 35.3%, 87.1% Internet users) to assess the prevalence of Internet addiction and at-risk Internet use by the Young Diagnostic Questionnaire (YDQ). The prevalence of Internet addiction (YDQ score 5-8) was 1.0% and an additional 5.2% were at-risk Internet users (YDQ score 3-4). Internet addiction and at-risk Internet use was strongly dependent on gender and age with highest prevalences among young males (16-29 years 4.1% and 19.0%, 30-39 years 3.3% and 10.7%). Logistic regression showed that male gender, young age, university level education, and an unsatisfactory financial situation were factors positively associated with "problematic Internet use" (at-risk and addicted use combined). Time spent on the Internet and prevalence of self-reported sleeping disorders, depression, and other psychological impairments increased linearly with YDQ score. Problematic Internet use clearly affects the lives of many people.


title: Pseudo random number generator and Hash function for embedded microprocessors

text: Embedded microprocessors are commonly used for future technologies such as Internet of Things(IoT), RFID and Wireless Sensor Networks(WSN). However, the microprocessors have limited computing power and storages so straight-forward implementation of traditional services on resource constrained devices is not recommenced. To overcome this problem, lightweight implementation techniques should be concerned for practical implementations. Among various requirements, security applications should be conducted on microprocessors for secure and robust service environments. In this paper, we presented a light weight implementation techniques for efficient Pseudo Random Number Generator(PRNG) and Hash function. To reduce memory consumption and accelerate performance, we adopted AES accelerator based implementation. This technique is firstly introduced in INDOCRYPT'12, whose idea exploits peripheral devices for efficient hash computations. With this technique, we presented block cipher based light-weight pseudo random number generator and simple hash function on embedded microprocessors.


title: Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition

text: One key challenge of facial trait recognition is the large non-rigid appearance variations due to some irrelevant real world factors, such as viewpoint and expression changes. In this paper, we explore how the shape information, i.e. facial landmark positions, can be explicitly deployed into the popular Convolutional Neural Network (CNN) architecture to disentangle such irrelevant non-rigid appearance variations. First, instead of using fixed kernels, we propose a kernel adaptation method to dynamically determine the convolutional kernels according to the spatial distribution of facial landmarks, which helps learning more robust features. Second, motivated by the intuition that different local facial regions may demand different adaptation functions, we further propose a tree-structured convolutional architecture to hierarchically fuse multiple local adaptive CNN subnetworks. Comprehensive experiments on WebFace, Morph II and MultiPIE databases well validate the effectiveness of the proposed kernel adaptation method and tree-structured convolutional architecture for facial trait recognition tasks, including identity, age and gender recognition. For all the tasks, the proposed architecture consistently achieves the state-of-the-art performances.


title: Story generation after TALE-SPIN

text: TALE-SPIN, the laat major AI attempt at atory generation, approached the problem of making up atoriea primarily from the perspective of an impartial world simulator. AUTHOR ia a program (under development) which generates atoriea as a creative reaaoner in purauit of her own narrative goals. It is thus intended to aimulate an author's mind aa ahe makes up a story, rather than the world as things happen in it. The four major forces driving the atory generation process, according to the AUTHOR model, are (1) author intentionality, (2) conceptual reformulation, (3) reminding, and (4) the opportunity enhancement metagoal. 1. World Simulation Based Story Generation TALE-SPIN [Meehan, 1976] is a program which made up Aesop-fable-like conceptual atoriea, which it then expressed in natural language. Each atory TALE-SPIN made up waa based on a model of the storyworld, including the characters in it and their goala, peraonalities, and interperaonal relatione. Storiea were apun by working out consequences of the model. Thus, central to TALE-SPIN waa the idea of world simulation. The three levels TALE-SPIN was most concerned with simulating were: 1. character intentionality-e.g., if Joe Bear was hungry, he would try to get something to eat 2. social conatrainta-e.g., if Joe Bear believed Irving Bird to be hia friend, he could aak an outright favor; if Joe Bear believed their relation to be a bit cooler, he would feel obliged to offer something in return (auch aa a worm) 3. phyaical cauaality-e.g., if Irving Bird ate a worm, the worm would cease to exist; after "fooling around", Aa oppoaed to Sheldon Klein's ayetem which only produced atory texts and which ia incapable, in principle, of understanding the atoriea it "makes up" [Klein et el., 1976]. characters felt "wiped out" World simulation, however, fails to reflect the proctaa that an author goes through in making up a atory-storyworlds are developed by authors aa needed, frequently as post hoc justification for eventa that the author has already decided ahe wants as part of the story. As illustration, let us consider the question of why, in the Star Wars aequel, "The Empire Strikes Back", Princess Leia and Solo go to visit Landeau. If one asks a reader (or viewer) this question, the anawer will be along the lines of, "They needed a place to hide from imperial stormtroopers until they got the hyperdrive fixed, and Landeau was an old buddy of Solo's, and ..." • …


title: A Multimodal Embedded Sensor System for Scalable Robotic and Prosthetic Fingers

text: The development of dexterous and robust anthropomorphic hands with rich sensor feedback remains a challenging task for both humanoid robotics as well as prosthetics as of today. The design of hands that are scalable in size and equipped with integrated multimodal sensor systems is a key requirement for advanced control schemes and reactive behaviour. In this paper, we present the design of a scalable and low cost robotic finger with a soft fingertip and position, temperature as well as normal and shear force sensors. All cables and sensors are completely enclosed inside the finger to ensure an anthropometric appearance. The finger is modelled based on a 50th percentile male little finger and can be easily adapted to other dimensions in terms of size and sensor system configuration. We describe the design of the sensor system, provide an experimental analysis for the characterization of the different sensor types in terms of sensor range, resolution, creep, spatial response as well as temperature flux.


title: A Fuzzy-Neural Intelligent Trading Model for Stock Price Prediction

text: In this paper, Fuzzy logic and Neural Network approaches for predicting financial stock price are investigated. A study of a knowledge based system for stock price prediction is carried out. We explore Trapezoidal membership function method and Sugeno-type fuzzy inference engine to optimize the estimated result. Our model utilizes the performance of artificial neural networks trained using back propagation and supervised learning methods respectively. The system is developed based on the selection of stock data history obtained from Nigerian Stock Exchange in Nigeria, which are studied and used in training the system. A computer simulation is designed to assist the experimental decision for the best control action. The system is developed using MySQL, NetBeans, Java, and MatLab. The experimental result shows that the model has such properties as fast convergence, high precision and strong function approximation ability. It has shown to perform well in the context of various trading strategies involving stocks.


title: Heterogeneous Feature Selection With Multi-Modal Deep Neural Networks and Sparse Group LASSO

text: Heterogeneous feature representations are widely used in machine learning and pattern recognition, especially for multimedia analysis. The multi-modal, often also high- dimensional , features may contain redundant and irrelevant information that can deteriorate the performance of modeling in classification. It is a challenging problem to select the informative features for a given task from the redundant and heterogeneous feature groups. In this paper, we propose a novel framework to address this problem. This framework is composed of two modules, namely, multi-modal deep neural networks and feature selection with sparse group LASSO. Given diverse groups of discriminative features, the proposed technique first converts the multi-modal data into a unified representation with different branches of the multi-modal deep neural networks. Then, through solving a sparse group LASSO problem, the feature selection component is used to derive a weight vector to indicate the importance of the feature groups. Finally, the feature groups with large weights are considered more relevant and hence are selected. We evaluate our framework on three image classification datasets. Experimental results show that the proposed approach is effective in selecting the relevant feature groups and achieves competitive classification performance as compared with several recent baseline methods.


title: On the Margin Theory of Feedforward Neural Networks

text: Past works have shown that, somewhat surprisingly, over-parametrization can help generalization in neural networks. Towards explaining this phenomenon, we adopt a margin-based perspective. We establish: 1) for multilayer feedforward relu networks, the global minimizer of a weakly-regularized cross-entropy loss has the maximum normalized margin among all networks, 2) as a result, increasing the over-parametrization improves the normalized margin and generalization error bounds for two-layer networks. In particular, an infinite-size neural network enjoys the best generalization guarantees. The typical infinite feature methods are kernel methods; we compare the neural net margin with that of kernel methods and construct natural instances where kernel methods have much weaker generalization guarantees. We validate this gap between the two approaches empirically. Finally, this infinite-neuron viewpoint is also fruitful for analyzing optimization. We show that a perturbed gradient flow on infinite-size networks finds a global optimizer in polynomial time.


title: Social interaction detection using a multi-sensor approach

text: In the context of a social gathering, such as a cocktail party, the memorable moments are often captured by professional photographers or the participants. The latter case is generally undesirable because many participants would rather enjoy the event instead of being occupied by the tedious photo capturing task. Motivated by this scenario, we propose an automated social event photo-capture framework for which, given the multiple sensor data streams and the information from the Web as input, will output the visually appealing photos of the social event. Our proposal consists of three components: (1) social attribute extraction from both the physical space and the cyberspace; (2) social attribute fusion; and (3) active camera control. Current work is presented and we conclude with expected contributions as well as future direction.


title: Psychopaths know right from wrong but don't care.

text: Adult psychopaths have deficits in emotional processing and inhibitory control, engage in morally inappropriate behavior, and generally fail to distinguish moral from conventional violations. These observations, together with a dominant tradition in the discipline which sees emotional processes as causally necessary for moral judgment, have led to the conclusion that psychopaths lack an understanding of moral rights and wrongs. We test an alternative explanation: psychopaths have normal understanding of right and wrong, but abnormal regulation of morally appropriate behavior. We presented psychopaths with moral dilemmas, contrasting their judgments with age- and sex-matched (i) healthy subjects and (ii) non-psychopathic, delinquents. Subjects in each group judged cases of personal harms (i.e. requiring physical contact) as less permissible than impersonal harms, even though both types of harms led to utilitarian gains. Importantly, however, psychopaths' pattern of judgments on different dilemmas was the same as those of the other subjects. These results force a rejection of the strong hypothesis that emotional processes are causally necessary for judgments of moral dilemmas, suggesting instead that psychopaths understand the distinction between right and wrong, but do not care about such knowledge, or the consequences that ensue from their morally inappropriate behavior.


title: The role of emotions for moral judgments depends on the type of emotion and moral scenario.

text: Emotions seem to play a critical role in moral judgment. However, the way in which emotions exert their influence on moral judgments is still poorly understood. This study proposes a novel theoretical approach suggesting that emotions influence moral judgments based on their motivational dimension. We tested the effects of two types of induced emotions with equal valence but with different motivational implications (anger and disgust), and four types of moral scenarios (disgust-related, impersonal, personal, and beliefs) on moral judgments. We hypothesized and found that approach motivation associated with anger would make moral judgments more permissible, while disgust, associated with withdrawal motivation, would make them less permissible. Moreover, these effects varied as a function of the type of scenario: the induced emotions only affected moral judgments concerning impersonal and personal scenarios, while we observed no effects for the other scenarios. These findings suggest that emotions can play an important role in moral judgment, but that their specific effects depend upon the type of emotion induced. Furthermore, induced emotion effects were more prevalent for moral decisions in personal and impersonal scenarios, possibly because these require the performance of an action rather than making an abstract judgment. We conclude that the effects of induced emotions on moral judgments can be predicted by taking their motivational dimension into account. This finding has important implications for moral psychology, as it points toward a previously overlooked mechanism linking emotions to moral judgments.


title: Gossip in Evolutionary Perspective

text: Conversation is a uniquely human phenomenon. Analyses of freely forming conversations indicate that approximately two thirds of conversation time is devoted to social topics, most of which can be given the generic label gossip. This article first explores the origins of gossip as a mechanism for bonding social groups, tracing these origins back to social grooming among primates. It then asks why social gossip in this sense should form so important a component of human interaction and presents evidence to suggest that, aside from servicing social networks, a key function may be related explicitly to controlling free riders. Finally, the author reviews briefly the role of social cognition in facilitating conversations of this kind.


title: Limbic abnormalities in affective processing by criminal psychopaths as revealed by functional magnetic resonance imaging

text:
BACKGROUND
Psychopathy is a complex personality disorder of unknown etiology. Central to the disorder are anomalies or difficulties in affective processing.

METHODS
Functional magnetic resonance imaging was used to elucidate the neurobiological correlates of these anomalies in criminal psychopaths during performance of an affective memory task.

RESULTS
Compared with criminal nonpsychopaths and noncriminal control participants, criminal psychopaths showed significantly less affect-related activity in the amygdala/hippocampal formation, parahippocampal gyrus, ventral striatum, and in the anterior and posterior cingulate gyri. Psychopathic criminals also showed evidence of overactivation in the bilateral fronto-temporal cortex for processing affective stimuli.

CONCLUSIONS
These data suggest that the affective abnormalities so often observed in psychopathic offenders may be linked to deficient or weakened input from limbic structures.


title: Impairment of social and moral behavior related to early damage in human prefrontal cortex

text: The long-term consequences of early prefrontal cortex lesions occurring before 16 months were investigated in two adults. As is the case when such damage occurs in adulthood, the two early-onset patients had severely impaired social behavior despite normal basic cognitive abilities, and showed insensitivity to future consequences of decisions, defective autonomic responses to punishment contingencies and failure to respond to behavioral interventions. Unlike adult-onset patients, however, the two patients had defective social and moral reasoning, suggesting that the acquisition of complex social conventions and moral rules had been impaired. Thus early-onset prefrontal damage resulted in a syndrome resembling psychopathy.


title: Multimodal Classification for Analysing Social Media

text: Classification of social media data is an important approach in understanding user behavior on the Web. Although information on social media can be of different modalities such as texts, images, audio or videos, traditional approaches in classification usually leverage only one prominent modality. Techniques that are able to leverage multiple modalities are often complex and susceptible to the absence of some modalities. In this paper, we present simple models that combine information from different modalities to classify social media content and are able to handle the above problems with existing techniques. Our models combine information from different modalities using a pooling layer and an auxiliary learning task is used to learn a common feature space. We demonstrate the performance of our models and their robustness to the missing of some modalities in the emotion classification domain. Our approaches, although being simple, can not only achieve significantly higher accuracies than traditional fusion approaches but also have comparable results when only one modality is available.


title: A Case Study on Quality Attribute Measurement using MARF and GIPSY

text: This literature focuses on doing a comparative analysis between Modular Audio Recognition Framework (MARF) and the General Intentional Programming System (GIPSY) with the help of different software metrics. At first, we understand the general principles, architecture and working of MARF and GIPSY by looking at their frameworks and running them in the Eclipse environment. Then, we study some of the important metrics including a few state of the art metrics and rank them in terms of their usefulness and their influence on the different quality attributes of a software. The quality attributes are viewed and computed with the help of the Logiscope and McCabe IQ tools. These tools perform a comprehensive analysis on the case studies and generate a quality report at the factor level, criteria level and metrics level. In next step, we identify the worst code at each of these levels, extract the worst code and provide recommendations to improve the quality. We implement and test some of the metrics which are ranked as the most useful metrics with a set of test cases in JDeodorant. Finally, we perform an analysis on both MARF and GIPSY by doing a fuzzy code scan using MARFCAT to find the list of weak and vulnerable classes.


title: Pair-Wise Temporal Pooling Method for Rapid Training of the HTM Networks Used in Computer Vision Applications

text: In the paper, several modifications to the conventional learning algorithms of the Hierarchical Temporal Memory (HTM) – a biologically inspired largescale model of the neocortex by Numenta – have been proposed. Firstly, an alternative spatial pooling method has been introduced, which makes use of a random pattern generator exploiting the Metropolis-Hastings algorithm. The original inference algorithm by Numenta has been reformulated, in order to reduce a number of tunable parameters and to optimize its computational efficiency. The main contribution of the paper consists in the proposal of a novel temporal pooling method – the pair-wise explorer – which allows faster and more reliable training of the HTM networks using data without inherent temporal information (e.g., static images). While the conventional temporal pooler trains the HTM network on a finite segment of the smooth Brownian-like random walk across the training images, the 902 S. Štolc, I. Bajla, K. Valent́ın, R. Škoviera proposed method performs training by means of the pairs of patterns randomly sampled (in a special manner) from a virtually infinite smooth random walk. We have conducted a set of experiments with the single-layer HTM network applied to the position, scale, and rotation-invariant recognition of geometric objects. The obtained results provide a clear evidence that the pair-wise method yields significantly faster convergence to the theoretical maximum of the classification accuracy with respect to both the length of the training sequence (defined by the maximum allowed number of updates of the time adjacency matrix – TAM) and the number of training patterns. The advantage of the proposed explorer manifested itself mostly in the lower range of TAM updates where it caused up to 10% relative accuracy improvement over the conventional method. Therefore we suggest to use the pair-wise explorer, instead of the smooth explorer, always when the HTM network is trained on a set of static images, especially when the exhaustive training is impossible due to the complexity of the given task.


title: Mapping interference resolution across task domains: A shared control process in left inferior frontal gyrus

text: Work in functional neuroimaging has mapped interference resolution processing onto left inferior frontal regions for both verbal working memory and a variety of semantic processing tasks. The proximity of the identified regions from these different tasks suggests the existence of a common, domain-general interference resolution mechanism. The current research specifically tests this idea in a within-subject design using fMRI to assess the activation associated with variable selection requirements in a semantic retrieval task (verb generation) and a verbal working memory task with a trial-specific proactive interference manipulation (recent-probes). High interference trials on both tasks were associated with activity in the midventrolateral region of the left inferior frontal gyrus, and the regions activated in each task strongly overlapped. The results indicate that an elemental component of executive control associated with interference resolution during retrieval from working memory and from semantic memory can be mapped to a common portion of the left inferior frontal gyrus.


title: Creativity support for novice digital filmmaking

text: Machinima is a new form of creative digital filmmaking that leverages the real time graphics rendering of computer game engines. Because of the low barrier to entry, machinima has become a popular creative medium for hobbyists and novices while still retaining borrowed conventions from professional filmmaking. Can novice machinima creators benefit from creativity support tools? A preliminary study shows novices generally have difficulty adhering to cinematographic conventions. We identify and document four cinematic conventions novices typically violate. We report on a Wizard-of-Oz study showing a rule-based intelligent system that can reduce the frequency of errors that novices make by providing information about rule violations without prescribing solutions. We discuss the role of error reduction in creativity support tools.


title: System architecture directions for networked sensors

text: Technological progress in integrated, low-power, CMOS communication devices and sensors makes a rich design space of networked sensors viable. They can be deeply embedded in the physical world and spread throughout our environment like smart dust. The missing elements are an overall system architecture and a methodology for systematic advance. To this end, we identify key requirements, develop a small device that is representative of the class, design a tiny event-driven operating system, and show that it provides support for efficient modularity and concurrency-intensive operation. Our operating system fits in 178 bytes of memory, propagates events in the time it takes to copy 1.25 bytes of memory, context switches in the time it takes to copy 6 bytes of memory and supports two level scheduling. The analysis lays a groundwork for future architectural advances.


title: Recent Development in Big Data Analytics for Business Operations and Risk Management

text: “Big data” is an emerging topic and has attracted the attention of many researchers and practitioners in industrial systems engineering and cybernetics. Big data analytics would definitely lead to valuable knowledge for many organizations. Business operations and risk management can be a beneficiary as there are many data collection channels in the related industrial systems (e.g., wireless sensor networks, Internet-based systems, etc.). Big data research, however, is still in its infancy. Its focus is rather unclear and related studies are not well amalgamated. This paper aims to present the challenges and opportunities of big data analytics in this unique application domain. Technological development and advances for industrial-based business systems, reliability and security of industrial systems, and their operational risk management are examined. Important areas for future research are also discussed and revealed.


title: Computational design of linkage-based characters

text: We present a design system for linkage-based characters, combining form and function in an aesthetically-pleasing manner. Linkage-based character design exhibits a mix of discrete and continuous problems, making for a highly unintuitive design space that is difficult to navigate without assistance. Our system significantly simplifies this task by allowing users to interactively browse different topology options, thus guiding the discrete set of choices that need to be made. A subsequent continuous optimization step improves motion quality and, crucially, safeguards against singularities. We demonstrate the flexibility of our method on a diverse set of character designs, and then realize our designs by physically fabricating prototypes.


title: Automated Summarization Evaluation with Basic Elements

text: As part of evaluating a summary automatically, it is usual to determine how much of the contents of one or more human-produced ‘ideal’ summaries it contains. Past automated methods such as ROUGE compare using fixed word ngrams, which are not ideal for a variety of reasons. In this paper we describe a framework in which summary evaluation measures can be instantiated and compared, and we implement a specific evaluation method using very small units of content, called Basic Elements, that address some of the shortcomings of ngrams. This method is tested on DUC 2003, 2004, and 2005 systems and produces very good correlations with human judgments.


title: Computational Intelligence, Networked Systems and Their Applications

text: This paper presents the metro constant illumination controller, which is designed to solve the current problem that the interior illumination in a moving metro fluctuates drastically with the changes of exterior environment. By detecting the real-time interior illumination values and adjusting the lights in a metro with PID controller based on BP neural network, the controller can keep the metro interior illumination values around the preset value. Simulations and actual test results show that the PID controller based on BP neural network has strong adaptability and robustness in a nonlinear system. It can both save energy and solve the problem of drastically fluctuating illumination in a moving metro, which cannot be achieved in conventional PID controllers.