title: Fostering Intrinsic Motivation through Avatar Identification in Digital Games

abstract: Fostering intrinsic motivation with interactive applications can increase the enjoyment that people experience when using technology, but can also translate into more invested effort. We propose that identifying with an avatar in a game will increase the intrinsic motivation of the player. We analyzed data from 126 participants playing a custom endless runner game and show that similarity identification, embodied identification, and wishful identification increases autonomy, immersion, invested effort, enjoyment, and positive affect. We also show that greater identification translates into motivated behaviour as operationalized by the time that players spent in an unending version of the infinite runner. Important for the design of games for entertainment and serious purposes, we discuss how identification with an avatar can be facilitated to cultivate intrinsic motivation within and beyond games.


title: Reference architecture and maturity levels for cyber-physical systems in the mechanical engineering industry

abstract: Nowadays mechanical engineering products change from mechatronic systems to Cyber-Physical Systems (CPS). CPS are connected, embedded systems which directly record physical data using sensors and affect physical processes using actuators. They evaluate and save recorded data, use globally available services and interact with operators via multimodal human-machine-interfaces. In context of industrial production CPS change production processes radically. Due to the change of technical systems, equipment suppliers, especially companies of the mechanical engineering industry, face the challenges of a rising complexity and a nearly unmanageable amount of new solutions based on information and communication technology. The contribution at hand provides a reference architecture and maturity levels for CPS. The reference architecture serves as an universal blueprint to structure CPS and to visualize all components and relationships. Two sets of CPS maturity levels help companies to assess the status quo, to determine the target state and to define concrete actions for improving their systems.


title: Ultra-Wideband (UWB) Bandpass Filters With Hybrid Microstrip/Slotline Structures

abstract: A novel ultra-wideband (UWB) bandpass filter is presented using back-to-back microstrip-slotline transition and triple-mode slotline resonator. A uniform slotline resonator is constructed with a length of one full wavelength at the central frequency. Two microstrip feed lines are then placed above the slotline resonator to make up a back-to-back microstrip-slotline transition, where two intersection points are equally located at one quarter-wavelength away from two short-ends. By properly stretching the microstrip line lengths, an ultra-wide passband is constituted with the lower/upper cut-off edges dominated by the first/third resonant modes.By forming a W-shaped slotline resonator, introducing a cross coupling and cascading two lowpass filters, two UWB bandpass filters with improved out-of-band performances are designed, fabricated and measured. Measured results achieve a 2.56-to-10.90 GHz or 124% passband with return loss >12.04 dB, group delay variation <0.58 ns, sharpened rejection skirts and extended upper-stopband.


Classifying Frames at the Sentence Level in News Articles.


title: Pin: building customized program analysis tools with dynamic instrumentation

abstract: Robust and powerful software instrumentation tools are essential for program analysis tasks such as profiling, performance evaluation, and bug detection. To meet this need, we have developed a new instrumentation system called Pin. Our goals are to provide easy-to-use, portable, transparent, and efficient instrumentation. Instrumentation tools (called Pintools) are written in C/C++ using Pin's rich API. Pin follows the model of ATOM, allowing the tool writer to analyze an application at the instruction level without the need for detailed knowledge of the underlying instruction set. The API is designed to be architecture independent whenever possible, making Pintools source compatible across different architectures. However, a Pintool can access architecture-specific details when necessary. Instrumentation with Pin is mostly transparent as the application and Pintool observe the application's original, uninstrumented behavior. Pin uses dynamic compilation to instrument executables while they are running. For efficiency, Pin uses several techniques, including inlining, register re-allocation, liveness analysis, and instruction scheduling to optimize instrumentation. This fully automated approach delivers significantly better instrumentation performance than similar tools. For example, Pin is 3.3x faster than Valgrind and 2x faster than DynamoRIO for basic-block counting. To illustrate Pin's versatility, we describe two Pintools in daily use to analyze production software. Pin is publicly available for Linux platforms on four architectures: IA32 (32-bit x86), EM64T (64-bit x86), Itanium®, and ARM. In the ten months since Pin 2 was released in July 2004, there have been over 3000 downloads from its website.


Application of Soil Electrical Conductivity to Precision Agriculture


title: Aerial Hose Type Robot by Water Jet for Fire Fighting

abstract: Disaster response, especially fire-fighting and rescue, is highly risky for firefighters engaged in action. As a result, many robots intended for fire-fighting have been proposed. However, it is difficult for them to directly access fire sources because their mobility is limited. Specifically, existing robots are large and heavy. Therefore, we propose a novel hose-type robot, which can fly directly into the fire source via a water jet. First, to control the reaction force for stable flying, we developed a nozzle module. By combining two nozzles whose outlet direction can be controlled, the resultant reaction force can be controlled. Finally, we developed a robot with a nozzle module and conducted an experiment. The experiment demonstrates that a robot with a length of approximately 2 m can fly stably in the air by leveraging the water jet. In addition, the head direction can also be controlled.


title: Rasch Analysis of the General Self-Efficacy Scale in Workers with Traumatic Limb Injuries

abstract: Purpose The purpose of this study was to apply Rasch analysis to examine the unidimensionality and reliability of the General Self-Efficacy Scale (GSE) in workers with traumatic limb injuries. Furthermore, if the items of the GSE fitted the Rasch model’s assumptions, we transformed the raw sum ordinal scores of the GSE into Rasch interval scores. Methods A total of 1076 participants completed the GSE at 1 month post injury. Rasch analysis was used to examine the unidimensionality and person reliability of the GSE. The unidimensionality of the GSE was verified by determining whether the items fit the Rasch model’s assumptions: (1) item fit indices: infit and outfit mean square (MNSQ) ranged from 0.6 to 1.4; and (2) the eigenvalue of the first factor extracted from principal component analysis (PCA) for residuals was <2. Person reliability was calculated. Results The unidimensionality of the 10-item GSE was supported in terms of good item fit statistics (infit and outfit MNSQ ranging from 0.92 to 1.32) and acceptable eigenvalues (1.6) of the first factor of the PCA, with person reliability = 0.89. Consequently, the raw sum scores of the GSE were transformed into Rasch scores. Conclusions The results indicated that the items of GSE are unidimensional and have acceptable person reliability in workers with traumatic limb injuries. Additionally, the raw sum scores of the GSE can be transformed into Rasch interval scores for prospective users to quantify workers’ levels of self-efficacy and to conduct further statistical analyses.


title: Algorithmic bias amplifies opinion polarization: A bounded confidence model

abstract: The flow of information reaching us via the online media platforms is optimized not by the information content or relevance but by popularity and proximity to the target. This is typically performed in order to maximise platform usage. As a side effect, this introduces an algorithmic bias that is believed to enhance fragmentation and polarization of the societal debate. To study this phenomenon, we modify the well-known continuous opinion dynamics model of bounded confidence in order to account for the algorithmic bias and investigate its consequences. In the simplest version of the original model the pairs of discussion participants are chosen at random and their opinions get closer to each other if they are within a fixed tolerance level. We modify the selection rule of the discussion partners: there is an enhanced probability to choose individuals whose opinions are already close to each other, thus mimicking the behavior of online media which suggest interaction with similar peers. As a result we observe: a) an increased tendency towards opinion fragmentation, which emerges also in conditions where the original model would predict consensus, b) increased polarisation of opinions and c) a dramatic slowing down of the speed at which the convergence at the asymptotic state is reached, which makes the system highly unstable. Fragmentation and polarization are augmented by a fragmented initial population.


title: The effect of news and public mood on stock movements

abstract: With technological advancements that cultivate vibrant creation, sharing, and collaboration among Web users, investors can rapidly obtain more valuable and timely information. Meanwhile, the adaption of user engagement in media effectively magnifies the information in the news. With such rapid information influx, investor decisions tend to be influenced by peer and public emotions. An effective methodology to quantitatively analyze the mechanism of information percolation and its degree of impact on stock markets has yet to be explored. In this article, we propose a quantitative media-aware trading strategy to investigate the media impact on stock markets. Our main findings are that (1) fundamental information of firm-specific news articles can enrich the knowledge of investors and affect their trading activities; (2) public sentiments cause emotional fluctuations in investors and intervene in their decision making; and (3) the media impact on firms varies according to firm characteristics and article content.


title: A Butterfly Substrate Integrated Waveguide Leaky-Wave Antenna

abstract: A new leaky-wave antenna taking advantage of substrate integrated waveguide technology has been introduced. This antenna has a butterfly-like configuration consisting of eight wings, i.e., eight parts. By this configuration, better gain and side-lobe level for lower elevation angles (12 $^{\circ}$ –45 $^{\circ}$ ) are obtained while in the uniform design, these angles were scanned with poor radiation performances. Besides, an effective matching part for good impedance matching has been employed. The simulated data have been compared with measurement results and showed good consistency.


title: Learning invariant feature hierarchies

abstract: Fast visual recognition in the mammalian cortex seems to be a hierarchical process by which the representation of the visual world is transformed in multiple stages from low-level retinotopic features to high-level, global and invariant features, and to object categories. Every single step in this hierarchy seems to be subject to learning. How does the visual cortex learn such hierarchical representations by just looking at the world? How could computers learn such representations from data? Computer vision models that are weakly inspired by the visual cortex will be described. A number of unsupervised learning algorithms to train these models will be presented, which are based on the sparse auto-encoder concept. The effectiveness of these algorithms for learning invariant feature hierarchies will be demonstrated with a number of practical tasks such as scene parsing, pedestrian detection, and object classification.


title: Implementation of Reverse Vending Machine Based on FPGA

abstract: In these modern days, Reverse Vending Machine is very popular to Korea, Japan, England, Britain, Greece and China. Reverse Vending Machine saves time, employee work and also save human's energy. This paper describes about the Reverse Vending Machine which works after inserting the empty beverage container into it. The project of Reverse Vending Machine (RVM) is useless without the cooperation of consumer. This is very attractive and motivated prize for the consumer through the RVM i.e. Refund. This paper addresses the implementation of Reverse Vending Machine based on FPGA. RVM is based on that process in which a machine accepts empty remaining containers and gives output according to the number of container. In this Paper, RVM supports empty containers, coins, snacks, chocolate, cold-drink etc. The algorithm for it is implemented in VHDL and simulated using Xilinx FPGA Spartan 3 development board.


title: Video Anomaly Identification

abstract: This article describes a family of unsupervised approaches to video anomaly detection based on statistical activity analysis. Approaches based on activity analysis provide intriguing possibilities for region-of-interest (ROI) processing since relevant activities and their locations are detected prior to higher-level processing such as object tracking, tagging, and classification. This strategy is essential for scalability of video analysis to cluttered environments with a multitude of objects and activities. Activity analysis approaches typically do not involve object tracking, and yet they inherently account for spatiotemporal dependencies. They are robust to clutter arising from multiple activities and contamination arising from poor background subtraction or occlusions. They can sometimes also be employed for fusing activities from multiple cameras. We illustrate successful application of activity analysis to anomaly detection in various scenarios, including the detection of abandoned objects, crowds of people, and illegal U-turns.


title: A simple and efficient hidden Markov model scheme for host-based anomaly intrusion detection

abstract: Extensive research activities have been observed on network-based intrusion detection systems (IDSs). However, there are always some attacks that penetrate traffic-profiling-based network IDSs. These attacks often cause very serious damages such as modifying host critical files. A host-based anomaly IDS is an effective complement to the network IDS in addressing this issue. This article proposes a simple data preprocessing approach to speed up a hidden Markov model (HMM) training for system-call-based anomaly intrusion detection. Experiments based on a public database demonstrate that this data preprocessing approach can reduce training time by up to 50 percent with unnoticeable intrusion detection performance degradation, compared to a conventional batch HMM training scheme. More than 58 percent data reduction has been observed compared to our prior incremental HMM training scheme. Although this maximum gain incurs more degradation of false alarm rate performance, the resulting performance is still reasonable.


title: A comprehensive analytical approach for policy analysis of system dynamics models

abstract: Formal tools to link system dynamics model's structure to the system modes of behavior have recently become available. In this paper, we aim to expand the use of these tools to perform the model's policy analysis in a more structured and formal way than the exhaustive exploratory approaches used to date. We consider how a policy intervention (a parameter change) affects a particular behavior mode by affecting the gains of particular feedback loops as well as how it affects the presence of that mode in the variable of interest. The paper demonstrates the utility of considering both of these aspects since the analysis provides an assessment of the overall impact of a policy on a variable and explains why the impact occurs in terms of structural changes in the model. Particularly in the context of larger models, this method enables a much more efficient search for leverage policies, by ranking the influence of each model parameter without the need for multiple simulation experiments.


title: Context-Aware Captions from Context-Agnostic Supervision

abstract: We introduce an inference technique to produce discriminative context-aware image captions (captions that describe differences between images or visual concepts) using only generic context-agnostic training data (captions that describe a concept or an image in isolation). For example, given images and captions of siamese cat and tiger cat, we generate language that describes the siamese cat in a way that distinguishes it from tiger cat. Our key novelty is that we show how to do joint inference over a language model that is context-agnostic and a listener which distinguishes closely-related concepts. We first apply our technique to a justification task, namely to describe why an image contains a particular fine-grained category as opposed to another closely-related category of the CUB-200-2011 dataset. We then study discriminative image captioning to generate language that uniquely refers to one of two semantically-similar images in the COCO dataset. Evaluations with discriminative ground truth for justification and human studies for discriminative image captioning reveal that our approach outperforms baseline generative and speaker-listener approaches for discrimination.


title: Cooperative Tx/Rx Caching in Interference Channels: A Storage-Latency Tradeoff Study

abstract: This paper studies the storage-latency tradeoff in the 3 × 3 wireless interference network with caches equipped at all transmitters and receivers. The tradeoff is characterized by the so-called fractional delivery time (FDT) at given normalized transmitter and receiver cache sizes. We first propose a generic cooperative transmitter/receiver caching strategy with adjustable file splitting ratios. Based on this caching strategy, we then design the delivery phase carefully to turn the considered interference channel opportunistically into broadcast channel, multicast channel, X channel, or a hybrid form of these channels. After that, we obtain an achievable upper bound of the minimum FDT by solving a linear programming problem of the file splitting ratios. The achievable FDT is a convex and piece-wise linear decreasing function of the cache sizes. Receiver local caching gain, coded multicasting gain, and transmitter cooperation gain (interference alignment and interference neutralization) are leveraged in different cache size regions.


Management solutions — Roadmap strategy and mplementation challenges hiva


Assembly Technologies for Integrated Transmitter/Receiver Optical Sub-Assembly Modules


title: Stiffness Analysis and Optimization of a Co-axial Spherical Parallel Manipulator

abstract: This paper investigates the stiness characteristics of spherical parallel manipulators. By virtue of singular


title: Bootstrapping techniques to improve the bandwidth of transimpedance amplifiers

abstract: Transimpedance amplifiers using voltage feedback operational amplifiers are widely used for current to voltage conversion in applications when a moderate/high bandwidth and a high sensitivity are required, such as photodiodes, photomultipliers, electron multipliers and capacitive sensors. The conventional circuit presents a virtual earth to the input and at low frequencies, the input capacitance is usually not a significant concern. However, at high frequencies and especially for high sensitivity circuits, the total input capacitance can severely limit the available bandwidth from the circuit. The input capacitance in effect constitutes part of the feedback network of the op-amp and hence reduces the available loop gain at high frequencies. In some cases a high input capacitance can cause the circuit to have a lightly damped or unstable dynamic response. Lag compensation by simply adding feedback capacitance is generally used to guarantee stability, however this approach does not permit the full gain-bandwidth characteristic of the op-amp to be fully exploited. Active techniques for reducing the input capacitance, for instance bootstrapping, have been previously reported and over the last ten years several papers have described successful examples of transimpedance circuits using bootstrapping. The basic bootstrapping principle is to use an additional buffer amplifier to actively charge and discharge to input capacitance as required. By doing so the effective source capacitance is reduced, enabling the overall bandwidth of the circuit to be increased. This paper presents a brief overview of the technique and introduces for the first time the four possible bootstrap configurations (series or shunt bootstrapping loops, with either floating or grounded sources) applied to the basic transimpedance circuit. Previous published techniques have employed the series technique, for example in the case of a photodiode bootstrapped by the source of the input FET. The shunt configuration has not been reported as in this paper. This work presents new analysis and practical results that demonstrate the shunt bootstrap configuration on a transimpedance circuit. A doubling of bandwidth over the standard transimpedance circuit has been realised using the same type of op-amp for both the amplifying and bootstrap elements of the circuit.


title: Demonstration of a home energy management system with smart thermostat control

abstract: A home energy management (HEM) system has the potential to enable demand response (DR) implementation for residential customers. This paper presents a detailed hardware architecture that allows control of an air conditioning (AC) unit by varying the thermostat set point. This can be accomplished by controlling a Wi-Fi enabled thermostat via an HEM system. The hardware demonstration of the proposed architecture is also discussed. This work showcases the applicability of the HEM system in managing an AC unit through a smart thermostat control. This approach can serve as an alternative to allowing an electric utility to perform remote disconnect to AC units. Controlling an AC unit by adjusting the thermostat set point can ensure lower average power consumption of the AC unit during a demand response period, along with better temperature control and improved equipment lifetime.


title: Learning to Predict Ego-Vehicle Poses for Sampling-Based Nonholonomic Motion Planning

abstract: Sampling-based motion planning is an effective tool to compute safe trajectories for automated vehicles in complex environments. However, a fast convergence to the optimal solution can only be ensured with the use of problem-specific sampling distributions. Due to the large variety of driving situations within the context of automated driving, it is very challenging to manually design such distributions. This letter introduces, therefore, a data-driven approach utilizing a deep convolutional neural network (CNN): Given the current driving situation, future ego-vehicle poses can be directly generated from the output of the CNN allowing to guide the motion planner efficiently toward the optimal solution. A benchmark highlights that the CNN predicts future vehicle poses with a higher accuracy compared to uniform sampling and a state-of-the-art A*-based approach. Combining this CNN-guided sampling with the motion planner bidirectional RRT* reduces the computation time by up to an order of magnitude and yields a faster convergence to a lower cost as well as a success rate of 100% in the tested scenarios.


title: A UWB Unidirectional Antenna With Dual-Polarization

abstract: A novel ±45° dual-polarized unidirectional antenna element is presented, consisting of two cross center-fed tapered mono-loops and two cross electric dipoles located against a reflector for ultrawideband applications. The operation principle of the antenna including the use of elliptically tapered transmission line for transiting the unbalanced energy to the balanced energy is described. Designs with different reflectors-planar or conical-are investigated. A measured overlapped impedance bandwidth of 126% (SWR <; 2) is demonstrated. Due to the complementary nature of the structure, the antenna has a relatively stable broadside radiation pattern with low cross polarization and low back lobe radiation over the operating band. The measured gain of the proposed antenna varies from 4 to 13 dBi and 7 to 14.5 dBi for port 1 and port 2, respectively, over the operating band, when mounted against a conical backed reflector. The measured coupling between the two ports is below -25 dB over the operating band.


title: Perceived usefulness, perceived ease of use, and user acceptance of information technology

abstract: Valid measurement scales for predicting user acceptance of computers are in short supply. Most subjective measures used in practice are unvalidated, and their relationship to system usage is unknown. The present research develops and validates new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance. Definitions of these two variables were used to develop scale items that were pretested for content validity and then tested for reliability and construct validity in two studies involving a total of 152 users and four application programs. The measures were refined and streamlined, resulting in two six-item scales with reliabilities of .98 for usefulness and .94 for ease of use. The scales exhibited hgih convergent, discriminant, and factorial validity. Perceived usefulness was significnatly correlated with both self-reported current usage r = .63, Study 1) and self-predicted future usage r = .85, Study 2). Perceived ease of use was also significantly correlated with current usage r = .45, Study 1) and future usage r = .59, Study 2). In both studies, usefulness had a signficnatly greater correaltion with usage behavior than did ease of use. Regression analyses suggest that perceived ease of use may actually be a causal antecdent to perceived usefulness, as opposed to a parallel, direct determinant of system usage. Implications are drawn for future research on user acceptance.


title: Avoiding Traffic Jam Using Ant Colony Optimization - A Novel Approach

abstract: Ant colony optimization (ACO) is a meta-heuristic based on colony of artificial ants which work cooperatively, building solutions by moving on the problem graph and by communicating through artificial pheromone trails mimicking real ants. One of the active research directions is the application of ACO algorithms to solve dynamic shortest path problems. Solving traffic jams is one such problem where the cost i.e. time to travel increases during rush hours resulting in tremendous strain on daily commuters and chaos. This paper describes a new approach-DSATJ (Dynamic System for Avoiding Traffic Jam) which aims at choosing an alternative optimum path to avoid traffic jam and then resuming that same path again when the traffic is regulated. The approach is inspired by variants of ACO algorithms. Traffic jam is detected through pheromone values on edges which are updated according to goodness of solution on the optimal tours only. Randomness is introduced in the probability function to ensure maximum exploration by ants. Experiments were carried out with the partial road map of North-West region of Delhi, India, to observe the performance of our approach.


title: An Enhanced Contextual Fire Detection Algorithm for MODIS

abstract: Abstract Experience with the first 2 years of high quality data from the Moderate Resolution Imaging Spectroradiometer (MODIS) through quality control and validation has suggested several improvements to the original MODIS active fire detection algorithm described by Kaufman, Justice et al. [Journal of Geophysical Research 103 (1998) 32215]. In this paper, we present an improved replacement detection algorithm that offers increased sensitivity to smaller, cooler fires as well as a significantly lower false alarm rate. Performance of both the original and improved algorithm is established using a theoretical simulation and high-resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes. In general, the new algorithm can detect fires roughly half the minimum size that could be detected with the original algorithm while having an overall false alarm rate 10–100 times smaller.


title: Compact Factorization of Matrices Using Generalized Round-Rank

abstract: Matrix factorization is a well-studied task in machine learning for compactly representing large, noisy data. In our approach, instead of using the traditional concept of matrix rank, we define a new notion of link-rank based on a non-linear link function used within factorization. In particular, by applying the round function on a factorization to obtain ordinal-valued matrices, we introduce generalized round-rank (GRR). We show that not only are there many full-rank matrices that are low GRR, but further, that these matrices cannot be approximated well by low-rank linear factorization. We provide uniqueness conditions of this formulation and provide gradient descent-based algorithms. Finally, we present experiments on real-world datasets to demonstrate that the GRR-based factorization is significantly more accurate than linear factorization, while converging faster and using lower rank representations.


title: Disaffection or expected outcomes: Understanding personal Internet use during work

abstract: Many contemporary analyses of personal Internet use during work explain the behavior in terms of workplace disaffection. However, evidence for this interpretation is mixed. This article posits that an approach emphasizing the expected outcomes of Internet use more effectively explains the behavior. The 2 approaches are tested using survey data collected from more than 1,000 U.S.-based computer-using workers. About 4/5 of those workers do engage in personal Internet use during work. Regression analyses show that workplace disaffection factors, such as stress and dissatisfaction, have no significant influence on the extent of web surfing or personal e-mail use during work. In contrast, factors which shape the expected outcomes of personal Internet use during work, such as a generalized positive perception of the utility of the Internet, routinized use of computers, job commitment, and organizational restrictions on computer use, are very significant predictors of such behavior. These results suggest that employees use the Internet for personal purposes at work for many of the same reasons that they use it elsewhere. Implications of these findings are explored.


title: Counting Pedestrians in Crowds Using Viewpoint Invariant Training

abstract: This paper describes a learning-based method for counting people in crowds from a single camera. Our method takes into account feature normalization to deal with perspective projection and different camera orientation. Thus, our system is trained to be viewpoint invariant and can be deployed with minimal setup for a new site. This is achieved by applying background subtraction and edge detection to each frame and extracting edge orientation and blob size histograms as features. A homography is computed between the ground plane and the image plane coordinates for the region of interest (ROI). A density map that measures the relative size of individuals and a global scale measuring camera orientation are also estimated and used for feature normalization. The relationship between the feature histograms and the number of pedestrians in the crowds is learned from labeled training data. The two training methods used in the current system are linear fitting and neural networks. Experimental results from different sites with different camera orientation demonstrate the performance and the potential of our method.


title: Contextual anomaly detection for cyber-physical security in Smart Grids based on an artificial neural network model

abstract: This paper presents a contextual anomaly detection method and its use in the discovery of malicious voltage control actions in the low voltage distribution grid. The model-based anomaly detection uses an artificial neural network model to identify a distributed energy resource's behaviour under control. An intrusion detection system observes distributed energy resource's behaviour, control actions and the power system impact, and is tested together with an ongoing voltage control attack in a co-simulation set-up. The simulation results obtained with a real photovoltaic rooftop power plant data show that the contextual anomaly detection performs on average 55% better in the control detection and over 56% better in the malicious control detection over the point anomaly detection.


title: Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

abstract: Soaring capacity and coverage demands dictate that future cellular networks need to migrate soon toward ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads, and higher backhaul costs. Interestingly, most of the problems that beleaguer network densification stem from legacy networks' one common feature, i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of the aforementioned challenges. In this survey, we review various proposals that have been presented in the literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification, namely: energy efficiency, system level capacity maximization, interference management, and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP) and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up-to-date survey on SARC, CoMP, and D2D. Most importantly, this survey provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.


title: Analgesic efficacy of peripheral opioids (all except intra-articular): a qualitative systematic review of randomised controlled trials

abstract: Abstract Anaesthetists, using basic scientific concepts of peripheral opioid activity, try to improve regional anaesthesia and postoperative analgesia by injecting opioids, with or without local anaesthetic, close to nerve trunks or nerve endings. To test the evidence that peripherally applied opioids (all except intra-articular) have an analgesic effect outside the knee joint. Systematic search for published reports of randomised controlled trials in the period 1966–1996 (MEDLINE, EMBASE, Oxford Data Base, reference lists) which compared efficacy of peripheral opioids with placebo, local anaesthetic, or systemic opioids in acute pain. Reports of pethidine or intra-articular opioids were not included. Data on intraoperative efficacy (onset, quality, duration of sensory block), and postoperative efficacy (pain intensity, analgesic consumption) were extracted. Statistical significance as indicated in the original reports and clinical relevance of differences between opioids and controls were taken into account to estimate qualitatively overall efficacy. Twenty-six trials with data from 952 patients were analysed. Opioids used were morphine (16 trials), fentanyl (8), alfentanil (1), buprenorphine (1), and butorphanol (1). Of four experimental pain trials, two reported a statistically significant difference in favour of the opioid. In 22 clinical trials efficacy of opioid injections into the brachial plexus (10), Bier's block (4), perineural (3), or other sites (5) was tested. Five of 10 clinical trials measuring intraoperative efficacy reported statistically significant efficacy with opioids compared with control; none of them were judged to be clinically relevant. Five of 17 clinical trials measuring postoperative efficacy reported a significant difference in favour of the opioid; none was judged to be clinical relevant. Trials of lower quality were more likely to report increased efficacy with opioids. Adverse events related to the route of administration were not reported. These trials provide no evidence for a clinically relevant peripheral analgesic efficacy of opioids in acute pain.


title: Brand analysis framework for online marketing: ranking web pages and analyzing popularity of brands on social media

abstract: Online marketing is one of the best practices used to establish a brand and to increase its popularity. Advertisements are used in a better way to showcase the company’s product/service and give rise to a worthy online marketing strategy. Posting an advertisement on utilitarian web pages helps to maximize brand reach and get a better feedback. Now-a-days companies are cautious of their brand image on the Internet due to the growing number of Internet users. Since there are billions of Web sites on the Internet, it becomes difficult for companies to really decide where to advertise on the Internet for brand popularity. What if, the company advertise on a page which is visited by less number of the interested (for a particular type of product) users instead of a web page which is visited by more number of the interested users?—this doubt and uncertainty—is a core issue faced by many companies. This research paper presents a Brand analysis framework and suggests some experimental practices to ensure efficiency of the proposed framework. This framework is divided into three components—(1) Web site network formation framework—a framework that forms a Web site network of a specific search query obtained from resultant web pages of three search engines-Google, Yahoo & Bing and their associated web pages; (2) content scraping framework—it crawls the content of web pages existing in the framework-formed Web site network; (3) rank assignment of networked web pages—text edge processing algorithm has been used to find out terms of interest and their occurrence associated with search query. We have further applied sentiment analysis to validate positive or negative impact of the sentences, having the search term and its associated terms (with reference to the search query) to identify impact of web page. Later, on the basis of both—text edge analysis and sentiment analysis results, we assigned a rank to networked web pages and online social network pages. In this research work, we present experiments for ‘Motorola smart phone,’ ‘LG smart phone’ and ‘Samsung smart phone’ as search query and sampled the Web site network of top 20 search results of all three search engines and examined up to 60 search results for each search engine. This work is useful to target the right online location for specific brand marketing. Once the brand knows the web pages/social media pages containing high brand affinity and ensures that the content of high affinity web page/social media page has a positive impact, we advertise at that respective online location. Thus, targeted brand analysis framework for online marketing not only has benefits for the advertisement agencies but also for the customers.


title: Inferring latent attributes of Twitter users with label regularization

abstract: Inferring latent attributes of online users has many applications in public health, politics, and marketing. Most existing approaches rely on supervised learning algorithms, which require manual data annotation and therefore are costly to develop and adapt over time. In this paper, we propose a lightly supervised approach based on label regularization to infer the age, ethnicity, and political orientation of Twitter users. Our approach learns from a heterogeneous collection of soft constraints derived from Census demographics, trends in baby names, and Twitter accounts that are emblematic of class labels. To counteract the imprecision of such constraints, we compare several constraint selection algorithms that optimize classification accuracy on a tuning set. We find that using no user-annotated data, our approach is within 2% of a fully supervised baseline for three of four tasks. Using a small set of labeled data for tuning further improves accuracy on all tasks.


title: Exactly-once delivery in a content-based publish-subscribe system

abstract: This paper presents a general knowledge model for propagating information in a content-based publish-subscribe system. The model is used to derive an efficient and scalable Protocol for exactly-once delivery to large numbers (tens of thousands per broker) of content-based subscribers in either publisher order or uniform total order Our protocol allows intermediate content filtering at each hop, but requires persistent storage only at the publishing site. It is tolerant of message drops, message reorderings, node failures, and link failures, and maintains only "soft" state at intermediate nodes. We evaluate the performance of our implementation both under failure-free conditions and with fault injection.


title: Delta-Sigma FDC Based Fractional-N PLLs

abstract: Fractional-N phase-locked loop frequency synthesizers based on time-to-digital converters (TDC-PLLs) have been proposed to reduce the area and linearity requirements of conventional PLLs based on delta-sigma modulation and charge pumps (ΔΣ-PLLs). Although TDC-PLLs with good performance have been demonstrated, TDC quantization noise has so far kept their phase noise and spurious tone performance below that of the best comparable ΔΣ-PLLs. An alternative approach is to use a delta-sigma frequency-to-digital converter (ΔΣ FDC) in place of a TDC to retain the benefits of TDC-PLLs and ΔΣ-PLLs. This paper proposes a practical ΔΣ FDC based PLL in which the quantization noise is equivalent to that of a ΔΣ-PLL. It presents a linearized model of the PLL, design criteria to avoid spurious tones in the ΔΣFDC quantization noise, and a design methodology for choosing the loop parameters in terms of standard PLL target specifications.


Guidance on the Conduct of Narrative Synthesis in Systematic Reviews A Product from the ESRC Methods Programme


title: Algebraic Distance on Graphs

abstract: Measuring the connection strength between a pair of vertices in a graph is one of the most important concerns in many graph applications. Simple measures such as edge weights may not be sufficient for capturing the effects associated with short paths of lengths greater than one. In this paper, we consider an iterative process that smooths an associated value for nearby vertices, and we present a measure of the local connection strength (called the algebraic distance; see [D. Ron, I. Safro, and A. Brandt, Multiscale Model. Simul., 9 (2011), pp. 407-423]) based on this process. The proposed measure is attractive in that the process is simple, linear, and easily parallelized. An analysis of the convergence property of the process reveals that the local neighborhoods play an important role in determining the connectivity between vertices. We demonstrate the practical effectiveness of the proposed measure through several combinatorial optimization problems on graphs and hypergraphs.


title: Elastic Distributed Bayesian Collaborative Filtering

abstract: In this paper, we consider learning a Bayesian collaborative filtering model on a shared cluster of commodity machines. Two main challenges arise: (1) How can we parallelize and distribute Bayesian collaborative filtering? (2) How can our distributed inference system handle elasticity events common in a shared, resource managed cluster, including resource ramp-up, preemption, and stragglers? To parallelize Bayesian inference, we adapt ideas from both matrix factorization partitioning schemes used with stochastic gradient descent and stale synchronous programming used with parameter servers. To handle elasticity events we offer a generalization of previous partitioning schemes that gives increased flexibility during system disruptions. We additionally describe two new scheduling algorithms to dynamically route work at runtime. In our experiments, we compare the effectiveness of both scheduling algorithms and demonstrate their robustness to system failure.


title: Are we ready for autonomous driving? The KITTI vision benchmark suite

abstract: Today, visual recognition systems are still rarely employed in robotics applications. Perhaps one of the main reasons for this is the lack of demanding benchmarks that mimic such scenarios. In this paper, we take advantage of our autonomous driving platform to develop novel challenging benchmarks for the tasks of stereo, optical flow, visual odometry/SLAM and 3D object detection. Our recording platform is equipped with four high resolution video cameras, a Velodyne laser scanner and a state-of-the-art localization system. Our benchmarks comprise 389 stereo and optical flow image pairs, stereo visual odometry sequences of 39.2 km length, and more than 200k 3D object annotations captured in cluttered scenarios (up to 15 cars and 30 pedestrians are visible per image). Results from state-of-the-art algorithms reveal that methods ranking high on established datasets such as Middlebury perform below average when being moved outside the laboratory to the real world. Our goal is to reduce this bias by providing challenging benchmarks with novel difficulties to the computer vision community. Our benchmarks are available online at: www.cvlibs.net/datasets/kitti


title: Hockey Action Recognition via Integrated Stacked Hourglass Network

abstract: A convolutional neural network (CNN) has been designed to interpret player actions in ice hockey video. The hourglass network is employed as the base to generate player pose estimation and layers are added to this network to produce action recognition. As such, the unified architecture is referred to as action recognition hourglass network, or ARHN. ARHN has three components. The first component is the latent pose estimator, the second transforms latent features to a common frame of reference, and the third performs action recognition. Since no benchmark dataset for pose estimation or action recognition is available for hockey players, we generate such an annotated dataset. Experimental results show action recognition accuracy of 65% for four types of actions in hockey. When similar poses are merged to three and two classes, the accuracy rate increases to 71% and 78%, proving the efficacy of the methodology for automated action recognition in hockey.


title: Modelocking of a thin-disk laser with the frequency-doubling nonlinear-mirror technique.

abstract: We demonstrate a frequency-doubling nonlinear-mirror (NLM) modelocked thin-disk laser. This modelocking technique, composed of an intracavity second harmonic crystal in combination with a dichroic output coupler, offers robust operation decoupled from cavity stability (as in semiconductor saturable absorber mirror (SESAM) modelocking) combined with an ultrafast saturable loss and high modulation depth (as in Kerr-lens modelocking (KLM)). With our NLM diode-pumped Yb:YAG thin-disk laser we achieve 21 W of average power at 323-fs pulse duration, which is an order of magnitude shorter than the previously obtained duration with the same technique in bulk lasers. Using these first results, we present a theoretical model for the NLM technique, which accurately predicts its loss modulation properties and the shortest achievable pulse duration without relying on any fitting parameters. Based on this simulation, we expect that the NLM technique will enable thin-disk lasers with average power of more than 100 W, with potentially sub-200 fs pulses. This could potentially solve the pulse duration limitations with SESAM modelocked Yb:YAG thin-disk lasers without imposing strong cavity stability constraints such as in KLM.


title: Formetric 4D Rasterstereography

abstract: This paper is a comment on “Intra- and interday reliability of spine rasterstereography” [1]. We think that supporting the feasibility of using the rasterstereographic tool Formetric 4D, both as a diagnostic tool and in scientific research, requires much more scientific exactness. The purpose of this paper is to underline what we consider to be substantial omission or error.


title: Making DDS really real-time with openflow

abstract: An increasing amount of distributed real-time systems and other critical infrastructure now rely on Data Distribution Service (DDS) middleware for timely dissementation of data between system nodes. While DDS has been designed specifically for use in distributed real-time systems and exposes a number of QoS properties to programmers, DDS fails to lift time fully into the programming abstraction. The reason for this is simple: DDS cannot directly control the underlying network to ensure that messages always arrive to their destination on time. In this paper we describe a prototype system that uses the OpenFlow SDN protocol to automatically and transparently learn the QoS requirements of DDS participants. Based on the QoS requirements, our system will manipulate the low-level packet forwarding rules in the network to ensure that the QoS requirements are always satisfied. We use real OpenFlow hardware to evaluate how well our prototype is able to manage network contention and guarantee the requested QoS. Additionally, we evaluate how well the reliability and resilience features of a real DDS implementation is able to compensate for network contention on an unmanaged (i.e., normal) ethernet. To the best of our knowledge, this is the first evaluation of performance DDS under extreme network contention conditions.


title: Multivariate time series classification with parametric derivative dynamic time warping

abstract: We improve DTW distance measure in multivariate time series classification.We use derivatives to improve DTW in multivariate time series classification.We test effectiveness on 18 real time series.We present a detailed comparison of proposed methods. Multivariate time series (MTS) data are widely used in a very broad range of fields, including medicine, finance, multimedia and engineering. In this paper a new approach for MTS classification, using a parametric derivative dynamic time warping distance, is proposed. Our approach combines two distances: the DTW distance between MTS and the DTW distance between derivatives of MTS. The new distance is used in classification with the nearest neighbor rule. Experimental results performed on 18 data sets demonstrate the effectiveness of the proposed approach for MTS classification.


title: Structural Behavior of Mortarless Interlocking Load Bearing Hollow Block Wall Panel under Out-Of-Plane Loading

abstract: Experimental and numerical investigation of interlocking mortarless wall panels with 1.0 m height, 1.2 m width and 150 mm thickness are conducted. Behaviour of both hollow and partially grouted masonry wall panels is studied. The panels were tested under constant pre-compressive vertical load and out-of-plane lateral load. Lateral load carrying capacity, deflection at mid height, dry joint opening between block layers and mode of failure are investigated. Strain characteristics throughout the loading process are also monitored. A finite element analysis is presented for the system and a good agreement between the experimental and modelling results is achieved. Parametric study using the finite element model is also presented and the effect of different parameters; amount of pre-compressive load and slenderness ratio is studied. The study reveals that pre-compressive vertical load and reinforcement significantly affect the structural behaviour of mortarless walls under out-of-plane loading. Useful expressi...


title: Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection

abstract: An integrated framework for density-based cluster analysis, outlier detection, and data visualization is introduced in this article. The main module consists of an algorithm to compute hierarchical estimates of the level sets of a density, following Hartigan’s classic model of density-contour clusters and trees. Such an algorithm generalizes and improves existing density-based clustering techniques with respect to different aspects. It provides as a result a complete clustering hierarchy composed of all possible density-based clusters following the nonparametric model adopted, for an infinite range of density thresholds. The resulting hierarchy can be easily processed so as to provide multiple ways for data visualization and exploration. It can also be further postprocessed so that: (i) a normalized score of “outlierness” can be assigned to each data object, which unifies both the global and local perspectives of outliers into a single definition; and (ii) a “flat” (i.e., nonhierarchical) clustering solution composed of clusters extracted from local cuts through the cluster tree (possibly corresponding to different density thresholds) can be obtained, either in an unsupervised or in a semisupervised way. In the unsupervised scenario, the algorithm corresponding to this postprocessing module provides a global, optimal solution to the formal problem of maximizing the overall stability of the extracted clusters. If partially labeled objects or instance-level constraints are provided by the user, the algorithm can solve the problem by considering both constraints violations/satisfactions and cluster stability criteria. An asymptotic complexity analysis, both in terms of running time and memory space, is described. Experiments are reported that involve a variety of synthetic and real datasets, including comparisons with state-of-the-art, density-based clustering and (global and local) outlier detection methods.


title: A Metamodel for Knowledge Management in Crisis Management

abstract: This article considers collaboration issue and data management issue as the key points of crisis management. In this context, the presented research works focus on a crisis management metamodel, dedicated to provide a way to class incoming data by connecting it to concepts of the metamodel. Such instances would then become intelligible for crisis management tools. This metamodel includes several points of view, specific to the domain of crisis management (including characteristics of the crisis, of the context, of the actors involved, etc.). Besides, the models built from this metamodel can be used as information bases for tools dedicated to support collaboration of actors: collaborative processes may be deduced from the models (and then orchestrated). Similarly, the evolution of the situation can be recorded as updates of the crisis models and contribute to the agility of the crisis management.


title: Web search query privacy: Evaluating query obfuscation and anonymizing networks

abstract: Web Search is one of the most rapidly growing applications on the internet today. However, the current practice followed by most search engines --of logging and analyzing users' queries --raises serious privacy concerns. In this paper, we concentrate on two existing solutions which are relatively easy to deploy --namely Query Obfuscation and Anonymizing Networks. In query obfuscation, a client-side software attempts to mask real user queries via injection of certain noisy queries. Anonymizing networks route the user queries through a series of relay servers, hiding the actual query source from the search engine. A fundamental problem with these solutions, however, is that user queries are still obviously revealed to the search engine, although they are “mixed” among queries generated either by a machine or by other users. We focus on TrackMeNot TMN, a popular query obfuscation tool, and the Tor anonymizing network, and try to analyse whether these solutions can actually preserve users' privacy in practice against an adversarial search engine. We demonstrate that a search engine, equipped with only a short-term history of a user's search queries, can break the privacy guarantees of TMN and Tor by only utilizing off-the-shelf machine learning techniques.


title: SPANIDS: a scalable network intrusion detection loadbalancer

abstract: Network intrusion detection systems (NIDS) are becoming an increasingly important security measure. With rapidly increasing network speeds, the capacity of the NIDS sensor can limit the ability of the system to detect intrusions. The SPANIDS parallel NIDS architecture overcomes this limitation by distributing network traffic load over an array of sensor nodes. Based on a custom hardware load balancer and cost-effective off-the-shelf sensors, the system employs novel stateless load balancing heuristics to thwart scalability limitations. It also uses dynamic feedback from the sensor nodes to adapt to changes in network traffic. This paper describes the overall system architecture, discusses some of the critical design decisions and presents experimental results that demonstrate the performance advantage of this approach


Research on Coordination Ability of Special Actions of Track and Field Jumping Events Based on Signal Autocorrelation


title: Deterministic Word Segmentation Using Maximum Matching with Fully Lexicalized Rules

abstract: We present a fast algorithm of word segmentation that scans an input sentence in a deterministic manner just one time. The algorithm is based on simple maximum matching which includes execution of fully lexicalized transformational rules. Since the process of rule matching is incorporated into dictionary lookup, fast segmentation is achieved. We evaluated the proposed method on word segmentation of Japanese. Experimental results show that our segmenter runs considerably faster than the state-of-the-art systems and yields a practical accuracy when a more accurate segmenter or an annotated corpus is available.


“ Chitty-Chitty-Chat Bot ” : Deep Learning for Conversational AI


title: Role of beta-dystrobrevin in nonmuscle dystrophin-associated protein complex-like complexes in kidney and liver.

abstract: beta-Dystrobrevin is a dystrophin-related and -associated protein that is highly expressed in brain, kidney, and liver. Recent studies with the kidneys of the mdx3Cv mouse, which lacks all dystrophin isoforms, suggest that beta-dystrobrevin, and not the dystrophin isoforms, may be the key component in the assembly of complexes similar to the muscle dystrophin-associated protein complexes (DPC) in nonmuscle tissues. To understand the role of beta-dystrobrevin in the function of nonmuscle tissues, we generated beta-dystrobrevin-deficient (dtnb(-/-)) mice by gene targeting. dtnb(-/-) mice are healthy, fertile, and normal in appearance. No beta-dystrobrevin was detected in these mice by Western blotting or immunocytochemistry. In addition, the levels of several beta-dystrobrevin-interacting proteins, namely Dp71 isoforms and the syntrophins, were greatly reduced from the basal membranes of kidney tubules and liver sinusoids and on Western blots of crude kidney and liver microsomes of beta-dystrobrevin-deficient mice. However, no abnormality was detected in the ultrastructure of membranes of kidney and liver cells or in the renal function of these mice. beta-Dystrobrevin may therefore be an anchor or scaffold for Dp71 and syntrophin isoforms, as well as other associating proteins at the basal membranes of kidney and liver, but is not necessary for the normal function of these mice.


title: A Secured Authentication Protocol for Wireless Sensor Networks Using Elliptic Curves Cryptography

abstract: User authentication is a crucial service in wireless sensor networks (WSNs) that is becoming increasingly common in WSNs because wireless sensor nodes are typically deployed in an unattended environment, leaving them open to possible hostile network attack. Because wireless sensor nodes are limited in computing power, data storage and communication capabilities, any user authentication protocol must be designed to operate efficiently in a resource constrained environment. In this paper, we review several proposed WSN user authentication protocols, with a detailed review of the M.L Das protocol and a cryptanalysis of Das’ protocol that shows several security weaknesses. Furthermore, this paper proposes an ECC-based user authentication protocol that resolves these weaknesses. According to our analysis of security of the ECC-based protocol, it is suitable for applications with higher security requirements. Finally, we present a comparison of security, computation, and communication costs and performances for the proposed protocols. The ECC-based protocol is shown to be suitable for higher security WSNs.


title: The embryonic development of the bodywall and nervous system of the cestode flatworm Hymenolepis diminuta

abstract: Cestodes (tapeworms) are a derived, parasitic clade of the phylum Platyhelminthes (flatworms). The cestode body wall represents an adaptation to its endoparasitic lifestyle. The epidermis forms a non-ciliated syncytium, and both muscular and nervous system are reduced. Morphological differences between cestodes and free-living flatworms become apparent already during early embryogenesis. Cestodes have a complex life cycle that begins with an infectious larva, called the oncosphere. In regard to cell number, cestode oncospheres are among the simplest multicellular organisms, containing in the order of 50–100 cells. As part of our continuing effort to analyze embryonic development in flatworms, we describe here the staining pattern obtained with acTub in embryos and larvae of the cestode Hymenolepis diminuta and, briefly, the monogenean Neoheterocotyle rhinobatidis. In addition, we labeled the embryonic musculature of Hymenolepis with phalloidin. In Hymenolepis embryos, two different cell types that we interpret as neurons and epidermal gland cells express acTub. There exist only two neurons that develop close to the midline at the anterior pole of the embryo. The axons of these two neurons project posteriorly into the center of the oncosphere, where they innervate the complex of muscles that is attached to the hooklets. In addition to neurons, acTub labels a small and invariant set of epidermal gland cells that develop at superficial positions, anteriorly adjacent to the neurons, in the dorsal midline, and around the posteriorly located hooklets. During late stages of embryogenesis they spread and form a complete covering of the embryo. We discuss these data in the broader context of platyhelminth embryology.


title: The self-regulating brain and neurofeedback: Experimental science and clinical promise

abstract: Neurofeedback, one of the primary examples of self-regulation, designates a collection of techniques that train the brain and help to improve its function. Since coming on the scene in the 1960s, electroencephalography-neurofeedback has become a treatment vehicle for a host of mental disorders; however, its clinical effectiveness remains controversial. Modern imaging technologies of the living human brain (e.g., real-time functional magnetic resonance imaging) and increasingly rigorous research protocols that utilize such methodologies begin to shed light on the underlying mechanisms that may facilitate more effective clinical applications. In this paper we focus on recent technological advances in the field of human brain imaging and discuss how these modern methods may influence the field of neurofeedback. Toward this end, we outline the state of the evidence and sketch out future directions to further explore the potential merits of this contentious therapeutic prospect.


Facial Emotion Recognition in Real Time


title: High efficiency coupling of light from a ridge to a photonic crystal waveguide.

abstract: We present theoretical and experimental demonstration of two designs to achieve group velocity insensitive coupling of light from a ridge waveguide to a photonic crystal waveguide. We demonstrate an average improvement of 62% in coupling to low group velocity modes and an average coupling enhancement of 11.5% at large group velocities.


title: WBAN Energy Efficiency and Dependability Improvement Utilizing Wake-Up Receiver

abstract: This paper discusses the perspectives of using a wake-up receiver (WUR) in wireless body area network (WBAN) applications with event-driven data transfers. First we compare energy efficiency between the WUR-based and the duty-cycled medium access control protocol -based IEEE 802.15.6 compliant WBAN. Then, we review the architectures of state-of-the-art WURs and discuss their suitability for WBANs. The presented results clearly show that the radio frequency envelope detection based architecture features the lowest power consumption at a cost of sensitivity. The other architectures are capable of providing better sensitivity, but consume more power. Finally, we propose the design modification that enables using a WUR to receive the control commands beside the wake-up signals. The presented results reveal that use of this feature does not require complex modifications of the current architectures, but enables to improve energy efficiency and latency for small data blocks transfers.


Virtual reality-augmented rehabilitation for patients in subacute phase post stroke : a feasibility study


How long until human-level AI? Results from an expert assessment, Technological Forecasting


title: Pattern and Adaptation in Individual Behavioral Differences

abstract: The existence of individual differences in behavior is a well-documented but poorly understood phenomenon. On the one hand, individual variation could be attributable to nonadaptive evolutionary processes, such as genetic drift, or to stochastic environmental forces that act upon a single genotype to produce an array of observed behavioral phenotypes. On the other hand, behavioral differences may reflect the action of the process of natural selection on the form, degree, context, and origins of differences in behavior. The characteristics of behavioral variation in any given species may themselves represent an adaptation which increases individual success. The nonadaptive “explanation” is common, but generally untestable; it makes no specific predictions, although implicitly it suggests that many observed types will be maladaptive. The functional interpretation leads us to expect pattern in the occurrence of individual variation both between and within species such that an individual’s behavior is more precisely fit for its peculiar circumstances. In this chapter, we explicitly pursue the possibility of functional and adaptive individuality. We review the reports of variation in social and foraging behavior and consider the existing explanations for this variation. We then try to develop a framework for further study which relates the appearance and controls on individuality to the life history of an organism and the dynamics of its foraging and social environments. This framework leads to some specific predictions both interspecifically and intraspecifically.


title: Building Multiversal Semantic Maps for Mobile Robot Operation

abstract: A probabilistic stance is proposed for tackling the symbol grounding problem.The outcome of such grounding is accommodated in a novel semantic map representation.This semantic map considers different interpretations of the robot workspace.A more coherent robot operation is achieved by exploring such interpretations.Our proposal has been assessed employing the Robot@Home dataset. Semantic maps augment metric-topological maps with meta-information, i.e. l semantic knowledge aimed at the planning and execution of high-level robotic tasks. Semantic knowledge typically encodes human-like concepts, like types of objects and rooms, which are connected to sensory data when symbolic representations of percepts from the robot workspace are grounded to those concepts. Such a symbol grounding is usually carried out by algorithms that individually categorize each symbol and provide a crispy outcome a symbol is either a member of a category or not. Such approach is valid for a variety of tasks, but it fails at: (i) dealing with the uncertainty inherent to the grounding process, and (ii) jointly exploiting the contextual relations among concepts (e.g. microwaves are usually in kitchens). This work provides a solution for probabilistic symbol grounding that overcomes these limitations. Concretely, we rely on Conditional Random Fields (CRFs) to model and exploit contextual relations, and to provide measurements about the uncertainty coming from the possible groundings in the form of beliefs (e.g. an object can be categorized (grounded) as a microwave or as a nightstand with beliefs 0.6 and 0.4, respectively). Our solution is integrated into a novel semantic map representation called Multiversal Semantic Map (MvSmap), which keeps the sets of different groundings, or universes, as instances of ontologies annotated with the obtained beliefs for their posterior exploitation. The suitability of our proposal has been proven with the Robot@Home dataset, a repository that contains challenging multi-modal sensory information gathered by a mobile robot in home environments.


Protective relaying for pumped storage hydro units rotating machinery protection subcommittee of the IEEE power system relaying committee


title: Position control of Robotino mobile robot using fuzzy logic

abstract: The appearance of fuzzy logic theory led to new trends in robotics field and solves various typical problems. In fact, fuzzy logic is a good alternative for increasing the capabilities of autonomous mobile robots in an unknown dynamic environment by integrating human experience. The paper presents the design and implementation of the position control using fuzzy logic for an omni directional mobile robot. The position control of the mobile robot was tested remotely using the Festo Robotino platform and Matlab environment. The obstacle avoidance and mobile robot odometry were also studied in this work.


title: Scaling of SOI FinFETs down to fin width of 4 nm for the 10nm technology node

abstract: Using a novel replacement gate SOI FinFET device structure, we have fabricated FinFETs with fin width (D Fin ) of 4nm, fin pitch (FP) of 40nm, and gate length (L G ) of 20nm. With this structure, we have achieved arrays of thousands of fins for D Fin down to 4nm with robust yield and structural integrity. We observe performance degradation, increased variability, and V T shift as D Fin is reduced. Capacitance measurements agree with quantum confinement behavior which has been predicted to pose a fundamental limit to scaling FinFETs below 10nm L G .


title: Interactive geometry remeshing

abstract: We present a novel technique, both flexible and efficient, for interactive remeshing of irregular geometry. First, the original (arbitrary genus) mesh is substituted by a series of 2D maps in parameter space. Using these maps, our algorithm is then able to take advantage of established signal processing and halftoning tools that offer real-time interaction and intricate control. The user can easily combine these maps to create a control map --- a map which controls the sampling density over the surface patch. This map is then sampled at interactive rates allowing the user to easily design a tailored resampling. Once this sampling is complete, a Delaunay triangulation and fast optimization are performed to perfect the final mesh.As a result, our remeshing technique is extremely versatile and general, being able to produce arbitrarily complex meshes with a variety of properties including: uniformity, regularity, semi-regularity, curvature sensitive resampling, and feature preservation. We provide a high level of control over the sampling distribution allowing the user to interactively custom design the mesh based on their requirements thereby increasing their productivity in creating a wide variety of meshes.


title: Riesz Fractional Based Model for Enhancing License Plate Detection and Recognition

abstract: One of the major causes of poor results in license plate recognition is low quality of images affected by multiple factors, such as severe illumination condition, complex background, different weather conditions, night light, and perspective distortions. In this paper, we propose a new mathematical model based on Riesz fractional operator for enhancing details of edge information in license plate images to improve the performances of text detection and recognition methods. The proposed model performs convolution operation of the Riesz fractional derivative over each input image by enhancing the edge strength in it. To test the performance of the proposed model, we conduct experiments on benchmark license plate image databases, namely, UCSD and ICDAR 2015-SR competition text image databases. Experimental results on enhancement show that the proposed model outperforms the existing baseline enhancement techniques in terms of quality measures. Furthermore, experimental results on text detection and recognition show that text detection and recognition rates are improved significantly after enhancement compared with before enhancement.


title: Broadband modified Wilkinson power divider fed antipodal Vivaldi antenna array

abstract: A broadband modified Wilkinson power divider feed network for a 4 element antipodal Vivaldi antenna array in the frequency range of 2-6 GHz is presented. A modified Wilkinson power divider formed by implementing one radial stub on each branch is designed to meet the antenna specifications. Measured results are shown to meet the array specifications. This wideband array is useful in applications such as electronic warfare.


title: Face recognition on partially occluded images using compressed sensing

abstract: In this work we have built a face recognition system using a new method based on recent advances in compressed sensing theory. The authors propose a method for recognizing faces that is robust to certain types and levels of occlusion. They also present tests that allow to assess the incidence of the proposed method.


title: Classification of sentiment reviews using n-gram machine learning approach

abstract: A large number of sentiment reviews, blogs and comments present online.These reviews must be classified to obtain a meaningful information.Four different supervised machine learning algorithm used for classification.Unigram, Bigram, Trigram models and their combinations used for classification.The classification is done on IMDb movie review dataset. With the ever increasing social networking and online marketing sites, the reviews and blogs obtained from those, act as an important source for further analysis and improved decision making. These reviews are mostly unstructured by nature and thus, need processing like classification or clustering to provide a meaningful information for future uses. These reviews and blogs may be classified into different polarity groups such as positive, negative, and neutral in order to extract information from the input dataset. Supervised machine learning methods help to classify these reviews. In this paper, four different machine learning algorithms such as Naive Bayes (NB), Maximum Entropy (ME), Stochastic Gradient Descent (SGD), and Support Vector Machine (SVM) have been considered for classification of human sentiments. The accuracy of different methods are critically examined in order to access their performance on the basis of parameters such as precision, recall, f-measure, and accuracy.


title: Knowledge And Meaning ... Chaos And Complexity

abstract: What are models good for? Taking a largely pedagogical view, the following essay discusses the semantics of measurement and the uses to which an observer can put knowledge of a process’s structure. To do this in as concrete a way as possible, it first reviews the reconstruction of probabilistic finite automata from time series of stochastic and chaotic processes. It investigates the convergence of an observer’s knowledge of the process’s state; assuming that the process is in, and the observer also uses for internal representation, that model class. The conventional notions of phase and phase-locking are extended beyond periodic behavior to include deterministic chaotic processes. The meaning of individual measurements of an unpredictable process is then defined in terms of the computational structure in a model that an observer built.


title: New xyelydid sawflies from the Lower Cretaceous of China

abstract: Abstract One new genus and three new species, Fissilyda compta gen. et sp. nov., F. alba sp. nov., and F. parilis sp. nov., and a new species, Novalyda decora sp. nov., are described and figured on the basis of five well-preserved fossils from the Lower Cretaceous Yixian Formation of China. All of these well-preserved new fossil specimens have a desclerotized pterostigma which is pale at the base, while F. alba sp. nov. has the crossvein cu-a linearly aligned with vein a in the hind wing, both are rather unusual and important characters in Hymenoptera. These new taxa broaden the diversity of Xyelydidae in the Early Cretaceous and their new morphological characters enhance our understanding of the evolution and relationships of this small extinct family.


title: Point-of-Interest Recommendation for Location Promotion in Location-Based Social Networks

abstract: With the wide application of location-based social networks (LBSNs), point-of-interest (POI) recommendation has become one of the major services in LBSNs. The behaviors of users in LBSNs are mainly checking in POIs, and these checking-in behaviors are influenced by user's behavior habits and his/her friends. In social networks, social influence is often used to help businesses to attract more users. Each target user has a different influence on different POI in social networks. This paper selects the list of POIs with the greatest influence for recommending users. Our goals are to satisfy the target user's service need, and simultaneously to promote businesses' locations (POIs). This paper defines a POI recommendation problem for location promotion. Additionally, we use submodular properties to solve the optimization problem. At last, this paper conducted a comprehensive performance evaluation for our method using two real LBSN datasets. Experimental results show that our proposed method achieves significantly superior POI recommendations comparing with other state-of-the-art recommendation approaches in terms of location promotion.


Multivariate Density Estimation using Dimension Reducing Information and Tail Flattening Transformations ∗ Tine Buch-Kromann † Montserrat Guillen ‡ Oliver Linton §


title: Customer Relationship Management (CRM) implementation: A soft issue in knowledge management scenario

abstract: Knowledge Management (KM) implementation is mainly linked to soft issues such as organizational culture and people. Recently, most organizations are struggling to effectively use KM tools and techniques. Previous study shows that there is a relationship between KM and Customer Relationship Management (CRM). KM, in particular, has been defined as the process of capturing the collective expertise and intelligence in an organization and using them to foster innovation through continued organizational learning whereas obtaining customer-related knowledge is specified as the means to attain CRM objectives. Since a major part of that expertise and intelligence refers to customers, it is concluded that CRM is strongly related to KM and especially to customer knowledge management. CRM is a combination of people, processes and technology that seeks to understand a company's customers. CRM has evolved from advances in information technology and organizational changes in customer centric processes. Managing a successful CRM implementation requires an integrated and balanced approach to technology, processes and people. This paper discuss about the implementation of CRM in Research and Development Centers of Public Universities in Malaysia. The purpose of this study is to investigate the current scenario of CRM implementation Research and Development Centers of Public Universities in Malaysia and what are their challenges in its implementation. This study has conducted the structured interview session with top management of eight public universities. Besides, a questionnaire was distributed to the R & D staffs in those eight public universities. Statistical analysis was carried out on data collected from 40 completed questionnaires. Descriptive analysis and correlation test has been performed to investigate the hypotheses. This study indicates that organization CRM knowledge has a relationship with organizational issues such as human, culture, financial and technology issues. Suggestions as to what future research should indicate are also made.


title: Dynamic modeling and obstacle-crossing capability of flexible pendulum-driven ball-shaped robots

abstract: Abstract Ball-shaped robots present a novel and widely studied approach for mobile robotics. Despite the essential benefit of the ball-robot that it cannot flip over or fall down, the robot’s physical construction often severely limits the ball mobility in uneven terrain. The customarily applied quasi-static motion model makes the anticipated theoretical robot mobility even worse, because it completely ignores ball dynamics and therefore seriously under-estimates the robot’s obstacle-crossing capability. The energy-based model, sometimes applied instead of the quasi-static model, over-estimates ball mobility and becomes inconvenient when an active driving motor is added to the system. This paper introduces a new extended dynamic model for flexible pendulum-driven ball-shaped robots, as well as a simulation-based method to predict the robot’s step-crossing capability. The extended dynamic model allows rolling, bouncing and slipping of the robot, and it includes a simplified contact model for the ball-obstacle-interaction. The simulation results have been compared to experimental results obtained with a physical robot. The comparison shows that the new dynamic model and contact model outperform the traditionally applied quasi-static and energy-based models. The new dynamic model may be applied in mobility analysis of ball-robot designs, for path planning, as well as for control algorithm development.


Innovative care for chronic conditions: building blocks for action


title: Sokoban: enhancing general single-agent search methods using domain knowledge

abstract: Abstract Artificial intelligence (AI) research has developed an extensive collection of methods to solve state-space problems. Using the challenging domain of Sokoban, this paper studies the effect of general search enhancements on program performance. We show that the current state of the art in AI generally requires a large research and programming effort to use domain-dependent knowledge to solve even moderately complex problems in such difficult domains. The application of domain-specific knowledge to exploit properties of the search space can result in large reductions in the size of the search tree, often several orders of magnitude per search enhancement. This application-specific knowledge is discovered and applied using application-independent search enhancements. Understanding the effect of these enhancements on the search leads to a new taxonomy of search enhancements, and a new framework for developing single-agent search applications. This is used to illustrate the large gap between what is portrayed in the literature versus what is needed in practice.


title: Storytelling by the StoryCake visualization

abstract: On the issue of how to explore the complex relationships of entities in a story, Storyline visualization has proven to be a useful approach. The traditional Storyline visualization technology only applies to a linear continuous story and has the limitation of reflecting the development of the plots in a story. In this paper, we propose a hierarchical plot visualization method called StoryCake to improve this problem. First, according to the story elements, inter-sessions are divided into groups, whereby the layout of the entities will be optimized in individual groups. Then, the hierarchical relationships of entities in polar coordinates can be confirmed. Finally, we use several skills, e.g., interaction, labels and a fan-shaped visualization view, to enable people better understand how story evolves and track the hierarchical relationship more conveniently. Experimental results show that the proposed hierarchical plot visualization method in polar coordinates can reveal discontinuous events and nonlinear stories. The results also show that our algorithm presents the narrative structure and development of the story.


title: Bioinspired Engineering of Thermal Materials

abstract: In the development of next-generation materials with enhanced thermal properties, biological systems in nature provide many examples that have exceptional structural designs and unparalleled performance in their thermal or nonthermal functions. Bioinspired engineering thus offers great promise in the synthesis and fabrication of thermal materials that are difficult to engineer through conventional approaches. In this review, recent progress in the emerging area of bioinspired advanced materials for thermal science and technology is summarized. State-of-the-art developments of bioinspired thermal-management materials, including materials for efficient thermal insulation and heat transfer, and bioinspired materials for thermal/infrared detection, are highlighted. The dynamic balance of bioinspiration and practical engineering, the correlation of inspiration approaches with the targeted applications, and the coexistence of molecule-based inspiration and structure-based inspiration are discussed in the overview of the development. The long-term outlook and short-term focus of this critical area of advanced materials engineering are also presented.


title: Compliance with Information Security Policies: An Empirical Investigation

abstract: Information security was the main topic in this paper. An investigation of the compliance to information security policies were discussed. The author mentions that the insignificant relationship between rewards and actual compliance with information security policies does not make sense. Quite possibly this relationship results from not applying rewards for security compliance. Also mentions that based on the survey conducted, careless employee behavior places an organization's assets and reputation in serious jeopardy. The major threat to information security arises from careless employees who fail to comply with organizations' information security policies and procedures.


title: Energy Efficiency Optimization for MISO SWIPT Systems With Zero-Forcing Beamforming

abstract: This paper considers a power splitting based multiuser multiple-input-single-output (MISO) downlink system with simultaneous wireless information and power transfer, where each single antenna receiver splits the received signal into two streams of different power for decoding information and harvesting energy separately. Assuming that the most common zero-forcing (ZF) beamforming scheme is employed by the base station, we aim to maximize the system energy efficiency in bits per Joule by joint beamforming and power splitting under both the signal-to-interference-plus-noise ratio constraints and energy harvesting constraints. The energy efficiency optimization (EEO) problem is nonconvex and very hard to solve. In this paper, by exploiting the problem structure, we first simplify the EEO problem to a joint transmit power allocation and receive power splitting problem. Then, with a tactful reformulation, we propose a Lagrangian relaxation (LR) method coupled with Dinkelbach method to address the simplified EEO problem, whilst devising a nearly closed-form solution for the subproblems involved in the Dinkelbach method. It is proven that the proposed LR method is optimum under some condition and can guarantee at least a feasible solution, which is a notable advantage over the existing methods. Besides, we develop a low complexity EEO algorithm by proportionally distributing the total power to users. Finally, numerical results validate the excellent efficiency of the proposed algorithms.


title: Decoding the Semantic Content of Natural Movies from Human Brain Activity

abstract: One crucial test for any quantitative model of the brain is to show that the model can be used to accurately decode information from evoked brain activity. Several recent neuroimaging studies have decoded the structure or semantic content of static visual images from human brain activity. Here we present a decoding algorithm that makes it possible to decode detailed information about the object and action categories present in natural movies from human brain activity signals measured by functional MRI. Decoding is accomplished using a hierarchical logistic regression (HLR) model that is based on labels that were manually assigned from the WordNet semantic taxonomy. This model makes it possible to simultaneously decode information about both specific and general categories, while respecting the relationships between them. Our results show that we can decode the presence of many object and action categories from averaged blood-oxygen level-dependent (BOLD) responses with a high degree of accuracy (area under the ROC curve > 0.9). Furthermore, we used this framework to test whether semantic relationships defined in the WordNet taxonomy are represented the same way in the human brain. This analysis showed that hierarchical relationships between general categories and atypical examples, such as organism and plant, did not seem to be reflected in representations measured by BOLD fMRI.


title: Learning Search Strategies from Human Demonstrations

abstract: Decision making and planning with partial state information is a problem faced by all forms of intelligent entities. The formulation of a problem under partial state information leads to an exorbitant set of choices with associated probabilistic outcomes making its resolution difficult when using traditional planning methods. Human beings have acquired the ability of acting under uncertainty through education and self-learning. Transferring our know-how to artificial agents and robots will make it faster for them to learn and even improve upon us in tasks in which incomplete knowledge is available, which is the objective of this thesis. We model how humans reason with respect to their beliefs and transfer this knowledge in the form of a parameterised policy, following a Programming by Demonstration framework, to a robot apprentice for two spatial navigation tasks: the first task consists of localising a wooden block on a table and for the second task a power socket must be found and connected. In both tasks the human teacher and robot apprentice only rely on haptic and tactile information. We model the human and robot's beliefs by a probability density function which we update through recursive Bayesian state space estimation. To model the reasoning processes of human subjects performing the search tasks we learn a generative joint distribution over beliefs and actions (end-effector velocities) which were recorded during the executions of the task. For the first search task the direct mapping from belief to actions is learned whilst for the second task we incorporate a cost function used to adapt the policy parameters in a Reinforcement Learning framework and show a considerable improvement over solely learning the behaviour with respect to the distance taken to accomplish the task. Both search tasks above can be considered as active localisation as the uncertainty originates only from the position of the agent in the world. We consider searches in which both the position of the robot and features of the environment are uncertain. Given the unstructured nature of the belief a histogram parametrisation of the joint distribution of the robots position and features is necessary. However, naively doing so becomes quickly intractable as the space and time complexity is exponential. We demonstrate that by only parametrising the marginals and by memorising the parameters of the measurement likelihood functions we can recover the exact same solution as the naive parametrisations at a cost which is linear in space and time complexity.


Image Titles-Variations on Show , Attend and Tell


title: Satellite multibeam antennas at airbus defence and space: State of the art and trends

abstract: This paper presents a survey of the current state of the art of multi beam antennas applied for mobile, broad band and regional broadcasting services. The pros and cons of the different architectures, like single feed per beam, multi feed per beam or direct radiating and imaging arrays are discussed and representative examples for the different architectures produced or currently under production at Airbus Defence and Space are presented. The paper concludes with an outlook on potential developments towards antennas with increased flexibility.


title: User Environment Detection with Acoustic Sensors Embedded on Mobile Devices for the Recognition of Activities of Daily Living

abstract: The detection of the environment where user is located, is of extreme use for the identification of Activities of Daily Living (ADL). ADL can be identified by use of the sensors available in many off-the-shelf mobile devices, including magnetic and motion, and the environment can be also identified using acoustic sensors. The study presented in this paper is divided in two parts: firstly, we discuss the recognition of the environment using acoustic sensors (i.e., microphone), and secondly, we fuse this information with motion and magnetic sensors (i.e., motion and magnetic sensors) for the recognition of standing activities of daily living. The recognition of the environments and the ADL are performed using pattern recognition techniques, in order to develop a system that includes data acquisition, data processing, data fusion, and artificial intelligence methods. The artificial intelligence methods explored in this study are composed by different types of Artificial Neural Networks (ANN), comparing the different types of ANN and selecting the best methods to implement in the different stages of the system developed. Conclusions point to the use of Deep Neural Networks (DNN) with normalized data for the identification of ADL with 85.89% of accuracy, the use of Feedforward neural networks with non-normalized data for the identification of the environments with 86.50% of accuracy, and the use of DNN with normalized data for the identification of standing activities with 100% of accuracy.


Chronic illness and school achievement.


title: A Broadband Circularly Polarized Printed Monopole Antenna With Parasitic Strips

abstract: A novel printed monopole antenna is presented for circular polarization (CP) operation with broad axial-ratio (AR) bandwidth. The antenna is composed of a monopole, a ground plane, and two parasitic strips. By utilizing two spiral strips coupled by the monopole, broadband CP radiation is achieved. The measured results show the 10-dB impedance bandwidth is 2.75 GHz (1.75–4.5 GHz) and the antenna also has a 3-dB AR bandwidth of 64.7% (2.2 GHz, 2.3–4.5 GHz). The proposed antenna features simple structure and broad AR bandwidth, which is desirable for various wireless communication systems.


title: Decision Trees for Uplift Modeling

abstract: Most classification approaches aim at achieving high prediction accuracy on a given dataset. However, in most practical cases, some action, such as mailing an offer or treating a patient, is to be taken on the classified objects and we should model not the class probabilities themselves, but instead, the change in class probabilities caused by the action. The action should then be performed on those objects for which it will be most profitable. This problem is known as uplift modeling, differential response analysis or true lift modeling, but has received very little attention in Machine Learning literature. In the paper we present a tree based classifier tailored specifically to this task. To this end, we design new splitting criteria and pruning methods. The experiments confirm the usefulness of the proposed approach and show significant improvement over previous uplift modeling techniques.


title: Design of L band cavity filter for GPS receiver

abstract: Filters play a critical role in RF and Microwave systems and the type used depends on the application. It is used in the receiver front end to improve selectivity and in transmitter for spurious /harmonic rejection where low insertion loss and narrow bandwidth are the major requirements. This paper, deals with the design of a narrow band cavity filter in L band with centre frequency of 1575.42 MHz. It is planned to use at the front end of a GPS receiver where the system has to withstand the harsh environment of launch vehicle. The cavity filter is designed for the required specification, simulated and optimized using the software Advanced Design System (ADS) and ElectroMagnetic Professional (EMPro).