title: Technological Frames Recursive Construction Approach: A Systemic Theory For Information Technology Incorporation In Organizations

abstract: Information Technology, during the last few years, has turned into a determining factor of modern organization development. In this line, a lot of studies have been conducted aimed at explaining the possible relations to company's productivity and competitiveness, which to some extent, leads to causal conclusions, casting structural individual relationships into a background in the organizational tasks. In this direction, social researchers have incorporated valuable elements to understand the individuals 'position in the construction of technological artifacts and the comprehension thereof in the organization scope. Such research led to the statement of the concept of so-called technological frames, which explicitly incorporate an individual's social and cognitive distinctions around the technological context in a community. Subsequent studies, however, fail to delve into the way such frames are constructed or influenced in a significant extent by the companies's tasks or technology incorporations. For such reasons, this paper, based on the assumption that Information Technology is the result of individuals' social interaction, is intended to explore the benefits of individuals' structural relations understanding in Information Technology incorporation to integrate the findings of causal research and systemic elements (the study of relations), focusing on information technology understanding in the organizational tasks.


title: Overview of the IALP 2016 shared task on Dimensional Sentiment Analysis for Chinese Words

abstract: This paper presents the IALP 2016 shared task on Dimensional Sentiment Analysis for Chinese Words (DSAW) which seeks to identify a real-value sentiment score of Chinese words in the both valence and arousal dimensions. Valence represents the degree of pleasant and unpleasant (or positive and negative) feelings, and arousal represents the degree of excitement and calm. Of the 22 teams registered for this shared task for two-dimensional sentiment analysis, 16 submitted results. We expected that this evaluation campaign could produce more advanced dimensional sentiment analysis techniques, especially for Chinese affective computing. All data sets with gold standards and scoring script are made publicly available to researchers.


title: Nature-inspired optimization method: Hydrozoan algorithm for solving continuous problems

abstract: In this article, a new optimization algorithm that is inspired by the biology of hydrozoa (HA) is proposed. Our aim was to develop an algorithm that is based on the regeneration and transplantation processes of hydrozoa for finding the best solutions for continuous optimization problems. Basically, HA follows the same general processes of evolutionary algorithm; however, its distinctive processes mimic the life cycle of 3 basic forms of hydrozoa: motile planula, polyps, and medusa. In particular, the growth of strong buds from the polyp stage depends on levels of morphogens: activators and inhibitors. These 3 forms develop or evolve into the best solution. HA was performance tested with 20 standard benchmark functions and compared with genetic algorithm and Particle Swarm Optimization (PSO). The test results have confirmed that the proposed algorithm is computationally more efficient than both GA and PSO. It works very well on most benchmark functions.


title: Jointly Predicting Links and Inferring Attributes using a Social-Attribute Network (SAN)

abstract: The eects of social inuence and homophily suggest that both network structure and node attribute information should inform the tasks of link prediction and node attribute inference. Recently, Yin et al. [28, 29] proposed Social-Attribute Network (SAN), an attribute-augmented social network, to integrate network structure and node attributes to perform both link prediction and attribute inference. They focused on generalizing the random walk with restart algorithm to the SAN framework and showed improved performance. In this paper, we extend the SAN framework with several leading supervised and unsupervised link prediction algorithms and demonstrate performance improvement for each algorithm on both link prediction and attribute inference. Moreover, we make the novel observation that attribute inference can help inform link prediction, i.e., link prediction accuracy is further improved by rst inferring missing attributes. We comprehensively evaluate these algorithms and compare them with other existing algorithms using a novel, largescale Google+ dataset, which we make publicly available 1 .


title: Hybrid precoding for millimeter wave cellular systems with partial channel knowledge

abstract: Next-generation cellular standards may leverage the large bandwidth available at millimeter wave (mmWave) frequencies to provide gigabit-per-second data rates in outdoor wireless systems. A main challenge in realizing mmWave cellular is achieving sufficient operating link margin, which is enabled via directional beamforming with large antenna arrays. Due to the high cost and power consumption of high-bandwidth mixed-signal devices, mmWave beamforming will likely include a combination of analog and digital processing. In this paper, we develop an iterative hybrid beamforming algorithm for the single user mmWave channel. The proposed algorithm accounts for the limitations of analog beamforming circuitry and assumes only partial channel knowledge at both the base and mobile stations. The precoding strategy exploits the sparse nature of the mmWave channel and uses a variant of matching pursuit to provide simple solutions to the hybrid beamforming problem. Simulation results show that the proposed algorithm can approach the rates achieved by unconstrained digital beamforming solutions.


title: The Case of the Poisoned Event Handler: Weaknesses in the Node.js Event-Driven Architecture

abstract: Node.js has seen rapid adoption in industry and the open-source community. Unfortunately, its event-driven architecture exposes Node.js applications to Event Handler-Poisoning denial of service attacks. Our evaluation of the state of practice in Node.js--- combining a study of 353 publicly reported security vulnerabilities and a survey of 151 representative npm modules --- demonstrates that the community is not equipped to combat this class of attack. We recommend several changes to the state of practice and propose both programming language and runtime approaches to defend against Event Handler-Poisoning attacks.


title: The Conflict Escalation Resolution (CONFER) Database

abstract: Abstract Conflict is usually defined as a high level of disagreement taking place when individuals act on incompatible goals, interests, or intentions. Research in human sciences has recognized conflict as one of the main dimensions along which an interaction is perceived and assessed. Hence, automatic estimation of conflict intensity in naturalistic conversations would be a valuable tool for the advancement of human-centered computing and the deployment of novel applications for social skills enhancement including conflict management and negotiation. However, machine analysis of conflict is still limited to just a few works, partially due to an overall lack of suitable annotated data, while it has been mostly approached as a conflict or (dis)agreement detection problem based on audio features only. In this work, we aim to overcome the aforementioned limitations by a) presenting the Conflict Escalation Resolution (CONFER) Database, a set of excerpts from audio–visual recordings of televised political debates where conflicts naturally arise, and b) reporting baseline experiments on audio–visual conflict intensity estimation. The database contains approximately 142 min of recordings in Greek language, split over 120 non-overlapping episodes of naturalistic conversations that involve two or three interactants. Subject- and session-independent experiments are conducted on continuous-time (frame-by-frame) estimation of real-valued conflict intensity, as opposed to binary conflict/non-conflict classification. For the problem at hand, the efficiency of various audio and visual features and fusion of them as well as various regression frameworks is examined. Experimental results suggest that there is much room for improvement in the design and development of automated multi-modal approaches to continuous conflict analysis. The CONFER Database is publicly available for non-commercial use at http://ibug.doc.ic.ac.uk/resources/confer/ .


title: Submillimeter Accuracy of InSAR Time Series: Experimental Validation

abstract: This paper presents the results of a blind experiment that is performed using two pairs of dihedral reflectors. The aim of the experiment was to demonstrate that interferometric synthetic aperture radar (InSAR) measurements can indeed allow a displacement time series estimation with submillimeter accuracy (both in horizontal and vertical directions), provided that the data are properly processed and the impact of in situ as well as atmospheric effects is minimized. One pair of dihedral reflectors was moved a few millimeters between SAR acquisitions, in the vertical and east-west (EW) directions, and the ground truth was compared with the InSAR data. The experiment was designed to allow a multiplatform and multigeometry analysis, i.e., each reflector was carefully pointed in order to be visible in both Envisat and Radarsat acquisitions. Moreover, two pairs of reflectors were used to allow the combination of data gathered along ascending and descending orbits. The standard deviation of the error is 0.75 mm in the vertical direction and 0.58 mm in the horizontal (EW) direction. GPS data were also collected during this experiment in order to cross-check the SAR results


title: Situational maturity models as instrumental artifacts for organizational design

abstract: In order to identify and explore the strength and weaknesses of particular organizational designs, a wide range of maturity models have been developed by both, practitioners and academics over the past years. However, a systematization and generalization of the procedure on how to design maturity models as well as a synthesis of design science research with the rather behavioural field of organization theory is still lacking. Trying to combine the best of both fields, a first design proposition of a situational maturity model is presented in this paper. The proposed maturity model design is illustrated with the help of an instantiation for the healthcare domain.


title: Rebuilding an Inclusive Political Community After War

abstract: This article argues that the central need in a society in the aftermath of violent conflict is to rebuild the state’s representation function, which should be constructed around an inclusive political and civic community. This inclusive community must overcome the fragmentation of society that occurs or is exacerbated during war, and view all community members as survivors of conflict. The main means of pursuing reconciliation in post-conflict societies has been through measures of ‘transitional justice’, primarily trials or truth commissions. However, this article argues that these transitional justice measures, while fulfilling certain needs and accruing some benefits, do not necessarily fulfil the specific need of delivering reconciliation or helping to rebuild inclusive societies. Instead, these measures could, paradoxically, deepen divisions within society. A new paradigm of ‘reparative justice’ is thus proposed as part of a comprehensive alternative, within the framework of a critical approach to po...


title: A Simple Machine Learning Method for Commonsense Reasoning? A Short Commentary on Trinh&Le (2018)

abstract: This is a short Commentary on Trinh & Le (2018) ("A Simple Method for Commonsense Reasoning") that outlines three serious flaws in the cited paper and discusses why data-driven approaches cannot be considered as serious models for the commonsense reasoning needed in natural language understanding in general, and in reference resolution, in particular.


title: Making Working Memory Work

abstract: This meta-analysis examined the effects of process-based executive-function and working memory training (49 articles, 61 independent samples) in older adults (> 60 years). The interventions resulted in significant effects on performance on the trained task and near-transfer tasks; significant results were obtained for the net pretest-to-posttest gain relative to active and passive control groups and for the net effect at posttest relative to active and passive control groups. Far-transfer effects were smaller than near-transfer effects but were significant for the net pretest-to-posttest gain relative to passive control groups and for the net gain at posttest relative to both active and passive control groups. We detected marginally significant differences in training-induced improvements between working memory and executive-function training, but no differences between the training-induced improvements observed in older adults and younger adults, between the benefits associated with adaptive and nonadapt...


title: Weighted Sampling for Large-Scale Boosting

abstract: This paper addresses the problem of learning from very large databases where batch learning is impractical or even infeasible. Bootstrap is a popular technique applicable in such situations. We show that sampling strategy used for bootstrapping has a significant impact on the resulting classifier performance. We design a new general sampling strategy ”quasi-random weighted sampling + trimming” (QWS+) that includes well established strategies as special cases. The QWS+ approach minimizes the variance of hypothesis error estimate and leads to significant improvement in performance compared to standard sampling techniques. The superior performance is demonstrated on several problems including profile and frontal face detection.


title: Two-sided Facility Location

abstract: Recent years have witnessed the rise of many successful e-commerce marketplace platforms like the Amazon marketplace, AirBnB, Uber/Lyft, and Upwork, where a central platform mediates economic transactions between buyers and sellers. Motivated by these platforms, we formulate a set of facility location problems that we term Two-sided Facility location. In our model, agents arrive at nodes in an underlying metric space, where the metric distance between any buyer and seller captures the quality of the corresponding match. The platform posts prices and wages at the nodes, and opens a set of"virtual markets"or facilities to route the agents to. The agents at any facility are assumed to be matched. The platform ensures high match quality by imposing a distance constraint between a node and the facilities it is routed to. It ensures high service availability by ensuring flow to the facility is at least a pre-specified lower bound. Subject to these constraints, the goal of the platform is to maximize the social surplus subject to weak budget balance, i.e., profit being non-negative. We present an approximation algorithm for this problem that yields a $(1 + \epsilon)$ approximation to surplus for any constant $\epsilon>0$, while relaxing the match quality (i.e., maximum distance of any match) by a constant factor. We use an LP rounding framework that easily extends to other objectives such as maximizing volume of trade or profit. We justify our models by considering a dynamic marketplace setting where agents arrive according to a stochastic process and have finite patience (or deadlines) for being matched. We perform queueing analysis to show that for policies that route agents to virtual markets and match them, ensuring a low abandonment probability of agents reduces to ensuring sufficient flow arrives at each virtual market.


title: The vulnerability of UAVs to cyber attacks - An approach to the risk assessment

abstract: By 2012 the U.S. military had increased its investment in research and production of unmanned aerial vehicles (UAVs) from $2.3 billion in 2008 to $4.2 billion [1]. Currently UAVs are used for a wide range of missions such as border surveillance, reconnaissance, transportation and armed attacks. UAVs are presumed to provide their services at any time, be reliable, automated and autonomous. Based on these presumptions, governmental and military leaders expect UAVs to improve national security through surveillance or combat missions. To fulfill their missions, UAVs need to collect and process data. Therefore, UAVs may store a wide range of information from troop movements to environmental data and strategic operations. The amount and kind of information enclosed make UAVs an extremely interesting target for espionage and endangers UAVs of theft, manipulation and attacks. Events such as the loss of an RQ-170 Sentinel to Iranian military forces on 4th December 2011 [2] or the “keylogging” virus that infected an U.S. UAV fleet at Creech Air Force Base in Nevada in September 2011 [3] show that the efforts of the past to identify risks and harden UAVs are insufficient. Due to the increasing governmental and military reliance on UAVs to protect national security, the necessity of a methodical and reliable analysis of the technical vulnerabilities becomes apparent. We investigated recent attacks and developed a scheme for the risk assessment of UAVs based on the provided services and communication infrastructures. We provide a first approach to an UAV specific risk assessment and take into account the factors exposure, communication systems, storage media, sensor systems and fault handling mechanisms. We used this approach to assess the risk of some currently used UAVs: The “MQ-9 Reaper” and the “AR Drone”. A risk analysis of the “RQ-170 Sentinel” is discussed.


title: Learning Features and Parts for Fine-Grained Recognition

abstract: This paper addresses the problem of fine-grained recognition: recognizing subordinate categories such as bird species, car models, or dog breeds. We focus on two major challenges: learning expressive appearance descriptors and localizing discriminative parts. To this end, we propose an object representation that detects important parts and describes fine grained appearances. The part detectors are learned in a fully unsupervised manner, based on the insight that images with similar poses can be automatically discovered for fine-grained classes in the same domain. The appearance descriptors are learned using a convolutional neural network. Our approach requires only image level class labels, without any use of part annotations or segmentation masks, which may be costly to obtain. We show experimentally that combining these two insights is an effective strategy for fine-grained recognition.


title: Sensor pillow system: monitoring respiration and body movement in sleep

abstract: This paper presents "Sensor Pillow System" to measure physiological parameters in sleep without restraint to a human. The system consists of an array of pressure sensors under the pillow, a one-chip microcomputer to digitize and transmit the pressure data to a desktop computer; and the computer to count respirations and turns in sleep. This paper also presents a simple motion model which explains the change of the head pressure distribution accompanied with respiration. Based on this model, respiration count algorithms is proposed. The effectiveness of this system is experimentally shown by comparing the number of respirations and turns counted by the sensor pillow system of a medical device and a video image.


title: Abstract Invited Review Two-dimensional packing problems: A survey

abstract: We consider problems requiring to allocate a set of rectangular items to larger rectangular standardized units by minimizing the waste. In two-dimensional bin packing problems these units are finite rectangles, and the objective is to pack all the items into the minimum number of units, while in two-dimensional strip packing problems there is a single standardized unit of given width, and the objective is to pack all the items within the minimum height. We discuss mathematical models, and survey lower bounds, classical approximation algorithms, recent heuristic and metaheuristic methods and exact enumerative approaches. The relevant special cases where the items have to be packed into rows forming levels are also discussed in detail.


title: Breaking BLE Beacons For Fun But Mostly Profit

abstract: Bluetooth Low Energy (BLE) Beacons introduced a novel technology that enables devices to advertise their presence in an area by constantly broadcasting a static unique identifier. The aim was to enhance services with location and context awareness. Although the hardware components of typical BLE Beacons systems are able to support adequate cryptography, the design and implementation of most publicly available BLE Beacon protocols appears to render them vulnerable to a plethora of attacks. Indeed, in this paper, we were able to perform user tracking, user behavior monitoring, spoofing as well as denial of service (DoS) of many supported services. Our aim is to show that these attacks stem from design flaws of the underlying protocols and assumptions made for the BLE beacons protocols. Using a clearly defined threat model, we provide a formal analysis of the adversarial capabilities and requirements and the attack impact on security and privacy for the end-user. Contrary to popular belief, BLE technology can be exploited even by low-skilled adversaries leading to exposure of user information. To demonstrate our attacks in practice, we selected Apple's iBeacon technology, as a case study. However, our analysis can be easily generalized to other BLE Beacon technologies.


title: Pulse glucometry: a new approach for noninvasive blood glucose measurement using instantaneous differential near-infrared spectrophotometry

abstract: We describe a new optical method for noninvasive blood glucose (BGL) measurement. Optical methods are confounded by basal optical properties of tissues, especially water and other biochemical species, and by the very small glucose signal. We address these problems by using fast spectrophotometric analysis in a finger, deriving 100 transmittance spectra per second, to resolve optical spectra (900 to 1700 nm) of blood volume pulsations throughout the cardiac cycle. Difference spectra are calculated from the pulsatile signals, thereby eliminating the effects of bone, other tissues, and nonpulsatile blood. A partial least squares (PLS) model is used with the measured spectral data to predict BGL levels. Using glucose tolerance tests in 27 healthy volunteers, periodic optical measurements were made simultaneously with collection of blood samples for in vitro glucose analysis. Altogether, 603 paired data sets were obtained in all subjects and two-thirds of the data or of the subjects randomly selected were used for the PLS calibration model and the rest for the prediction. Bland-Altman and error-grid analyses of the predicted and measured BGL levels indicated clinically acceptable accuracy. We conclude that the new method, named pulse glucometry, has adequate performance for safe, noninvasive estimation of BGL.


title: FIMESS: filtering mobile external SMS spam

abstract: The widespread use of mobile devices has attracted the attention of cyber-criminals, who exploit their functionality for malevolent purposes. A very popular and well-known such approach is the use of unsolicited electronic messages, also known as spam. Such messages can be used by attackers in order to tempt the recipient to visit a malicious page, or to reply to a message and be charged at premium rates, or even for advertising goods and offers. Several of the mechanisms developed for fighting mobile spam have been based on the well-known and widely adopted e-mail spam techniques. Mobile spam on the other hand has specific properties, such as limited text size, particular linguistic style with specific abbreviations, also known as "the SMS language" or "textese", etc. Our algorithm, FIMESS (FIltering Mobile External SMS Spam), performs simple, yet effective checks on the message headers so as to classify an SMS as being spam or not. In contrast to linguistic-only approaches of spam detection algorithms, FIMESS is able to utilise the important information in the SMS headers and identify SMS spam messages. Contrary to the email metadata which can easily be manipulated by the spammers, the SMS protocol provides useful information to build more efficient spam filters. The proposed scheme was tested on the Android platform and yielded encouraging results.


title: Influenza Virus Vaccine Based on the Conserved Hemagglutinin Stalk Domain

abstract: Although highly effective in the general population when well matched to circulating influenza virus strains, current influenza vaccines are limited in their utility due to the narrow breadth of protection they provide. The strain specificity of vaccines presently in use mirrors the exquisite specificity of the neutralizing antibodies that they induce, that is, antibodies which bind to the highly variable globular head domain of hemagglutinin (HA). Herein, we describe the construction of a novel immunogen comprising the conserved influenza HA stalk domain and lacking the globular head. Vaccination of mice with this headless HA construct elicited immune sera with broader reactivity than those obtained from mice immunized with a full-length HA. Furthermore, the headless HA vaccine provided full protection against death and partial protection against disease following lethal viral challenge. Our results suggest that the response induced by headless HA vaccines is sufficiently potent to warrant their further development toward a universal influenza virus vaccine.


title: A variational Shearlet-based model for aortic stent detection

abstract: In medical applications, stent segmentation in the abdominal aorta has to be carried out in challenging conditions, since one has to deal with noise, low contrast, objects having similar appearances and missing or blurred edges. Variational segmentation methods eases this task by carrying prior information on the target region or on the regularity of its boundaries. In this paper, we propose a new approach based on the global minimization of the Active Contour model using the L1-norm of the Shearlet Transform instead of Total Variation (TV -norm). One of the distinctive features of such a regularization is that it allows the detection of anisotropic structures in images like stents boundaries. The sparsity imposed by the minimization provides piecewise smooth solutions with C2-singularities. We also use the shearlet coefficients to construct an edge function for more faithful contour detection. Performances of our algorithm are evaluated on a stent segmentation from post-operative CT data. Results show that the proposed method drastically improves the detection of the stent placement compared to the TV based approach.


title: Malware-aware processors: A framework for efficient online malware detection

abstract: Security exploits and ensuant malware pose an increasing challenge to computing systems as the variety and complexity of attacks continue to increase. In response, software-based malware detection tools have grown in complexity, thus making it computationally difficult to use them to protect systems in real-time. Therefore, software detectors are applied selectively and at a low frequency, creating opportunities for malware to remain undetected. In this paper, we propose Malware-Aware Processors (MAP) — processors augmented with an online hardware-based detector to serve as the first line of defense to differentiate malware from legitimate programs. The output of this detector helps the system prioritize how to apply more expensive software-based solutions. The always-on nature of MAP detector helps protect against intermittently operating malware. Our work improves on the state of the art in the following ways: (1) We define and explore the use of sub-semantic features for online detection of malware. (2) We explore hardware implementations and show that simple classifiers appropriate for such implementations can effectively classify malware. We also study different classifiers, develop implementation optimizations, and explore complexity to performance trade-offs. (3) We propose a two-level detection framework where the hardware classifier prioritizes the work of a more accurate but more expensive software defense mechanism. (4) We integrate the MAP implementation with an open-source x86-compatible core, synthesizing the resulting design to run on an FPGA.


title: Nonparametrically Guided Autoencoder with Laplace Approximation for dimensionality reduction

abstract: Unsupervised learning aims to discovery latent representation embedded in the observation, which is useful for data visualization, dimensionality reduction, and density modeling. Autoencoders have been successfully used to learn the latent variations in data, especially with the recent reintroduction by deep learning. For some specific tasks, there are supervised information or labels that can be used to further guide the unsupervised autoencoder model for finding latent representation. The Non-Parametrically Guided Autoencoder (NPGA) has been proved to be an effective model. It tries to utilize Gaussian Process Regression (GPR) to model the unknown mapping from unknown latent representation to extra supervised information. However for the discrete label information in classification tasks, using GPR could be unwise and inefficient. In this paper, we propose the Non-Parametrically Guided Autoencoder with Laplace Approximation (NPGA-LA) to effectively handle discrete labels. The idea of NPGA-LA is to make use of Gaussian Process Classification (GPC) rather than GPR to model the transformation between the latent space and the discrete label space. The experimental results verify the excellent performance of the newly developed method.


title: [Observation on the analgesic effect of repeated electroacupuncture and its relation to changes of plasma beta-EP, ACTH and COR levels].

abstract: OBJECTIVE ::: To observe the regularity of electroacupuncture (EA) analgesia and to analyze its relation to hypothalamus-pituitary-adrenal cortex axis function in chronic constrictive injury (CCI) and ovariectomy (OVX) rats. ::: ::: ::: METHODS ::: A total of 110 Wistar rats anesthetized with mixture solution of 1.5% chloralose (50 mg/kg) and 25% urethrane (420 mg/kg, i.p.) were randomized into control (n=10), CCI (n =10), CCI + EA (n=30), OVX + CCI (n=30), OVX + CCI + EA (n=30) groups, and the later 3 groups were further divided into 2 times (2 t), 2 weeks (2 w) and 3 w time courses, with 10 cases in each. Forty-five days after OVX, Morris water maze test was conducted to evaluate the OVX rats' memory-learning ability. Chronic pain model was established by ligating the right sciatic nerve with a piece of suture. EA (2/15 Hz, 1 mA) was applied to bilateral "Zusanli" (ST 36)-"Yanglingquan" (GB 34) for 30 min, once daily for 2 t, 2 w, 3 w respectively. The paw withdrawal latency (PWL) was detected to be used as the pain threshold. Under deep anesthesia, the rat was decapitated for collecting blood samples. Plasma adrenocorticotropic hormone (ACTH), beta-endorphine (EP), cortisone (COR) contents were detected using radioimmunoassay. ::: ::: ::: RESULTS ::: In comparison with control group, PWL difference values (PWLDV) of CCI + EA, OVX + CCI and OVX + CCI + EA groups increased significantly (P<0.05), suggesting a marked decrease of pain threshold. Compared with CCI group, PWLDV of CCI + EA group at 3 w decreased significantly (P<0.05), being close to normal control group (P>0.05). Compared with OVX + CCI group, PWLDV of OVX + CCI + EA at 2 w and 3 w lowered significantly (P<0.05), suggesting an evident pain relief. In comparison with CCI + EA group, PWLDV of OVX + CCI + EA group at most time courses were considerably higher (P<0.05), showing a better cumulative analgesic effect in CCI rats without OVX. In CCI group, plasma beta-EP increased slightly, ACTH had no clear changes, and COR lowered lightly at 2 t. No significant differences were found between control and CCI + EA groups in beta-EP and ACTH levels at 2 w and 3 w (P>0.05), only COR levels of CCI + EA group at 2 w and 3 w increased significantly (P<0.05). Compared with control group, beta-EP levels of OVX + CCI group at 2 w and 3 w increased significantly (P<0.05); plasma ACTH had no marked changes (P>0.05); COR contents of OVX + CCI group decreased evidently at 2 t and increased markedly at 2 w (P<0.05). Compared with OVX + CCI group, in OVX + CCI + EA group, beta-EP levels of 2 t and 2 w decreased significantly (P<0.05); ACTH levels increased evidently at 2 t and 3 w, and decreased markedly at 2 w (P<0.05); and COR level at 2 w decreased significantly (P<0.05). It indicated that when cumulative EA analgesia appeared, in CCI animals, only plasma COR level increased pronouncedly, while in OVX + CCI rats, both plasma beta-EP and COR contents decreased evidently. ::: ::: ::: CONCLUSION ::: Both plasma beta-EP and COR are involved in the accumulative analgesic effect of EA in the rat, and decline of learning memory ability may weaken the analgesic effect of EA to a certain degree, but no clear correlation tendency is found between changes of plasma beta-EP and COR and the improvement of pain behavior.


title: A Unified Software Architecture to Enable Cross-Layer Design in the Future Internet

abstract: While research on cross-layer network optimization has been progressing, useful implementations have been lagging because the current Internet architecture does not accommodate cross-layering gracefully. As part of our FIND project, we propose a software architecture for the future Internet that is designed to accommodate such interactions. We present a conceptual overview as well as high level software design and an early prototype implementation, and point out the strengths of our architecture.


title: Wafer Warpage Experiments and Simulation for Fan-Out Chip on Substrate

abstract: Fan-out chip on substrate (FOCoS) is defined as the fan-out package flip-chip mounts on high pin counts ball grid array substrate. 12-inch advanced wafer level package (aWLP) process is implemented on FOCoS for cost saving advantage. The fan-out package constructs from multi-chips with short interconnection between die to die (D2D) by re-distribution layer (RDL) process, which has the potential for high speed communication and multi-function application. In this paper, a 45x45 FOCoS package size with interconnection between two large size D2D area by three metal layers RDL process is presented. Wafer level warpage control is critical in large size fan-out package of FOCoS. Warpage performance of FOCoS is examined by advanced warpage metrology analyzer (WMA) and finite element model (FEM) in this report. 12-inch mold wafer warpage is dramatically changed through multi-RDL processes. The reliability performance of FOCoS is also evaluated to show the qualification of mass production.


title: A Study on Cloud Forensics and Challenges in SaaS Application Environment

abstract: With virtualization technique, cloud computing is developed rapidly. However, aspects of the collecting of digital proofs for forensics in cloud computing environment, certain guideline is not yet. Therefore, cloud forensic researches in progress for reactivating security incidents in cloud computing. In this paper, we analyze forensic issue, challenges, already proposed forensic model in previous researches for adopting cloud forensic. And we suggest a novel model and approach for cloud forensic in SaaS and discuss needs of Service Level Agreements (SLA) and possibilities of Forensic-as-a-Service.


title: Automatic text classification and focused crawling

abstract: A focused crawler is a web crawler that traverse the web to explore information that is related to a particular topic of interest only. On the other hand, generic web crawlers try to search the entire web, which is impossible due to the size and the complexity of WWW. In this paper we make a survey of some of the latest focused web crawling approaches discussing each with their experimental results. We categorize them as focused crawling based on content analysis, focused crawling based on link analysis and focused crawling based on both the content and link analysis. We also give an insight to the future research and draw the overall conclusions.


title: Minimizing Development Cost With Reliability Goal for Automotive Functional Safety During Design Phase

abstract: ISO 26262 is a functional safety standard specifically made for automotive systems, in which the automotive safety integrity level (ASIL) is the representation of the criticality level. Recently, most studies have used ASIL decomposition to reduce the development cost of automotive functions. However, these studies have not paid special attention to the problem that the reliability goal may not be satisfied when ASIL decomposition is performed. In this study, we solve the problem of minimizing the development cost of a distributed automotive function while satisfying its reliability goal during the design phase by presenting two heuristic algorithms, reliabilitycalculation of scheme (RCS) and minimizing development cost with reliability goal (MDCRG). We first use RCS to calculate the reliability value of each ASIL decomposition scheme; then, the MDCRG is used to select the scheme with the minimum development cost while satisfying the reliability goal. Real-life benchmark and simulated functions based on real parameter values are used in experiments, and results show the effectiveness of the proposed algorithms.


title: Do Consumers Want More Nutritional and Health Information on Wine Labels? Insights from the EU and USA

abstract: The global strategy to reduce the harmful use of alcohol launched in 2010 by the World Health Organization includes, amongst several areas of recommended actions, providing consumer information about, and labelling, alcoholic beverages to indicate alcohol-related harm. Labelling requirements worldwide for alcoholic drinks are currently quite diverse and somewhat limited compared to labelling on food products and on tobacco. In this context, the current paper contributes to the academic and political debate on the inclusion of nutritional and health information on wine labelling, providing some insights into consumer interest in, and preferences for, such information in four core wine-producing and -consuming countries: Italy, France, Spain, and the United States of America. A rating-based conjoint analysis was performed in order to ascertain consumer preferences for different formats of additional information on wine labels, and a segmentation of the sample was performed to determine the existence of homogeneous groups of consumers in relation to the degrees of usefulness attached to the nutritional and health information on wine labels. Our results highlight the interest expressed by European and United States consumers for introducing nutrition and health information on wine labels. However, the results of conjoint analysis show some significant differences among stated preferences of the information delivery modes in different countries. In addition, segmentation analysis reveal the existence of significant differences between consumer groups with respect to their interest in receiving additional information on wine labels. These differences are not only linked to the geographic origin of the consumers, or to socio-demographic variables, but are also related to wine consumption habits, attitudes towards nutritional information, and the degree of involvement with wine. This heterogeneity of consumer preferences indicates a need for a careful consideration of wine labelling regulations and merits further investigation in order to identify labelling guidelines in terms of the message content and presentation method to be used.


title: Selecting the optimal mining method with extended multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) approach

abstract: Mining method selection (MMS) is one of the core contents in mining design. As the influencing factors of MMS are in general of ambiguity or uncertainty, MMS can therefore be deemed as a complex fuzzy multi-criteria decision making problem. To resolve this problem, an extended multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) approach is studied in this paper. Three major contributions of this study are included. At first, comparative importance is crisp number in most stepwise weight assessment ratio analysis (SWARA) method, while linguistic terms are utilized to determine the criteria weights in the improved SWARA. Then, several Heronian mean (HM) operators related to linguistic neutrosophic numbers (LNNs) are defined to cope with evaluating indicators’ interrelations. Special cases are discussed, and primary properties are proved as well. At last, in combination with improved SWARA and HM operators, a novel decision making method, extended MULTIMOORA approach based on LNNs, is proposed. Applying this new approach to selecting the optimal mining method through an example, the feasibility is indicated. In addition, analyses of the strengths of the presented method are performed in comparison with other existing approaches.


title: Neural network control for a closed-loop System using Feedback-error-learning

abstract: This paper presents new learning schemes using feedback-error-learning for a neural network model applied to adaptive nonlinear feedback control. Feedback-error-learning was proposed as a learning method for forming a feedforward controller that uses the output of a feedback controller as the error for training a neural network model. Using new schemes for nonlinear feedback control, the actual responses after learning correspond to the desired responses which are defined by an inverse reference model implemented as a conventional feedback controller. In this respect, these methods are similar to Model Reference Adaptive Control (MRAC) applied to linear or linearized systems. It is shown that learning impedance control is derived when one proposed scheme is used in Cartesian space. We show the results of applying these learning schemes to an inverted pendulum and a 2-link manipulator. We also discuss the convergence properties of the neural network models employed in these learning schemes by applying the Lyapunov method to the averaged equations associated with the stochastic differential equations which describe the system dynamics.


title: Ontology based syndrome diagnostic system: (OBSDS)

abstract: Disease Diagnosis is a process of assessment of all possible grounds for a set of symptoms to identify the disease in order to recommend treatment carried out by the physician all over the world. But, one of the major problems faced by medical experts in this process is in possessing a diverse amount of knowledge for better disease diagnosis. In the Past few years the use of Ontologies in Medical domain is basically focused on the representation of the Medical terminologies. Furthermore small number of Ontologies can be found in the domain of Syndrome. A syndrome can refer to the group of symptoms that suggest the presence of syndrome disease. There exist dependencies among causes and symptoms of syndrome diseases. We have proposed a Syndrome ontology model for disease diagnosis. We have done model formalization in Web Ontology language (OWL) and have handled reasoning upon ontology using SPARQL Query. Then apply evaluation matrices on these queries to evaluate the performance of our system. Ontology is developed in Protege and use SPARQL interface which support various output formats. We use Apache Tomcat 7.0 since it is an online System. This system offer support for better medical decision in Syndrome Diagnosing.


title: A Computer Vision Framework for Detecting and Preventing Human-Elephant Collisions

abstract: Human Elephant Collision (HEC) is a problem that is quite common across many parts of the world. There have been many incidents in the past where conflict between humans and elephants has caused serious damage and resulted in the loss of lives as well as property. The paper proposes a frame-work that relies on computer vision approaches for detecting and preventing HEC. The technique initially recognizes the areas of conflict where accidents are most likely to occur. This is followed by elephant detection system that identifies an elephant in the video frame. Two different algorithms to detect the presence of elephants having accuracies of 98.621% and 98.667% have been proposed in the paper. The position of the elephant once detected is tracked with respect to the area of conflict with a particle filter. A warning message is displayed as soon as the position of the elephant overlaps with the area of conflict. The results of the techniques that were applied on videos were discussed in the paper


title: Creativity workshops: Tools for innovation in organizations?

abstract: Abstract There are good reasons for encouraging creativity in organizations, teams, project groups, and networks: firstly, creativity is one of the main prerequisites for innovation; secondly, the breaking down of hierarchies reinforces imaginativeness, dynamism, and autonomy at all levels of the company; and thirdly, the creativity of the employees is one of the few domains, after all the rationalization and restructuring has been done, in which there is still considerable development potential to be found. In this way creativity becomes one of the basic qualifications for all employees. So it is only logical that companies should want to stimulate and support the creative potential of their staff, work groups, and teams. Unfortunately, it isn't all that easy to free up creative energy: new ideas and innovative vigour don't just materialize of their own free will–they require a special background and specific conditions for growth while also demanding nurturing and stimulation. “Creativity workshops” are...


title: In Search of the Ultimate Autonomous Driving Simulator

abstract: This technical paper provides an overview of features found in publicly available simulators used for autonomous driving research. Selection of simulators has been limited to ones running smoothly on a typical desktop computer. Evaluated functionalities were selected with young scientists in mind to help them decide which platform would suit their research needs best. Not all features were found in one ultimate autonomous driving simulator, leaving the title open. A set of sought features has been discovered and can be used as reference for future simulation platforms.


title: Approximate Reverse Carry Propagate Adder for Energy-Efficient DSP Applications

abstract: In this paper, a reverse carry propagate adder (RCPA) is presented. In the RCPA structure, the carry signal propagates in a counter-flow manner from the most significant bit to the least significant bit; hence, the carry input signal has higher significance than the output carry. This method of carry propagation leads to higher stability in the presence of delay variations. Three implementations of the reverse carry propagate full-adder (RCPFA) cell with different delay, power, energy, and accuracy levels are introduced. The proposed structure may be combined with an exact (forward) carry adder to form hybrid adders with tunable levels of accuracy. The design parameters of the proposed RCPA implementations and some hybrid adders realized utilizing these structures are studied and compared with those of the state-of-the-art approximate adders using HSPICE simulations in a 45-nm CMOS technology. The results indicate that employing the proposed RCPAs in the hybrid adders may provide, on average, 27%, 6%, and 31% improvements in delay, energy, and energy-delay-product while providing higher levels of accuracy. In addition, the structure is more resilient to delay variation compared to the conventional approximate adder. Finally, the efficacy of the proposed RCPAs is investigated in the discrete cosine transform (DCT) block of the JPEG compression and finite-impulse response (FIR) filter applications. The investigation reveals 60% and 39% energy saving in the DCT of JPEG and FIR filter, respectively, for the proposed RCPAs.


title: Designing inflatable structures

abstract: We propose an interactive, optimization-in-the-loop tool for designing inflatable structures. Given a target shape, the user draws a network of seams defining desired segment boundaries in 3D. Our method computes optimally-shaped flat panels for the segments, such that the inflated structure is as close as possible to the target while satisfying the desired seam positions. Our approach is underpinned by physics-based pattern optimization, accurate coarse-scale simulation using tension field theory, and a specialized constraint-optimization method. Our system is fast enough to warrant interactive exploration of different seam layouts, including internal connections, and their effects on the inflated shape. We demonstrate the resulting design process on a varied set of simulation examples, some of which we have fabricated, demonstrating excellent agreement with the design intent.


title: Big data science for predicting insurance claims fraud

abstract: The cost of insurance is on the rise globally with contributing factors being new legislation and fraud. Both of these factors are problematic with new legislation not easily controllable by insurance companies and fraud very difficult to proactively detect. Hence, controlling the upward trend of cost is problematic. We therefore investigate the use of Big Data and Data Science (Big Data Science) to predict insurance claims fraud. Big Data, Data Science and Predictive Analytics were applied to the shortterm insurance industry with a use-case in the developing world. This study was performed within the bounds of the constraints of privacy legislation that invokes standards on how data can be kept and shared — we suggest a privacy preservation method of predicting insurance claims fraud.


title: Field Experience With Performances Evaluation of a Single-Crystalline Photovoltaic Panel in an Underwater Environment

abstract: In this paper, the electrical and thermal performances of a single-crystalline submerged photovoltaic (PV) solar panel (SP2) is investigated. In particular, due to the presence of water, several phenomena occur such as the modification of solar-radiation spectrum and the reduction of the module operating temperature. These phenomena have different impacts on the global energy performance of the PV module, which depends on the environmental conditions, on the PV technology, and on the water depth. Energy performances of an SP2 module are measured using two different experimental setups in different environmental conditions, with an increase in power ranging from 10% to 15%.


title: The Impact of Observational Learning and Electronic Word of Mouth on Consumer Purchase Decisions: The Moderating Role of Consumer Expertise and Consumer Involvement

abstract: The social media revolution has created a dynamic shift in the digital marketing landscape. The voice of influence is moving from traditional marketers towards consumers through online social interactions. In this study, we focus on two types of online social interactions, namely, electronic word of mouth (eWOM) and observational learning (OL), and explore how they influence consumer purchase decisions. We also examine how receiver characteristics, consumer expertise and consumer involvement, moderate consumer purchase decision process. Analyzing panel data collected from a popular online beauty forum, we found that consumer purchase decisions are influenced by their online social interactions with others and that action-based OL information is more influential than opinion-based eWOM. Further, our results show that both consumer expertise and consumer involvement play an important moderating role, albeit in opposite direction: Whereas consumer expertise exerts a negative moderating effect, consumer involvement is found to have a positive moderating effect. The study makes important contributions to research and practice.


title: Explainable Fashion Recommendation with Joint Outfit Matching and Comment Generation

abstract: Most previous work on fashion recommendation focuses on designing visual features to enhance recommendations. Existing work neglects user comments of fashion items, which have been proved effective in generating explanations along with better recommendation results. We propose a novel neural network framework, neural fashion recommendation (NFR), that simultaneously provides fashion recommendations and generates abstractive comments. NFR consists of two parts: outfit matching and comment generation. For outfit matching, we propose a convolutional neural network with a mutual attention mechanism to extract visual features of outfits. The visual features are then decoded into a rating score for the matching prediction. For abstractive comment generation, we propose a gated recurrent neural network with a cross-modality attention mechanism to transform visual features into a concise sentence. The two parts are jointly trained based on a multi-task learning framework in an end-to-end back-propagation paradigm. Extensive experiments conducted on an existing dataset and a collected real-world dataset show NFR achieves significant improvements over state-of-the-art baselines for fashion recommendation. Meanwhile, our generated comments achieve impressive ROUGE and BLEU scores in comparison to human-written comments. The generated comments can be regarded as explanations for the recommendation results. We release the dataset and code to facilitate future research.


title: Falling in love with online games: The uses and gratifications perspective

abstract: Playing online games is experience-oriented but few studies have explored the user's initial (trial) reaction to game playing and how this further influences a player's behavior. Drawing upon the Uses and Gratifications theory, we investigated players' multiple gratifications for playing (i.e. achievement, enjoyment and social interaction) and their experience with the service mechanisms offered after they had played an online game. This study explores the important antecedents of players' proactive ''stickiness'' to a specific online game and examines the relationships among these antecedents. The results show that both the gratifications and service mechanisms significantly affect a player's continued motivation to play, which is crucial to a player's proactive stickiness to an online game.


title: Research on nursing handoffs for medical and surgical settings: an integrative review

abstract: AIMS ::: To synthesize outcomes from research on handoffs to guide future computerization of the process on medical and surgical units. ::: ::: ::: BACKGROUND ::: Handoffs can create important information gaps, omissions and errors in patient care. Authors call for the computerization of handoffs; however, a synthesis of the literature is not yet available that might guide computerization. ::: ::: ::: DATA SOURCES ::: PubMed, CINAHL, Cochrane, PsycINFO, Scopus and a handoff database from Cohen and Hilligoss. ::: ::: ::: DESIGN ::: Integrative literature review. ::: ::: ::: REVIEW METHODS ::: This integrative review included studies from 1980-March 2011 in peer-reviewed journals. Exclusions were studies outside medical and surgical units, handoff education and nurses' perceptions. ::: ::: ::: RESULTS ::: The search strategy yielded a total of 247 references; 81 were retrieved, read and rated for relevance and research quality. A set of 30 articles met relevance criteria. ::: ::: ::: CONCLUSION ::: Studies about handoff functions and rituals are saturated topics. Verbal handoffs serve important functions beyond information transfer and should be retained. Greater consideration is needed on analysing handoffs from a patient-centred perspective. Handoff methods should be highly tailored to nurses and their contextual needs. The current preference for bedside handoffs is not supported by available evidence. The specific handoff structure for all units may be less important than having a structure for contextually based handoffs. Research on pertinent information content for contextually based handoffs is an urgent need. Without it, handoff computerization is not likely to be successful. Researchers need to use more sophisticated experimental research designs, control for individual and unit differences and improve sampling frames.


title: A Study of Android Malware Detection Techniques and Machine Learning

abstract: Android OS is one of the widely used mobile Operating Systems. The number of malicious applications and adwares are increasing constantly on par with the number of mobile devices. A great number of commercial signature based tools are available on the market which prevent to an extent the penetration and distribution of malicious applications. Numerous researches have been conducted which claims that traditional signature based detection system work well up to certain level and malware authors use numerous techniques to evade these tools. So given this state of affairs, there is an increasing need for an alternative, really tough malware detection system to complement and rectify the signature based system. Recent substantial research focused on machine learning algorithms that analyze features from malicious application and use those features to classify and detect unknown malicious applications. This study summarizes the evolution of malware detection techniques based on machine learning algorithms focused on the Android OS.


title: Chronic urticaria: aetiology, management and current and future treatment options.

abstract: Chronic urticaria is a common condition that can be very disabling when severe. A cause for chronic idiopathic urticaria (CIU) is only infrequently identified. Potential causes include reactions to food and drugs, infections (rarely) and, apart from an increased incidence of thyroid disease, uncomplicated urticaria is not usually associated with underlying systemic disease or malignancy. About one-third of patients with CIU have circulating functional autoantibodies against the high affinity IgE receptor or against IgE, although it is not known why such antibodies are produced, or how the presence of such antibodies alters the course of the disease or response to treatment. There are only a few publications relating to childhood urticaria, but it is probably similar to the adult form, except that adult urticaria is more common. The diagnosis is based on patient history and it is vital to spend time documenting this in detail. Extensive laboratory tests are not required in the vast majority of patients. Chronic urticaria resolves spontaneously in 30-55% of patients within 5 years, but it can persist for many years. Treatment is aimed firstly at avoiding underlying causative or exacerbating factors. Histamine H1 receptor antagonists remain the mainstay of oral treatment for all forms of urticaria. The newer low-sedating antihistamines desloratadine, fexofenadine, levocetirizine and mizolastine should be tried first. Sedating antihistamines have more adverse effects but are useful if symptoms are causing sleep disturbance. Low-dose dopexin is effective and especially suitable for patients with associated depression. There is controversy as to whether the addition of an histamine H2 receptor antagonist or a leukotriene antagonist is helpful. For CIU, second-line agents include ciclosporin (cyclosporine) [which is effective in approximately 75% of patients], short courses of oral corticosteroids, intravenous immunoglobulins and plasmapheresis, although the last two were found to be beneficial in small trials only. Treatments for CIU with only limited or anecdotal supportive evidence include sulphasalazine, methotrexate, stanazol, rofecoxib and cyclophosphamide. The efficacy of photo(chemo)therapy is controversial. Physical urticarias may respond to H1 receptor antagonists, although in delayed pressure urticaria, and cold, solar and aquagenic urticaria, the response may be disappointing. Second-line agents for physical urticarias vary depending on the urticaria and most have limited supportive evidence. The potential for spontaneous resolution, the variation in the disease activity and the unpredictable nature of the disease makes the efficacy of treatments difficult to assess.


title: Oxidation and Synthesis of Fatty Acids in Human and Rat Placental and Fetal Tissues

abstract: The rat fetus (day 21 of pregnancy) covers its fatty acid (FA) demands at equal amounts both by maternal-fetal FA transfer and fetal FA synthesis. At the end of the first trimester the human fetal FA synthesis is too small to cover the fetal FA requirements. Therefore, the transfer of FA from the mother to the fetus seems to be the predominant source of fetal FA. The FA oxidation is greater than the FA synthesis in human and rat placenta as well as in the human fetus at the end of the nrst trimester, whereas the rat fetus (day 21 of pregnancy) oxidizes and synthesizes FA at equal amounts.


title: ROBIN, a Telepresence Robot to Support Older Users Monitoring and Social Inclusion: Development and Evaluation

abstract: INTRODUCTION ::: This article describes an enhanced telepresence robot named ROBIN, part of a telecare system derived from the GIRAFFPLUS project for supporting and monitoring older adults at home. ROBIN is integrated in a sensor-rich environment that aims to continuously monitor physical and psychological wellbeing of older persons living alone. The caregivers (formal/informal) can communicate through it with their assisted persons. Long-term trials in real houses highlighted several user requirements that inspired improvements on the robotic platform. The enhanced telepresence robot was assessed by users to test its suitability to support social interaction and provide motivational feedback on health-related aspects. ::: ::: ::: METHODS ::: Twenty-five users (n = 25) assessed the new multimodal interaction capabilities and new communication services. A psychophysiological approach was adopted to investigate aspects like engagement, usability, and affective impact, as well as the possible role of individual differences on the quality of human-robot interaction. ::: ::: ::: RESULTS ::: ROBIN was overall judged usable, the interaction with/through it resulted pleasant and the required workload was limited, thus supporting the idea of using it as a central component for remote assistance and social participation. Open-minded users tended to have a more positive interaction with it. ::: ::: ::: CONCLUSIONS ::: This work describes an enabling technology for remote assistance and social communication. It highlights the importance of being compliant with users' needs to develop solutions easy to use and able to foster their social connections. The role of personality appeared to be relevant for the interaction, underscoring a clear role of the service personalization.


title: HMFlow: Accelerating FPGA Compilation with Hard Macros for Rapid Prototyping

abstract: The FPGA compilation process (synthesis, map, place, and route) is a time consuming task that severely limits designer productivity. Compilation time can be reduced by saving implementation data in the form of hard macros. Hard macros consist of previously synthesized, placed and routed circuits that enable rapid design assembly because of the native FPGA circuitry (primitives and nets)which they encapsulate. This work presents results from creating a new FPGA design flow based on hard macros called HMF low. HMF low has shown speedups of 10-50X over the fastest configuration of the Xilinx tools. Designed for rapid prototyping, HMF low achieves these speedups by only utilizing up to 50 percent of the resources on an FPGA and produces implementations that run 2-4X slower than those produced by Xilinx. These speedups are obtained on a wide range of benchmark designs with some exceeding 18,000 slices on a Virtex 4 LX200.


Biometric cryptosystems based fuzzy commitment scheme: a security evaluation.


title: Surgical Data Science: Enabling Next-Generation Surgery

abstract: This paper introduces Surgical Data Science as an emerging scientific discipline. Key perspectives are based on discussions during an intensive two-day international interactive workshop that brought together leading researchers working in the related field of computer and robot assisted interventions. Our consensus opinion is that increasing access to large amounts of complex data, at scale, throughout the patient care process, complemented by advances in data science and machine learning techniques, has set the stage for a new generation of analytics that will support decision-making and quality improvement in interventional medicine. In this article, we provide a consensus definition for Surgical Data Science, identify associated challenges and opportunities and provide a roadmap for advancing the field.


title: Reliability and validity of Somali version of the PHQ-9 in primary care practice

abstract: ObjectivesDepression is one of the common mental health problems noticed in immigrants because of the experiences related to their resettlement which is the case for Somali population. Depression increases mortality, morbidity, disability, and costs of health care which can be controlled by screening depression in the primary care setting using a culturally and linguistically congruent screening tool. The aim of the current study is to translate the English PHQ-9 into Somali language using evidence-based translational methodology and establish psychometric properties of the Somali PHQ-9.MethodsThe initial validation of the Somali PHQ-9 was studied by comparing the original and back translation versions using the comparability and interpretability tool in a sample of 56 monolingual health care professionals. The reliability and validity of the Somali version were established by psychometric analysis in a sample of 47 bilingual health-care workers.ResultsCronbach’s alpha was 0.79 for the Somali version with...


title: Investigating Patterns of Writing Errors for Different L1 Groups through Error-Coded ESL Learners’ Essays

abstract: Automated error detection and feedback systems are becoming an important component of online writing practice services for ESL/EFL (English as a second/foreign language) learners. The main purposes of the study are to: (a) collect samples of essays written by ESL learners with different native language (or L1) backgrounds that are error-coded by an early version of an automated error-detection system (CritiqueTM) and trained human coders; and (b) identify some unique patterns of writing errors for different first language (L1) groups. Data analyzed in this study included 18, 439 TOEFL◯R CBT essays error-coded by CritiqueTM and a much smaller, combined sample of 480 TOEFL◯R CBT/TOEFL iBT◯R essays error-coded by trained human coders. A comparison of error rates across five different language groups showed some unique patterns: (a) the Arabic and Spanish groups were the highest on both spelling and punctuation errors; (b) the Korean and Japanese groups had the highest article error frequency; and (c) the Chinese group had the highest number of errors related to verb conjugations or adjective and noun inflections. The implications of these findings are discussed in terms of understanding the nature of L1-related writing errors and enhancing the automated error detection and feedback systems.


title: Moral credentials and the expression of prejudice.

abstract: Three experiments supported the hypothesis that people are more willing to express attitudes that could be viewed as prejudiced when their past behavior has established their credentials as nonprejudiced persons. In Study 1, participants given the opportunity to disagree with blatantly sexist statements were later more willing to favor a man for a stereotypically male job. In Study 2, participants who first had the opportunity to select a member of a stereotyped group (a woman or an African American) for a category-neutral job were more likely to reject a member of that group for a job stereotypically suited for majority members. In Study 3, participants who had established credentials as nonprejudiced persons revealed a greater willingness to express a politically incorrect opinion even when the audience was unaware of their credentials. The general conditions under which people feel licensed to act on illicit motives are discussed.


title: An Intelligent Anti-Money Laundering Method for Detecting Risky Users in the Banking Systems

abstract: During the last decades, universal economy has experienced money laundering and its destructive impact on the economy of the countries. Money laundering is the process of converting or transferring an asset in order to conceal its illegal source or assist someone that is involved in such crimes. Criminals generally attempt to clean the sources of the funds obtained by crime, using the banking system. Due to the large amount of information in the banks, detecting such behaviors is not feasible without anti-money laundering systems. Money laundering detection is one of the areas, where data mining tools can be useful and effective. In this research, some of the features of the users are extracted from their profiles by studying them. These features may include large financial transactions in risky areas regarding money laundering, reactivation of dormant accounts with considerable amounts, etc. Network training is performed by designing a fuzzy system, developing an adaptive neuro-fuzzy inference system and adding feature vectors of the users to it. The network output can determine the riskiness of the user behavior. The evaluation results reveal that the proposed method increases the accuracy of detecting risky users.


title: A 60 GHz single-chip 256-element wafer-scale phased array with EIRP of 45 dBm using sub-reticle stitching

abstract: This paper presents a 60 GHz wafer-scale transmit phased-array with 256-elements spaced λ/2 apart in the x and y directions, and occupying an area of 4.14×4.2 cm2 (1740 mm2). The phased array is built using independent RF, transmission-line and control circuit blocks which are stitched together to form an aggregate chip which is much larger than a standard reticle (22×22 mm2). This method allows for a wafer-scale design and can be extended to any size and any shape (rectangular, hexagonal, etc.) up to the edge of the wafer. The blocks include high-efficiency on-wafer antennas, phased-array channels with 3-bits amplitude and 5-bits phase control together with an amplifier having an output power of +3 dBm at 60 GHz. Also, a highly redundant RF distribution network is synthesized from several stitched blocks for improved yield, and the control blocks have redundant SPI control and power strips, also for improved yield. The 256-element array results in a half-power beamwidth of 6° in the E- and H-planes, a directivity of 29 dB, and scans to +/− 55° in the E- and H-planes with near-ideal patterns and a cross-polarization level of <−25 dB. The measured EIRP is 45 dBm at 61 GHz and with a 3-dB bandwidth from 58 to 64 GHz. To our knowledge, this is the largest single-chip phased-array ever developed and allows the construction of large-scale (1000+ elements) phased-array systems, either on a single wafer or by assembling several of these chips together.


title: Intentional attunement: Mirror neurons and the neural underpinnings of interpersonal relations

abstract: The neural circuits activated in a person carrying out actions, expressing emotions, and experiencing sensations are activated also, automatically via a mirror neuron system, in the observer of those actions, emotions, and sensations. It is proposed that this finding of shared activation suggests a functional mechanism of “embodied simulation” that consists of the automatic, unconscious, and noninferential simulation in the observer of actions, emotions, and sensations carried out and experienced by the observed. It is proposed also that the shared neural activation pattern and the accompanying embodied simulation constitute a fundamental biological basis for understanding another's mind. The implications of this perspective for psychoanalysis are discussed, particularly regarding unconscious communication, projective identification, attunement, empathy, autism, therapeutic action, and transference-countertransference interactions.


title: Reinforcement Learning for Trading

abstract: We propose to train trading systems by optimizing financial objective functions via reinforcement learning. The performance functions that we consider are profit or wealth, the Sharpe ratio and our recently proposed differential Sharpe ratio for online learning. In Moody & Wu (1997), we presented empirical results that demonstrate the advantages of reinforcement learning relative to supervised learning. Here we extend our previous work to compare Q-Learning to our Recurrent Reinforcement Learning (RRL) algorithm. We provide new simulation results that demonstrate the presence of predictability in the monthly S&P 500 Stock Index for the 25 year period 1970 through 1994, as well as a sensitivity analysis that provides economic insight into the trader's structure.


Ground: A Data Context Service


title: Keyword advertising is not what you think: Clicking and eye movement behaviors on keyword advertising

abstract: This study examined the behavior of online searchers in relation to keyword advertising according to the theory of advertising avoidance. A total of 451 volunteers were recruited for an experiment. A computer program and an eye-tracking device were used to measure the number of clicks and eye movements. The findings show that the number of clicks for to obtain organic search results is higher than that for keyword advertising. There is no difference in observation count between the area of keyword advertising placed above the organic search results and the area of the organic search results themselves. However, observation counts for the organic search results and keyword advertising placed above the organic search results are higher than for the keyword advertising placed at the right-hand side of the page. Searchers seem to spend the longest observation time on the organic search results, then on the keyword advertising placed above the organic search results, and finally on the keyword advertising placed at the right-hand side of the page.


Neural Network Tool for Data Mining: SOM Toolbox


title: Who views online extremism? Individual attributes leading to exposure

abstract: Who is likely to view materials online maligning groups based on race, nationality, ethnicity, sexual orientation, gender, political views, immigration status, or religion? We use an online survey (Nź=ź1034) of youth and young adults recruited from a demographically balanced sample of Americans to address this question. By studying demographic characteristics and online habits of individuals who are exposed to online extremist groups and their messaging, this study serves as a precursor to a larger research endeavor examining the online contexts of extremism.Descriptive results indicate that a sizable majority of respondents were exposed to negative materials online. The materials were most commonly used to stereotype groups. Nearly half of negative material centered on race or ethnicity, and respondents were likely to encounter such material on social media sites. Regression results demonstrate African-Americans and foreign-born respondents were significantly less likely to be exposed to negative material online, as are younger respondents. Additionally, individuals expressing greater levels of trust in the federal government report significantly less exposure to such materials. Higher levels of education result in increased exposure to negative materials, as does a proclivity towards risk-taking. Demographics and Internet habits affect exposure to hate material online.Distrust in the government is associated with exposure to hate material online.People who have been targets of hate are more likely to be exposed to online hate.Hours per day spent online affects the likelihood of being exposed to online hate.


title: Convolutional Models for Joint Object Categorization and Pose Estimation

abstract: In the task of Object Recognition, there exists a dichotomy between the categorization of objects and estimating object pose, where the former necessitates a view-invariant representation, while the latter requires a representation capable of capturing pose information over different categories of objects. With the rise of deep architectures, the prime focus has been on object category recognition. Deep learning methods have achieved wide success in this task. In contrast, object pose regression using these approaches has received relatively much less attention. In this paper we show how deep architectures, specifically Convolutional Neural Networks (CNN), can be adapted to the task of simultaneous categorization and pose estimation of objects. We investigate and analyze the layers of various CNN models and extensively compare between them with the goal of discovering how the layers of distributed representations of CNNs represent object pose information and how this contradicts with object category representations. We extensively experiment on two recent large and challenging multi-view datasets. Our models achieve better than state-of-the-art performance on both datasets.


title: Reelin and brain development

abstract: Over the last 50 years, the reeler mutant mouse has become an important model for studying normal and abnormal development in the cerebral cortex and other regions of the brain. However, we are only just beginning to understand the actions of reelin — the protein that is affected by the reeler mutation — at the molecular and cellular level. This review discusses the most recent advances in this research field, and considers the merits of the various models that have been put forward to explain how reelin works.


title: How to Enhance Cloud Architectures to Enable Cross-Federation: Towards Interoperable Storage Providers

abstract: Small/medium cloud storage providers can hardly compete with the biggest cloud players such as Google, Amazon, Dropbox, etc. As a consequence, the cloud storage market depends on such mega-providers and each small/medium provider cannot face alone the challenge of Big Data storage. A possible solution consists in establishing stronger partnerships among small-medium providers where they can borrow/lend resources each other, according to the rules of the federated cloud ecosystem they belong to. According to such an approach, the challenge consists in creating federated cloud ecosystems able to compete with mega-provides and one of the major problems for the achievement of such an ecosystem is the management of inter-domain communications. In this paper, we propose an architecture addressing such an issue. In particular, we present and test a solution integrating the CLEVER Message Oriented Middleware (MOM) with the Hadoop Distribute File System (HDFS), i.e., one of the major massive storage solutions currently available on the market.


title: Transfer learning of weakly labelled audio

abstract: Many machine learning tasks have been shown solvable with impressive levels of success given large amounts of training data and computational power. For the problems which lack data sufficient to achieve high performance, methods for transfer learning can be applied. These refer to performing the new task while having prior knowledge of the nature of the data, gained by first performing a different task, for which training data is abundant. Shown successful for other machine learning tasks, transfer learning is now investigated in audio analysis. We propose to solve the weakly labelled problem of sound event tagging with small amounts of training data by transferring the abstract knowledge about the nature of audio data from another tagging task. The proposed methods constitute pre-training of a recurrent neural network or its parts to perform one tagging task given abundant and diverse training data, and then using it or its parts for a new task of tagging sound events of different nature, for which the data is limited. Several architectures for such transfer are proposed and evaluated, showing impressive classification accuracy of 83.4% with gains of up to 20 percentage points over the baseline given as little as 36 training samples for the target task.


title: Factors Influencing Online Shopping Behavior of Urban Consumers in India

abstract: This study examines the factors influencing online shopping behavior of urban consumers in the State of Andhra Pradesh, India and provides a better understanding of the potential of electronic marketing for both researchers and online retailers. Data from a sample of 1500 Internet users distributed evenly among six selected major cities was collected by a structured questionnaire covering demographic profile and the factors influencing online shopping. Factor analysis and multiple regression analysis are used to establish relationship between the factors influencing online shopping and online shopping behavior. The study identified that perceived risk and price positively influenced online shopping behavior. Results also indicated that positive attitude, product risk and financial risk affect negatively the online shopping behavior.


title: Improved Representation Learning for Question Answer Matching

abstract: Passage-level question answer matching is a challenging task since it requires effective representations that capture the complex semantic relations between questions and answers. In this work, we propose a series of deep learning models to address passage answer selection. To match passage answers to questions accommodating their complex semantic relations, unlike most previous work that utilizes a single deep learning structure, we develop hybrid models that process the text using both convolutional and recurrent neural networks, combining the merits on extracting linguistic information from both structures. Additionally, we also develop a simple but effective attention mechanism for the purpose of constructing better answer representations according to the input question, which is imperative for better modeling long answer sequences. The results on two public benchmark datasets, InsuranceQA and TREC-QA, show that our proposed models outperform a variety of strong baselines.


title: Cognitive inefficiency in depressive undergraduates: Stroop processing and ERPs

abstract: Evidence from neuroimaging studies indicates that depressive symptomatology is associated with inefficient recruitment of prefrontal brain regions while performing tasks that tax executive function. In the current study, we investigated the time-course and ERP signature of inefficient executive functioning using a verbal Stroop color-naming task. Twenty (20) undergraduates with moderate to severe BDI-II depression scores and 20 low-scoring controls completed the task. Performance measures did not differ between the two groups. Overt reaction and P300 latencies indicated that all participants showed prominent Stroop effects, such that incongruent responses were delayed compared to congruent. Effects of task condition on the frontal N450 indicated that depressive participants differentiated congruent and incongruent trials earlier than did controls, and that the size of the congruency effect on the N450 was related to self-reported trait rumination among depressive participants. Following this effect, depressive participants showed larger P300s, suggesting an over-commitment of cognitive control resources in the depressive participants. These data lend further evidence to the cortical inefficiency hypothesis and extend the literature by indicating possible improper timing of neural activations during an executive task in depressive undergraduates.


title: Constrained Nearest Neighbor Queries

abstract: In this paper we introduce the notion of constrained nearest neighbor queries (CNN) and propose a series of methods to answer them. This class of queries can be thought of as nearest neighbor queries with range constraints. Although both nearest neighbor and range queries have been analyzed extensively in previous literature, the implications of constrained nearest neighbor queries have not been discussed. Due to their versatility, CNN queries are suitable to a wide range of applications from GIS systems to reverse nearest neighbor queries and multimedia applications. We develop methods for answering CNN queries with different properties and advantages. We prove the optimality (with respect to I/O cost) of one of the techniques proposed in this paper. The superiority of the proposed technique is shown by a performance analysis.


title: Polar format algorithm for bistatic SAR

abstract: Matched filtering (MF) of phase history data is a mathematically ideal but computationally expensive approach to bistatic synthetic aperture radar (SAR) image formation. Fast backprojection algorithms (BPAs) for image formation have recently been shown to give improved O(N/sup 2/ log/sub 2/N) performance. An O(N/sup 2/ log/sub 2/N) bistatic polar format algorithm (PFA) based on a bistatic far-field assumption is derived. This algorithm is a generalization of the popular PFA for monostatic SAR image formation and is highly amenable to implementation with existing monostatic image formation processors. Limits on the size of an imaged scene, analogous to those in monostatic systems, are derived for the bistatic PFA.


title: Local trajectory planning and tracking of autonomous vehicles, using clothoid tentacles method

abstract: In general, autonomous navigation requires three key steps, the perception of the environment surrounding the vehicle, the trajectory planning and the actuators control. Numerous works on the localization, perception, generation of occupancy grids and control of vehicles were developed within the ASER team at Heudiasyc laboratory. The work presented in this paper covers, essentially, trajectory planning and is based on the results of these works. The challenge is to avoid static and dynamic obstacles at high speed, using real time algorithms. The planning method developed in this work uses an empirical approach for local path planning. This approach consists on drawing clothoid tentacles in the ego-centered reference frame related to the vehicle. An occupancy grid represents the environment surrounding the vehicle and is considered to be ego-centered around it. Using the information of the occupancy grid, each tentacle is classified as navigable or not navigable. Among the navigable tentacles, only one tentacle is chosen as the vehicle reference trajectory using several criteria. The chosen tentacle is then applied to the vehicle using a lateral controller based on Immersion and Invariance principle (I&I).


title: LR error repair using the A* algorithm

abstract: This article presents a local LR error repair method that repairs syntax errors quickly by adoption of the A* algorithm that helps remove unproductive configurations. The new method also enhances the repair quality by adoption of a flexible edit strategy to support shifting symbols unrestrictedly, as well as inserting and deleting symbols, in order to repair invalid input strings. Experimental results show that the new method excels existing works in repair quality and efficiency.


title: Adaptive security for multilevel ad hoc networks

abstract: Secure communication is critical in military environments in which the network infrastructure is vulnerable to various attacks and compromises. A conventional centralized solution breaks down when the security servers are destroyed by the enemies. In this paper we design and evaluate a security framework for multilevel ad hoc wireless networks with unmanned aerial vehicles (UAVs). In battlefields, the framework adapts to the contingent damages on the network infrastructure. ::: ::: Depending on the availability of the network infrastructure, our design is composed of two modes. In infrastructure mode, security services, specifically the authentication services, are implemented on UAVs that feature low overhead and flexible managements. When the UAVs fail or are destroyed, our system seamlessly switches to infrastructureless mode, a backup mechanism that maintains comparable security services among the surviving units. In the infrastructureless mode, the security services are localized to each node's vicinity to comply with the ad hoc communication mechanism in the scenario. We study the instantiation of these two modes and the transitions between them. Our implementation and simulation measurements confirm the effectiveness of our design. Copyright © 2002 John Wiley & Sons, Ltd.


title: Green initiatives in IoT

abstract: Internet of things (IoT) is going to be ubiquitous in the next few years. In the smart city initiative, millions of sensors will be deployed for the implementation of IoT related services. Even in the normal cellular architecture, IoT will be deployed as a value added service for several new applications. Such massive deployment of IoT sensors and devices would certainly cost a large sum of money. In addition to the cost of deployment, the running costs or the operational expenditure of the IoT networks will incur huge power bills and spectrum license charges. As IoT is going to be a pervasive technology, its sustainability and environmental effects too are important. Energy efficiency and overall resource optimization would make it the long term technology of the future. Therefore, green IoT is essential for the operators and the long term sustainability of IoT itself. In this article we consider the green initiatives being worked out for IoT. We also show that narrowband IoT as the greener version right now.


title: Evolution of Data into an Information Hierarchy

abstract: The data, information, knowledge, and wisdom (DIKW) hierarchy manifests a structural and functional description of the processes that transform data into information, information into knowledge, and knowledge into wisdom. This paper introduces a new modeling methodology that treats data and information as the fundamental notions in theories of processing and transformation, thus avoiding the controversy surrounding the DIKW hierarchy. The resultant description aims at building a conceptual architecture of the evolution of data into higher levels in the ladder of processes in an organization. A sample project is used to illustrate the new approach.


title: Brief survey on full-duplex relaying and its applications on 5G

abstract: This paper gives an brief survey on full-duplex relaying. First, we discuss the importance of full-duplex communications and its major drawback, known as self-interference. We provide a overview of the state-of-the-art on self-interference cancellation techniques from antenna design to digital cancellation. Then, we assess current developments in full-duplex relaying, some promising protocols and we discuss some of the performance analysis carried out so far. Finally, we discuss the importance of full-duplex, more specifically full-duplex relaying, on 5G networks.


Competitive probabilistic neural network


title: Best practice in forensic entomology—standards and guidelines

abstract: Forensic entomology, the use of insects and other arthropods in forensic investigations, is becoming increasingly more important in such investigations. To ensure its optimal use by a diverse group of professionals including pathologists, entomologists and police officers, a common frame of guidelines and standards is essential. Therefore, the European Association for Forensic Entomology has developed a protocol document for best practice in forensic entomology, which includes an overview of equipment used for collection of entomological evidence and a detailed description of the methods applied. Together with the definitions of key terms and a short introduction to the most important methods for the estimation of the minimum postmortem interval, the present paper aims to encourage a high level of competency in the field of forensic entomology.


title: Learning Abstract Concept Embeddings from Multi-Modal Data: Since You Probably Can't See What I Mean

abstract: Models that acquire semantic representations from both linguistic and perceptual input are of interest to researchers in NLP because of the obvious parallels with human language learning. Performance advantages of the multi-modal approach over language-only models have been clearly established when models are required to learn concrete noun concepts. However, such concepts are comparatively rare in everyday language. In this work, we present a new means of extending the scope of multi-modal models to more commonly-occurring abstract lexical concepts via an approach that learns multimodal embeddings. Our architecture outperforms previous approaches in combining input from distinct modalities, and propagates perceptual information on concrete concepts to abstract concepts more effectively than alternatives. We discuss the implications of our results both for optimizing the performance of multi-modal models and for theories of abstract conceptual representation.


title: Optimal Design of an Axial Flux Permanent Magnet Synchronous Motor for the Electric Bicycle

abstract: Design of an electric machine such as the axial flux permanent magnet synchronous motor (AFPMSM) requires a 3-D finite-element method (FEM) analysis. The AFPMSM with a 3-D FEM model involves too much time and effort to analyze. To deal with this problem, we apply a surrogate assisted multi-objective optimization (SAMOO) algorithm that can realize an accurate and well-distributed Pareto front set with a few number of function calls, and considers various design variables in the motor design process. The superior performance of the SAMOO is verified by comparing it with conventional multi-objective optimization algorithms in a test function. Finally, the optimal design result of the AFPMSM for the electric bicycle is obtained by using the SAMOO algorithm.


title: A comparative study of language support for generic programming

abstract: Many modern programming languages support basic generic programming, sufficient to implement type-safe polymorphic containers. Some languages have moved beyond this basic support to a broader, more powerful interpretation of generic programming, and their extensions have proven valuable in practice. This paper reports on a comprehensive comparison of generics in six programming languages: C++, Standard ML, Haskell, Eiffel, Java (with its proposed generics extension), and Generic C. By implementing a substantial example in each of these languages, we identify eight language features that support this broader view of generic programming. We find these features are necessary to avoid awkward designs, poor maintainability, unnecessary run-time checks, and painfully verbose code. As languages increasingly support generics, it is important that language designers understand the features necessary to provide powerful generics and that their absence causes serious difficulties for programmers.


title: BlueDBM: an appliance for big data analytics

abstract: Complex data queries, because of their need for random accesses, have proven to be slow unless all the data can be accommodated in DRAM. There are many domains, such as genomics, geological data and daily twitter feeds where the datasets of interest are 5TB to 20 TB. For such a dataset, one would need a cluster with 100 servers, each with 128GB to 256GBs of DRAM, to accommodate all the data in DRAM. On the other hand, such datasets could be stored easily in the flash memory of a rack-sized cluster. Flash storage has much better random access performance than hard disks, which makes it desirable for analytics workloads. In this paper we present BlueDBM, a new system architecture which has flash-based storage with in-store processing capability and a low-latency high-throughput inter-controller network. We show that BlueDBM outperforms a flash-based system without these features by a factor of 10 for some important applications. While the performance of a ram-cloud system falls sharply even if only 5%~10% of the references are to the secondary storage, this sharp performance degradation is not an issue in BlueDBM. BlueDBM presents an attractive point in the cost-performance trade-off for Big Data analytics.


Green IoT Agriculture and HealthcareApplication (GAHA)


title: OMG UR Funny! Computer-Aided Humor with an Application to Chat

abstract: In this paper we explore Computer-Aided Humor (CAH), where a computer and a human collaborate to be humorous. CAH systems support people’s natural desire to be funny by helping them express their own idiosyncratic sense of humor. Artificial intelligence research has tried for years to create systems that are funny, but found the problem to be extremely hard. We show that by combining the strengths of a computer and a human, CAH can foster humor better than either alone. We present CAHOOTS, an online chat system that suggests humorous images to its users to include in the conversation. We compare CAHOOTS to a regular chat system, and to a system that automatically inserts funny images using an artificial humor-bot. Users report that CAHOOTS made their conversations more enjoyable and funny, and helped them to express their personal senses of humor. Computer-Aided Humor offers an example of how systems can algorithmically augment human intelligence to create rich, creative experiences.