title: Trust management mechanism for Internet of Things

abstract: Trust management has been proven to be a useful technology for providing security service and as a consequence has been used in many applications such as P2P, Grid, ad hoc network and so on. However, few researches about trust mechanism for Internet of Things (IoT) could be found in the literature, though we argue that considerable necessity is held for applying trust mechanism to IoT. In this paper, we establish a formal trust management control mechanism based on architecture modeling of IoT. We decompose the IoT into three layers, which are sensor layer, core layer and application layer, from aspects of network composition of IoT. Each layer is controlled by trust management for special purpose: self-organized, affective routing and multi-service respectively. And the final decision-making is performed by service requester according to the collected trust information as well as requester' policy. Finally, we use a formal semantics-based and fuzzy set theory to realize all above trust mechanism, the result of which provides a general framework for the development of trust models of IoT.


Sampling Variations of Sequences for Structured Music Generation


title: Theory of Deep Learning IIb: Optimization Properties of SGD

abstract: In Theory IIb we characterize with a mix of theory and experiments the optimization of deep convolutional networks by Stochastic Gradient Descent. The main new result in this paper is theoretical and experimental evidence for the following conjecture about SGD: SGD concentrates in probability -- like the classical Langevin equation -- on large volume, "flat" minima, selecting flat minimizers which are with very high probability also global minimizers


title: Gameplay Design Patterns for Game Dialogues

abstract: Dialogues are natural models for human communication and have also been used to model interaction within computer games. In this paper, we look at current models of dialogues from within the field of computational linguistics and explore their usefulness of games, and especially for the design of gameplay through interaction with nonplaying characters in games. This is done by analyzing several examples of computer-based games and similar playful activities, both to see which models of dialogues are used but also to suggest possible ways of expanding gameplay through using other dialogue models. Uses of existing models for dialogues within games are identified but an additional model, the Game State-based approach to dialogues, is introduced. The possible implications of the changes in gameplay are described through the use of gameplay design patterns, offering a way to encode design knowledge explicitly and link that knowledge to other gameplay design pattern collections.


title: Pairwise Relational Networks for Face Recognition

abstract: Existing face recognition using deep neural networks is difficult to know what kind of features are used to discriminate the identities of face images clearly. To investigate the effective features for face recognition, we propose a novel face recognition method, called a pairwise relational network (PRN), that obtains local appearance patches around landmark points on the feature map, and captures the pairwise relation between a pair of local appearance patches. The PRN is trained to capture unique and discriminative pairwise relations among different identities. Because the existence and meaning of pairwise relations should be identity dependent, we add a face identity state feature, which obtains from the long short-term memory (LSTM) units network with the sequential local appearance patches on the feature maps, to the PRN. To further improve accuracy of face recognition, we combined the global appearance representation with the pairwise relational feature. Experimental results on the LFW show that the PRN using only pairwise relations achieved 99.65% accuracy and the PRN using both pairwise relations and face identity state feature achieved 99.76% accuracy. On the YTF, both the PRN using only pairwise relations and the PRN using pairwise relations and the face identity state feature achieved the state-of-the-art (95.7% and 96.3%). The PRN also achieved comparable results to the state-of-the-art for both face verification and face identification tasks on the IJB-A, and the state-of-the-art on the IJB-B.


Encoding Sentiment Information into Word Vectors for Sentiment Analysis


title: Lessons in Neural Network Training: Overfitting May be Harder than Expected

abstract: For many reasons, neural networks have become very popular AI machine learning models. Two of the most important aspects of machine learning models are how well the model generalizes to unseen data, and how well the model scales with problem complexity. Using a controlled task with known optimal training error, we investigate the convergence of the backpropagation (BP) algorithm. We find that the optimal solution is typically not found. Furthermore, we observe that networks larger than might be expected can result in lower training and generalization error. This result is supported by another real world example. We further investigate the training behavior by analyzing the weights in trained networks (excess degrees of freedom are seen to do little harm and to aid convergence), and contrasting the interpolation characteristics of multi-layer perceptron neural networks (MLPs) and polynomial models. (overfitting behavior is very different - the MLP is often biased towards smoother solutions). Finally, we analyze relevant theory outlining the reasons for significant practical differences. These results bring into question common beliefs about neural network training regarding convergence and optimal network size, suggest alternate guidelines for practical use (lower fear of excess degrees of freedom), and help to direct future work (e.g. methods for creation of more parsimonious solutions, importance of the MLP/BP bias and possibly worse performance of "improved" training algorithms).


title: Optimal personalized filtering against spear-phishing attacks

abstract: To penetrate sensitive computer networks, attackers can use spear phishing to sidestep technical security mechanisms by exploiting the privileges of careless users. In order to maximize their success probability, attackers have to target the users that constitute the weakest links of the system. The optimal selection of these target users takes into account both the damage that can be caused by a user and the probability of a malicious e-mail being delivered to and opened by a user. Since attackers select their targets in a strategic way, the optimal mitigation of these attacks requires the defender to also personalize the e-mail filters by taking into account the users' properties. ::: ::: In this paper, we assume that a learned classifier is given and propose strategic per-user filtering thresholds for mitigating spear-phishing attacks. We formulate the problem of filtering targeted and non-targeted malicious e-mails as a Stackelberg security game. We characterize the optimal filtering strategies and show how to compute them in practice. Finally, we evaluate our results using two real-world datasets and demonstrate that the proposed thresholds lead to lower losses than nonstrategic thresholds.


title: AMSOM: Adaptive Moving Self-organizing Map for Clustering and Visualization

abstract: Self-Organizing Map (SOM) is a neural network model which is used to obtain a topology-preserving mapping from the (usually high dimensional) input/feature space to an output/map space of fewer dimensions (usually two or three in order to facilitate visualization). Neurons in the output space are connected with each other but this structure remains fixed throughout training and learning is achieved through the updating of neuron reference vectors in feature space. Despite the fact that growing variants of SOM overcome the fixed structure limitation they increase computational cost and also do not allow the removal of a neuron after its introduction. In this paper, a variant of SOM is proposed called AMSOM (Adaptive Moving Self-Organizing Map) that on the one hand creates a more flexible structure where neuron positions are dynamically altered during training and on the other hand tackles the drawback of having a predefined grid by allowing neuron addition and/or removal during training. Experiments using multiple literature datasets show that the proposed method improves training performance of SOM, leads to a better visualization of the input dataset and provides a framework for determining the optimal number and structure of neurons.


title: Hadoop Recognition of Biomedical Named Entity Using Conditional Random Fields

abstract: Processing large volumes of data has presented a challenging issue, particularly in data-redundant systems. As one of the most recognized models, the conditional random fields (CRF) model has been widely applied in biomedical named entity recognition (Bio-NER). Due to the internally sequential feature, performance improvement of the CRF model is nontrivial, which requires new parallelized solutions. By combining and parallelizing the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and Viterbi algorithms, we propose a parallel CRF algorithm called MapReduce CRF (MRCRF) in this paper, which contains two parallel sub-algorithms to handle two time-consuming steps of the CRF model. The MapReduce L-BFGS (MRLB) algorithm leverages the MapReduce framework to enhance the capability of estimating parameters. Furthermore, the MapReduce Viterbi (MRVtb) algorithm infers the most likely state sequence by extending the Viterbi algorithm with another MapReduce job. Experimental results show that the MRCRF algorithm outperforms other competing methods by exhibiting significant performance improvement in terms of time efficiency as well as preserving a guaranteed level of correctness.


title: The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists

abstract: The DAVID Gene Functional Classification Tool http://david.abcc.ncifcrf.gov uses a novel agglomeration algorithm to condense a list of genes or associated biological terms into organized classes of related genes or biology, called biological modules. This organization is accomplished by mining the complex biological co-occurrences found in multiple sources of functional annotation. It is a powerful method to group functionally related genes and terms into a manageable number of biological modules for efficient interpretation of gene lists in a network context.


title: Experiences of Mandarin-speaking and English-speaking women of undergoing non-invasive prenatal genetic screening

abstract: Summary Introduction Cell-free DNA testing for fetal aneuploidies has been broadly commercialized internationally, especially in the United States and China. It is a relatively new technology, and allows for the non-invasive screening of common aneuploidies more accurately than traditional serum screening. However, the implementation of cfDNA differs according to the healthcare context in which it takes place. Methods Using content analysis of online pregnancy forums in China and the US, we compared the views of Mandarin-speaking and English-speaking participants on cfDNA testing with a focus on cfDNA within the broader prenatal testing landscape. Results We found factually incorrect beliefs regarding cfDNA expressed in both the Mandarin-speaking and English-speaking groups. Though some English-speaking women knew cfDNA was for screening purposes, many nevertheless discussed the test and its results as diagnostic. Many Mandarin-speaking women expressed the belief that cfDNA was completely accurate, and none mentioned the possibility of a false positive. Although English-speaking women discussed speaking with a genetic counseling about their results, there was almost no mention of genetic counseling in the Mandarin-speaking group; some Mandarin-speaking women mentioned receiving their cfDNA results via text message. Discussion These findings raise concerns about the informed consent that women are giving to undergo cfDNA screening. The price and availability of cfDNA also appeared to pose a significant barrier to many women considering cfDNA. Both Mandarin and English speakers viewed cfDNA as a very expensive test, and English-speaking women frequently discussed difficulty in getting insurance to cover it. Women in both groups also discussed the lack of convenient access to cfDNA in their area, an issue that was particularly prevalent in the Mandarin-speaking group, many of whom mentioned that cfDNA was only available in large cities or certain hospitals. These concerns introduce the possibility of inequities in access to certain prenatal screening technologies. Conclusion Overall, our findings illustrate that while some of the barriers cfDNA testing is facing in China and US are dependent on cultural and structural context, many of the central issues of informed consent, affordability and access are consistent cross-culturally.


title: APA: Adaptive Pose Alignment for Pose-Invariant Face Recognition

abstract: Face alignment is a crucial step in face recognition. Through making the position of face consistent, face alignment reduces intra-class variability due to factors such as lighting, background, pose, and perspective transformation, and further facilitating the recognition tasks. In this paper, we propose a new face alignment method for pose-invariant face recognition, called adaptive pose alignment (APA), which can greatly reduce the intra-class difference and correct the noise caused by the traditional method in the alignment process, especially in unconstrained settings. Instead of aligning all faces to the pre-defined, uniform frontal shape, we adaptively learn the alignment templates according to the facial poses and then align each face of training or testing sets to its related template. To further improve face recognition performance, we propose a simple, yet effective feature normalization method which can generate more discriminative feature representation of a face or a set of faces (template) combined with the APA method. Furthermore, we introduce a pose-invariant face recognition pipeline that sequentially applies APA-based alignment, deep representation by softmax or ArcFace loss function, and the effective feature normalization procedure. We empirically show that APA-based images can accelerate the training of deep face recognition model by aligning all the images to the optimal templates. Moreover, experimental results show that the proposed method achieves the state-of-the-art performance on popular challenging face datasets including IJB-A, IJB-C, and CPLFW datasets.


title: A Super Wideband printed antenna with enhanced gain using FSS structure

abstract: Super Wideband (SWB) printed antenna with enhanced gain covering the impedance bandwidth ranging from 3–20GHz is presented. Gain enhancement of the antenna is achieved using appropriately designed frequency selective structure (FSS) acting as a band stop filter i.e. reflector, while keeping the bandwidth of antenna unchanged. The FSS used effectively reflects the in-phase radiations with a very low transmission coefficient over the entire bandwidth which is key requirement for providing antenna gain enhancement for wide frequency range. Hence significant improvement in antenna gain has been achieved using low profile and without compromising the impedance bandwidth. Results show a significant improvement in gain (i.e. 4–5dB) over the whole frequency range.


title: Design of area and delay efficient Vedic multiplier using Carry Select Adder

abstract: In this paper, Vedic multiplier is designed using area-efficient Carry Select Adder (CSLA). As the multiplication is the process of subsequent addition, adder is important block in implementation of multiplier. Digital adder has problem of carry propagation, thus carry select adder is used instead of simple Ripple Carry Adder (RCA). Carry select adder is known to be one of the fastest adder structures. Here Vedic multiplier is implemented instead of normal multipliers like add and shift multiplier, array multiplier etc. The goal of this paper is to design Vedic multiplier based on crosswise and vertical algorithms using area-efficient CSLA. Conventional CSLA designs like Binary to Excess one Converter (BEC) based CSLA and Modified CSLA (MCSLA) are compared with proposed CSLA design to prove its efficiency. It shows improved performance in terms of area. This renovated CSLA is used to design proposed Vedic multiplier. It has 6% less area than Vedic multiplier using MCSLA and 16% less area than Vedic multiplier using BEC-based CSLA. Proposed design is also compared with the Booth multiplier. Proposed multiplier showed more excellent results than Booth multiplier.


title: Physiological and Behavioral Stress and Anxiety in Children with Autism Spectrum Disorders during Routine Oral Care

abstract: Background. Children with autism spectrum disorders (ASD) commonly exhibit uncooperative behaviors which impede oral care. Previous studies have utilized dentist-report measures of uncooperative behaviors in children with ASD but none have utilized an objective measure of children's behavior or a physiological measure of distress. This study investigated behavioral and physiological distress in children with ASD during routine oral care and examined factors associated with this distress. Methods. Participants were 44 children (n = 22 typical, n = 22 ASD) aged 6–12 receiving routine dental cleanings. Behavioral and physiological measures of stress and anxiety were collected during dental cleanings. Results. Children with ASD exhibited greater distress, compared to the typical group, on dentist-report and researcher-coded measures of overt distress behaviors and on physiological measures. Correlations between physiological and behavioral measures of distress were found in the ASD but not in the typical group. Behavioral distress was correlated with age in the typical group and with expressive communication ability and sensory processing difficulties in the ASD group; physiological distress was correlated with parent-report of anxiety in the typical group and sensory processing difficulties in the ASD group. Conclusions. Novel strategies may be required to decrease behavioral and physiological distress in children with ASD in the dental clinic.


title: Knowledge discovery from database Using an integration of clustering and classification

abstract: Clustering and classification are two important techniques of data mining. Classification is a supervised learning problem of assigning an object to one of several pre-defined categories based upon the attributes of the object. While, clustering is an unsupervised learning problem that group objects based upon distance or similarity. Each group is known as a cluster. In this paper we make use of a large database ‘Fisher’s Iris Dataset’ containing 5 attributes and 150 instances to perform an integration of clustering and classification techniques of data mining. We compared results of simple classification technique (using J48 classifier) with the results of integration of clustering and classification technique, based upon various parameters using WEKA (Waikato Environment for Knowledge Analysis), a Data Mining tool. The results of the experiment show that integration of clustering and classification gives promising results with utmost accuracy rate and robustness even when the data set is containing missing values.


Enterprise resource planning: cultural fits and misfits: is ERP a universal solution?


title: Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

abstract: The main aim of this paper is to solve a path planning problem for an autonomous mobile robot in static and dynamic environments by determining the collision-free path that satisfies the chosen criteria for shortest distance and path smoothness. The algorithm mimics the real world by adding the actual size of the mobile robot to that of the obstacles and formulating the problem as a moving point in the free-space. The proposed path planning algorithm consists of three modules: in the first module, the path planning algorithm forms an optimised path by conducting a hybridized Particle Swarm Optimization-Modified Frequency Bat (PSO-MFB) algorithm that minimises distance and follows path smoothness criteria; in the second module, any infeasible points generated by the proposed PSO-MFB Algorithm are detected by a novel Local Search (LS) algorithm and integrated with the PSO-MFB algorithm to be converted into feasible solutions; the third module features obstacle detection and avoidance (ODA), which is triggered when the mobile robot detects obstacles within its sensing region, allowing it to avoid collision with obstacles. Simulations have been carried out that indicated that this method generates a feasible path even in complex dynamic environments and thus overcomes the shortcomings of conventional approaches such as grid methods. Comparisons with previous examples in the literature are also included in the results.


title: Deep Learning Assisted Heuristic Tree Search for the Container Pre-marshalling Problem

abstract: One of the key challenges for operations researchers solving real-world problems is designing and implementing high-quality heuristics to guide their search procedures. In the past, machine learning techniques have failed to play a major role in operations research approaches, especially in terms of guiding branching and pruning decisions. We integrate deep neural networks into a heuristic tree search procedure to decide which branch to choose next and to estimate a bound for pruning the search tree of an optimization problem. We call our approach Deep Learning assisted heuristic Tree Search (DLTS) and apply it to a well-known problem from the container terminals literature, the container pre-marshalling problem (CPMP). Our approach is able to learn heuristics customized to the CPMP solely through analyzing the solutions to CPMP instances, and applies this knowledge within a heuristic tree search to produce the highest quality heuristic solutions to the CPMP to date.


Course Assessment Practices and Student Learning Strategies in Online Courses COURSE ASSESSMENT PRACTICES AND STUDENT LEARNING STRATEGIES IN ONLINE COURSES


CSTR Control by Using Model Reference Adaptive Control and PSO


title: Wireless photoplethysmographic device for heart rate variability signal acquisition and analysis

abstract: The photoplethysmographic (PPG) signal has the potential to aid in the acquisition and analysis of heart rate variability (HRV) signal: a non-invasive quantitative marker of the autonomic nervous system that could be used to assess cardiac health and other physiologic conditions. A low-power wireless PPG device was custom-developed to monitor, acquire and analyze the arterial pulse in the finger. The system consisted of an optical sensor to detect arterial pulse as variations in reflected light intensity, signal conditioning circuitry to process the reflected light signal, a microcontroller to control PPG signal acquisition, digitization and wireless transmission, a receiver to collect the transmitted digital data and convert them back to their analog representations. A personal computer was used to further process the captured PPG signals and display them. A MATLAB program was then developed to capture the PPG data, detect the RR peaks, perform spectral analysis of the PPG data, and extract the HRV signal. A user-friendly graphical user interface (GUI) was developed in LabView to display the PPG data and their spectra. The performance of each module (sensing unit, signal conditioning, wireless transmission/reception units, and graphical user interface) was assessed individually and the device was then tested as a whole. Consequently, PPG data were obtained from five healthy individuals to test the utility of the wireless system. The device was able to reliably acquire the PPG signals from the volunteers. To validate the accuracy of the MATLAB codes, RR peak information from each subject was fed into Kubios software as a text file. Kubios was able to generate a report sheet with the time domain and frequency domain parameters of the acquired data. These features were then compared against those calculated by MATLAB. The preliminary results demonstrate that the prototype wireless device could be used to perform HRV signal acquisition and analysis.


title: Low-Altitude Unmanned Aerial Vehicles-Based Internet of Things Services: Comprehensive Survey and Future Perspectives

abstract: Recently, unmanned aerial vehicles (UAVs), or drones, have attracted a lot of attention, since they represent a new potential market. Along with the maturity of the technology and relevant regulations, a worldwide deployment of these UAVs is expected. Thanks to the high mobility of drones, they can be used to provide a lot of applications, such as service delivery, pollution mitigation, farming, and in the rescue operations. Due to its ubiquitous usability, the UAV will play an important role in the Internet of Things (IoT) vision, and it may become the main key enabler of this vision. While these UAVs would be deployed for specific objectives (e.g., service delivery), they can be, at the same time, used to offer new IoT value-added services when they are equipped with suitable and remotely controllable machine type communications (MTCs) devices (i.e., sensors, cameras, and actuators). However, deploying UAVs for the envisioned purposes cannot be done before overcoming the relevant challenging issues. These challenges comprise not only technical issues, such as physical collision, but also regulation issues as this nascent technology could be associated with problems like breaking the privacy of people or even use it for illegal operations like drug smuggling. Providing the communication to UAVs is another challenging issue facing the deployment of this technology. In this paper, a comprehensive survey on the UAVs and the related issues will be introduced. In addition, our envisioned UAV-based architecture for the delivery of UAV-based value-added IoT services from the sky will be introduced, and the relevant key challenges and requirements will be presented.


title: Spatiotemporal Saliency Detection for Video Sequences Based on Random Walk With Restart

abstract: A novel saliency detection algorithm for video sequences based on the random walk with restart (RWR) is proposed in this paper. We adopt RWR to detect spatially and temporally salient regions. More specifically, we first find a temporal saliency distribution using the features of motion distinctiveness, temporal consistency, and abrupt change. Among them, the motion distinctiveness is derived by comparing the motion profiles of image patches. Then, we employ the temporal saliency distribution as a restarting distribution of the random walker. In addition, we design the transition probability matrix for the walker using the spatial features of intensity, color, and compactness. Finally, we estimate the spatiotemporal saliency distribution by finding the steady-state distribution of the walker. The proposed algorithm detects foreground salient objects faithfully, while suppressing cluttered backgrounds effectively, by incorporating the spatial transition matrix and the temporal restarting distribution systematically. Experimental results on various video sequences demonstrate that the proposed algorithm outperforms conventional saliency detection algorithms qualitatively and quantitatively.


title: Solving Stochastic Path Problem: Particle Swarm Optimization Approach

abstract: An stochastic version of the classical shortest path problem whereby for each node of a graph, a probability distribution over the set of successor nodes must be chosen so as to reach a certain destination node with minimum expected cost. In this paper, we propose a new algorithm based on Particle Swarm Optimization (PSO) for solving Stochastic Shortest Path Problem (SSPP). The comparison of our algorithm with other algorithms indicates that its performance is suitable even by the less number of iterations.


title: PREDICTING CONSUMER INFORMATION SEARCH BENEFITS FOR PERSONALIZED ONLINE PRODUCT RANKING: A CONFIDENCE-BASED APPROACH

abstract: Product ranking mechanism is an important service for e-commerce that facilitates consumers’ decision-making process. This paper studies online product ranking under uncertainty. Different from previous studies that generally rank products merely based on predicted ratings, a new personalized product ranking method is proposed based on estimating consumer information search benefits and taking prediction uncertainty and confidence into consideration. Experiments using real data of movie ratings illustrate that the proposed method is advantageous over traditional point estimation methods, thus may help enhance customers’ satisfaction with the decision-making process and choices through saving their time and efforts.


title: A music recommendation system with consideration of personal emotion

abstract: Emotions evoked by were analyzed. An estimation of the correlation coefficient was applied to determine features of music that evoke an emotion. These features were then used to train two support vector machines (SVMs) for an individual subject to classify music that evokes happiness, anger, sadness, and peacefulness. The proposed approach can be used to classify music that evokes an emotion and to build a personal emotion-cognitive music recommendation system for an individual subject. Experiment results show the effectiveness of the proposed approach.


title: Cooperative Frequency-Domain Beamforming for Broadband SC-FDE Systems

abstract: In this work, we investigate cooperative frequency-domain beamforming (FD-BF) for single-carrier frequency-domain equalization (SC-FDE) systems. In particular, we propose optimal and suboptimal FD-BF schemes for frequency-domain linear equalization (FD-LE) and an idealized matched filter receiver. For both receiver structures, we derive the optimal processing at the destination and develop corresponding expressions for the signal-to-interference-plus-noise ratio (SINR). For a given power allocation, the optimal FD-BF filters maximizing the SINR are obtained in closed form and the power allocation problem is shown to be convex. For computation of the optimal power allocation a numerical method is developed and two low-complexity suboptimal power allocations are provided. Simulation results confirm the excellent performance of all proposed FD-BF schemes.


Temporal SMAS lift using Serdev sutures


title: DataHub: Collaborative Data Science&Dataset Version Management at Scale

abstract: Relational databases have limited support for data collaboration, where teams collaboratively curate and analyze large datasets. Inspired by software version control systems like git, we propose (a) a dataset version control system, giving users the ability to create, branch, merge, difference and search large, divergent collections of datasets, and (b) a platform, DATAHUB, that gives users the ability to perform collaborative data analysis building on this version control system. We outline the challenges in providing dataset version control at scale.


title: 380v/1.9A GaN power-HEMT: current collapse phenomena under high applied voltage and demonstration of 27.1 MHz class-E amplifier

abstract: The current collapse phenomena in 380V/1.9A GaN power-HEMTs designed for high-voltage power electronics application is reported. The influence of these phenomena to the power-electronics circuit performance under high applied voltage is discussed using a 27.1 MHz class-E amplifier, which can be one of an industrial application candidate. It has been found that the optimized field plate structure minimizes the increase of conduction loss caused by the current collapse phenomena and thus improves the power efficiency of the circuit. The minimized device achieved the output power of 13.8 W and the power efficiency of 89.6 % for the demonstrated circuit even with the applied drain voltage of 330 V and the switching frequency of 27.1 MHz. These results show the nature possibility of a new GaN-device application with both high voltage and high frequency condition


title: Towards online anti-opinion spam: Spotting fake reviews from the review sequence

abstract: Detecting review spam is important for current e-commerce applications. However, the posted order of review has been neglected by the former work. In this paper, we explore the issue on fake review detection in review sequence, which is crucial for implementing online anti-opinion spam. We analyze the characteristics of fake reviews firstly. Based on review contents and reviewer behaviors, six time sensitive features are proposed to highlight the fake reviews. And then, we devise supervised solutions and a threshold-based solution to spot the fake reviews as early as possible. The experimental results show that our methods can identify the fake reviews orderly with high precision and recall.


title: Learning query intent from regularized click graphs

abstract: This work presents the use of click graphs in improving query intent classifiers, which are critical if vertical search and general-purpose search services are to be offered in a unified user interface. Previous works on query classification have primarily focused on improving feature representation of queries, e.g., by augmenting queries with search engine results. In this work, we investigate a completely orthogonal approach --- instead of enriching feature representation, we aim at drastically increasing the amounts of training data by semi-supervised learning with click graphs. Specifically, we infer class memberships of unlabeled queries from those of labeled ones according to their proximities in a click graph. Moreover, we regularize the learning with click graphs by content-based classification to avoid propagating erroneous labels. We demonstrate the effectiveness of our algorithms in two different applications, product intent and job intent classification. In both cases, we expand the training data with automatically labeled queries by over two orders of magnitude, leading to significant improvements in classification performance. An additional finding is that with a large amount of training data obtained in this fashion, classifiers using only query words/phrases as features can work remarkably well.


Assessing the organizational social context (OSC) of mental health services for implementation research and practice


A NOVEL BANDPASS WAVEGUIDE FILTER STRUCTURE ON SIW TECHNOLOGY


A Comparative Study on Feature Selection in Text Categorization


title: Understanding mobile SNS continuance usage in China from the perspectives of social influence and privacy concern

abstract: Social influence has a significant effect on continuance usage of mobile SNS.Social influence includes compliance, identification and internalization.Privacy concern affects privacy risk, which in turn affects continuance usage. Retaining users and facilitating continuance usage are crucial to the success of mobile social network services (SNS). This research examines the continuance usage of mobile SNS in China by integrating both the perspectives of social influence and privacy concern. Social influence includes three processes: compliance, identification and internalization, which are respectively represented by subjective norm, social identity, and group norm. The results indicate that these three factors and privacy concern have significant effects on continuance usage. The results suggest that service providers should address the issues of social influence and privacy concern to encourage mobile SNS continuance usage.


Convolutional Neural Networks as Feature Generators for Near-duplicate Video Detection


Using Homomorphic Encryption for Large Scale Statistical Analysis


title: Sparse Online Learning via Truncated Gradient

abstract: We propose a general method called truncated gradient to induce sparsity in the weights of online-learning algorithms with convex loss. This method has several essential properties. First, the degree of sparsity is continuous—a parameter controls the rate of sparsification from no sparsification to total sparsification. Second, the approach is theoretically motivated, and an instance of it can be regarded as an online counterpart of the popular L1-regularization method in the batch setting. We prove small rates of sparsification result in only small additional regret with respect to typical online-learning guarantees. Finally, the approach works well empirically. We apply it to several datasets and find for datasets with large numbers of features, substantial sparsity is discoverable.


title: A stochastic collocation approach to Bayesian inference in inverse problems

abstract: We present an efficient numerical strategy for the Bayesian solution of inverse problems. Stochastic collocation methods, based on generalized polynomial chaos (gPC), are used to construct a polynomial approximation of the forward solution over the support of the prior distribution. This approximation then defines a surrogate posterior probability density that can be evaluated repeatedly at minimal computational cost. The ability to simulate a large number of samples from the posterior distribution results in very accurate estimates of the inverse solution and its associated uncertainty. Combined with high accuracy of the gPC-based forward solver, the new algorithm can provide great efficiency in practical applications. A rigorous error analysis of the algorithm is conducted, where we establish convergence of the approximate posterior to the true posterior and obtain an estimate of the convergence rate. It is proved that fast (exponential) convergence of the gPC forward solution yields similarly fast (exponential) convergence of the posterior. The numerical strategy and the predicted convergence rates are then demonstrated on nonlinear inverse problems of varying smoothness and dimension. AMS subject classifications: 41A10, 60H35, 65C30, 65C50


title: Text Simplification as Tree Transduction

abstract: Lexical and syntactic simplification aim to make texts more accessible to certain audiences. Syntactic simplification uses either hand-crafted linguis- tic rules for deep syntactic transformations, or machine learning techniques to model simpler transformations. Lexical simplification performs a lookup for synonyms followed by context and/or frequency-based models. In this paper we investigate modelling both syntactic and lexical simplification through the learning of general tree transduction rules. Experiments with the Simple En- glish Wikipedia corpus show promising results but highlight the need for clever filtering strategies to remove noisy transformations.


title: Maternal transfer, distribution, and metabolism of BDE-47 and its related hydroxylated, methoxylated analogs in zebrafish (Danio rerio).

abstract: OH-PBDEs have been reported to be more potent than the postulated precursor PBDEs or corresponding MeO-PBDEs. However, there are contradictory reports for transformation of these compounds in organisms, particularly, for biotransformation of OH-PBDEs and MeO-PBDEs, only one study reported transformation of 6-OH-BDE-47 and 6-MeO-BDE-47 in Japanese medaka. In present study zebrafish (Danio rerio) were exposed to BDE-47, 6-OH-BDE-47, 6-MeO-BDE-47, 2'-OH-BDE-28 and 2'-MeO-BDE-28 in the diet for 20 d. Concentrations of each exposed compound were measured in eggs collected on days 0, 5, 10, 15 or 20. After 20 d exposure, concentrations of precursor and biotransformation products in liver and liver-free residual carcass were measured by use of GC-MS/MS. Total mass of the five compounds in bodies of adults were: 2'-MeO-BDE-28 ∼ 6-MeO-BDE-47>BDE-47>2'-OH-BDE-28>6-OH-BDE-47. MeO-PBDEs were also accumulated more into parental fish body than in liver, while OH-PBDEs accumulated in liver more than in liver-free residual carcass. Concentrations in liver of males were greater than those of females. This result suggests sex-related differences in accumulation. Ratios between concentration in eggs and liver (E/L) were: 2.9, 1.7, 0.8, 0.4 and 0.1 for 6-MeO-BDE-47, BDE-47, 6-OH-BDE-47, 2'-MeO-BDE-28 and 2'-OH-BDE-28, respectively. This result suggests transfer from adult females to eggs. BDE-47 was not transformed into OH-PBDEs or MeO-PBDEs. Inter-conversions of 6-OH-BDE-47 and 6-MeO-BDE-47, 2'-OH-BDE-28 and 2'-MeO-BDE-28 were observed, with metabolite/precursor concentration ratios for 6-OH-BDE-47, 6-MeO-BDE-47, 2'-OH-BDE-28 and 2'-MeO-BDE-28 being 3.8%, 14.6%, 2.9% and 76.0%, respectively. Congener-specific differences were observed in distributions between liver and carcass, maternal transfer and transformation. The two MeO-PBDEs were accumulated into adults, transferred to eggs, and were transformed to the structural similar OH-PBDEs, which might be more toxic. BDE-47 was accumulated into adults and transferred from females to eggs, but not transformed to MeO-PBDEs and/or OH-PBDEs. Accumulation of OH-PBDEs into adults as well as rates of transformation of OH-PBDEs to MeO-PBDEs were all several orders of magnitude less. Thus, MeO-PBDEs are likely to present more of a risk in the environment.


title: Visually comprehensible QR codes via embedding of big logos

abstract: In this paper, we tackle the problem of embedding logos into QR codes. For the ease of visual comprehension by human, it is preferred that the logo should be big, if possible. A logo-embedding scheme, which is referred to as module displacement, is proposed. In module displacement, the data modules that are corrupted by the logo are moved to hide behind those modules that are not corrupted by the logo. Moreover, the problem of decoding a logo-embedded QR code posted on a plane or a cylinder is addressed. In both cases, the QR image as captured by a camera is distorted. Therefore, some operation needs to be performed to rectify the deformation.


title: Positive effects of entertainment technology on human behaviour

abstract: Worldwide the pros and cons of games and social behaviour are discussed. In Western countries the discussion is focussing on violent game and media content; in Japan on intensive game usage and the impact on the intellectual development of children. A lot is already discussed on the harmful and negative effects of entertainment technology on human behaviour, therefore we decided to focus primarily on the positive effects. Based on an online document search we could find and select 393 online available publications according the following categories: meta review (N=34), meta analysis (N=13), literature review (N=38), literature survey (N=36), empirical study (N=91), survey study (N=44), design study (N=91), any other document (N=46). In this paper a first preliminary overview over positive effects of entertainment technology on human behaviour is presented and discussed. The drawn recommendations can support developers and designers in entertainment industry.


title: The design of a channel-access protocol for a wireless ad hoc network with sectored directional antennas

abstract: A packet scheduler and a medium access control (MAC) protocol are presented for a direct-sequence spread-spectrum, wireless ad hoc network that contains a mix of nodes with directional antennas and nodes with omnidirectional antennas. The scheduler and MAC protocol are designed to prevent the co-site interference problem that arises in some types of nodes employing directional antennas. It is shown that the presence of nodes with directional antennas exacerbates the vulnerability of the network to the receiver blocking problem. A modification of the MAC protocol is presented that mitigates the receiver blocking problem, and it is shown to improve the performance of a network that includes nodes with directional antennas.


title: A Baseline for the Multivariate Comparison of Resting-State Networks

abstract: As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting state networks of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12 to 71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. Resting state networks were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.


title: From Mind Perception to Mental Connection: Synchrony as a Mechanism for Social Understanding

abstract: Connecting deeply with another mind is as enigmatic as it is fulfilling. Why people ‘‘click’’ with some people but not others is one of the great unsolved mysteries of science. However, researchers from psychology and neuroscience are converging on a likely physiological basis for connection ‐ neural synchrony (entrainment). Here, we review research on the necessary precursors for interpersonal synchrony: the ability to detect a mind and resonate with its outputs. Further, We describe potential mechanisms for the development of synchrony between two minds. We then consider recent neuroimaging and behavioral evidence for the adaptive benefits of synchrony, including neural efficiency and the release of a reward signal that promotes future social interaction. In nature, neural synchrony yields behavioral synchrony. Humans use behavioral synchrony to promote neural synchrony, and thus, social bonding. This reverse-engineering of social connection is an important innovation likely underlying this distinctively human capacity to create large-scale social coordination and cohesion.


THE SPEECH-LANGUAGE PATHOLOGIST'S ROLE IN THE NEONATAL INTENSIVE CARE UNIT


title: Enabling security on 6LoWPAN / IPv6 Wireless Sensor Networks

abstract: The increasing interest in the development of open-source, IPv6 platforms for Wireless Sensor Networks (WSN) and the Internet of Things (IoT), offers a significant potential ubiquitous monitoring and control. The usage of IPv6 in WSNs enables the integration of sensing applications with the Internet. For relevant goals, we consider security should properly be addressed as an integral part of high-level layers of the protocol stack. This paper describes and evaluates the usage of new compressed 6LoWPAN security headers, with a focus on the link-layer. Leveraging the Contiki operating system for resource constrained devices, along with link-layer security sublayers and IPv6, helpful insight is achieved for evaluation and deployment.


title: Combined Haze and Cirrus Removal for Multispectral Imagery

abstract: Multispectral satellite images are often contaminated by haze and/or cirrus. A previous paper presented a haze removal method that calculates a haze thickness map (HTM) based on a local search of dark objects. The haze-free signal is restored by subtracting the HTM from the hazy image assuming an additive model of the haze influence. The HTM method is substantially improved by employing the 1.38-μm cirrus band. The top-of-atmosphere reflectance cirrus band is used as an additional source of information. The method restores the information in highly inhomogeneous surfaces attenuated by a low-altitude haze and high-altitude cirrus, improving the interpretation of the scene content while preserving the shape of the spectral signatures. The new enhanced HTM method is successfully applied to Landsat-8 OLI and Sentinel-2 real and simulated scenes.


title: Multimodal distributional semantics

abstract: Distributional semantic models derive computational representations of word meaning from the patterns of co-occurrence of words in text. Such models have been a success story of computational linguistics, being able to provide reliable estimates of semantic relatedness for the many semantic tasks requiring them. However, distributional models extract meaning information exclusively from text, which is an extremely impoverished basis compared to the rich perceptual sources that ground human semantic knowledge. We address the lack of perceptual grounding of distributional models by exploiting computer vision techniques that automatically identify discrete "visual words" in images, so that the distributional representation of a word can be extended to also encompass its co-occurrence with the visual words of images it is associated with. We propose a flexible architecture to integrate text- and image-based distributional information, and we show in a set of empirical tests that our integrated model is superior to the purely text-based approach, and it provides somewhat complementary semantic information with respect to the latter.


title: Gamification by Design: Implementing Game Mechanics in Web and Mobile Apps

abstract: What do Foursquare, Zynga, Nike+, and Groupon have in common? These and many other brands use gamification to deliver a sticky, viral, and engaging experience to their customers. This book provides the design strategy and tactics you need to integrate game mechanics into any kind of consumer-facing website or mobile app. Learn how to use core game concepts, design patterns, and meaningful code samples to a create fun and captivating social environment.Whether you're an executive, developer, producer, or product specialist, Gamification by Design will show you how game mechanics can help you build customer loyalty.Discover the motivational framework game designers use to segment and engage consumers Understand core game mechanics such as points, badges, levels, challenges, and leaderboards Engage your consumers with reward structures, positive reinforcement, and feedback loops Combine game mechanics with social interaction for activities such as collecting, gifting, heroism, and status Dive into case studies on Nike and Yahoo!, and analyze interactions at Google, Facebook, and Zynga Get the architecture and code to gamify a basic consumer site, and learn how to use mainstream gamification APIs from Badgeville "Turning applications into games is a huge trend. This book does a great job of identifying the core lasting principals you need to inspire your users to visit again and again." Adam Loving Freelance Social Game Developer and founder of Twibes Twitter Groups


Bayesian Neural Networks for Predicting Learning Curves


title: Deep brain stimulation: a novel strategy for treating Alzheimer's disease.

abstract: Recent studies have identified an association between memory deficits and defects of the integrated neuronal cortical areas known collectively as the default mode network. It is conceivable that the amyloid deposition or other molecular abnormalities seen in patients with Alzheimer’s disease may interfere with this network and disrupt neuronal circuits beyond the localized brain areas. Therefore, Alzheimer’s disease may be both a degenerative disease and a broader system-level disorder affecting integrated neuronal pathways involved in memory. In this paper, we describe the rationale and provide some evidence to support the study of deep brain stimulation of the hippocampal fornix as a novel treatment to improve neuronal circuitry within these integrated networks and thereby sustain memory function in early Alzheimer’s disease.


title: Value-Based Software Engineering : Seven Key Elements and Ethical Considerations

abstract: This chapter presents seven key elements that provide candidate foundations for value-based software engineering: ::: ::: 1. ::: ::: Benefits Realization Analysis ::: ::: ::: ::: ::: 2. ::: ::: Stakeholder Value Proposition Elicitation and Reconciliation ::: ::: ::: ::: ::: 3. ::: ::: Business Case Analysis ::: ::: ::: ::: ::: 4. ::: ::: Continuous Risk and Opportunity Management ::: ::: ::: ::: ::: 5. ::: ::: Concurrent System and Software Engineering ::: ::: ::: ::: ::: 6. ::: ::: Value-Based Monitoring and Control ::: ::: ::: ::: ::: 7. ::: ::: Change as Opportunity ::: ::: ::: ::: ::: ::: ::: Using a case study we show how some of these elements can be used to incorporate ethical considerations into daily software engineering practice.


title: Micro-Doppler effect in radar: phenomenon, model, and simulation study

abstract: When, in addition to the constant Doppler frequency shift induced by the bulk motion of a radar target, the target or any structure on the target undergoes micro-motion dynamics, such as mechanical vibrations or rotations, the micro-motion dynamics induce Doppler modulations on the returned signal, referred to as the micro-Doppler effect. We introduce the micro-Doppler phenomenon in radar, develop a model of Doppler modulations, derive formulas of micro-Doppler induced by targets with vibration, rotation, tumbling and coning motions, and verify them by simulation studies, analyze time-varying micro-Doppler features using high-resolution time-frequency transforms, and demonstrate the micro-Doppler effect observed in real radar data.


title: Generating narrative variation in interactive fiction

abstract: A general method for the generation of natural language narrative is described. It allows the expression, or narrative discourse, to vary independently of the underlying events and existents that are the narrative's content. Specifically, this variation is accomplished in an interactive fiction (IF) system which replies to typed input by narrating what has happened in a simulated world. IF works have existed for about 30 years as forms of text-based computer simulation, instances of dialog systems, and examples of literary art. Theorists of narrative have carefully distinguished between the level of underlying content (corresponding to the simulated world in interactive fiction) and that of expression (corresponding to the textual exchange between computer and user) since the mid-1960s, when the field of narratology began to develop, but IF systems have not yet made use of this distinction. The current project contributes new techniques for automatic narration by building on work done in computational linguistics, specifically natural language generation, and in narratology. First, types of narrative variation that are possible in IF are identified and formalized in a way that is suitable for a natural language generation system. An architecture for an IF system is then described and implemented; the result allows multiple works of interactive fiction to be realized and, using a general plan for narrating, allows them to be narrated in different ways during interaction. The system's ability to generate text is considered in a pilot evaluation. Plans for future work are also discussed. They include publicly released systems for IF development and narratology education, adding a planning capability that uses actors' individual perspectives, and adapting automatic narration to different sorts of interactive systems.


title: Spherical symmetric 3D local ternary patterns for natural, texture and biomedical image indexing and retrieval

abstract: Abstract In this paper, we propose a new algorithm using spherical symmetric three dimensional local ternary patterns (SS-3D-LTP) for natural, texture and biomedical image retrieval applications. The existing local binary patterns (LBP), local ternary patterns (LTP), local derivative patterns (LDP), local tetra patterns (LTrP) etc., are encode the relationship between the center pixel and its surrounding neighbors in two dimensional (2D) local region of an image. The proposed method encodes the relationship between the center pixel and its surrounding neighbors with five selected directions in 3D plane which is generated from 2D image using multiresolution Gaussian filter bank. In addition, we propose the color SS-3D-LTP (CSS-3D-LTP) where we consider the RGB spaces as three planes of 3D volume. Three experiments have been carried out for proving the worth of our algorithm for natural, texture and biomedical image retrieval applications. It is further mentioned that the databases used for natural, texture and biomedical image retrieval applications are Corel-10K, Brodatz and open access series of imaging studies (OASIS) magnetic resonance databases respectively. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to the start-of-art spatial as well as transform domain techniques on respective databases.


title: Software Defined naval network for satellite communications (SDN-SAT)

abstract: We propose Software Defined Networking (SDN) framework to a fleet of naval ships that relies on multiple satellite communication systems for onboard communication. Our solution addresses practical issues in current shipboard naval networks such as sharing and load balancing of multiple satellite communication links as well as overcoming limited bandwidth constraints. To ameliorate link intermittence and outage, we propose Multi-Path Transmission Control Protocol (MPTCP), which improves end-to-end data delivery by creating several subflows under one TCP session. In our SDN framework, each ship is an SDN switch with multiple SATCOM connections. The management and classification of MPTCP subflows are handled by a remote SDN controller. The cooperation between MPTCP and SDN controller leads to an agile, bandwidth efficient, robust naval network. System analysis and numerical evaluation validate the feasibility and efficacy of our SDN-based solution for such a network.


title: FPGA implementation for stereo matching algorithm

abstract: To reach a real-time stereo vision in embedded systems, we propose in this paper, the adaptation and optimization of the well-known Disparity Space Image (DSI) on a single FPGA(Field programmable gate Arrays) that is designed for high efficiency when realized in hardware. An initial disparity map was calculated using the DSI structure and then a median filter was applied to smooth the disparity map. Many methods reported in the literature are mainly restricted to implement the SAD algorithm (Sum of Absolute Differences) on an FPGA. An evaluation of our method is done by comparing the obtained results of our method with a very fast and well-known sum of absolute differences algorithm using hardware-based implementations.


title: Prediction of protein structural classes by support vector machines

abstract: In this paper, we apply a new machine learning method which is called support vector machine to approach the prediction of protein structural class. The support vector machine method is performed based on the database derived from SCOP which is based upon domains of known structure and the evolutionary relationships and the principles that govern their 3D structure. As a result, high rates of both self-consistency and jackknife test are obtained. This indicates that the structural class of a protein inconsiderably correlated with its amino acid composition, and the support vector machine can be referred as a powerful computational tool for predicting the structural classes of proteins.


title: Preliminary indications of the effect of a brief yoga intervention on markers of inflammation and DNA methylation in chronically stressed women

abstract: Yoga is associated with reduced stress and increased well-being, although the molecular basis for these benefits is not clear. Mounting evidence implicates the immune response, with current studies focused on protein immune markers (such as cytokines) in clinical populations. To explore the molecular impact, this pilot study uses a subsample (n=28) from a randomised waitlist control trial investigating the impact of an 8-week yoga intervention in a community population of women reporting psychological distress (N=116). We measured interleukin-6 (IL-6), tumour necrosis factor (TNF) and C-reactive protein (CRP) protein levels, and the DNA methylation of these genes and the global indicator, LINE-1. Correlations between these and psychological variables were explored, identifying moderate correlations with CRP protein levels, and methylation of IL-6, CRP and LINE-1. Many cytokine samples were below detection, however a Mann-Whitney U demonstrated a trend of moderate between-group effect for elevated IL-6 in the yoga group. Methylation analyses applied cross-sectional and non-controlled longitudinal analyses. Waist-to-height ratio and age were covaried. We demonstrated reduced methylation of the TNF region in the yoga group relative to the waitlist control group. No other genes demonstrated a significant difference. Longitudinal analysis further supported these results. This study is one of the first to explore yoga and immunological markers in a non-clinical population, and is the first study to explore DNA methylation. These findings indicate that further research into molecular impact of yoga on markers of immune function is warranted, with larger studies required.


title: Informational needs for participation in bioequivalence studies: the perspectives of experienced volunteers

abstract: The present study aimed to determine the extent of information that healthy volunteers need in an informed consent form (ICF) to support their decision whether to participate in a bioequivalence study, a type of clinical studies involving the testing of pharmacokinetic equivalence of a generic drug with a brand-name drug in volunteer subjects. This cross-sectional, descriptive study determined the perspectives of individuals who used to participate in bioequivalence studies, using an electronic-based questionnaire. A 5-point modified Likert scale was used to indicate the importance of each element of the ICF content, with an anchored rating scale from 1 (not important) to 5 (very important) for each item. Of 300 questionnaires distributed, all (100%) were returned. The respondents considered most items to be necessary for their decision, with the score ranging from 3.25 to 4.60 (mean overall score = 4.16 ± 0.30). The four top-rated items were the “major foreseeable risk” (4.60 ± 0.72), “participant’s responsibility during participation” (4.52 ± 0.72), “confidentiality and the limit of confidentiality” (4.52 ± 0.82), and “all possible adverse effects of the drug” (4.47 ± 0.74), while the relatively less concerned items were related to general information. Most elements of the ICF content required were considered as important by the previously experienced volunteers in bioequivalence studies, notwithstanding that some elements were perceived as more important than others. The data from this study could be used to better tailor relevant information in an ICF to the needs of research participants in bioequivalence studies.


title: A MULTI-ATTRIBUTE ANALYSIS OF PREFERENCES FOR ONLINE AND OFFLINE SHOPPING : DIFFERENCES ACROSS PRODUCTS , CONSUMERS , AND SHOPPING STAGES

abstract: In order to develop the optimal mix of online and offline services for a particular product, marketers must determine which key attributes are perceived by their target market to be delivered better online or offline. A multipart survey was administered to assess how product attribute evaluations drive differences in online/offline shopping preferences between products, between consumers, and between stages of the shopping experience. Two groups of participants - a student sample and a sample from a national survey panel - were asked to rate their likelihood of shopping online or offline for each of a series of products, both at the search stage and at the purchase stage. They were then asked to rate the importance of attributes that might have differential importance for products like clothing and books, and to rate the extent to which they thought each attribute was delivered better online or offline. For both samples, differences in importance weights assigned to attributes that favor online shopping and attributes that favor offline shopping were key predictors of observed differences in shopping mode preference across products and across consumers.


title: Towards visual smart metering exploiting wM-Bus and DLMS/COSEM

abstract: Automatic Meter Reading (AMR) is one of the key issues in modern energy efficiency technologies, which are expected to bring on great benefits to both customers and utility companies (electricity, gas and water). Several efforts have been recently made in developing suitable communication protocols for metering data management and transmission. In order to match the cost requirements for the water market, an original solution represented by a visual sensor is proposed as an add-on module for the smart meter. The adoption of developed electronic device and ad-hoc protocol at application layer within the last mile of a multiservice Advanced Metering Infrastructure is investigated in terms of energy issues and interoperability. This study also analyses the communication performance that can be expected when using wireless M-Bus technology to send DLMS/COSEM messages and complete an image file transmission from the smart meter to data concentrator unit.


Implications of population ageing for economic growth


title: Extracting flow features via supervised streamline segmentation

abstract: Effective flow feature extraction enables users to explore complex flow fields by reducing visual clutter. Existing methods usually use streamline segmentation as a preprocessing step for feature extraction. In our work, features are directly extracted as a result of streamline segmentation. In order to achieve this, we first ask users to specify desired features by manually segmenting a few streamlines from a flow field. Users only need to pick the segmentation points (i.e., positive examples) along a streamline, remaining points will be used as negative examples. Next we compute multiscale features for each positive/negative example and feed them into a binary support vector machine (SVM) trainer. The trained classifier is then used to segment all the streamlines in a flow field. Finally, the segments are clustered based on their shape similarities. Our experiment shows that very good segmentation results can be obtained with only a small number of streamlines to be segmented by users for each data set. We also propose a novel heuristic based on the minimum bounding ellipsoid volume to help determine where to segment a streamline. Graphical abstractDisplay Omitted HighlightsWe proposed a supervised segmentation algorithm to extract flow features.We propose an effective heuristic which captures how human beings segment streamlines.We illustrate the utility of our streamline segmentation method using both synthesized and real-simulation data sets.We compare our method with the state-of-the-art to show our method performs much better.


title: On the Design of Microfluidic Implant Coil for Flexible Telemetry System

abstract: This paper describes the realization of a soft, flexible, coil fabricated by means of a liquid metal alloy encased in a biocompatible elastomeric substrate for operation in a telemetry system, primarily for application to biomedical implantable devices. Fluidic conductors are in fact well suited for applications that require significant flexibility as well as conformable and stretchable devices, such as implantable coils for wireless telemetry. A coil with high conductivity, and therefore low losses and high unloaded Q factor, is required to realize an efficient wireless telemetry system. Unfortunately, the conductivity of the liquid metal alloy considered-eutectic gallium indium (EGaIn)-is approximately one order of magnitude lower than gold or copper. The goal of this paper is to demonstrate that despite the lower conductivity of liquid metal alloys, such as EGaIn, compared with materials, such as copper or gold, it is still possible to realize an efficient biomedical telemetry system employing liquid metal coils on the implant side. A wireless telemetry system for an artificial retina to restore partial vision to the blind is used as a testbed for the proposed liquid metal coils. Simulated and measured results show that power transfer efficiency of 43% and 21% are obtained at operating distances between coils of 5 and 12 mm, respectively. Further, liquid metal based coil retains more than 72% of its performance (voltage gain, resonance bandwidth, and power transfer efficiency) when physically deformed over a curved surface, such as the surface of the human eye. This paper demonstrates that liquid metal-based coils for biomedical implant provide an alternative to stiff and uncomfortable traditional coils used in biomedical implants.


title: FOXP2 and the role of cortico-basal ganglia circuits in speech and language evolution

abstract: PURPOSE OF THE REVIEW ::: A reduced dosage of the transcription factor FOXP2 leads to speech and language impairments probably owing to deficits in cortical and subcortical neural circuits. Based on evolutionary sequence analysis it has been proposed that the two amino acid substitutions that occurred on the human lineage have been positively selected. Here I review recent studies investigating the functional consequences of these two substitutions and discuss how these first endeavors to study human brain evolution can be interpreted in the context of speech and language evolution. ::: ::: ::: RECENT FINDINGS ::: Mice carrying the two substitutions in their endogenous Foxp2 gene show specific alterations in dopamine levels, striatal synaptic plasticity and neuronal morphology. Mice carrying only one functional Foxp2, show additional and partly opposite effects suggesting that FOXP2 has contributed to tuning cortico-basal ganglia circuits during human evolution. Evidence from human and songbird studies suggest that this could have been relevant during language acquisition or vocal learning, respectively. ::: ::: ::: SUMMARY ::: FOXP2 could have contributed to the evolution of human speech and language by adapting cortico-basal ganglia circuits. More generally the recent studies allow careful optimism that aspects of human brain evolution can be investigated in model systems such as the mouse.


title: Deep learning model based breast cancer histopathological image classification

abstract: The automatic and precision classification for breast cancer histopathological image has a great significance in clinical application. However, the existing analysis approaches are difficult to addressing the breast cancer classification problem because the feature subtle differences of inter-class histopathological image and the classification accuracy still hard to meet the clinical application. Recent advancements in data-driven sharing processing and multi-level hierarchical feature learning have made available considerable chance to dope out a solution to this problem. To address the challenging problem, we propose a novel breast cancer histopathological image classification method based on deep convolutional neural networks, named as BiCNN model, to address the two-class breast cancer classification on the pathological image. This deep learning model considers class and sub-class labels of breast cancer as prior knowledge, which can restrain the distance of features of different breast cancer pathological images. In addition, an advanced data augmented method is proposed to fit tolerance whole slide image recognition, which can full reserve image edge feature of cancerization region. The transfer learning and fine-tuning method are adopted as an optimal training strategy to improve breast cancer histopathological image classification accuracy. The experiment results show that the proposed method leads to a higher classification accuracy (up to 97%) and displays good robustness and generalization, which provides efficient tools for breast cancer clinical diagnosis.


title: Rough set based hybrid algorithm for text classification

abstract: Automatic classification of text documents, one of essential techniques for Web mining, has always been a hot topic due to the explosive growth of digital documents available on-line. In text classification community, k-nearest neighbor (kNN) is a simple and yet effective classifier. However, as being a lazy learning method without premodelling, kNN has a high cost to classify new documents when training set is large. Rocchio algorithm is another well-known and widely used technique for text classification. One drawback of the Rocchio classifier is that it restricts the hypothesis space to the set of linear separable hyperplane regions. When the data does not fit its underlying assumption well, Rocchio classifier suffers. In this paper, a hybrid algorithm based on variable precision rough set is proposed to combine the strength of both kNN and Rocchio techniques and overcome their weaknesses. An experimental evaluation of different methods is carried out on two common text corpora, i.e., the Reuters-21578 collection and the 20-newsgroup collection. The experimental results indicate that the novel algorithm achieves significant performance improvement.


title: Treemap: An O(log n) algorithm for indoor simultaneous localization and mapping

abstract: This article presents a very efficient SLAM algorithm that works by hierarchically dividing a map into local regions and subregions. At each level of the hierarchy each region stores a matrix representing some of the landmarks contained in this region. To keep those matrices small, only those landmarks are represented that are observable from outside the region. ::: ::: A measurement is integrated into a local subregion using O(k2) computation time for k landmarks in a subregion. When the robot moves to a different subregion a full least-square estimate for that region is computed in only O(k3 log n) computation time for n landmarks. A global least square estimate needs O(kn) computation time with a very small constant (12.37 ms for n = 11300). ::: ::: The algorithm is evaluated for map quality, storage space and computation time using simulated and real experiments in an office environment.


Reproducibility in Machine Learning-Based Studies: An Example of Text Mining


title: CodeTube: extracting relevant fragments from software development video tutorials

abstract: Nowadays developers heavily rely on sources of informal documentation, including Q&A forums, slides, or video tutorials, the latter being particularly useful to provide introductory notions for a piece of technology. The current practice is that developers have to browse sources individually, which in the case of video tutorials is cumbersome, as they are lengthy and cannot be searched based on their contents. We present C ode T ube , a Web-based recommender system that analyzes the contents of video tutorials and is able to provide, given a query, cohesive and self-contained video fragments, along with links to relevant Stack Overflow discussions. C ode T ube relies on a combination of textual analysis and image processing applied on video tutorial frames and speech transcripts to split videos into cohesive fragments, index them and identify related Stack Overflow discussions. DEMO URL: http://codetube.inf.usi.ch VIDEO URL: https://youtu.be/yUsUG3g87Dg


title: A transformable wheel robot with a passive leg

abstract: In this paper, we present a novel wheeled robot that transforms from a circled configuration to a spoke-like legged configuration. Wheeled mobile robots are able to quickly and efficiently move on flat surfaces. However they may sink into a terrain with dynamic surface such as snow, sand, dirt, moss, and small gravel. The transformable wheel robot presented in this paper is able to overcome obstacles and navigate these dynamics surfaces, yet can still move quickly over flat ground. The wheel is comprised of five spokes or legs, four of which are actively driven by a motor via four slider-crank linkages. The last one is designed be passive in order to significantly decrease the actuation force of the transformer mechanism. Analysis shows that the maximum driving force required to actuate the passive leg design is 1/187.5 times that of the active leg design. Experiments demonstrate that the passive leg design allows the mobile robot to open the transformation mechanism with a maximum external loading of 1.809 times the original robot weight. The diameter of the legged configuration wheel is 1.576 times that of the circled configuration.


title: Vertical augmentation with interpositional blocks of anorganic bovine bone vs. 7-mm-long implants in posterior mandibles: 1-year results of a randomized clinical trial

abstract: OBJECTIVES ::: To evaluate whether 7-mm-long implants could be an alternative to longer implants placed in vertically augmented posterior mandibles. ::: ::: ::: MATERIALS AND METHODS ::: Sixty patients with posterior mandibular edentulism with 7-8 mm bone height above the mandibular canal were randomized to either vertical augmentation with anorganic bovine bone blocks and delayed 5-month placement of ≥10 mm implants or to receive 7-mm-long implants. Four months after implant placement, provisional prostheses were delivered, replaced after 4 months, by definitive prostheses. The outcome measures were prosthesis and implant failures, any complications and peri-implant marginal bone levels. All patients were followed to 1 year after loading. ::: ::: ::: RESULTS ::: One patient dropped out from the short implant group. In two augmented mandibles, there was not sufficient bone to place 10-mm-long implants possibly because the blocks had broken apart during insertion. One prosthesis could not be placed when planned in the 7 mm group vs. three prostheses in the augmented group, because of early failure of one implant in each patient. Four complications (wound dehiscence) occurred during graft healing in the augmented group vs. none in the 7 mm group. No complications occurred after implant placement. These differences were not statistically significant. One year after loading, patients of both groups lost an average of 1 mm of peri-implant bone. There no statistically significant differences in bone loss between groups. ::: ::: ::: CONCLUSIONS ::: When residual bone height over the mandibular canal is between 7 and 8 mm, 7 mm short implants might be a preferable choice than vertical augmentation, reducing the chair time, expenses and morbidity. These 1-year preliminary results need to be confirmed by follow-up of at least 5 years.


title: Mean-field theory for scale-free random networks

abstract: Random networks with complex topology are common in Nature, describing systems as diverse as the world wide web or social and business networks. Recently, it has been demonstrated that most large networks for which topological information is available display scale-free features. Here we study the scaling properties of the recently introduced scale-free model, that can account for the observed power-law distribution of the connectivities. We develop a mean-field method to predict the growth dynamics of the individual vertices, and use this to calculate analytically the connectivity distribution and the scaling exponents. The mean-field method can be used to address the properties of two variants of the scale-free model, that do not display power-law scaling.


title: DYNAMICAL RECURRENT NEURO-FUZZY IDENTIFICATION SCHEMES EMPLOYING SWITCHING PARAMETER HOPPING

abstract: In this paper we analyze the identification problem which consists of choosing an appropriate identification model and adjusting its parameters according to some adaptive law, such that the response of the model to an input signal (or a class of input signals), approximates the response of the real system for the same input. For identification models we use fuzzy-recurrent high order neural networks. High order networks are expansions of the first-order Hopfield and Cohen-Grossberg models that allow higher order interactions between neurons. The underlying fuzzy model is of Mamdani type assuming a standard defuzzification procedure such as the weighted average. Learning laws are proposed which ensure that the identification error converges to zero exponentially fast or to a residual set when a modeling error is applied. There are two core ideas in the proposed method: (1) Several high order neural networks are specialized to work around fuzzy centers, separating in this way the system into neuro-fuzzy subsystems, and (2) the use of a novel method called switching parameter hopping against the commonly used projection in order to restrict the weights and avoid drifting to infinity.


title: Organogenesis and Ultrastructural Features of In Vitro Grown Canna indica L.

abstract: An efficient protocol for micropropagation of Canna indica L., an economically and pharmaceutically important plant, was standardized using rhizome explants, excised from two-month-old aseptic seedlings. Complete plant regeneration was induced on MS medium supplemented with 3.0 mg/L BAP plus 1.5 mg/L NAA, which produced the highest number of shoots (73.3 ± 0.5%) and roots (86.7 ± 0.4%) after 2 weeks. Furthermore, the optimum media for multiple shoots regeneration were recorded on MS enriched with 7.0 mg/L BAP (33.0 ± 0.5%). Plantlets obtained were transplanted to pots after two months and acclimatized in the greenhouse, with 75% survival. In addition, ultrastructural studies showed that rhizomes of in vitro grown specimens were underdeveloped compared to the in vivo specimens, possibly due to the presence of wide spaces. Meanwhile, the leaves of in vivo specimens had more open stomata compared to in vitro specimens, yet their paracytic stomata structures were similar. Hence, there were no abnormalities or major differences between in vitro regenerants and mother plants.


title: Current Status and Prospects for Cannabidiol Preparations as New Therapeutic Agents.

abstract: States and the federal government are under growing pressure to legalize the use of cannabis products for medical purposes in the United States. Sixteen states have legalized (or decriminalized possession of) products high in cannabidiol (CBD) and with restricted ∆(9) -tetrahydrocannabinol (∆(9) -THC) content. In most of these states, the intent is for use in refractory epileptic seizures in children, but in a few states, the indications are broader. This review provides an overview of the pharmacology and toxicology of CBD; summarizes some of the regulatory, safety, and cultural issues relevant to the further exploitation of its antiepileptic or other pharmacologic activities; and assesses the current status and prospects for clinical development of CBD and CBD-rich preparations for medical use in the United States. Unlike Δ(9) -THC, CBD elicits its pharmacologic effects without exerting any significant intrinsic activity on the cannabinoid receptors, whose activation results in the psychotropic effects characteristic of Δ(9) -THC, and CBD possesses several pharmacologic activities that give it a high potential for therapeutic use. CBD exhibits neuroprotective, antiepileptic, anxiolytic, antipsychotic, and antiinflammatory properties. In combination with Δ(9) -THC, CBD has received regulatory approvals in several European countries and is currently under study in trials registered by the U.S. Food and Drug Administration in the United States. A number of states have passed legislation to allow for the use of CBD-rich, limited Δ(9) -THC-content preparations of cannabis for certain pathologic conditions. CBD is currently being studied in several clinical trials and is at different stages of clinical development for various medical indications. Judging from clinical findings reported so far, CBD and CBD-enriched preparations have great potential utility, but uncertainties regarding sourcing, long-term safety, abuse potential, and regulatory dilemmas remain.


title: A Continuous Beam Steering Slotted Waveguide Antenna Using Rotating Dielectric Slabs

abstract: The design, simulation, and measurement of a beam steerable slotted waveguide antenna operating in X band are presented. The proposed antenna consists of a standard rectangular waveguide (RWG) section with longitudinal slots in the broad wall. The beam steering in this configuration is achieved by rotating two dielectric slabs inside the waveguide and consequently changing the phase of the slots excitations. In order to confirm the usefulness of this concept, a nonresonant 20-slot waveguide array antenna with an element spacing of $d = 0.6\lambda _{0}$ has been designed, built, and measured. A 14° beam scanning from near broadside ( $\theta = 4^{\circ }$ ) toward end-fire ( $\theta = 18^{\circ }$ ) direction is observed. The gain varies from 18.33 to 19.11 dBi and the antenna efficiency from 95% to 79%. The sidelobe level is −14 dB at the design frequency of 9.35 GHz. The simulated and measured co-polarized realized gains are in good agreement.


title: AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring

abstract: AutoTutor is a natural language tutoring system that has produced learning gains across multiple domains (e.g., computer literacy, physics, critical thinking). In this paper, we review the development, key research findings, and systems that have evolved from AutoTutor. First, the rationale for developing AutoTutor is outlined and the advantages of natural language tutoring are presented. Next, we review three central themes in AutoTutor’s development: human-inspired tutoring strategies, pedagogical agents, and technologies that support natural-language tutoring. Research on early versions of AutoTutor documented the impact on deep learning by co-constructed explanations, feedback, conversational scaffolding, and subject matter content. Systems that evolved from AutoTutor added additional components that have been evaluated with respect to learning and motivation. The latter findings include the effectiveness of deep reasoning questions for tutoring multiple domains, of adapting to the affect of low-knowledge learners, of content over surface features such as voices and persona of animated agents, and of alternative tutoring strategies such as collaborative lecturing and vicarious tutoring demonstrations. The paper also considers advances in pedagogical agent roles (such as trialogs) and in tutoring technologies, such semantic processing and tutoring delivery platforms. This paper summarizes and integrates significant findings produced by studies using AutoTutor and related systems.


title: Variability and relationships of important alfalfa germplasm agronomic traits

abstract: Background and Purpose: The objective of this paper was to establish variability as well as to estimate stability of the most important alfalfa agronomic traits, with respect to germplasms of different origin. Correlations between the investigated traits were also analysed, since data on relationships are of great importance in selection, especially in traits with low genetic variability. Material and Methods: Experimental material consisted of 12 alfalfa germplasms. Investigation was carried out during a two-year period on two locations, at two different soil types. The field trial was arranged according to the randomised block design with four replications. Results and Conclusions: There was a statistically significant difference of green mass yield from 59.43 t ha –1 (Astra) to 69.04 t ha –1 (PCP), of dry matter from 12.26 t ha –1 (Astra) to 14.10 t ha –1 (PCP), of proteins from 2.49 t ha –1 (Panonija) to 3.03 t ha –1 (PCP), of plant height from 54.67 cm (OS – 90) to 60.82 (Elena), of leaf ratio to green mass from 51.03% (Elena) to 53.56% (Panonija) and of protein content in dry matter from 19.86% (Posavina) to 20.96% (PCP). There were positive correlations between green mass yield, dry matter, protein level and plant height, leaf ratio in green mass was in a negative correlation to yields and plant height. High and stable green mass yields were provided by germplasm OS-88, Du Puits, Elena, Posavina, Vuka and the breeding population PCP in various environmental conditions of plant growth.


title: Papyrus: a UML2 tool for domain-specific language modeling

abstract: This chapter outlines Papyrus, a tool for graphical modeling of UML2 applications. It is an open-source project, designed as an Eclipse component, and based on the existing EMF-based realization of the UML2 meta-model. The goal of this open-source project is twofold. First, it is a complete, efficient, robust, and methodologically agnostic implementation of a UML2 tool to both industry and academia. Second, it is an open and flexible facility for defining and utilizing domain-specific modeling languages using a very advanced implementation of the UML profile concept.


title: A survey on feature weighting based K-Means algorithms

abstract: In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analysing some of the most popular, or innovative, feature weighting mechanisms based in K-Means.


title: Fast planar surface 3D SLAM using LIDAR

abstract: In this paper we propose a fast 3D pose based SLAM system that estimates a vehicle’s trajectory by registering sets of planar surface segments, extracted from 360∘360∘ field of view (FOV) point clouds provided by a 3D LIDAR. Full FOV and planar representation of the map gives the proposed SLAM system the capability to map large-scale environments while maintaining fast execution time. For efficient point cloud processing we apply image-based techniques to project it to three two-dimensional images. The SLAM backend is based on Exactly Sparse Delayed State Filter as a non-iterative way of updating the pose graph and exploiting sparsity of the SLAM information matrix. Finally, our SLAM system enables reconstruction of the global map by merging the local planar surface segments in a highly efficient way. The proposed point cloud segmentation and registration method was tested and compared with the several state-of-the-art methods on two publicly available datasets. Complete SLAM system was also tested in one indoor and one outdoor experiment. The indoor experiment was conducted using a research mobile robot Husky A200 to map our university building and the outdoor experiment was performed on the publicly available dataset provided by the Ford Motor Company, in which a car equipped with a 3D LIDAR was driven in the downtown Dearborn Michigan.


title: Depression, anxiety and stress in dental students

abstract: To measure the occurrence and levels of depression, anxiety and stress in undergraduate dental students using the Depression, Anxiety and Stress Scale (DASS-21).


title: A simple approach for bubble modelling from multiphase fluid simulation

abstract: This article presents a novel and flexible bubble modelling technique for multi-fluid simulations using a volume fraction representation. By combining the volume fraction data obtained from a primary multi-fluid simulation with simple and efficient secondary bubble simulation, a range of real-world bubble phenomena are captured with a high degree of physical realism, including large bubble deformation, sub-cell bubble motion, bubble stacking over the liquid surface, bubble volume change, dissolving of bubbles, etc. Without any change in the primary multi-fluid simulator, our bubble modelling approach is applicable to any multi-fluid simulator based on the volume fraction representation.


SDN-Based Enterprise and Campus Networks: A Case of VLAN Management


title: Minutiae-based Fingerprint Extraction and Recognition

abstract: In our electronically inter-connected society, reliable and user-friendly recognition and verification system is essential in many sectors of our life. The person’s physiological or behavioral characteristics, known as biometrics, are important and vital methods that can be used for identification and verification. Fingerprint recognition is one of the most popular biometric techniques used in automatic personal identification and verification. Many researchers have addressed the fingerprint classification problem and many approaches to automatic fingerprint classification have been presented in the literature; nevertheless, the research on this topic is still very active. Although significant progress has been made in designing automatic fingerprint identification systems over the past two decades, a number of design factors (lack of reliable minutia extraction algorithms, difficulty in quantitatively defining a reliable match between fingerprint images, poor image acquisition, low contrast images, the difficulty of reading the fingerprint for manual workers, etc.) create bottlenecks in achieving the desired performance. Nowadays, investigating the influence of the fingerprint quality on recognition performances also gains more and more attention. A fingerprint is the pattern of ridges and valleys on the surface of a fingertip. Each individual has unique fingerprints. Most fingerprint matching systems are based on four types of fingerprint representation schemes (Fig. 1): grayscale image (Bazen et al., 2000), phase image (Thebaud, 1999), skeleton image (Feng, 2006; Hara & Toyama, 2007), and minutiae (Ratha et al., 2000; Bazen & Gerez, 2003). Due to its distinctiveness, compactness, and compatibility with features used by human fingerprint experts, minutiae-based representation has become the most widely adopted fingerprint representation scheme. The uniqueness of a fingerprint is exclusively determined by the local ridge characteristics and their relationships. The ridges and valleys in a fingerprint alternate, flowing in a local constant direction. The two most prominent local ridge characteristics are: 1) ridge ending and, 2) ridge bifurcation. A ridge ending is defined as the point where a ridge ends abruptly. A ridge bifurcation is defined as the point where a ridge forks or diverges into branch ridges. Collectively, these features are called minutiae. Detailed description of fingerprint minutiae will be given in the next section. The widespread deployment of fingerprint recognition systems in various applications has caused concerns that compromised fingerprint templates may be used to make fake fingers, which could then be used to deceive all fingerprint systems the same person is enrolled in.


title: WULoRa: An energy efficient IoT end-node for energy harvesting and heterogeneous communication

abstract: Intelligent connected objects, which build the IoT, are electronic devices usually supplied by batteries that significantly limit their life-time. These devices are expected to be deployed in very large numbers, and manual replacement of their batteries will severely restrict their large-scale or wide-area deployments. Therefore energy efficiency is of the utmost importance in the design of these devices. The wireless communication between the distributed sensor devices and the host stations can consume significant energy, even more when data needs to reach several kilometers of distance. In this paper, we present an energy-efficient multi-sensing platform that exploits energy harvesting, long-range communication and ultra-low-power short-range wake-up radio to achieve self sustainability in a kilometer range network. The proposed platform is designed with power efficiency in mind and exploits the always-on wake-up radio as both receiver and a power management unit to significantly reduce the quiescent current even continuously listening the wireless channel. Moreover the platform allows the building of an heterogeneous long-short range network architecture to reduce the latency and reduce the power consumption in listening phase at only 4.6 μW. Experimental results and simulations demonstrate the benefits of the proposed platform and heterogeneous network.


title: 3-Stream Convolutional Networks for Video Action Recognition with Hybrid Motion Field

abstract: Two-stream based architectures for video action recognition exhibit great success recently. They encode the appearance with RGB frame, and the motion with optical flow. It is observed that optical flow depicts pixel-level motion field, focusing much on detail information, is hard to tackle the large displacement. In fact, human always focus the global motion rather than pixel-level motion. Inspired by this, we propose a novel 3-stream network structure with a spatial ConvNet, a pixel-level temporal ConvNet and a block-level temporal ConvNet. Integrating multi-granularity motion representation significantly outperforms single pixel-level motion field based architectures. Further, we can obtain the block-level motion vector field from compressed videos without extra calculation. We address missing and noisy motion patterns of motion vector field with intra-encoded block rectifying and flow guided filtering, building a hybrid motion field for our block-level temporal ConvNet. Our approach obtains state-of-the-art accuracy on UCF101 (95.27%) and HMDB 51 (69.21 %).