title: Processing Social Media Messages in Mass Emergency: A Survey

abstract: Social media platforms provide active communication channels during mass convergence and emergency events such as disasters caused by natural hazards. As a result, first responders, decision makers, and the public can use this information to gain insight into the situation as it unfolds. In particular, many social media messages communicated during emergencies convey timely, actionable information. Processing social media messages to obtain such information, however, involves solving multiple challenges including: handling information overload, filtering credible information, and prioritizing different classes of messages. These challenges can be mapped to classical information processing operations such as filtering, classifying, ranking, aggregating, extracting, and summarizing. We survey the state of the art regarding computational methods to process social media messages, focusing on their application in emergency response scenarios. We examine the particularities of this setting, and then methodically examine a series of key sub-problems ranging from the detection of events to the creation of actionable and useful summaries.


title: Cross-Cultural Analysis of Blogs and Forums with Mixed-Collection Topic Models

abstract: This paper presents preliminary results on the detection of cultural differences from people's experiences in various countries from two perspectives: tourists and locals. Our approach is to develop probabilistic models that would provide a good framework for such studies. Thus, we propose here a new model, ccLDA, which extends over the Latent Dirichlet Allocation (LDA) (Blei et al., 2003) and cross-collection mixture (ccMix) (Zhai et al., 2004) models on blogs and forums. We also provide a qualitative and quantitative analysis of the model on the cross-cultural data.


title: Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles

abstract: Many practical perception systems exist within larger processes that include interactions with users or additional components capable of evaluating the quality of predicted solutions. In these contexts, it is beneficial to provide these oracle mechanisms with multiple highly likely hypotheses rather than a single prediction. In this work, we pose the task of producing multiple outputs as a learning problem over an ensemble of deep networks -- introducing a novel stochastic gradient descent based approach to minimize the loss with respect to an oracle. Our method is simple to implement, agnostic to both architecture and loss function, and parameter-free. Our approach achieves lower oracle error compared to existing methods on a wide range of tasks and deep architectures. We also show qualitatively that the diverse solutions produced often provide interpretable representations of task ambiguity.


title: Corpus domain effects on distributional semantic modeling of medical terms

abstract: Automatically quantifying semantic similarity and relatedness between clinical terms is an important aspect of text mining from electronic health records, which are increasingly recognized as valuable sources of phenotypic information for clinical genomics and bioinformatics research. A key obstacle to development of semantic relatedness measures is the limited availability of large quantities of clinical text to researchers and developers outside of major medical centers. Text from general English and biomedical literature are freely available; however, their validity as a substitute for clinical domain to represent semantics of clinical terms remains to be demonstrated.We constructed neural network representations of clinical terms found in a publicly available benchmark dataset manually labeled for semantic similarity and relatedness. Similarity and relatedness measures computed from text corpora in three domains (Clinical Notes, PubMed Central articles and Wikipedia) were compared using the benchmark as reference. We found that measures computed from full text of biomedical articles in PubMed Central repository (rho = 0.62 for similarity and 0.58 for relatedness) are on par with measures computed from clinical reports (rho = 0.60 for similarity and 0.57 for relatedness). We also evaluated the use of neural network based relatedness measures for query expansion in a clinical document retrieval task and a biomedical term word sense disambiguation task. We found that, with some limitations, biomedical articles may be used in lieu of clinical reports to represent the semantics of clinical terms and that distributional semantic methods are useful for clinical and biomedical natural language processing applications.The software and reference standards used in this study to evaluate semantic similarity and relatedness measures are publicly available as detailed in the article.pakh0002@umn.eduSupplementary information: Supplementary data are available at Bioinformatics online.


title: Recognizing MSTAR target variants and articulations

abstract: The focus of this paper is recognizing articulated vehicles and actual vehicle configuration variants in real SAR images from the MSTAR public data. Using SAR scattering center locations and magnitudes as features, the invariance of these features is shown with articulation (i.e. turret rotation for the T72 tank and ZSU 23/4 gun), with configuration variants and with a small change in depression angle. This scatterer location and magnitude quasi-invariance (e.g. location within one pixel, magnitude within about ten percent in radar cross- section) is used as a basis for development of a SAR recognition engine that successfully identified real articulated and non-standard configuration vehicles based on non-articulated, standard recognition models. Identification performance results are presented as vote space scatter plots and ROC curves for configuration variants, for articulated objects and for a small change in depression angle with the MSTAR data.


title: Lagged correlation-based deep learning for directional trend change prediction in financial time series

abstract: Trend change prediction in complex systems with a large number of noisy time series is a problem with many applications for real-world phenomena, with stock markets as a notoriously difficult to predict example of such systems. We approach predictions of directional trend changes via complex lagged correlations between them, excluding any information about the target series from the respective inputs to achieve predictions purely based on such correlations with other series. We propose the use of deep neural networks that employ step-wise linear regressions with exponential smoothing in the preparatory feature engineering for this task, with regression slopes as trend strength indicators for a given time interval. We apply this method to historical stock market data from 2011 to 2016 as a use case example of lagged correlations between large numbers of time series that are heavily influenced by externally arising new information as a random factor. The results demonstrate the viability of the proposed approach, with state-of-the-art accuracies and accounting for the statistical significance of the results for additional validation, as well as important implications for modern financial economics.


title: Interfirm Collaboration Networks: The Impact of Large-Scale Network Structure on Firm Innovation

abstract: The structure of alliance networks influences their potential for knowledge creation. Dense local clustering provides information transmission capacity in the network by fostering communication and cooperation. Nonredundant connections contract the distance between firms and give the network greater reach by tapping a wider range of knowledge resources. We propose that firms embedded in alliance networks that exhibit both high clustering and high reach (short average path lengths to a wide range of firms) will have greater innovative output than firms in networks that do not exhibit these characteristics. We find support for this proposition in a longitudinal study of the patent performance of 1,106 firms in 11 industry-level alliance networks.


title: Deep Learning in Drug Discovery

abstract: Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks.


title: The EULER project: application of software defined radio in joint security operations

abstract: The task of improving the effectiveness of public safety communications has become a main priority for governments. This is partly motivated by the increased risk of natural disasters such as flooding, earthquakes, and fires, and partly due to the risks and consequent impact of terrorist attacks. This article focuses on the experience from the European Commission Seventh Framework Programme project known as EULER, which seeks to demonstrate the benefits of software defined radio technology to support the resolution of natural disasters of significant stature, which require the participation of different public safety and military organizations, potentially of different nations. In such scenarios, the presence of interoperability barriers in the disaster area is a major challenge because different organizations may use different wireless communication systems. In this context, the main aspect investigated in EULER is the definition of a common waveform that respects the software communications architecture constraints, and guarantees maximum portability across SDR platforms. This article discusses a range of issues that have been identified thus far within the EULER project; in particular, the perceived pan-European interoperability needs of public safety, and coordination with military devices and networks. Aspects of interoperability are also extended to the three dimensions of platform, waveform, and information assurance.


title: IoT-cloud based framework for patient's data collection in smart healthcare system using raspberry-pi

abstract: The Internet of things(IoT) has brought the vision of the smarter world into reality and including healthcare it has a many application domains. The convergence of IOT-cloud can play a significant role in the smart healthcare by offering better insight of healthcare content to support affordable and quality patient care. In this paper, we proposed a model that allows the sensor to monitor the patient's symptom. The collected monitored data transmitted to the gateway via Bluetooth and then to the cloud server through docker container using the internet. Thus enabling the physician to diagnose and monitor health problems wherever the patient is. Also, we address the several challenges related to health monitoring and management using IoT.


title: A Novel Approach to Automatic Road-Accident Detection using Machine Vision Techniques

abstract: In this paper, a novel approach for automatic road accident detection is proposed. The approach is based on detecting damaged vehicles from footage received from surveillance cameras installed in roads and highways which would indicate the occurrence of a road accident. Detection of damaged cars falls under the category of object detection in the field of machine vision and has not been achieved so far. In this paper, a new supervised learning method comprising of three different stages which are combined into a single framework in a serial manner which successfully detects damaged cars from static images is proposed. The three stages use five support vector machines trained with Histogram of gradients (HOG) and Gray level co-occurrence matrix (GLCM) features. Since damaged car detection has not been attempted, two datasets of damaged cars - Damaged Cars Dataset-1 (DCD-1) and Damaged Cars Dataset-2 (DCD-2) – was compiled for public release. Experiments were conducted on DCD-1 and DCD-2 which differ based on the distance at which the image is captured and the quality of the images. The accuracy of the system is 81.83% for DCD-1 captured at approximately 2 meters with good quality and 64.37% for DCD-2 captured at approximately 20 meters with poor quality.


title: Semi-anthropomorphic 3D printed multigrasp hand for industrial and service robots

abstract: This paper presents the preliminary prototype design and implementation of the Nazarbayev University (NU) Hand, a new semi-anthropomorphic multigrasp robotic hand. The hand is designed to be an end effector for industrial and service robots. The main objective is to develop a low-cost, low-weight and easily manufacturable robotic hand with a sensor module allowing acquisition of data for autonomous intelligent object manipulation. 3D printing technologies were extensively used in the implementation of the hand. Specifically, the structure of the hand is printed using a 3D printer as a complete assembly voiding the need of using fasteners and bearings for the assembly of the hand and decreasing the total weight. The hand also incorporates a sensor module containing a LIDAR, digital camera and non-contact infrared temperature sensor for intelligent automation. As an alternative to teach pendants for the industrial manipulators, a teaching glove was developed, which acts as the primary human machine interface between the user and the NU Hand. The paper presents an extensive performance characterization of the robotic hand including finger forces, weight, audible noise level during operation and sensor data acquisition.


title: New oral anticoagulant drugs in cardiovascular disease

abstract: Oral anticoagulation has been limited to vitamin K antagonists (VKAs) for over 60 years. VKAs are effective and recommended for the prevention of venous and arterial thromboembolism in cardiovascular disease, but their pharmacodynamics are difficult to predict and the highly variable interindividual and intraindividual response to treatment accounts for the need of continuous monitoring. This prompted the intensive exploration of numerous substances within the last decade in an attempt to meet the shortcomings of current oral anticoagulation with VKAs. The development and clinical investigation of two novel groups of oral anticoagulants targeting central factors of the coagulation system either factor Xa or thrombin (factor IIa) has now reached the daily clinical practice with the approval of the oral direct thrombin inhibitor dabigatran etexilate and the oral direct factor Xa inhibitor rivaroxaban. Ongoing clinical trials are investigating these substances and other novel oral anticoagulants with similar mechanisms of action in patients with atrial fibrillation and acute coronary syndromes. This review article discusses the clinical evaluation and pharmacological properties of novel oral anticoagulants in late and earlier stages of clinical development, thereby providing a critical analysis and an outlook on the future of oral anticoagulation in cardiovascular disease.


title: Finding high-quality content in social media

abstract: The quality of user-generated content varies drastically from excellent to abuse and spam. As the availability of such content increases, the task of identifying high-quality content sites based on user contributions --social media sites -- becomes increasingly important. Social media in general exhibit a rich variety of information sources: in addition to the content itself, there is a wide array of non-content information available, such as links between items and explicit quality ratings from members of the community. In this paper we investigate methods for exploiting such community feedback to automatically identify high quality content. As a test case, we focus on Yahoo! Answers, a large community question/answering portal that is particularly rich in the amount and types of content and social interactions available in it. We introduce a general classification framework for combining the evidence from different sources of information, that can be tuned automatically for a given social media type and quality definition. In particular, for the community question/answering domain, we show that our system is able to separate high-quality items from the rest with an accuracy close to that of humans


title: Structured Literature Image Finder : Extracting Information from Text and Images in Biomedical Literature

abstract: Slif uses a combination of text-mining and image processing to extract information from figures in the biomedical literature. It also uses innovative extensions to traditional latent topic modeling to provide new ways to traverse the literature. Slif provides a publicly available searchable database (http://slif.cbi.cmu.edu). ::: ::: Slif originally focused on fluorescence microscopy images. We have now extended it to classify panels into more image types. We also improved the classification into subcellular classes by building a more representative training set. To get the most out of the human labeling effort, we used active learning to select images to label. ::: ::: We developed models that take into account the structure of the document (with panels inside figures inside papers) and the multi-modality of the information (free and annotated text, images, information from external databases). This has allowed us to provide new ways to navigate a large collection of documents.


title: Optimization and simulation of crank-rocker mechanism based on Excel

abstract: To find the optimum track and visualize the movement of the mechanism, a method based on Excel to optimize and simulate the motion behavior of crank-rocker mechanism is put forward. The mathematical model is established firstly, and then optimization is performed with the linear program toolbox in MS-Excel software. Finally, the dynamic animation of crank rocker mechanism is realized with the optimum parameters obtained and the link control as well as functions tools, compared with professional simulating software and advanced program language, the way proposed in the paper is convenient and practical.


title: Mycobiota and mycotoxins in Brazil nut samples from different states of the Brazilian Amazon region.

abstract: The objective of this study was to evaluate the presence of fungi and mycotoxins (aflatoxins and cyclopiazonic acid) in Brazil nut samples collected in different states of the Brazilian Amazon region: Acre, Amazonas, Amapá, and Pará. A total of 200 husk samples and 200 almond samples were inoculated onto Aspergillus flavus-parasiticus agar for the detection of fungi. Mycotoxins were analyzed by high-performance liquid chromatography. The mycobiota comprised the following fungi, in decreasing order of frequency: almonds - Phialemonium spp. (54%), Penicillium spp. (16%), Fusarium spp. (13%), Phaeoacremonium spp. (11%), and Aspergillus spp. (4%), husks - Phialemonium spp. (62%), Phaeoacremonium spp. (11%), Penicillium spp. (10%), Fusarium spp. (9%), and Aspergillus spp. A polyphasic approach was used for identification of Aspergillus species. Aflatoxins were detected in 22 (11%) of the 200 almond samples, with 21 samples presenting aflatoxin B(1) levels above 8μg/kg, the limit established by the European Commission for Brazil nuts for further processing. Nineteen (9.5%) of the 200 husk samples contained aflatoxins, but at levels lower than those seen in almonds. Cyclopiazonic acid (CPA) was detected in 44 (22%) almond samples, with levels ranging from 98.65 to 161.2μg/kg. Aspergillus nomius and A. flavus were the most frequent Aspergillus species. The presence of fungi does not necessarily imply mycotoxin contamination, but almonds of the Brazil nut seem to be a good substrate for fungal growth.


title: User-specific chip-level interleaver design for IDMA systems

abstract: A user-specific interleaver design for interleave-division multiple-access (IDMA) systems is proposed. This method can solve the memory cost problem for chip-level interleavers, and reduce the amount of information exchange between mobile stations and base stations to specify the interleaver used as their identifications.


title: Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma

abstract: Lung cancer is the most common cause of cancer-related deaths in the USA. It can be detected and diagnosed using computed tomography images. For an automated classifier, identifying predictive features from medical images is a key concern. Deep feature extraction using pretrained convolutional neural networks (CNNs) has recently been successfully applied in some image domains. Here, we applied a pretrained CNN to extract deep features from 40 computed tomography images, with contrast, of non-small cell adenocarcinoma lung cancer, and combined deep features with traditional image features and trained classifiers to predict short- and long-term survivors. We experimented with several pretrained CNNs and several feature selection strategies. The best previously reported accuracy when using traditional quantitative features was 77.5% (area under the curve [AUC], 0.712), which was achieved by a decision tree classifier. The best reported accuracy from transfer learning and deep features was 77.5% (AUC, 0.713) using a decision tree classifier. When extracted deep neural network features were combined with traditional quantitative features, we obtained an accuracy of 90% (AUC, 0.935) with the 5 best post-rectified linear unit features extracted from a vgg-f pretrained CNN and the 5 best traditional features. The best results were achieved with the symmetric uncertainty feature ranking algorithm followed by a random forests classifier.


A Review of Kernel Methods in Machine Learning


title: Privacy Preserving Back-Propagation Neural Network Learning Made Practical with Cloud Computing

abstract: To improve the accuracy of learning result, in practice multiple parties may collaborate through conducting joint Back-Propagation neural network learning on the union of their respective data sets. During this process no party wants to disclose her/his private data to others. Existing schemes supporting this kind of collaborative learning are either limited in the way of data partition or just consider two parties. There lacks a solution that allows two or more parties, each with an arbitrarily partitioned data set, to collaboratively conduct the learning. This paper solves this open problem by utilizing the power of cloud computing. In our proposed scheme, each party encrypts his/her private data locally and uploads the ciphertexts into the cloud. The cloud then executes most of the operations pertaining to the learning algorithms over ciphertexts without knowing the original private data. By securely offloading the expensive operations to the cloud, we keep the computation and communication costs on each party minimal and independent to the number of participants. To support flexible operations over ciphertexts, we adopt and tailor the BGN "doubly homomorphic" encryption algorithm for the multiparty setting. Numerical analysis and experiments on commodity cloud show that our scheme is secure, efficient, and accurate.


title: Solid phase peptide synthesis utilizing 9-fluorenylmethoxycarbonyl amino acids.

abstract: 9-Fluorenylmethoxycarbonyl (Fmoc) amino acids were first used for solid phase peptide synthesis a little more than a decade ago. Since that time, Fmoc solid phase peptide synthesis methodology has been greatly enhanced by the introduction of a variety of solid supports, linkages, and side chain protecting groups, as well as by increased understanding of solvation conditions. These advances have led to many impressive syntheses, such as those of biologically active and isotopically labeled peptides and small proteins. The great variety of conditions under which Fmoc solid phase peptide synthesis may be carried out represents a truly "orthogonal" scheme, and thus offers many unique opportunities for bioorganic chemistry.


title: W-band sub-harmonic mixer in hybrid technology

abstract: A sub-harmonic mixer in hybrid technology is reported in this paper. Flip chip Schottky diode pairs are used as nonlinear elements. A novel solutions with split stubs is used for improving both W-band and LO signals' confinement near diode terminals. The design is validated with mixer structures of good dynamic range and similar conversion loss over 90–100 GHz band.


title: UAV long range surveillance system based on BiQuad antenna for the Ground Control Station

abstract: Unmanned Aerial Vehicle (UAV) that used in long-range surveillance missions need an optimal performance of signal communication. The goal of this system is to communicate between the UAV and ground station in real time. The maximal performances characteristics of antenna such as gain, Standing Wave Ratio (SWR), impedance, polarization, and frequency are important for the UAV communication. Nowadays, antennas are easy to find, but many of them are not appropriate because the characteristic and mission of antenna are not compatible. In this research, to optimize the communications between UAV and Ground Control System (GCS), we use BiQuad antenna for telemetry. Our BiQuad antenna has a vertical polarization which is made by two loops that work in the frequency of 433 MHz and it is the best performance. For higher performance, this antenna component is made by unusual BiQuad material such as Printed Circuit Board (PCB) for the reflector, Teflon cable to connect the element with RP-SMA connector, and without using RG-8 cable. BiQuad antenna simulated by using the MMANA-GAL software to analysis and simulation characteristic of antenna. The characteristic of these antenna based on the research SWR= 1.2, F/B = −0.14 dB, Impedance = 50.9 ohm, Gain = 11.24 dBi, and frequency 433 Mhz. Based on these data result, this antenna is very appropriate for UAV long-range surveillance missions.


title: Adoption of Social Media and the Quality of Life of Older Adults

abstract: In this paper we discuss how social media can be used by older adults to enhance their quality of life. We develop a research model to investigate the factors that may affect the adoption of social media by older adults. This approach differs from previous information systems (IS) oriented adoption studies, since we focus on analyzing older adults' characteristics from biopsychosocial perspectives. Building on Abraham Maslow's hierarchy of needs model, we define older adult needs from a quality of life perspective. We then incorporate Maslow's model into a universal user adoption model with the constructs specifically adjusted to reflect the characteristics of older adults.


title: Cell Surface Lactate Receptor GPR81 Is Crucial for Cancer Cell Survival

abstract: The mechanisms that allow cancer cells to adapt to the typical tumor microenvironment of low oxygen and glucose and high lactate are not well understood. GPR81 is a lactate receptor recently identified in adipose and muscle cells that has not been investigated in cancer. In the current study, we examined GPR81 expression and function in cancer cells. We found that GPR81 was present in colon, breast, lung, hepatocellular, salivary gland, cervical, and pancreatic carcinoma cell lines. Examination of tumors resected from patients with pancreatic cancer indicated that 94% (148 of 158) expressed high levels of GPR81. Functionally, we observed that the reduction of GPR81 levels using shRNA-mediated silencing had little effect on pancreatic cancer cells cultured in high glucose, but led to the rapid death of cancer cells cultured in conditions of low glucose supplemented with lactate. We also observed that lactate addition to culture media induced the expression of genes involved in lactate metabolism, including monocarboxylase transporters in control, but not in GPR81-silenced cells. In vivo, GPR81 expression levels correlated with the rate of pancreatic cancer tumor growth and metastasis. Cells in which GPR81 was silenced showed a dramatic decrease in growth and metastasis. Implantation of cancer cells in vivo was also observed to lead to greatly elevated levels of GPR81. These data support that GPR81 is important for cancer cell regulation of lactate transport mechanisms. Furthermore, lactate transport is important for the survival of cancer cells in the tumor microenvironment. Cancer Res; 74(18); 5301-10. ©2014 AACR.


title: CELLULAR AND NETWORK ARCHITECTURE FOR 5G WIRELESS COMMUNICATION NETWORKS IN MOBILE TECHNOLOGY

abstract: G Technology stands for Fifth Generation Mobile technology. From generation 1G to 2.5G and from 3G to 5G this world of telecommunication has seen a number of improvements along with improved performance with every passing day. Fifth generation network provide affordable broadband wireless connectivity (very high speed). The paper throws light on network architecture of fifth generation technology. Currently 5G term is not officially used. In fifth generation researches are being made on development of World Wide Wireless Web (WWWW), Dynamic Adhoc Wireless Networks (DAWN) and Real Wireless World. Fifth generation focus on (Voice over IP) VOIP-enabled devices that user will experience a high level of call volume and data transmission. Wire-less system designers have been facing the continuously increasing demand for high data rates and mobility required by new wireless applications and therefore has started research on fifth generation wireless systems that are expected to be deployed beyond 2020. In this article, we propose a potential cellular architecture that separates indoor and outdoor scenarios, and discuss various promising technologies for 5G wireless communication systems, such as massive MIMO, energy-efficient communications, cognitive radio networks, and visible light communications. The proposed network is enforced by nanotechnology, cloud computing and based on all IP Platform. The main features in 5G mobile network is that user can simultaneously connect to the multiple wireless technologies and can switch between them. This forthcoming mobile technology will support IPv6 and flat IP. Index Terms- 5G, Telecommunication, Network Architecture, WWWW, DAWN.


Estimating Scale and Scope Effects in Public Health Delivery: Implications for Regionalization


title: A machine competence based analytical model to study trust calibration in supervised autonomous systems

abstract: Modern day autonomous systems are moving away form mere automation of manual tasks to true autonomy that require them to apply human-like judgment when dealing with uncertain situations in performing complex tasks. Trust in these systems is a key enabler to fully realize this dream. A lack of trust leads to inefficient use of these systems and increases the supervision workload for humans. Conversely, an over trust in these systems leads to increased risks and exposure to catastrophic events. This paper presents a high-level analytical model to study trust dynamics in supervised autonomous system environments. Trust, in this context, is defined as a function of machine competence and the level of human control required to achieve this competence. A parametric model of machine competence is presented that allows generating different machine competence behaviors based on the task difficulty, level of supervision and machine's learning ability. The notions of perceived and desired or optimal trust, computed based on perceived and observed machine competence respectively, are introduced. This allows treating trust calibration as an optimization or control problem. The presented models provide a formal framework for developing higher-fidelity simulation models to study trust dynamics in supervised autonomous systems and develop appropriate controllers for optimizing the trust between humans and machines in these systems.


title: Progressive Sparse Local Attention for Video Object Detection

abstract: Transferring image-based object detectors to the domain of videos remains a challenging problem. Previous efforts mostly exploit optical flow to propagate features across frames, aiming to achieve a good trade-off between accuracy and efficiency. However, introducing an extra model to estimate optical flow can significantly increase the overall model size. The gap between optical flow and high-level features can also hinder it from establishing spatial correspondence accurately. Instead of relying on optical flow, this paper proposes a novel module called Progressive Sparse Local Attention (PSLA), which establishes the spatial correspondence between features across frames in a local region with progressively sparser stride and uses the correspondence to propagate features. Based on PSLA, Recursive Feature Updating (RFU) and Dense Feature Transforming (DenseFT) are proposed to model temporal appearance and enrich feature representation respectively in a novel video object detection framework. Experiments on ImageNet VID show that our method achieves the best accuracy compared to existing methods with smaller model size and acceptable runtime speed.


title: Always Playable: Recombining Routines for Creative Efficiency at Ubisoft Montreal’s Video Game Studio

abstract: This study examines how an organization can redefine its core process when faced with a major crisis of creative efficiency. We analyze the case of the experimentation of the “Always Playable” project at Ubisoft Montreal, a leading video game development studio, where a crisis of creativity triggered an active reconfiguration of routines and their artifacts. To address this crisis, the organization attempted to restore a balance between efficiency and flexibility by modifying the generative relationship among the ostensive aspect, the performative aspect, and artifacts of routines. Our study shows that one way to reach this balance is by deliberately breaking, partitioning, and recombining aspects from different routines.


title: SORT: A Self-ORganizing Trust Model for Peer-to-Peer Systems

abstract: Open nature of peer-to-peer systems exposes them to malicious activity. Building trust relationships among peers can mitigate attacks of malicious peers. This paper presents distributed algorithms that enable a peer to reason about trustworthiness of other peers based on past interactions and recommendations. Peers create their own trust network in their proximity by using local information available and do not try to learn global trust information. Two contexts of trust, service, and recommendation contexts, are defined to measure trustworthiness in providing services and giving recommendations. Interactions and recommendations are evaluated based on importance, recentness, and peer satisfaction parameters. Additionally, recommender's trustworthiness and confidence about a recommendation are considered while evaluating recommendations. Simulation experiments on a file sharing application show that the proposed model can mitigate attacks on 16 different malicious behavior models. In the experiments, good peers were able to form trust relationships in their proximity and isolate malicious peers.


title: Solving 0–1 knapsack problem by a novel binary monarch butterfly optimization

abstract: Abstract ::: This paper presents a novel binary monarch butterfly optimization (BMBO) method, intended for addressing the 0–1 knapsack problem (0–1 KP). Two tuples, consisting of real-valued vectors and binary vectors, are used to represent the monarch butterfly individuals in BMBO. Real-valued vectors constitute the search space, whereas binary vectors form the solution space. In other words, monarch butterfly optimization works directly on real-valued vectors, while solutions are represented by binary vectors. Three kinds of individual allocation schemes are tested in order to achieve better performance. Toward revising the infeasible solutions and optimizing the feasible ones, a novel repair operator, based on greedy strategy, is employed. Comprehensive numerical experimentations on three types of 0–1 KP instances are carried out. The comparative study of the BMBO with four state-of-the-art classical algorithms clearly points toward the superiority of the former in terms of search accuracy, convergent capability and stability in solving the 0–1 KP, especially for the high-dimensional instances.


title: Design and implementation of anti-theft ATM machine using embedded systems

abstract: Automated Teller Machines (ATMs) security is the field of study that aims at solutions that provide multiple points of protection against physical and electronic theft from ATMs and protecting their installations. From anti-skimming defend systems to silent indicate systems, integrated ATM video surveillance cameras and ATM monitoring options, security specialists are ready to help the people get more out of the ATM security and ATM loss prevention systems. The implementation is achieved with the use of Machine-to-machine (M2M) communications technology. M2M communications is a topic that has recently attracted much attention It provides real-time monitoring and control without the need for human intervention. The idea of M2M platform suggests new system architecture for positioning and monitoring applications with wider coverage and higher communication efficiency. The aim of the proposed work is to implement a low cost stand-alone Embedded Web Server (EWS) based on ARM11 processor and Linux operating system using Raspberry Pi. It offers a robust networking solution with wide range of application areas over internet. The Web server can be run on an embedded system having limited resources to serve embedded web page to a web browser. The setup is proposed for ATM security, comprising of the modules namely, authentication of shutter lock, web enabled control, sensors and camera control.


title: Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks

abstract: As autonomous vehicles (AVs) need to interact with other road users, it is of importance to comprehensively understand the dynamic traffic environment, especially the future possible trajectories of surrounding vehicles. This paper presents an algorithm for long-horizon trajectory prediction of surrounding vehicles using a dual long short term memory (LSTM) network, which is capable of effectively improving prediction accuracy in strongly interactive driving environments. In contrast to traditional approaches which require trajectory matching and manual feature selection, this method can automatically learn high-level spatial-temporal features of driver behaviors from naturalistic driving data through sequence learning. By employing two blocks of LSTMs, the proposed method feeds the sequential trajectory to the first LSTM for driver intention recognition as an intermediate indicator, which is immediately followed by a second LSTM for future trajectory prediction. Test results from real-world highway driving data show that the proposed method can, in comparison to state-of-art methods, output more accurate and reasonable estimate of different future trajectories over 5s time horizon with root mean square error (RMSE) for longitudinal and lateral prediction less than 5.77m and 0.49m, respectively.


title: THE EXPRESSION AND PERCEPTION OF EMOTIONS: COMPARING ASSESSMENTS OF SELF VERSUS OTHERS

abstract: In the study of expressive speech communication, it is commonly accepted that the emotion perceived by the listener is a good approximation of the intended emotion conveyed by the speaker. This paper analyzes the validity of this assumption by comparing the mismatches between the assessments made by naive listeners and by the speakers that generated the data. The analysis is based on the hypothesis that people are better decoders of their own emotions. Therefore, self-assessments will be closer to the intended emotions. Using the IEMOCAP database, discrete (categorical) and continuous (attribute) emotional assessments evaluated by the actors and naive listeners are compared. The results indicate that there is a mismatch between the expression and perception of emotion. The speakers in the database assigned their own emotions to more specific emotional categories, which led to more extreme values in the activation-valence space.


title: Anaphoric annotation in the ARRAU corpus

abstract: Arrau is a new corpus annotated for anaphoric relations, with information about agreement and explicit representation of multiple antecedents for ambiguous anaphoric expressions and discourse antecedents for expressions which refer to abstract entities such as events, actions and plans. The corpus contains texts from different genres: task-oriented dialogues from the Trains-91 and Trains-93 corpus, narratives from the English Pear Stories corpus, newspaper articles from the Wall Street Journal portion of the Penn Treebank, and mixed text from the Gnome corpus.


title: Visualizing Conversation

abstract: Although the archive of text generated by a persistent conversation (i.e. newsgroup, mailing list, recorded chat, etc.) is searchable, it is not very expressive of the underlying social patterns. We discuss the design of graphical interfaces that reveal the social structure of the conversation by visualizing patterns such as bursts of activity, the arrival of new members, or the evolution of conversational topics. Our focus is on two projects: Chat Circles, a graphical interface for synchronous conversation and Loom, a visualization of threaded discussion. Through these examples we explore key issues in the generation, design and use of graphical interfaces for persistent conversations.


title: A Ku-Band 8-Element Phased-Array Transmitter with Built-in-Self-Test Capability

abstract: An 8-elements Ku-band phased-array transmitter with built-in-self-test (BIST) capability is presented. A fully symmetrical coupler feed line is used to improve the accuracy of the BIST and calibration procedure. 5-bit phase shifters and 4-bit attenuators with less phase and amplitude errors are designed to control the beam direction and side lobes. The RMS phase and amplitude errors of the phase shifters are less than 3.6° and 0.8 dB, respectively. The RMS amplitude and phase errors of the attenuators are less than 0.94 dB and 3.2°. The measured single path power gain including the power divider is 11.9 dB. The output referred PldB is 13 dBm for each channel. ESD protection and temperature sensors are integrated. The chip is fabricated in 180 nm bulk CMOS technology. Compact design is adopted to minimize the size of each channel, which is suitable for the larze-scale phased-array system. The size of the whole chip is $\mathbf{8.5x3\ mm}^{\mathbf{2}}$ , and that of each channel is $\mathbf{3.8x0.5\ mm}^{\mathbf{2}}$ .


title: Joint embeddings of shapes and images via CNN image purification

abstract: Both 3D models and 2D images contain a wealth of information about everyday objects in our environment. However, it is difficult to semantically link together these two media forms, even when they feature identical or very similar objects. We propose a joint embedding space populated by both 3D shapes and 2D images of objects, where the distances between embedded entities reflect similarity between the underlying objects. This joint embedding space facilitates comparison between entities of either form, and allows for cross-modality retrieval. We construct the embedding space using 3D shape similarity measure, as 3D shapes are more pure and complete than their appearance in images, leading to more robust distance metrics. We then employ a Convolutional Neural Network (CNN) to "purify" images by muting distracting factors. The CNN is trained to map an image to a point in the embedding space, so that it is close to a point attributed to a 3D model of a similar object to the one depicted in the image. This purifying capability of the CNN is accomplished with the help of a large amount of training data consisting of images synthesized from 3D shapes. Our joint embedding allows cross-view image retrieval, image-based shape retrieval, as well as shape-based image retrieval. We evaluate our method on these retrieval tasks and show that it consistently out-performs state-of-the-art methods, and demonstrate the usability of a joint embedding in a number of additional applications.


title: Latency analysis for M2M and online gaming traffic in an HSPA network

abstract: This paper analyzes latency in a High-Speed Packet Access network appropriately resourced for emulated Machine-to-Machine and Online Gaming traffic. Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) are used for conveying application data and the measurement results from the end-user perspective are compared. In TCP case, traffic traces were recorded in two points of the core network as well (Gn interface and the firewall), and the overall delay is dissected to portions belonging to different parts of the network (access, core, backbone). The diversity of the traffic patterns used made it possible to draw conclusions concerning selectivity of certain parts of the network towards different traffic patterns, indicating the direction of future research on reducing latency for subject emerging application domains in legacy networks.


title: The Experience of Nature: A Psychological Perspective

abstract: A study of the natural environment, people, and the relationship between them. The authors offer a research-based analysis of the vital psychological role that nature plays. They try to understand how people perceive nature and what kinds of natural environments they prefer.


title: Ant Diversity and Function in Disturbed and Changing Habitats

abstract: Habitat transformation and disturbance are significant threats to biodiversity conservation and ecosystem function. Disturbance is generally defined as any event that removes biomass (Townsend and Hildrew 1994), and is distinguished from habitat transformation or stress, which reduces available resources or changes the microclimate or structure of the habitat (Andersen 2000; Pickett and White 1985). Habitat disturbance and transformation affect communities in many ways either by altering the balance of competitive interactions, often in effect resetting the process of competitive exclusion, or by clearing space for colonization of new organisms. The degree to which habitat disturbance and transformation affect animal communities in general, and ants in particular, depends largely on the frequency and intensity of disturbance, the permanence with which habitats are transformed, and the distance from which propagules travel to recolonize affected habitats. Ant habitats of all kinds are modified by natural disturbances, such as fire, forest gap formation, hurricanes, and flooding, which vary in their extent, magnitude, and frequency. Furthermore, many terrestrial ecosystems, especially in tropical regions, have been altered by human activities including deforestation, urbanization, agriculture, agricultural intensification, grazing, and mining. At the same time, ants themselves are also instigators of habitat modification via their roles as mound builders and ecosystem engineers (Decaens et al. 2002; Folgarait 1998). The impacts of habitat disturbance and transformation for ants arewidespread, yet theyvarywith region and ecosystem. Ants can be very sensitive to habitat transformation and disturbance, and for this reason have been extensively used as indicator species (Hoffmann and Andersen 2003; see Box 8.1). Because ants are colonial organisms, removal of individuals (mortality) caused by habitat disturbance or transformation may not translate to extirpation of the colony from the habitat (Andersen 2000). This may mean the responses of ants to disturbance may differ from other terrestrial animals and plants that may become locally extinct after disturbances. Disturbance effects on ant communities include loss of diversity, changes in species composition, alteration of interspecific interactions, changes in trophic interactions with ant-plants and honeydew-producing hemipterans, and modification of ant-provided ecosystem services such as seed dispersal, predation, and soil modification. Virtually all habitats are subject to some sort of disturbance, although the disturbance will obviously vary in origin (natural or human-induced), in scale, and in magnitude. Many insights to basic ecology are thus gained by investigating ecology in disturbed habitats. For example, seminal work examining predator-caused disturbance in intertidal zones has formed the basis for the field of disturbance ecology (e.g. Paine 1996). Similarly, research in tropical forests affected by hurricanes and tree-fall gaps has shaped our knowledge


title: Original article: Parallel collision detection of ellipsoids with applications in large scale multibody dynamics

abstract: This contribution describes a parallel approach for determining the collision state of a large collection of ellipsoids. Collision detection is required in granular dynamics simulation where it can combine with a differential variational inequality solver or discrete element method to approximate the time evolution of a collection of rigid bodies interacting through frictional contact. The approach proposed is structured on three levels. At the lowest level, the collision information associated with two colliding ellipsoids is obtained as the solution of a two-variable unconstrained optimization problem for which first and second order sensitivity information is derived analytically. Although this optimization approach suffices to resolve the collision problem between any two arbitrary ellipsoids, a less versatile but more efficient approach precedes it to gauge whether two ellipsoids are actually in contact and require the more costly optimization approach. This intermediate level draws on the analytical solution of a 3rd order polynomial obtained from the characteristic equation of two arbitrary ellipsoids. Finally, this intermediate level is invoked by the outer level only when a 3D spatial binning algorithm indicates that two ellipsoids share the same bin (box) and therefore could potentially collide. This multi-level approach is implemented in parallel and when executed on a ubiquitous Graphics Processing Unit (GPU) card scales linearly and yields a two orders of magnitude speedup over a similar algorithm executed on the Central Processing Unit (CPU). The GPU-based ellipsoid contact detection algorithm yields a 14-fold speedup over a CPU-based sphere contact detection algorithm implemented in the third party open source Bullet Physics Library (BPL). The proposed methodology provides the efficiency demanded by granular dynamics applications, which routinely handle scenarios with millions of collision events.


title: Adaptive Feature Ranking for Unsupervised Transfer Learning

abstract: Transfer Learning is concerned with the application of knowledge gained from solving a problem to a different but related problem domain. In this paper, we propose a method and efficient algorithm for ranking and selecting representations from a Restricted Boltzmann Machine trained on a source domain to be transferred onto a target domain. Experiments carried out using the MNIST, ICDAR and TiCC image datasets show that the proposed adaptive feature ranking and transfer learning method offers statistically significant improvements on the training of RBMs. Our method is general in that the knowledge chosen by the ranking function does not depend on its relation to any specific target domain, and it works with unsupervised learning and knowledge-based transfer.


title: Nonlinear Adaptive Lumped Parameter Magnetic Circuit Analysis for Spoke-Type Fault-Tolerant Permanent-Magnet Motors

abstract: This paper proposes a nonlinear adaptive lumped parameter magnetic circuit (LPMC) model for a new spoke-type fault-tolerant permanent-magnet (ST-FTPM) brushless motor. The LPMC method, which reduces the total computational time greatly, can be used as a finite-element method (FEM) assistant design tool. Hence, it is used to calculate the flux linkage, back-electromotive force (back-EMF), inductance, and torque performance of the ST-FTPM motor. The novelty of this paper is to predict short-circuit current based on the developed LPMC model. Meanwhile, in order to ensure the analytical accuracy, the magnetic saturation is considered in the model design. The effectiveness of the presented LPMC model is verified by FEM and the experiments.


title: Modeling and comparative assessment of municipal solid waste gasification for energy production

abstract: Abstract Gasification is the thermochemical conversion of organic feedstocks mainly into combustible syngas (CO and H 2 ) along with other constituents. It has been widely used to convert coal into gaseous energy carriers but only has been recently looked at as a process for producing energy from biomass. This study explores the potential of gasification for energy production and treatment of municipal solid waste (MSW). It relies on adapting the theory governing the chemistry and kinetics of the gasification process to the use of MSW as a feedstock to the process. It also relies on an equilibrium kinetics and thermodynamics solver tool (Gasify®) in the process of modeling gasification of MSW. The effect of process temperature variation on gasifying MSW was explored and the results were compared to incineration as an alternative to gasification of MSW. Also, the assessment was performed comparatively for gasification of MSW in the United Arab Emirates, USA, and Thailand, presenting a spectrum of socioeconomic settings with varying MSW compositions in order to explore the effect of MSW composition variance on the products of gasification. All in all, this study provides an insight into the potential of gasification for the treatment of MSW and as a waste to energy alternative to incineration.


title: A11y Attacks: Exploiting Accessibility in Operating Systems

abstract: Driven in part by federal law, accessibility (a11y) support for disabled users is becoming ubiquitous in commodity OSs. Some assistive technologies such as natural language user interfaces in mobile devices are welcomed by the general user population. Unfortunately, adding new features in modern, complex OSs usually introduces new security vulnerabilities. Accessibility support is no exception. Assistive technologies can be defined as computing subsystems that either transform user input into interaction requests for other applications and the underlying OS, or transform application and OS output for display on alternative devices. Inadequate security checks on these new I/O paths make it possible to launch attacks from accessibility interfaces. In this paper, we present the first security evaluation of accessibility support for four of the most popular computing platforms: Microsoft Windows, Ubuntu Linux, iOS, and Android. We identify twelve attacks that can bypass state-of-the-art defense mechanisms deployed on these OSs, including UAC, the Yama security module, the iOS sandbox, and the Android sandbox. Further analysis of the identified vulnerabilities shows that their root cause is that the design and implementation of accessibility support involves inevitable trade-offs among compatibility, usability, security, and (economic) cost. These trade-offs make it difficult to secure a system against misuse of accessibility support. Based on our findings, we propose a number of recommendations to either make the implementation of all necessary security checks easier and more intuitive, or to alleviate the impact of missing/incorrect checks. We also point out open problems and challenges in automatically analyzing accessibility support and identifying security vulnerabilities.


title: Investigation of a New Flux-Modulated Permanent Magnet Brushless Motor for EVs

abstract: This paper presents a flux-modulated direct drive (FMDD) motor. The key is to integrate the magnetic gear with the PM motor while removing the gear inner-rotor. Hence, the proposed FMDD motor can achieve the low-speed high-torque output and high-speed compact design requirements as well as high-torque density with a simple structure. The output power equation is analytically derived. By using finite element analysis (FEA), the static characteristics of the proposed motor are obtained. Based on these characteristics, the system mathematical model can be established. Hence, the evaluation of system performances is conducted by computer simulation using the Matlab/Simulink. A prototype is designed and built for experimentation. Experimental results are given to verify the theoretical analysis and simulation.


Entropic sub-cell shock capturing schemes via Jin-Xin relaxation and Glimm front sampling for scalar conservation laws


title: Near field focusing circular microstrip antenna array for RFID applications

abstract: A circular microstrip array with beam focused for RFID applications was presented. An analogy with the optical lens and optical diffraction was made to describe the behaviour of this system. The circular configuration of the array requires less phase shift and exhibite smaller side lobe level compare to a square array. The measurement result shows a good agreement with simulation and theory. This system is a good way to increase the efficiency of RFID communication without useless power. This solution could also be used to develop RFID devices for the localization problematic. The next step of this work is to design a system with an adjustable focus length.


title: Semantics and service technologies for the automatic generation of online MCQ tests

abstract: Active learning requires that students receive a continuous feedback about their understanding. Multiple-Choice Questions (MCQ) tests have been frequently used to provide students the required feedback and to measure the effectiveness of this learning model. To construct a test is a challenging task, which is time consuming and requires experience. For these reasons, research efforts have been focused on the automatic generation of well-constructed tests. The semantic technologies have played a relevant role in the implementation of these testgeneration systems. Nevertheless, the existing proposals present a set of drawbacks that restrict their applicability to different learning domains and the type of test to be composed. In this paper, we propose a service-oriented and semantic-based system that solves these drawbacks. The system consists of a dynamic strategy of generating candidate distractors (alternatives to the correct answer), a set of heuristics for scoring the distractors' suitability, and a selection of distractors that considers the difficulty level of tests. Besides, the final version of tests is created using the Google Form service, a de-facto standard for elaborating online questionnaires.


title: A Flexible, Low-Power Analog PLL for SoC and Processors in 14nm CMOS

abstract: This paper presents a PLL supporting diverse low-power clocking needs including wide input (6–200 MHz) and output (0.15–5 GHz) frequency ranges and SSC operation. Fabricated in 14nm FinFET CMOS, a low-power switched-cap loop filter is employed to enable high −3dB PLL bandwidth (>40% of $f_{\mathrm{ REF}}=19.2$ MHz), and the proposed reference current generator (IrefGen) provides accurate current with <4% tolerance without the need for external components or on-chip precision resistors. IrefGen decouples PLL loop dynamics from feedback divide ratio and provides immunity to systematic capacitor variation. Power gating of switched-cap loop filter’s bias circuits results in more than 10% PLL total power savings. The PLL achieves 1.6-ps integrated RMS jitter at 4 GHz using 100-MHz reference while consuming 2.6 mW from 0.95 V. The PLL performance satisfies the stringent PCIe Gen2/3 jitter specifications without resorting to inductors.


title: A Meta-Analytic Investigation of the Relationship between Attentional Bias and Subjective Craving in Substance Abuse.

abstract: Theoretical models of addiction suggest that attentional bias for substance-related cues should be associated with self-reported craving. The authors evaluated the strength of the association by performing a meta-analysis on 68 independent data sets from which correlation coefficients between subjective craving and attentional bias indices were derived. Additional stratified analyses were conducted to identify any variables that might moderate the association between craving and attentional bias. The primary meta-analysis indicated a significant, albeit weak (r = .19), association between attentional bias and craving. Stratified analyses revealed that the association was larger for illicit drug and caffeine craving than for alcohol and tobacco craving, larger for direct measures of attention (eye movement measures and event-related potential measures) than for indirect behavioral measures of attentional bias, and larger when craving strength was high than when it was low (all ps < .05). The size of the correlation did not differ among patients in treatment and individuals who were not seeking treatment. These results suggest that attentional bias and craving are related phenomena, although the relationship is generally modest and appears to be moderated by various factors. Theoretical implications are discussed.


title: A Design Analysis Approach for Improving the Stability of Dynamic Systems with Application to the Design of Car-Trailer Systems

abstract: A design analysis approach is developed for improving the stability of dynamic systems subject to non-conservative forces. It combines genetic algorithms, sequential quadratic programming (SQP), and dynamic mode tracking (DMT). The proposed approach automatically optimizes the stability criterion and is applicable to rotor dynamics, wind turbine dynamics, aeronautics, and ground vehicle dynamics. The Routh-Hurwitz criterion has traditionally been used for determining the stability characteristics of these dynamic systems. In the conventional trial and error approaches, designers iteratively change the values of the design variables and reanalyze until an acceptable stability characteristic is achieved. This is both time-consuming and tedious. The proposed approach automates the design/analysis cycle by using the DMT technique to identify the modes; then, the SQP algorithm determines the stability criterion; and finally a genetic algorithm is applied to optimize design variables. The proposed integrated ap...


title: Tracking Hit-and-Run Vehicle with Sparse Video Surveillance Cameras and Mobile Taxicabs

abstract: Due to the sparse distribution of road video surveillance cameras, precise trajectory tracking for hit-and-run vehicles remains a challenging task. Previous research on vehicle trajectory recovery mostly focuses on recovering trajectory with low-sampling-rate GPS coordinates by retrieving road traffic flow patterns from collected GPS information. However, to the best of our knowledge, none of them considered using on-road taxicabs as mobile video surveillance cameras as well as the time-varying characteristics of vehicle traveling and road traffic flow patterns, therefore not suitable for recovering trajectories of hit-and-run vehicles. With this insight, we model the travel time-cost of a road segment during various time periods precisely with LNDs (Logarithmic Normal Distributions), then use LSNDs (Log Skew Normal Distributions) to approximate the time-cost of an urban trip during various time periods. We propose a novel approach to calculate possible location and time distribution of the hit-and-run vehicle in parallel, select the optimal taxicab to verify the distribution by uploading and checking video clips of this taxicab, finally refine the restoring trajectory in a parallel and recursive manner. We evaluate our solution on real-world taxicab and road surveillance system datasets. Experimental results demonstrate that our approach outperforms alternative solutions in terms of accuracy ratio of vehicle tracking.


title: The role of transportation in the persuasiveness of public narratives.

abstract: Transportation was proposed as a mechanism whereby narratives can affect beliefs. Defined as absorption into a story, transportation entails imagery, affect, and attentional focus. A transportation scale was developed and validated. Experiment 1 (N = 97) demonstrated that extent of transportation augmented story-consistent beliefs and favorable evaluations of protagonists. Experiment 2 (N = 69) showed that highly transported readers found fewer false notes in a story than less-transported readers. Experiments 3 (N = 274) and 4 (N = 258) again replicated the effects of transportation on beliefs and evaluations; in the latter study, transportation was directly manipulated by using processing instructions. Reduced transportation led to reduced story-consistent beliefs and evaluations. The studies also showed that transportation and corresponding beliefs were generally unaffected by labeling a story as fact or as fiction.


title: Automatic extraction of titles from general documents using machine learning

abstract: In this paper, we propose a machine learning approach to title extraction from general documents. By general documents, we mean documents that can belong to any one of a number of specific genres, including presentations, book chapters, technical papers, brochures, reports, and letters. Previously, methods have been proposed mainly for title extraction from research papers. It has not been clear whether it could be possible to conduct automatic title extraction from general documents. As a case study, we consider extraction from Office including Word and PowerPoint. In our approach, we annotate titles in sample documents (for Word and PowerPoint respectively) and take them as training data, train machine learning models, and perform title extraction using the trained models. Our method is unique in that we mainly utilize formatting information such as font size as features in the models. It turns out that the use of formatting information can lead to quite accurate extraction from general documents. Precision and recall for title extraction from Word is 0.810 and 0.837 respectively, and precision and recall for title extraction from PowerPoint is 0.875 and 0.895 respectively in an experiment on intranet data. Other important new findings in this work include that we can train models in one domain and apply them to another domain, and more surprisingly we can even train models in one language and apply them to another language. Moreover, we can significantly improve search ranking results in document retrieval by using the extracted titles.


title: Multi-scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation

abstract: This paper addresses the problem of depth estimation from a single still image. Inspired by recent works on multi- scale convolutional neural networks (CNN), we propose a deep model which fuses complementary information derived from multiple CNN side outputs. Different from previous methods, the integration is obtained by means of continuous Conditional Random Fields (CRFs). In particular, we propose two different variations, one based on a cascade of multiple CRFs, the other on a unified graphical model. By designing a novel CNN implementation of mean-field updates for continuous CRFs, we show that both proposed models can be regarded as sequential deep networks and that training can be performed end-to-end. Through extensive experimental evaluation we demonstrate the effective- ness of the proposed approach and establish new state of the art results on publicly available datasets.


title: Excitotoxic spinal cord injury: behavioral and morphological characteristics of a central pain model

abstract: Intraspinal injections of the AMPA-metabotropic receptor agonist quisqualic acid (QUIS) were made in an effort to simulate injury induced elevations of excitatory amino acids (EAAs), a well documented neurochemical change following spinal cord injury (SCI). The progressive pathological sequela associated with QUIS injections closely resembles the cascade of events described following ischemic and traumatic SCI and the pathogenesis of cavities in the clinical condition of post-traumatic syringomyelia. Using different injection parameters, i.e. depth and volume, to deliver QUIS into the cord the results have shown that the technique of intraspinal injection can be used to produce graded patterns of neuronal loss in specific regions of the spinal gray matter. Furthermore, neuronal loss in the dorsal horn, sparing the superficial laminae, results in the onset of spontaneous (excessive grooming behavior) and evoked (mechanical allodynia and thermal hyperalgesia) behaviors commonly associated with experimental models of chronic neuropathic pain. Thus, the present results provide a morphological correlate of spontaneous and evoked pain related behaviors following excitotoxic SCI. The behavioral characteristics combined with the similarities between QUIS induced injury and the clinical pathology of SCI support the use of the excitotoxic model in studies related to the central mechanism(s) of altered sensation, including pain, following spinal injury.


title: Endogenous vasotocin exerts context-dependent behavioral effects in a semi-naturalistic colony environment

abstract: Arginine vasotocin (VT), and its mammalian homologue arginine vasopressin (VP), are neuropeptides involved in the regulation of social behaviors and stress responsiveness. Previous research has demonstrated opposing effects of VT/VP on aggression in different species. However, these divergent effects were obtained in different social contexts, leading to the hypothesis that different populations of VT/VP neurons regulate behaviors in a context-dependent manner. We here use VP antagonists to block endogenous VT function in male zebra finches (Taeniopygia guttata) within a semi-natural, mixed-sex colony setting. We examine the role of VT in the regulation of aggression and courtship, and in pair bond formation and maintenance, over the course of three days. Although our results confirm previous findings, in that antagonist treatment reduces aggressive mate competition during an initial behavioral session during which males encounter novel females, we find that the treatment effects are completely reversed within hours of colony establishment, and the antagonist treatment instead facilitates aggression in later sessions. This reversal occurs as aggression shifts from mate competition to nest defense, but is not causally associated with pairing status per se. Instead, we hypothesize that these divergent effects reflect context-specific activation of hypothalamic and amygdalar VT neurons that exert opposing influences on aggression. Across contexts, effects were highly specific to aggression and the antagonist treatment clearly failed to alter latency to pair bond formation, pair bond stability, and courtship. However, VT may still potentially influence these behaviors via promiscuous oxytocin-like receptors, which are widely distributed in the zebra finch brain.


title: Influence of diet on the gut microbiome and implications for human health

abstract: Recent studies have suggested that the intestinal microbiome plays an important role in modulating risk of several chronic diseases, including inflammatory bowel disease, obesity, type 2 diabetes, cardiovascular disease, and cancer. At the same time, it is now understood that diet plays a significant role in shaping the microbiome, with experiments showing that dietary alterations can induce large, temporary microbial shifts within 24 h. Given this association, there may be significant therapeutic utility in altering microbial composition through diet. This review systematically evaluates current data regarding the effects of several common dietary components on intestinal microbiota. We show that consumption of particular types of food produces predictable shifts in existing host bacterial genera. Furthermore, the identity of these bacteria affects host immune and metabolic parameters, with broad implications for human health. Familiarity with these associations will be of tremendous use to the practitioner as well as the patient.


A Behavioural Biometric System Based on Human Computer Interaction


title: Condor-G: a computation management agent for multi-institutional grids

abstract: In recent years, there has been a dramatic increase in the amount of available computing and storage resources, yet few have been able to exploit these resources in an aggregated form. We present the Condor-G system, which leverages software from Globus and Condor to allow users to harness multi-domain resources as if they all belong to one personal domain. We describe the structure of Condor-G and how it handles job management, resource selection, security and fault tolerance.


title: Smartphone-Based Dual-Modality Imaging System for Quantitative Detection of Color or Fluorescent Lateral Flow Immunochromatographic Strips

abstract: Nowadays, lateral flow immunochromatographic assays are increasingly popular as a diagnostic tool for point-of-care (POC) test based on their simplicity, specificity, and sensitivity. Hence, quantitative detection and pluralistic popular application are urgently needed in medical examination. In this study, a smartphone-based dual-modality imaging system was developed for quantitative detection of color or fluorescent lateral flow test strips, which can be operated anywhere at any time. In this system, the white and ultra-violet (UV) light of optical device was designed, which was tunable with different strips, and the Sobel operator algorithm was used in the software, which could enhance the identification ability to recognize the test area from the background boundary information. Moreover, this technology based on extraction of the components from RGB format (red, green, and blue) of color strips or only red format of the fluorescent strips can obviously improve the high-signal intensity and sensitivity. Fifty samples were used to evaluate the accuracy of this system, and the ideal detection limit was calculated separately from detection of human chorionic gonadotropin (HCG) and carcinoembryonic antigen (CEA). The results indicated that smartphone-controlled dual-modality imaging system could provide various POC diagnoses, which becomes a potential technology for developing the next-generation of portable system in the near future.


title: The power of naive query segmentation

abstract: We address the problem of query segmentation: given a keyword query submitted to a search engine, the task is to group the keywords into phrases, if possible. Previous approaches to the problem achieve good segmentation performance on a gold standard but are fairly intricate. Our method is easy to implement and comes with a comparable accuracy.


title: A decentralized model for information flow control

abstract: This paper presents a new model for controlling information flo w in systems with mutual distrust and decentralized authority. The model allows users to share information with distrusted code (e.g., downloaded applets), yet still control how that code disseminates the shared information to others. The model improves on existing multilevel security models by allowing users to declassify information in a decentralized way, and by improving support for fine-graineddata sharing. The paper also shows how static program analysis can be used to certify proper information flo ws in this model and to avoid most run-time information flo w checks.


title: Opinion Mining on YouTube

abstract: This paper defines a systematic approach to Opinion Mining (OM) on YouTube comments by (i) modeling classifiers for predicting the opinion polarity and the type of comment and (ii) proposing robust shallow syntactic structures for improving model adaptability. We rely on the tree kernel technology to automatically extract and learn features with better generalization power than bag-of-words. An extensive empirical evaluation on our manually annotated YouTube comments corpus shows a high classification accuracy and highlights the benefits of structural models in a cross-domain setting.


title: Stopped Object Detection by Learning Foreground Model in Videos

abstract: The automatic detection of objects that are abandoned or removed in a video scene is an interesting area of computer vision, with key applications in video surveillance. Forgotten or stolen luggage in train and airport stations and irregularly parked vehicles are examples that concern significant issues, such as the fight against terrorism and crime, and public safety. Both issues involve the basic task of detecting static regions in the scene. We address this problem by introducing a model-based framework to segment static foreground objects against moving foreground objects in single view sequences taken from stationary cameras. An image sequence model, obtained by learning in a self-organizing neural network image sequence variations, seen as trajectories of pixels in time, is adopted within the model-based framework. Experimental results on real video sequences and comparisons with existing approaches show the accuracy of the proposed stopped object detection approach.


title: A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard

abstract: The paper proposed a Fuzzy Multiple Criteria Decision Making (FMCDM) approach for banking performance evaluation. Drawing on the four perspectives of a Balanced Scorecard (BSC), this research first summarized the evaluation indexes synthesized from the literature relating to banking performance. Then, for screening these indexes, 23 indexes fit for banking performance evaluation were selected through expert questionnaires. Furthermore, the relative weights of the chosen evaluation indexes were calculated by Fuzzy Analytic Hierarchy Process (FAHP). And the three MCDM analytical tools of SAW, TOPSIS, and VIKOR were respectively adopted to rank the banking performance and improve the gaps with three banks as an empirical example. The analysis results highlight the critical aspects of evaluation criteria as well as the gaps to improve banking performance for achieving aspired/desired level. It shows that the proposed FMCDM evaluation model of banking performance using the BSC framework can be a useful and effective assessment tool.


title: Robot placement based on reachability inversion

abstract: Having a representation of the capabilities of a robot is helpful when online queries, such as solving the inverse kinematics (IK) problem for grasping tasks, must be processed efficiently in the real world. When workspace representations, e.g. the reachability of an arm, are considered, additional quality information such as manipulability or self-distance can be employed to enrich the spatial data. In this work we present an approach of inverting such precomputed reachability representations in order to generate suitable robot base positions for grasping. Compared to existing works, our approach is able to generate a distribution in SE(2), the cross-space consisting of 2D position and 1D orientation, that describes potential robot base poses together with a quality index. We show how this distribution can be queried quickly in order to find oriented base poses from which a target grasping pose is reachable without collisions. The approach is evaluated in simulation using the humanoid robot ARMAR-III [1] and an extension is presented that allows to find suitable base poses for trajectory execution.


Verb Phrase Ellipsis using Frobenius Algebras in Categorical Compositional Distributional Semantics


title: Evaluation of Short and Tall Stature in Children.

abstract: Short stature is defined as a height more than two standard deviations below the mean for age (less than the 3rd percentile). Tall stature is defined as a height more than two standard deviations above the mean for age (greater than the 97th percentile). The initial evaluation of short and tall stature should include a history and physical examination, accurate serial measurements, and determination of growth velocity, midparental height, and bone age. Common normal variants of short stature are familial short stature, constitutional delay of growth and puberty, and idiopathic short stature. Pathologic causes of short stature include chronic diseases; growth hormone deficiency; and genetic disorders, such as Turner syndrome. Tall stature has the same prevalence as short stature, but it is a much less common reason for referral to subspecialty care. Common causes of tall stature include familial tall stature, obesity, Klinefelter syndrome, Marfan syndrome, and precocious puberty. Although most children with short or tall stature have variants of normal growth, children who are more than three standard deviations from the mean for age are more likely to have underlying pathology. Evaluation for pathologic etiologies is guided by history and physical examination findings.


title: LED Based Indoor Visible Light Communications: State of the Art

abstract: Visible Light Communication (VLC) is an emerging field in Optical Wireless Communication (OWC) which utilizes the superior modulation bandwidth of Light Emitting Diodes (LEDs) to transmit data. In modern day communication systems, the most popular frequency band is Radio Frequency (RF) mainly due to little interference and good coverage. However, the rapidly dwindling RF spectrum along with increasing wireless network traffic has substantiated the need for greater bandwidth and spectral relief. By combining illumination and communication, VLC provides ubiquitous communication while addressing the shortfalls and limitations of RF communication. This paper provides a comprehensive survey on VLC with an emphasis on challenges faced in indoor applications over the period 1979–2014. VLC is compared with infrared (IR) and RF systems and the necessity for using this beneficial technology in communication systems is justified. The advantages of LEDs compared to traditional lighting technologies are discussed and comparison is done between different types of LEDs currently available. Modulation schemes and dimming techniques for indoor VLC are discussed in detail. Methods needed to improve VLC system performance such as filtering, equalization, compensation, and beamforming are also presented. The recent progress made by various research groups in this field is discussed along with the possible applications of this technology. Finally, the limitations of VLC as well as the probable future directions are presented.


title: Learning concepts by asking questions

abstract: Tw o important issues in machine learning are explored: the role that memory plays in acquiring new concepts; and the extent to which the learner can take an active part in acquiring these concepts. This chapter describes a program, called Marvin, which uses concepts it has learned previously to learn new concepts. The program forms hypotheses about the concept being learned and tests the hypotheses by asking the trainer questions. Learning begins when the trainer shows Marvin an example of the concept to be learned. The program determines which objects in the example belong to concepts stored in the memory. A description of the new concept is formed by using the information obtained from the memory to generalize the description of the training example. The generalized description is tested when the program constructs new examples and shows these to the trainer, asking if they belong to the target concept.


title: DeepSTCL: A Deep Spatio-temporal ConvLSTM for Travel Demand Prediction

abstract: Urban resource scheduling is an important part of the development of a smart city, and transportation resources are the main components of urban resources. Currently, a series of problems with transportation resources such as unbalanced distribution and road congestion disrupt the scheduling discipline. Therefore, it is significant to predict travel demand for urban resource dispatching. Previously, the traditional time series models were used to forecast travel demand, such as AR, ARIMA and so on. However, the prediction efficiency of these methods is poor and the training time is too long. In order to improve the performance, deep learning is used to assist prediction. But most of the deep learning methods only utilize temporal dependence or spatial dependence of data in the forecasting process. To address these limitations, a novel deep learning traffic demand forecasting framework which based on Deep Spatio-Temporal ConvLSTM is proposed in this paper. In order to evaluate the performance of the framework, an end-to- end deep learning system is designed and a real dataset is used. Furthermore, the proposed method can capture temporal dependence and spatial dependence simultaneously. The closeness, period and trend components of spatio-temporal data are used in three predicted branches. These branches have the same network structures, but do not share weights. Then a linear fusion method is used to get the final result. Finally, the experimental results on DIDI order dataset of Chengdu demonstrate that our method outperforms traditional models with accuracy and speed.


title: Spatial PixelCNN: Generating Images from Patches

abstract: In this paper we propose Spatial PixelCNN, a conditional autoregressive model that generates images from small patches. By conditioning on a grid of pixel coordinates and global features extracted from a Variational Autoencoder (VAE), we are able to train on patches of images, and reproduce the full-sized image. We show that it not only allows for generating high quality samples at the same resolution as the underlying dataset, but is also capable of upscaling images to arbitrary resolutions (tested at resolutions up to $50\times$) on the MNIST dataset. Compared to a PixelCNN++ baseline, Spatial PixelCNN quantitatively and qualitatively achieves similar performance on the MNIST dataset.


title: On-chip active power rectifiers for biomedical applications

abstract: In this paper, we describe a new active power rectifying element (an 'ideal' diode) compatible with a standard CMOS process. We use this element to implement two types of on-chip active power rectifier for biomedical applications, such as cochlear implants and retinal prostheses. The rectifiers are simulated in a standard 0.35 /spl mu/m CMOS process and show a considerable increase in efficiency compared with passive diode based rectifiers. Measurements on fabricated circuits also confirm this.


title: Distributed Coordination Control for Multiple Bidirectional Power Converters in a Hybrid AC/DC Microgrid

abstract: This paper proposes a distributed coordination control method for multiple bidirectional power converters (BPCs) in a hybrid ac/dc microgrid. The researched hybrid ac/dc microgrid is composed of both ac and dc subgrids connected by multiple parallel BPCs, and only the ac subgrid contains storages for the sake of economy. The proposed control method can realize the appropriate power interaction between the two subgrids and make the two subgrids to support each other, so the hybrid microgrid can operate well in both the grid-connected and islanded modes. Concretely, there are three improvements in this proposed control methods. First, a d-q-0 three-axis control strategy instead of the conventional d-q two-axis control strategy is adopted to suppress the circulating current on the ac side generated by the multiple parallel BPCs. And then the square of the dc voltage rather than the dc voltage itself is controlled, which can make the control of the dc voltage linearized and can make the BPCs provide well voltage support for the dc subgrid. Second, the dc droop control is adopted for the dc current sharing. Also, in order to relieve the storages from generating too much reactive power if the ac load reactive power is very large in the ac subgrid, the reactive power droop control is adopted to make the BPCs share some reactive power. Third, taking the capacities and load types of the two subgrids into consideration, a coordinated control strategy based on the dc voltage versus the frequency droop is designed to realize the power interaction between the two subgrids. Furthermore, the stability analysis of the proposed control method is conducted. All the conclusions are verified by the real-time hardware-in-loop tests.


title: Competing on analytics.

abstract: Some companies have built their very businesses on their ability to collect, analyze, and act on data.


Evaluation of an Australian health literacy training program for socially disadvantaged adults attending basic education classes: study protocol for a cluster randomised controlled trial


title: Gimbal Influence on the Stability of Exterior Orientation Parameters of UAV Acquired Images

abstract: In this paper, results from the analysis of the gimbal impact on the determination of the camera exterior orientation parameters of an Unmanned Aerial Vehicle (UAV) are presented and interpreted. Additionally, a new approach and methodology for testing the influence of gimbals on the exterior orientation parameters of UAV acquired images is presented. The main motive of this study is to examine the possibility of obtaining better geometry and favorable spatial bundles of rays of images in UAV photogrammetric surveying. The subject is a 3-axis brushless gimbal based on a controller board (Storm32). Only two gimbal axes are taken into consideration: roll and pitch axes. Testing was done in a flight simulation, and in indoor and outdoor flight mode, to analyze the Inertial Measurement Unit (IMU) and photogrammetric data. Within these tests the change of the exterior orientation parameters without the use of a gimbal is determined, as well as the potential accuracy of the stabilization with the use of a gimbal. The results show that using a gimbal has huge potential. Significantly, smaller discrepancies between data are noticed when a gimbal is used in flight simulation mode, even four times smaller than in other test modes. In this test the potential accuracy of a low budget gimbal for application in real conditions is determined.


title: Security issues and challenges of big data analytics and visualization

abstract: The big data environment supports to resolve the issues of cyber security in terms of finding the attacker. There are security challenges of big data as well as security issues the analyst must understand. In this paper, the challenges faced by an analyst include the fraud detection, network forensics, data privacy issues and data provenance problems are well studied. It is more focused on the tools and techniques of data mining for the use of big data analytics in terms of security and the use of techniques for security to protect big data in terms of applying encryption capabilities. This rigorous study leads to make use of available tools and techniques of data analytics in the systematic implementation of a security system as well as forensic system. The paper also proposes the Bayesian classification algorithm of classification mining is suitable for predicting the attack type in the internet worked environment. The forensic aspects of big data issues and challenges are more emphasized in order to help the forensic investigator in case of a network attack.


title: Robust edge-based 3D object tracking with direction-based pose validation

abstract: In this paper we propose a robust edge-based approach for 3D textureless object tracking. We first introduce an edge-based pose estimation method, which minimizes the holistic distance between the projected object contour and the query image edges, without explicitly searching for 3D-2D correspondences. This method is accurate with a good initialization; however, it is sensitive to occlusion and fast motion, thus often gets lost in real environments. To improve robustness, we exploit consistency of edge direction for validating the correctness of the estimated 3D pose, and further incorporate the validation scheme for robust estimation, non-local searching and failure recovery. The robust estimation adopts point-wise validation to reduce the effect of outlier, resulting in a direction-based robust estimator. The non-local searching is based on particle filter, with the pose validation for a faithful weighting of particles, which is shown to be better than the distance-based weighting. The failure recovery is based on fast 2D detection, and estimates the recovered pose by searching for 3D-2D point correspondences, with the validation scheme to adaptively determine state transition. The effectiveness of our approach is demonstrated using comparative experiments on real image sequences with occlusions, large motions and background clutters.


Integrated Information Systems (IIS) and Management Accounting Evidence from the Danish Practice


title: Tolerating Transient Late-Timing Faults in Cloud-Based Real-Time Stream Processing

abstract: Real-time stream processing is a frequently deployed application within Cloud datacenters that is required to provision high levels of performance and reliability. Numerous fault-tolerant approaches have been proposed to effectively achieve this objective in the presence of crash failures. However, such systems struggle with transient late-timing faults -- a fault classification challenging to effectively tolerate -- that manifests increasingly within large-scale distributed systems. Such faults represent a significant threat towards minimizing soft real-time execution of streaming applications in the presence of failures. This work proposes a fault-tolerant approach for QoS-aware data prediction to tolerate transient late-timing faults. The approach is capable of determining the most effective data prediction algorithm for imposed QoS constraints on a failed stream processor at run-time. We integrated our approach into Apache Storm with experiment results showing its ability to minimize stream processor end-to-end execution time by 61% compared to other fault-tolerant approaches. The approach incurs 12% additional CPU utilization while reducing network usage by 44%.


title: Specifying the Neurobiological Basis of Human Attachment: Brain, Hormones, and Behavior in Synchronous and Intrusive Mothers

abstract: The mother–infant bond provides the foundation for the infant's future mental health and adaptation and depends on the provision of species-typical maternal behaviors that are supported by neuroendocrine and motivation-affective neural systems. Animal research has demonstrated that natural variations in patterns of maternal care chart discrete profiles of maternal brain–behavior relationships that uniquely shape the infant's lifetime capacities for stress regulation and social affiliation. Such patterns of maternal care are mediated by the neuropeptide Oxytocin and by stress- and reward-related neural systems. Human studies have similarly shown that maternal synchrony—the coordination of maternal behavior with infant signals—and intrusiveness—the excessive expression of maternal behavior—describe distinct and stable maternal styles that bear long-term consequences for infant well-being. To integrate brain, hormones, and behavior in the study of maternal–infant bonding, we examined the fMRI responses of synchronous vs intrusive mothers to dynamic, ecologically valid infant videos and their correlations with plasma Oxytocin. In all, 23 mothers were videotaped at home interacting with their infants and plasma OT assayed. Sessions were micro-coded for synchrony and intrusiveness. Mothers were scanned while observing several own and standard infant-related vignettes. Synchronous mothers showed greater activations in the left nucleus accumbens (NAcc) and intrusive mothers exhibited higher activations in the right amygdala. Functional connectivity analysis revealed that among synchronous mothers, left NAcc and right amygdala were functionally correlated with emotion modulation, theory-of-mind, and empathy networks. Among intrusive mothers, left NAcc and right amygdala were functionally correlated with pro-action areas. Sorting points into neighborhood (SPIN) analysis demonstrated that in the synchronous group, left NAcc and right amygdala activations showed clearer organization across time, whereas among intrusive mothers, activations of these nuclei exhibited greater cross-time disorganization. Correlations between Oxytocin with left NAcc and right amygdala activations were found only in the synchronous group. Well-adapted parenting appears to be underlay by reward-related motivational mechanisms, temporal organization, and affiliation hormones, whereas anxious parenting is likely mediated by stress-related mechanisms and greater neural disorganization. Assessing the integration of motivation and social networks into unified neural activity that reflects variations in patterns of parental care may prove useful for the study of optimal vs high-risk parenting.


title: Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text

abstract: This paper investigates how linguistic knowledge mined from large text corpora can aid the generation of natural language descriptions of videos. Specifically, we integrate both a neural language model and distributional semantics trained on large text corpora into a recent LSTM-based architecture for video description. We evaluate our approach on a collection of Youtube videos as well as two large movie description datasets showing significant improvements in grammaticality while modestly improving descriptive quality.