Query: Nested LSTM : Modeling Taxonomy and Temporal Dynamics in Location-Based Social Network

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Long Short-Term Memory <=SEP=> Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.

Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval <=SEP=> This paper develops a model that addresses sentence embedding, a hot topic in current natural language processing research, using recurrent neural networks (RNN) with Long Short-Term Memory (LSTM) cells. The proposed LSTM-RNN model sequentially takes each word in a sentence, extracts its information, and embeds it into a semantic vector. Due to its ability to capture long term memory, the LSTM-RNN accumulates increasingly richer information as it goes through the sentence, and when it reaches the last word, the hidden layer of the network provides a semantic representation of the whole sentence. In this paper, the LSTM-RNN is trained in a weakly supervised manner on user click-through data logged by a commercial web search engine. Visualization and analysis are performed to understand how the embedding process works. The model is found to automatically attenuate the unimportant words and detect the salient keywords in the sentence. Furthermore, these detected keywords are found to automatically activate different cells of the LSTM-RNN, where words belonging to a similar topic activate the same cell. As a semantic representation of the sentence, the embedding vector can be used in many different applications. These automatic keyword detection and topic allocation abilities enabled by the LSTM-RNN allow the network to perform document retrieval, a difficult language processing task, where the similarity between the query and documents can be measured by the distance between their corresponding sentence embedding vectors computed by the LSTM-RNN. On a web search task, the LSTM-RNN embedding is shown to significantly outperform several existing state of the art methods. We emphasize that the proposed model generates sentence embedding vectors that are specially useful for web document retrieval tasks. A comparison with a well known general sentence embedding method, the Paragraph Vector, is performed. The results show that the proposed method in this paper significantly outperforms Paragraph Vector method for web document retrieval task.

Recurrent Recommender Networks <=SEP=> Recommender systems traditionally assume that user profiles and movie attributes are static. Temporal dynamics are purely reactive, that is, they are inferred after they are observed, e.g. after a user's taste has changed or based on hand-engineered temporal bias corrections for movies. We propose Recurrent Recommender Networks (RRN) that are able to predict future behavioral trajectories. This is achieved by endowing both users and movies with a Long Short-Term Memory (LSTM) autoregressive model that captures dynamics, in addition to a more traditional low-rank factorization. On multiple real-world datasets, our model offers excellent prediction accuracy and it is very compact, since we need not learn latent state but rather just the state transition function.

A Survey of Point-of-interest Recommendation in Location-based Social Networks <=SEP=> Point-of-interest (POI) recommendation that suggests new places for users to visit arises with the popularity of location-based social networks (LBSNs). Due to the importance of POI recommendation in LBSNs, it has attracted much academic and industrial interest. In this paper, we offer a systematic review of this field, summarizing the contributions of individual efforts and exploring their relations. We discuss the new properties and challenges in POI recommendation, compared with traditional recommendation problems, e.g., movie recommendation. Then, we present a comprehensive review in three aspects: influential factors for POI recommendation, methodologies employed for POI recommendation, and different tasks in POI recommendation. Specifically, we propose three taxonomies to classify POI recommendation systems. First, we categorize the systems by the influential factors check-in characteristics, including the geographical information, social relationship, temporal influence, and content indications. Second, we categorize the systems by the methodology, including systems modeled by fused methods and joint methods. Third, we categorize the systems as general POI recommendation and successive POI recommendation by subtle differences in the recommendation task whether to be bias to the recent check-in. For each category, we summarize the contributions and system features, and highlight the representative work. Moreover, we discuss the available data sets and the popular metrics. Finally, we point out the possible future directions in this area and conclude this survey.

The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions <=SEP=> Received () Revised () Recurrent nets are in principle capable to store past inputs to produce the currently desired output. Because of this property recurrent nets are used in time series prediction and process control. Practical applications involve temporal dependencies spanning many time steps, e.g. between relevant inputs and desired outputs. In this case, however, gradient based learning methods take too much time. The extremely increased learning time arises because the error vanishes as it gets propagated back. In this article the decaying error ow is theoretically analyzed. Then methods trying to overcome vanishing gradients are brieey discussed. Finally, experiments comparing conventional algorithms and alternative methods are presented. With advanced methods long time lag problems can be solved in reasonable time.

An Empirical Analysis of the Influence of Fault Space on Search-Based Automated Program Repair <=SEP=> Automated program repair (APR) has attracted great research attention, and various techniques have been proposed. Search-based APR is one of the most important categories among these techniques. Existing researches focus on the design of effective mutation operators and searching algorithms to better find the correct patch. Despite various efforts, the effectiveness of these techniques are still limited by the search space explosion problem. One of the key factors attribute to this problem is the quality of fault spaces as reported by existing studies. This motivates us to study the importance of the fault space to the success of finding a correct patch. Our empirical study aims to answer three questions. Does the fault space significantly correlate with the performance of search-based APR? If so, are there any indicative measurements to approximate the accuracy of the fault space before applying expensive APR techniques? Are there any automatic methods that can improve the accuracy of the fault space? We observe that the accuracy of the fault space affects the effectiveness and efficiency of search-based APR techniques, e.g., the failure rate of GenProg could be as high as 60% when the real fix location is ranked lower than 10 even though the correct patch is in the search space. Besides, GenProg is able to find more correct patches and with fewer trials when given a fault space with a higher accuracy. We also find that the negative mutation coverage, which is designed in this study to measure the capability of a test suite to kill the mutants created on the statements executed by failing tests, is the most indicative measurement to estimate the efficiency of search-based APR. Finally, we confirm that automated generated test cases can help improve the accuracy of fault spaces, and further improve the performance of search-based APR techniques. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. c © 2017 ACM. ISBN 978-1-4503-2138-9. DOI: 10.1145/1235 CCS Concepts •Software and its engineering→Automatic programming; Empirical software validation;

Software Reuse Through Information Retrieval <=SEP=> There is widespread need for safe, verifiable, efficient, and reliable software that can be delivered in a timely manner. Software reuse can make a valuable contrbution toward this goal by increasing programmer productivity and software quality. Unfortunately, the amount of software reuse currently done is quite small. DeMarco [1] estimates that in the average software development environment only about five percent of code is reused.

Unsupervised speech representation learning using WaveNet autoencoders <=SEP=> We consider the task of unsupervised extraction of meaningful latent representations of speech by applying autoencoding neural networks to speech waveforms. The goal is to learn a representation able to capture high level semantic content from the signal, e.g. phoneme identities, while being invariant to confounding low level details in the signal such as the underlying pitch contour or background noise. The behavior of autoencoder models depends on the kind of constraint that is applied to the latent representation. We compare three variants: a simple dimensionality reduction bottleneck, a Gaussian Variational Autoencoder (VAE), and a discrete Vector Quantized VAE (VQVAE). We analyze the quality of learned representations in terms of speaker independence, the ability to predict phonetic content, and the ability to accurately reconstruct individual spectrogram frames. Moreover, for discrete encodings extracted using the VQVAE, we measure the ease of mapping them to phonemes. We introduce a regularization scheme that forces the representations to focus on the phonetic content of the utterance and report performance comparable with the top entries in the ZeroSpeech 2017 unsupervised acoustic unit discovery task.

Monotony of road environment and driver fatigue: a simulator study. <=SEP=> Studies have shown that drowsiness and hypovigilance frequently occur during highway driving and that they may have serious implications in terms of accident causation. This paper focuses on the task induced factors that are involved in the development of these phenomena. A driving simulator study was conducted in order to evaluate the impact of the monotony of roadside visual stimulation using a steering wheel movement (SWM) analysis procedure. Fifty-six male subjects each drove during two different 40-min periods. In one case, roadside visual stimuli were essentially repetitive and monotonous, while in the other one, the environment contained disparate visual elements aiming to disrupt monotony without changing road geometry. Subject's driving performance was compared across these conditions in order to determine whether disruptions of monotony can have a positive effect and help alleviate driver fatigue. Results reveal an early time-on-task effect on driving performance for both driving periods and more frequent large SWM when driving in the more monotonous road environment, which implies greater fatigue and vigilance decrements. Implications in terms of environmental countermeasures for driver fatigue are discussed.

Speech responsive mobile robot for transporting objects of different weight categories <=SEP=> In this paper we present a robot with mechanical gripper which has the capability of transporting limited sized objects from one place to another with pick-up and drop capabilities. The robot's responses are based on speech recognition of verbal commands. In our project we have used Google speech recognition module in order to understand verbal commands. We also categorized the objects into six specific categories according to the amount of gripping force required to lift the objects. The categories are divided according to object stiffness. An android application was used to communicate with the robot through Bluetooth communication. The application decodes the human speech into an array of characters which are transmitted to the robot using Bluetooth technology. The robot uses microcontroller which decodes the messages into executable functions. In this project we have designed the robot such that it understands only fifteen distinct verbal commands and ignores others. Being a speech responsive mobile robot it can be effectively used to move objects from one place to another by people with disability or handicap.

The A2iA Arabic Handwritten Text Recognition System at the Open HaRT2013 Evaluation <=SEP=> This paper describes the Arabic handwriting recognition systems proposed by A2iA to the NIST OpenHaRT2013 evaluation. These systems were based on an optical model using Long Short-Term Memory (LSTM) recurrent neural networks, trained to recognize the different forms of the Arabic characters directly from the image, without explicit feature extraction nor segmentation.Large vocabulary selection techniques and n-gram language modeling were used to provide a full paragraph recognition, without explicit word segmentation. Several recognition systems were also combined with the ROVER combination algorithm. The best system exceeded 80% of recognition rate.

Dynamic resource management in SDN-based virtualized networks <=SEP=> Network virtualization allows for an abstraction between user and physical resources by letting a given physical infrastructure to be shared by multiple service providers. However, network virtualization presents some challenges, such as, efficient resource management, fast provisioning and scalability. By separating a network's control logic from the underlying routers and switches, software defined networking (SDN) promises an unprecedented simplification in network programmability, management and innovation by service providers, and hence, its control model presents itself as a candidate solution to the challenges in network virtualization. In this paper, we use the SDN control plane to efficiently manage resources in virtualized networks by dynamically adjusting the virtual network (VN) to substrate network (SN) mappings based on network status. We extend an SDN controller to monitor the resource utilisation of VNs, as well as the average loading of SN links and switches, and use this information to proactively add or remove flow rules from the switches. Simulations show that, compared with three state-of-art approaches, our proposal improves the VN acceptance ratio by about 40% and reduces VN resource costs by over 10%.

CIRCULAR POLARIZATION DUAL FEED MICROSTRIP PATCH ANTENNA WITH 3 dB HYBRID COUPLER FOR WLAN ( 2 . 4 GHZ <=SEP=> Microstrip patch antennas represent one family of compact antennas that offer a conformal nature and the capability of ready integration with communication system’s printed circuitry. In this project, a 2.4 GHz circular polarization microstrip antenna is designed, constructed and measured. The selected microstrip antenna is a dual –fed circular polarized microstrip antenna. The antenna consists of square patch and 3 dB hybrid coupler. The dual – fed circular polarized microstrip antenna is etched on a FR4 with dielectric substrate of 4.6 with the height of 1.6 mm. Circular polarization is obtained when two orthogonal modes are equally excited with 90° phase difference between them. Circular polarization is important because regardless of the receiver orientation, it will always be receiving a component of the signal. This is due to the resulting wave having an angular variation. KEYWORD:MICROSTRIP,CIRCULAR POLARIZATION

Social semantic query expansion <=SEP=> Weak semantic techniques rely on the integration of Semantic Web techniques with social annotations and aim to embrace the strengths of both. In this article, we propose a novel weak semantic technique for query expansion. Traditional query expansion techniques are based on the computation of two-dimensional co-occurrence matrices. Our approach proposes the use of three-dimensional matrices, where the added dimension is represented by semantic classes (i.e., categories comprising all the terms that share a semantic property) related to the folksonomy extracted from social bookmarking services, such as delicious and StumbleUpon. The results of an indepth experimental evaluation performed on both artificial datasets and real users show that our approach outperforms traditional techniques, such as relevance feedback and personalized PageRank, so confirming the validity and usefulness of the categorization of the user needs and preferences in semantic classes. We also present the results of a questionnaire aimed to know the users opinion regarding the system. As one drawback of several query expansion techniques is their high computational costs, we also provide a complexity analysis of our system, in order to show its capability of operating in real time.

Scene classification based on single-layer SAE and SVM <=SEP=> Scene classification aims to group images into semantic categories. It is a challenging problem in computer vision due to the difficulties of intra-class variability and inter-class similarity. In this paper, a scene classification approach based on single-layer sparse autoencoder (SAE) and support vector machine (SVM) is proposed. This approach consists of two steps: SAE-based feature learning step and SVM-based classification step. In the first step, a single-layer SAE network is constructed and trained by the patches which are sampled randomly from the source images. The feature representation of images is learned by the trained single-layer SAE network. Meanwhile, a pooling operation is used to reduce the dimension of the learned feature vectors. In the second step, in order to improve the classification accuracy, the parameters of SVM are optimized by a particle swarm optimization (PSO) based algorithm. The one-versus-one strategy is employed for the multiple scene classification problem. To show the efficiency of the proposed approach, several public data sets are employed. The results reveal that the proposed approach achieves better classification accuracy than the existing state-of-the-art methods. 2014 Elsevier Ltd. All rights reserved.

A Novel Low-Cost, Large Curvature Bend Sensor Based on a Bowden-Cable <=SEP=> Bend sensors have been developed based on conductive ink, optical fiber, and electronic textiles. Each type has advantages and disadvantages in terms of performance, ease of use, and cost. This study proposes a new and low-cost bend sensor that can measure a wide range of accumulated bend angles with large curvatures. This bend sensor utilizes a Bowden-cable, which consists of a coil sheath and an inner wire. Displacement changes of the Bowden-cable's inner wire, when the shape of the sheath changes, have been considered to be a position error in previous studies. However, this study takes advantage of this position error to detect the bend angle of the sheath. The bend angle of the sensor can be calculated from the displacement measurement of the sensing wire using a Hall-effect sensor or a potentiometer. Simulations and experiments have shown that the accumulated bend angle of the sensor is linearly related to the sensor signal, with an R-square value up to 0.9969 and a root mean square error of 2% of the full sensing range. The proposed sensor is not affected by a bend curvature of up to 80.0 m(-1), unlike previous bend sensors. The proposed sensor is expected to be useful for various applications, including motion capture devices, wearable robots, surgical devices, or generally any device that requires an affordable and low-cost bend sensor.

Brief Announcement: Dynamic Determinacy Race Detection for Task Parallelism with Futures <=SEP=> Existing dynamic determinacy race detectors for task-parallel programs are limited to programs with strict computation graphs, where a task can only wait for its descendant tasks to complete. In this paper, we present the first known determinacy race detector for non-strict computation graphs with futures. The space and time complexity of our algorithm are similar to those of the classical SP-bags algorithm, when using only structured parallel constructs such as spawn-sync and async-finish. In the presence of point-to-point synchronization using futures, the complexity of the algorithm increases by a factor determined by the number of future operations, which includes future task creation and future get operations. The experimental results show that the slowdown factor observed for our algorithm relative to the sequential version is in the range of 1.00x to 9.92x, which is very much in line with slowdowns experienced for fully strict computation graphs.

Slot-Coupled Multisection Quadrature Hybrid for UWB Applications <=SEP=> We present a three-section quadrature hybrid based on slot-coupled directional couplers, which operates over a bandwidth from 3.1 to 10.6 GHz. A prototype has been fabricated which exhibits a return loss better than 20 dB, isolation around 20 dB, an amplitude imbalance between output ports of less than plusmn0.75 dB and a phase imbalance between +1deg and -3deg across the 3.1-10.6 GHz band. This design outperforms previously reported results for ultra wide band operation.

A High-Performance FPGA-Based Implementation of the LZSS Compression Algorithm <=SEP=> The increasing growth of embedded networking applications has created a demand for high-performance logging systems capable of storing huge amounts of high-bandwidth, typically redundant data. An efficient way of maximizing the logger performance is doing a real-time compression of the logged stream. In this paper we present a flexible high-performance implementation of the LZSS compression algorithm capable of processing up to 50 MB/s on a Virtex-5 FPGA chip. We exploit the independently addressable dual-port block RAMs inside the FPGA chip to achieve an average performance of 2 clock cycles per byte. To make the compressed stream compatible with the ZLib library [1] we encode the LZSS algorithm output using a fixed Huffman table defined by the Deflate specification [2]. We also demonstrate how changing the amount of memory allocated to various internal tables impacts the performance and compression ratio. Finally, we provide a cycle-accurate estimation tool that allows finding a trade-off between FPGA resource utilization, compression ratio and performance for a specific data sample.

Mining (Social) Network Graphs to Detect Random Link Attacks <=SEP=> Modern communication networks are vulnerable to attackers who send unsolicited messages to innocent users, wasting network resources and user time. Some examples of such attacks are spam emails, annoying tele-marketing phone calls, viral marketing in social networks, etc. Existing techniques to identify these attacks are tailored to certain specific domains (like email spam filtering), but are not applicable to a majority of other networks. We provide a generic abstraction of such attacks, called the Random Link Attack (RLA), that can be used to describe a large class of attacks in communication networks. In an RLA, the malicious user creates a set of false identities and uses them to communicate with a large, random set of innocent users. We mine the social networking graph extracted from user interactions in the communication network to find RLAs. To the best of our knowledge, this is the first attempt to conceptualize the attack definition, applicable to a variety of communication networks. In this paper, we formally define RLA and show that the problem of finding an RLA is NP-complete. We also provide two efficient heuristics to mine subgraphs satisfying the RLA property; the first (GREEDY) is based on greedy set-expansion, and the second (TRWALK) on randomized graph traversal. Our experiments with a real-life data set demonstrate the effectiveness of these algorithms.

A Solution to Forecast Demand Using Long Short-Term Memory Recurrent Neural Networks for Time Series Forecasting <=SEP=> This study focuses on predicting demand based on data collected which spans across many periods. To help our client build a solution to forecast demand effectively, we developed a model using Long Short-Term Memory (LSTM) Networks, a type of Recurrent Neural Network, to estimate demand based on historical patterns. While there may be many available models for dealing with a time series problem, the LSTM model is relatively new and highly sophisticated to its counterpart. By comparing this study which works excellently for sequential learning, to the other existing models and techniques, we are now closer to solving at least one of many complications apparent across industries. The study becomes even more important for supply chain professionals, especially those in the purchase department, as they can now rely on a highly accurate model instead of basing their forecasts purely on intuition and recent customer behavior. Using data from the M3-Competition, which is a competition conducted by the International Institute of Forecasters, we develop a working framework to help our client compare their existing models (feedforward neural network and exponential smoothing model) with our LSTM model.

CRISPR interference (CRISPRi) for sequence-specific control of gene expression <=SEP=> Sequence-specific control of gene expression on a genome-wide scale is an important approach for understanding gene functions and for engineering genetic regulatory systems. We have recently described an RNA-based method, CRISPR interference (CRISPRi), for targeted silencing of transcription in bacteria and human cells. The CRISPRi system is derived from the Streptococcus pyogenes CRISPR (clustered regularly interspaced palindromic repeats) pathway, requiring only the coexpression of a catalytically inactive Cas9 protein and a customizable single guide RNA (sgRNA). The Cas9-sgRNA complex binds to DNA elements complementary to the sgRNA and causes a steric block that halts transcript elongation by RNA polymerase, resulting in the repression of the target gene. Here we provide a protocol for the design, construction and expression of customized sgRNAs for transcriptional repression of any gene of interest. We also provide details for testing the repression activity of CRISPRi using quantitative fluorescence assays and native elongating transcript sequencing. CRISPRi provides a simplified approach for rapid gene repression within 1–2 weeks. The method can also be adapted for high-throughput interrogation of genome-wide gene functions and genetic interactions, thus providing a complementary approach to RNA interference, which can be used in a wider variety of organisms.

Serendipitous recommendation for scholarly papers considering relations among researchers <=SEP=> Serendipity occurs when one finds an interesting discovery while searching for something else. While search engines seek to report work relevant to a targeted query, recommendation engines are particularly well-suited for serendipitous recommendations as such processes do not need to fulfill a targeted query. Junior researchers can use such an engine to broaden their horizon and learn new areas, while senior researchers can discover interdisciplinary frontiers to apply integrative research. We adapt a state-of-the-art scholarly paper recommendation system's user profile construction to make use of information drawn from 1) dissimilar users and 2) co-authors to specifically target serendipitous recommendation.

Cross-Lingual Ontology Mapping - An Investigation of the Impact of Machine Translation <=SEP=> Ontologies are at the heart of knowledge management and make use of information that is not only written in English but also in many other natural languages. In order to enable knowledge discovery, sharing and reuse of these multilingual ontologies, it is necessary to support ontology mapping despite natural language barriers. This paper examines the soundness of a generic approach that involves machine translation tools and monolingual ontology matching techniques in cross-lingual ontology mapping scenarios. In particular, experimental results collected from case studies which engage mappings of independent ontologies that are labeled in English and Chinese are presented. Based on findings derived from these studies, limitations of this generic approach are discussed. It is shown with evidence that appropriate translations of conceptual labels in ontologies are of crucial importance when applying monolingual matching techniques in cross-lingual ontology mapping. Finally, to address the identified challenges, a semantic-oriented cross-lingual ontology mapping (SOCOM) framework is proposed and discussed.

Usability and Design Guidelines of Smart Canes for Users with Visual Impairments <=SEP=> The percentage of the population with visual impairments is increasing rapidly. Every year, the number of visually impaired people grows by up to 2 million worldwide. The World Health Organization (WHO, 2011) estimates that there are 39 million blind people and 246 million visually impaired people internationally. In addition, visual impairments are closely associated with aging. About 65% of the visually impaired are 50 years of age or older, with about 20% of the world’s population in this age group (WHO, 2011). In Europe, in particular, about 90% of visually impaired and partially sighted people are over the age of 60. One of the most severe difficulties for the visually impaired is safe independent mobility. Having independent mobility is a significant factor in ensuring that this aging group can perform simple daily tasks without depending on others. Clark-Carter (1985) indicates that visually impaired people have a low level of mobility. Further, a recent survey in France revealed that only 2% of the overall visually impaired population uses mobility aids (Sander, Lelievre, & Tallec, 2005). One model of an independent mobility aid for the visually impaired is the traditional white cane, which is inexpensive, lightweight and foldable. Unfortunately, white cane users have difficulty detecting protruding bars or moving vehicles until they are dangerously close, which can lead to collisions and falls. The limited capability of the white cane corresponds to its length and a user’s maneuvering skills. As such, users rarely detect overhanging obstacles at head-level or ranges further than approximately 1 m from the user. Manduchi and Kurniawan (2011) report in a recent survey of 300 visually impaired people that over 40% of the respondents experienced head-level accidents at least once a month. Further, Clark-Carter (1985) found that visually impaired people were shown to be potentially dangerous when using a white cane. To address these difficulties, many “smart” products for the visually impaired have been introduced in the last four decades, including smart canes and handheld or wearable devices that are equipped with a sensor system. A smart cane offers an improvement over a traditional white cane because it has the ability to detect objects above the cane and up to a range of 2 m away using an ultrasonic sensor. A white cane allows objects to be sensed through touch and echolocation from tapping. A smart cane has the same capabilities, except that it uses vibrotactile information and produces vibration alerts for obstacles in front of the user. However, these smart products have not been successfully adopted and used by a large number of visually impaired people (Roentgen, Gelderblom, Soede, & de Witte, 2008). Several ORIGINAL ARTICLE

Heart Rate Monitoring as an Easy Way to Increase Engagement in Human-Agent Interaction <=SEP=> Physiological sensors are gaining the attention of manufacturers and users. As denoted by devices such as smartwatches or the newly released Kinect 2 – which can covertly measure heartbeats – or by the popularity of smartphone apps that track heart rate during fitness activities. Soon, physiological monitoring could become widely accessible and transparent to users. We demonstrate how one could take advantage of this situation to increase users’ engagement and enhance user experience in human-agent interaction. We created an experimental protocol involving embodied agents – “virtual avatars”. Those agents were displayed alongside a beating heart. We compared a condition in which this feedback was simply duplicating the heart rates of users to another condition in which it was set to an average heart rate. Results suggest a superior social presence of agents when they display feedback similar to users’ internal state. This physiological “similarity-attraction” effect may lead, with little effort, to a better acceptance of agents and robots by the general public.

Ultrasound guided posterior femoral cutaneous nerve block. <=SEP=> The posterior femoral cutaneous nerve (PFCN) is a branch of the sacral plexus. It needs to be implemented as a complementary block for anesthesia or in the surgeries necessitating tourniquet in the suitable cases. We consider target oriented block concept within the PFCN block in the anesthesia implementations with the emergence of ultrasonic regional anesthesia in the practice and with the better understanding of sonoanatomy.

Dynamic Resource Discovery Based on Preference and Movement Pattern Similarity for Large-Scale Social Internet of Things <=SEP=> Given the wide range deployment of disconnected delay-tolerant social Internet of Things (SIoT), efficient resource discovery remains a fundamental challenge for large-scale SIoT. The existing search mechanisms over the SIoT do not consider preference similarity and are designed in Cartesian coordinates without sufficient consideration of real-world network deployment environments. In this paper, we propose a novel resource discovery mechanism in a 3-D Cartesian coordinate system with the aim of enhancing the search efficiency over the SIoT. Our scheme is based on both of preference and movement pattern similarity to achieve higher search efficiency and to reduce the system overheads of SIoT. Simulation experiments have been conducted to evaluate this new scheme in a large-scale SIoT environment. The simulation results show that our proposed scheme outperforms the state-of-the-art resource discovery schemes in terms of search efficiency and average delay.

Through mold vias for stacking of mold embedded packages <=SEP=> The constant drive towards further miniaturization and heterogeneous system integration leads to a need for new packaging technologies which also allow large area processing and 3D integration with potential for low cost applications. Large area mold embedding technologies and embedding of active components into printed circuit boards (Chip-in-Polymer) are two major packaging trends in this area. This paper describes the use of a novel S2iP (Stacked System in Package) interconnect technique using advanced molding process for multi chip embedding in combination with large area and low cost redistribution technology derived from printed circuit board manufacturing with a focus on integration of through mold vias for package stacking. The use of compression molding equipment with liquid or granular epoxy molding compounds for the targeted integration process flow is a new technology that has been especially developed to allow large area embedding of single chips but also of multiple chips or heterogeneous systems on wafer scale, typically 8” to 12”. Future developments will deal with panel sizes up to 470 × 370 mm². The wiring of the embedded components in this novel type of SiP is done using PCB manufacturing technologies, i.e. a resin coated copper (RCC) film is laminated over the embedded components — whichever no matter which shape they are: a compression molded wafer or a larger rectangular area of a Molded Array Package (MAP). Interconnects are formed by laser drilling to die pads and electroplating — all of them making use of standard PCB processes. Thus, through vias which are standard features in PCB manufacturing and can be also integrated in the proposed process flow for mold embedding in combination with RCC based redistribution. Vias were drilled by laser or mechanically after RCC lamination and were metalized together with the vias for chip interconnection. Within this study different liquid and granular molding compounds have been intensively evaluated on their processability. Via drilling process by laser and mechanical drilling is systematically developed and analyzed with focus on via diameter, pitch, mold thickness and molding compound composition and here especially on filler particle sizes and distribution. The feasibility of the entire process chain is demonstrated by fabrication of a Ball Grid Array (BGA) type of system package with two embedded dies and through mold vias allowing the stacking of these BGA packages. Finally, a technology demonstrator is described consisting of two BGAs stacked on each other and mounted on a base substrate enabling the electrical test of a daisy chain structure through the stacked module, allowing the evaluation of the technology and the applied processes.

Mindfulness-based cognitive therapy for depression: a new approach to preventing relapse. <=SEP=> Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.