title: Generalized Consensus and Paxos

abstract: Theoretician’s Abstract Consensus has been regarded as the fundamental problem that must be solved to implement a fault-tolerant distributed system. However, only a weaker problem than traditional consensus need be solved. We generalize the consensus problem to include both traditional consensus and this weaker version. A straightforward generalization of the Paxos consensus algorithm implements general consensus. The generalizations of consensus and of the Paxos algorithm require a mathematical detour de force into a type of object called a command-structure set.


title: Hybrid Approach for Emotion Classification of Audio Conversation Based on Text and Speech Mining

abstract: Abstract One of the greatest challenges in speech technology is estimating the speaker's emotion. Most of the existing approaches concentrate either on audio or text features. In this work, we propose a novel approach for emotion classification of audio conversation based on both speech and text. The novelty in this approach is in the choice of features and the generation of a single feature vector for classification. Our main intention is to increase the accuracy of emotion classification of speech by considering both audio and text features. In this work we use standard methods such as Natural Language Processing, Support Vector Machines, WordNet Affect and SentiWordNet. The dataset for this work have been taken from Semval -2007 and eNTERFACE’05 EMOTION Database.


title: Estimation of Sunlight Direction Using 3D Object Models

abstract: The direction of sunlight is an important informative cue in a number of applications in image processing, such as augmented reality and object recognition. In general, existing methods to estimate the direction of the sunlight rely on different image features (e.g., sky, texture, shadows, and shading). These features can be considered as weak informative cues as no single feature can reliably estimate the sunlight direction. Moreover, existing methods may require that the camera parameters are known limiting their applicability. In this paper, we present a new method to estimate the sunlight direction from a single (outdoor) image by inferring casts shadows through object modeling and recognition. First, objects (e.g., cars or persons) are first (automatically) recognized in images by exemplar-SVMs. Instead of training the Support Vector Machine (SVMs) using natural images (limited variation in viewpoints), we propose to train on 2D object samples generated from 3D object models. Then, the recognized objects are used as sundial cues (probes) to estimate the sunlight direction by inferring the corresponding shadows generated by 3D object models considering different illumination directions. We demonstrate the effectiveness of our approach on synthetic and real images. Experiments show that our method estimates the azimuth angle accurately within a quadrant (smaller than 45°) and compute the zenith angle with mean angular error of 23°.


title: Think-aloud protocols: Analyzing three different think-aloud protocols with counts of verbalized frustrations in a usability study of an information-rich Web site

abstract: We describe an empirical, between-subjects study on the use of think-aloud protocols in usability testing of an information-rich Web site. This double-blind study used three different types of think-aloud protocols: a traditional protocol, a speech-communication protocol, and a coaching protocol. A silent condition served as the control. Eighty participants were recruited and randomly pre-assigned to one of four conditions. With the goal of keeping unintended bias to a minimum, data analysis did not count the number of identified usability problems by condition, which was considered too subjective. Rather, the study collected the number of verbalized and non-verbalized counts of frustration by condition that users experienced. The study also did a count of the number of verbalized and non-verbalized instances of positive comments by condition that users expressed. Results show that there were no statistical differences in the number of counts by condition with respect to the traditional, speech communication, or coaching condition. The study concludes that simply counting the verbalizations of users by condition does not give enough information to determine whether any of the conditions would lead to a better understanding of the usability problems associated with the Web site.


title: Behavior-based network access control: A proof-of-concept

abstract: Current NAC technologies implement a pre-connect phase where the status of a device is checked against a set of policies before being granted access to a network, and a post-connect phase that examines whether the device complies with the policies that correspond to its role in the network. In order to enhance current NAC technologies, we propose a new architecture based on behaviorsrather than rolesor identity, where the policies are automatically learned and updated over time by the members of the network in order to adapt to behavioral changes of the devices. Behavior profiles may be presented as identity cards that can change over time. By incorporating an Anomaly Detector (AD) to the NAC server or to each of the hosts, their behavior profile is modeled and used to determine the type of behaviors that should be accepted within the network. These models constitute behavior-based policies. In our enhanced NAC architecture, global decisions are made using a group voting process. Each host's behavior profile is used to compute a partial decision for or against the acceptance of a new profile or traffic. The aggregation of these partial votes amounts to the model-group decision. This voting process makes the architecture more resilient to attacks. Even after accepting a certain percentage of malicious devices, the enhanced NAC is able to compute an adequate decision. We provide proof-of-concept experiments of our architecture using web traffic from our department network. Our results show that the model-group decision approach based on behavior profiles has a 99% detection rate of anomalous traffic with a false positive rate of only 0.005%. Furthermore, the architecture achieves short latencies for both the pre- and post-connect phases.


title: A method for obtaining digital signatures and public-key cryptosystems

abstract: An encryption method is presented with the novel property that publicly revealing an encryption key does not thereby reveal the corresponding decryption key. This has two important consequences: (1) Couriers or other secure means are not needed to transmit keys, since a message can be enciphered using an encryption key publicly revealed by the intented recipient. Only he can decipher the message, since only he knows the corresponding decryption key. (2) A message can be “signed” using a privately held decryption key. Anyone can verify this signature using the corresponding publicly revealed encryption key. Signatures cannot be forged, and a signer cannot later deny the validity of his signature. This has obvious applications in “electronic mail” and “electronic funds transfer” systems. A message is encrypted by representing it as a number M, raising M to a publicly specified power e, and then taking the remainder when the result is divided by the publicly specified product, n, of two large secret primer numbers p and q. Decryption is similar; only a different, secret, power d is used, where e * d ≡ 1(mod (p - 1) * (q - 1)). The security of the system rests in part on the difficulty of factoring the published divisor, n.


title: Gaussian Processes for Regression

abstract: The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior distribution over functions. In this paper we investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis for fixed values of hyperparameters to be carried out exactly using matrix operations. Two methods, using optimization and averaging (via Hybrid Monte Carlo) over hyperparameters have been tested on a number of challenging problems and have produced excellent results.


title: Abstract Automatic information extraction from semi-structured Web pages by pattern discovery

abstract: The World Wide Web is now undeniably the richest and most dense source of information; yet, its structure makes it difficult to make use of that information in a systematic way. This paper proposes a pattern discovery approach to the rapid generation of information extractors that can extract structured data from semi-structured Web documents. Previous work in wrapper induction aims at learning extraction rules from user-labeled training examples, which, however, can be expensive in some practical applications. In this paper, we introduce IEPAD (an acronym for Information Extraction based on PAttern Discovery), a system that discovers extraction patterns from Web pages without user-labeled examples. IEPAD applies several pattern discovery techniques, including PAT-trees, multiple string alignments and pattern matching algorithms. Extractors generated by IEPAD can be generalized over unseen pages from the same Web data source. We empirically evaluate the performance of IEPAD on an information extraction task from 14 real Web data sources. Experimental results show that with the extraction rules discovered from a single page, IEPAD achieves 96% average retrieval rate, and with less than five example pages, IEPAD achieves 100% retrieval rate for 10 of the sample Web data sources.


title: Revisiting Requirements Elicitation Techniques

abstract: The importance of Requirements Engineering (RE) has been well recognized by the research community in the last decade. There is no doubt to say that requirements phase is the foundation of the entire Software Development Life Cycle (SDLC) on which the entire software is built. With the proper management of various activities falling under requirements phase, a project can deliver the right solution within the time and budget. Requirement elicitation, requirement specification, and requirement validation are the important stages to assure the quality of requirement documentations. Out of these, elicitation is the first major activity, which requires proper attention by Requirement Engineers and other related stakeholders. Literature reveals various elicitation techniques, which can be used for the purpose depending upon the nature of project/s. In this paper, an attempt is made to cover all the major elicitation techniques along with their significant aspects at one place. Such a review would enable the concerned stakeholders to understand and select the most appropriate technique to be used for their project/s.


title: A correlation game for unsupervised learning yields computational interpretations of Hebbian excitation, anti-Hebbian inhibition, and synapse elimination

abstract: Much has been learned about plasticity of biological synapses from empirical studies. Hebbian plasticity is driven by correlated activity of presynaptic and postsynaptic neurons. Synapses that converge onto the same neuron often behave as if they compete for a fixed resource; some survive the competition while others are eliminated. To provide computational interpretations of these aspects of synaptic plasticity, we formulate unsupervised learning as a zero-sum game between Hebbian excitation and anti-Hebbian inhibition in a neural network model. The game formalizes the intuition that Hebbian excitation tries to maximize correlations of neurons with their inputs, while anti-Hebbian inhibition tries to decorrelate neurons from each other. We further include a model of synaptic competition, which enables a neuron to eliminate all connections except those from its most strongly correlated inputs. Through empirical studies, we show that this facilitates the learning of sensory features that resemble parts of objects.


title: A novel silicon "star-comb" microphone concept for enhanced signal-to-noise-ratio: Modeling, design and first prototype

abstract: A novel comb-structure-based, capacitive MEMS microphone concept is proposed, which is expected to significantly reduce viscous damping losses, challenging the high performance of conventional MEMS microphones. To this end, we derived a dedicated, fully energy-coupled and properly calibrated system-level model scaling with all relevant design parameters. It enables to discriminate in detail the impact of the individual components like transducer, package and electrostatic read out to the overall signal-to-noise-ratio (SNR) of the microphone and hence, to identify the optimal design of the device. Measurements of first prototypes show promising results and agree very well with simulations demonstrating the predictive power of the model w.r.t. to further optimization.


Dynamic control of the 6 WD skid-steering mobile robot RobuROC 6 using sliding mode technique


title: Project Telepathy: Targeted Verbal Communication using 3D Beamforming Speakers and Facial Electromyography

abstract: Speech is our innate way of communication. However, it has limitation such as being a broadcast process, it has limited reach and it only works in air. Here, we explore the combination of two technologies for realizing natural targeted communication with potential applications in coordination of tasks or private conversations. For detecting words, we measure the bioelectric signals produced by facial muscles during speech. An electromyographic system composed of 4 surface electrodes had an accuracy of 80% when discriminating between 10 words. More importantly, the system was equally effective in discriminating spoken and silently mouthed words. For transferring the words, we used the sound-through-ultrasound phenomenon to generate audible sound within a narrow beam. We built a phased array of ultrasonic emitters, capable of emitting sound that can be steered electronically without physically moving the array. Two prototypes that combine detection and transfer of words are presented and their limitations analysed.


title: Short text similarity based on probabilistic topics

abstract: In this paper, we propose a new method for measuring the similarity between two short text snippets by comparing each of them with the probabilistic topics. Specifically, our method starts by firstly finding the distinguishing terms between the two short text snippets and comparing them with a series of probabilistic topics, extracted by Gibbs sampling algorithm. The relationship between the distinguishing terms of the short text snippets can be discovered by examining their probabilities under each topic. The similarity between two short text snippets is calculated based on their common terms and the relationship of their distinguishing terms. Extensive experiments on paraphrasing and question categorization show that the proposed method can calculate the similarity of short text snippets more accurately than other methods including the pure TF-IDF measure.


title: Stimulating and sustaining interest in a language course: An experimental comparison of Chatbot and Human task partners

abstract: Novel technology can be a powerful tool for enhancing students' interest in many learning domains. However, the sustainability and overall impact of such interest is unclear. This study tests the longer-term effects of technology on students' task and course interest. The experimental study was conducted with students in foreign language classes (n = 122): a 12-week experimental trial that included pre- and post-course interest, and a sequence of task interest measures. Employing a counterbalanced design, at three week intervals students engaged in separate speaking tasks with each of a Human and “Chatbot” partner. Students' interest in successive tasks and in the course (pre-post), were used to assess differential partner effects and course interest development trajectories. Comparisons of task interest under different partner conditions over time indicated a significant drop in students' task interest with the Chatbot but not Human partner. After accounting for initial course interest, Structural Equation Modelling indicated that only task interest with the Human partner contributed to developing course interest. While Human partner task interest predicted future course interest, task interest under Chatbot partner conditions did not. Under Chatbot partner conditions there was a drop in task interest after the first task: a novelty effect. Implications for theory and practice are discussed.


ECNU at SemEval-2016 Task 1: Leveraging Word Embedding From Macro and Micro Views to Boost Performance for Semantic Textual Similarity


title: A Comparison of Face-based and Interaction-based Affect Detectors in Physics Playground

abstract: Increased attention to the relationships between affect and learning has led to the development of machine-learned models that are able to identify students’ affective states in computerized learning environments. Data for these affect detectors have been collected from multiple modalities including physical sensors, dialogue logs, and logs of students’ interactions with the learning environment. While researchers have successfully developed detectors based on each of these sources, little work has been done to compare the performance of these detectors. In this paper, we address this issue by comparing interaction-based and video-based affect detectors for a physics game called Physics Playground. Specifically, we report on the development and detection accuracy of two suites of affect and behavioral detectors. The first suite of detectors applies facial expression recognition to video data collected with webcams, while the second focuses on students’ interactions with the game as recorded in log-files. Ground–truth affect and behavior annotations for both faceand interactionbased detectors were obtained via live field observations during game-play. We first compare the performance of these detectors in predicting students’ affective states and off-task behaviors, and then proceed to outline the strengths and weakness of each approach.


title: Single-Folded Leaky-Wave Antennas for Automotive Radars at 77 GHz

abstract: Low-cost multilayer antennas are proposed for the next-generation automotive radar systems at millimeter waves. The antennas consist of a multilayer pillbox leaky-wave antenna structure where the feeding (integrated horn) and radiating parts are connected by a quasi-optical system made by an integrated parabola and several coupling slots (pillbox transition). In addition, a scanning capability is obtained by displacing the feeding part in the focal plane of the pillbox transition. Three prototypes with different feed locations have been manufactured and tested. The antenna stack-up consists of two Rogers 5880 substrates piled up by a diffusion bonding process. The integrated horn and pillbox transition are synthesized by perfect vertical metallic walls and closely spaced metallic pins, respectively. The E- and H-plane radiation patterns measured in the 74-78-GHz band show an average half-power beamwidth of about 11.5° and 5.5°, respectively, and sidelobe levels better than -15 dB up to a scan angle of 18°. The measured gains are about 24 dB around 77 GHz, resulting in a total efficiency higher than 50%.


title: [PENS (papular epidermal nevus with "skyline" basal cell layer)].

abstract: BACKGROUND ::: PENS is a rare neuro-cutaneous syndrome that has been recently described. It involves one or more congenital epidermal hamartomas of the papular epidermal nevus with "skyline" basal cell layer type (PENS) as well as non-specific neurological anomalies. Herein, we describe an original case in which the epidermal hamartomas are associated with autism spectrum disorder (ASD). ::: ::: ::: PATIENTS AND METHODS ::: A 6-year-old boy with a previous history of severe ASD was referred to us for asymptomatic pigmented congenital plaques on the forehead and occipital region. Clinical examination revealed a light brown verrucous mediofrontal plaque in the form of an inverted comma with a flat striated surface comprising coalescent polygonal papules, and a clinically similar round occipital plaque. Repeated biopsies revealed the presence of acanthotic epidermis covered with orthokeratotic hyperkeratosis with occasionally broadened epidermal crests and basal hyperpigmentation, pointing towards an anatomoclinical diagnosis of PENS. ::: ::: ::: DISCUSSION ::: A diagnosis of PENS hamartoma was made on the basis of the clinical characteristics and histopathological analysis of the skin lesions. This condition is defined clinically as coalescent polygonal papules with a flat or rough surface, a round or comma-like shape and light brown coloring. Histopathological examination showed the presence of a regular palisade "skyline" arrangement of basal cell epidermal nuclei which, while apparently pathognomonic, is neither a constant feature nor essential for diagnosis. Association of a PENS hamartoma and neurological disorders allows classification of PENS as a new keratinocytic epidermal hamartoma syndrome. The early neurological signs, of varying severity, are non-specific and include psychomotor retardation, learning difficulties, dyslexia, hyperactivity, attention deficit disorder and epilepsy. There have been no reports hitherto of the presence of ASD as observed in the case we present. ::: ::: ::: CONCLUSION ::: This new case report of PENS confirms the autonomous nature of this neuro-cutaneous disorder associated with keratinocytic epidermal hamartoma syndromes.


Generating Clinical Text from Dialogue 1


title: Plant Classification from Leaf Textures

abstract: This work describes a methodology for plant classification based on the analysis of leaf textures by combining a multi-resolution technique, such as the two-dimensional (2D) Discrete Wavelet Transform (2D-DWT), statistical models and Gray-Level Co-occurrence Matrices (GLCM) in which some invariance (e.g. rotation and scale) are achieved. As a second step, an Artificial Neural Network (ANN) model is trained for automatic classifying plant species. The proposed approach was tested on the Flavia database. An overall classification accuracy of $91.85\%$ was achieved which demonstrates that plants can be reliably classified using texture samples extracted from leaf tissues.


title: A Discriminative Approach to Topic-based Citation Recommendation ⋆

abstract: In this paper, we present a study of a novel problem, i.e. topic-based citation recommendation, which involves recommending papers to be referred to. Traditionally, this problem is usually treated as an engineering issue and dealt with using heuristics. This paper gives a formalization of topic-based citation recommendation and proposes a discriminative approach to this problem. Specifically, it proposes a two-layer Restricted Boltzmann Machine model, called RBM-CS, which can discover topic distributions of paper content and citation relationship simultaneously. Experimental results demonstrate that RBM-CS can significantly outperform baseline methods for citation recommendation.


title: An improved algorithm for triangle to triangle intersection test

abstract: The triangle-to-triangle intersection test is a basic component of all collision detection data structures and algorithms. There are many algorithms that have been put forward in this area until now. Among these algorithms, there is one proposed by YU Hai-yan based on projection theory which can simplify the three-dimensional test into the two-dimensional test and certificate its robustness theoretically. However, the long calculation time can not satisfy the real-time performance. In this paper, an improved algorithm is proposed firstly. Then, the GPU parallel computing is adopted to accelerate our algorithm. Finally, some experiments were carried to validate the improved algorithm. Through the experiments, it shows that the improved algorithm is up to 13 times faster than the original algorithm on average when the triangles' number increases to 2 million and the acceleration times will also increase when much more triangles are detected at the same time and however the algorithm is also stable.


title: Stitching Web Tables for Improving Matching Quality

abstract: HTML tables on web pages ("web tables") cover a wide variety of topics. Data from web tables can thus be useful for tasks such as knowledge base completion or ad hoc table extension. Before table data can be used for these tasks, the tables must be matched to the respective knowledge base or base table. The challenges of web table matching are the high heterogeneity and the small size of the tables. ::: ::: Though it is known that the majority of web tables are very small, the gold standards that are used to compare web table matching systems mostly consist of larger tables. In this experimental paper, we evaluate T2K Match, a web table to knowledge base matching system, and COMA, a standard schema matching tool, using a sample of web tables that is more realistic than the gold standards that were previously used. We find that both systems fail to produce correct results for many of the very small tables in the sample. As a remedy, we propose to stitch (combine) the tables from each web site into larger ones and match these enlarged tables to the knowledge base or base table afterwards. For this stitching process, we evaluate different schema matching methods in combination with holistic correspondence refinement. Limiting the stitching procedure to web tables from the same web site decreases the heterogeneity and allows us to stitch tables with very high precision. Our experiments show that applying table stitching before running the actual matching method improves the matching results by 0.38 in F1-measure for T2K Match and by 0.14 for COMA. Also, stitching the tables allows us to reduce the amount of tables in our corpus from 5 million original web tables to as few as 100,000 stitched tables.


title: A Scalable Probabilistic Change Detection Algorithm for Very High Resolution (VHR) Satellite Imagery

abstract: Detecting landscape changes using very high-resolution multispectral imagery demands an accurate and scalable algorithm that is robust to geometric and atmospheric errors. Existing pixel-based change detection approaches, however, have several drawbacks, which render them ineffective for VHR imagery analysis. A recent probabilistic change detection framework provides more accurate assessment of changes than traditional approaches by analyzing image patches than pixels. However, this patch (grid)-based approach produces coarse-resolution (patch size) changes. In this work we present a sliding window based approach that produces changes at the native image resolution. The increased computational demand of the sliding window based approach is addressed through thread-level parallelization on shared memory architectures. Our experimental evaluation showed a 91% performance improvement compared to its sequential counterpart on a sq. KM aerial image with varying window sizes on a 16-core (32 virtual threads) Intel Xeon processor.


title: Facial expression recognition based on local region specific features and support vector machines

abstract: Facial expressions are one of the most powerful, natural and immediate means for human being to communicate their emotions and intensions. Recognition of facial expression has many applications including human-computer interaction, cognitive science, human emotion analysis, personality development etc. In this paper, we propose a new method for the recognition of facial expressions from single image frame that uses combination of appearance and geometric features with support vector machines classification. In general, appearance features for the recognition of facial expressions are computed by dividing face region into regular grid (holistic representation). But, in this paper we extracted region specific appearance features by dividing the whole face region into domain specific local regions. Geometric features are also extracted from corresponding domain specific regions. In addition, important local regions are determined by using incremental search approach which results in the reduction of feature dimension and improvement in recognition accuracy. The results of facial expressions recognition using features from domain specific regions are also compared with the results obtained using holistic representation. The performance of the proposed facial expression recognition system has been validated on publicly available extended Cohn-Kanade (CK+) facial expression data sets.


Taxonomy Embedding in Poincaré Space : A General Exploration


title: A systematic approach to improve multiple Lyapunov function stability and stabilization conditions for fuzzy systems

abstract: This paper presents a systematic approach for decreasing conservativeness in stability analysis and control design for Takagi-Sugeno (TS) systems. This approach is based on the idea of multiple Lyapunov functions together with simple techniques for introducing slack matrices. Unlike some previous approaches based on multiple Lyapunov functions, both the stability and the stabilization conditions are written as linear matrix inequality (LMI) problems. The proposed approach reduces the number of inequalities and guarantees extra degrees of freedom to the LMI problems. Numeric examples illustrate the effectiveness of this method.


title: Comparative Analysis of Global Feature Extraction Methods for Off-line Signature Recognition

abstract: this paper, a method is proposed for feature extraction of offline signature recognition system. The proposed method is based on global features to identify forgeries and also median filter is introduces for noise reduction. The Proposed feature extraction method is compared with Discrete Radon Transform (DRT). Both the feature extraction method extracts one dimensional global features and the alignment between features is performed by Dynamic Time Warping (DTW). When being trained using 6 genuine signatures of each person and 250 forgeries taken from our database, the proposed method obtained an equal error rate (EER) of 8.40%. The false acceptance rate (FAR) for proposed method was also kept as low as 8.80%. Keywordsfilter, Global features, Discrete Radon transform, Dynamic time warping.


title: A Vision Based Method for Automatic Evaluation of Germination Rate of Rice Seeds

abstract: Rice is one of the most cultivated cereal in Asian countries and Vietnam in particular. Good seed germination is important for rice seed quality, that impacts the rice production and crop yield. Currently, seed germination evaluation is carried out manually by experienced persons. This is a tedious and time-consuming task. In this paper, we present a system for automatic evaluation of rice seed germination rate based on advanced techniques in computer vision and machine learning. We propose to use U-Net - a convolutional neural network - for segmentation and separation of rice seeds. Further processing such as computing distance transform and thresholding will be applied on the segmented regions for rice seed detection. Finally, ResNet is utilized to classify segmented rice seed regions into two classes: germinated and non- germinated seeds. Our contributions in this paper are three-fold. Firstly, we propose a framework which confirms that convolutional neural networks are better than traditional methods for both segmentation and classification tasks (with F1- scores of 93.38\% and 95.66\% respectively). Secondly, we deploy successfully the automatic tool in a real application for estimating rice germination rate. Finally, we introduce a new dataset of 1276 images of rice seeds from 7 to 8 seed varieties germinated during 6 to 10 days. This dataset is publicly available for research purpose.


title: A SECURITY FRAMEWORK IN CLOUD COMPUTING INFRASTRUCTURE

abstract: In a typical cloud computing diverse facilitating components like hardware, software, firmware, networking, and services integrate to offer different computational facilities, while Internet or a private network (or VPN) provides the required backbone to deliver the services. The security risks to the cloud system delimit the benefits of cloud computing like “on-demand, customized resource availability and performance management”. It is understood that current IT and enterprise security solutions are not adequate to address the cloud security issues. This paper explores the challenges and issues of security concerns of cloud computing through different standard and novel solutions. We propose analysis and architecture for incorporating different security schemes, techniques and protocols for cloud computing, particularly in Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) systems. The proposed architecture is generic in nature, not dependent on the type of cloud deployment, application agnostic and is not coupled with the underlying backbone. This would facilitate to manage the cloud system more effectively and provide the administrator to include the specific solution to counter the threat. We have also shown using experimental data how a cloud service provider can estimate the charging based on the security service it provides and security-related cost-benefit analysis can be estimated.


title: The Evolution and Future of National Customer Satisfaction Index Models

abstract: A number of both national and international customer satisfaction barometers or indices have been introduced in the last decade. For the most part, these satisfaction indices are embedded within a system of cause and effect relationships or satisfaction model. Yet there has been little in the way of model development. Of critical importance to the validity and reliability of such indices is that the models and methods used to measure customer satisfaction and related constructs continue to learn, adapt and improve over time. The primary goal of this research is to propose and test a number of modifications and improvements to the national index models. Using survey data from the Norwegian Customer Satisfaction Barometer (NCSB), we find general support for the proposed modifications.


Semantic Complex Event Processing for Social Media Monitoring- A Survey


The consequence of caring too much: compassion fatigue and the trauma nurse.


title: A high-sensitivity battery-less wake-up receiver for 915 MHz ISM band applications

abstract: A high-sensitivity fully passive 902–928MHz ISM band wake-up radio (WuRx) receiver for implantable biomedical devices is presented. The WuRx does not have an external power source and harvests the entire energy from the radio-frequency (RF) signal received at the antenna. A high-efficiency differential RF-to-DC converter rectifies the input RF signal and supplies the circuit blocks including a low-power comparator and reference generators; and at the same time retrieves the envelope of the On-Off Keying (OOK) wake-up signal. To enhance the power conversion efficiency (PCE) of the proposed rectifier when the input power is low, appropriate gate-drive voltages for each stage of the rectifier are generated using a chain of auxiliary cells. The WuRx is designed and implemented in 0.13 μm CMOS and achieves a sensitivity of −43 dBm for a 100 kbps wake-up signal.


title: Per-channel color barcodes for displays

abstract: We present a per-channel framework for extending monochrome barcodes to color for display applications, offering increased data rates and capacity. Data is independently encoded into barcodes, incorporated within a color image as the red (R), green (G), and blue (B) channels and decoded from the corresponding R, G, and B channels in the image of the displayed barcode captured with a smartphone. Using a physical model for the display and capture processes, we show that the cross-channel interference in this situation is appropriately modeled by a linear mixing relation. Via experiments done over multiple smartphones and displays, we demonstrate that the impact of the interference is small and can be further mitigated through the use of interference cancellation. Two methods are presented for estimating and canceling the interference: either using pilot blocks in coherent designs where the synchronization marker patterns align between the codes or using expectation maximization. Results are presented for both QR and Aztec color codes in this framework, highlighting its versatility. Overall bit error rates are shown to be below 1.23% and are further reduced by interference cancellation. These low error rates are well within the correction capacity of the error correction codes used for typical color barcodes.


title: A game-theoretic method of fair resource allocation for cloud computing services

abstract: As cloud-based services become more numerous and dynamic, resource provisioning becomes more and more challenging. A QoS constrained resource allocation problem is considered in this paper, in which service demanders intend to solve sophisticated parallel computing problem by requesting the usage of resources across a cloud-based network, and a cost of each computational service depends on the amount of computation. Game theory is used to solve the problem of resource allocation. A practical approximated solution with the following two steps is proposed. First, each participant solves its optimal problem independently, without consideration of the multiplexing of resource assignments. A Binary Integer Programming method is proposed to solve the independent optimization. Second, an evolutionary mechanism is designed, which changes multiplexed strategies of the initial optimal solutions of different participants with minimizing their efficiency losses. The algorithms in the evolutionary mechanism take both optimization and fairness into account. It is demonstrated that Nash equilibrium always exists if the resource allocation game has feasible solutions.


title: Ultrasound assessment of schistosomiasis

abstract: In 2000, the World Health Organization (WHO) issued an ultrasound field protocol for assessing the morbidity due to Schistosoma (S.) haematobium and S. mansoni. The experience with this classification has recently been reviewed systematically. The WHO protocol was well accepted worldwide. Here we review the use of ultrasound to assess the morbidity due to schistosomiasis with emphasis on easy, quick, and reproducible ways that can be used in the field. Findings obtained with high-end ultrasound scanners in the hospital setting that might eventually have applications in the field are also described.


title: www.elsevier.com/locate/neucom Methodology for long-term prediction of time series

abstract: In this paper, a global methodology for the long-term prediction of time series is proposed. This methodology combines direct prediction strategy and sophisticated input selection criteria: k-nearest neighbors approximation method (k-NN), mutual information (MI) and nonparametric noise estimation (NNE). A global input selection strategy that combines forward selection, backward elimination (or pruning) and forward-backward selection is introduced. This methodology is used to optimize the three input selection criteria (k-NN, MI and NNE). The methodology is successfully applied to a real life benchmark: the Poland Electricity Load dataset.


title: Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks

abstract: Building a voice conversion (VC) system from non-parallel speech corpora is challenging but highly valuable in real application scenarios. In most situations, the source and the target speakers do not repeat the same texts or they may even speak different languages. In this case, one possible, although indirect, solution is to build a generative model for speech. Generative models focus on explaining the observations with latent variables instead of learning a pairwise transformation function, thereby bypassing the requirement of speech frame alignment. In this paper, we propose a non-parallel VC framework with a variational autoencoding Wasserstein generative adversarial network (VAW-GAN) that explicitly considers a VC objective when building the speech model. Experimental results corroborate the capability of our framework for building a VC system from unaligned data, and demonstrate improved conversion quality.


title: Combining SAX and Piecewise Linear Approximation to Improve Similarity Search on Financial Time Series

abstract: Efficient and accurate similarity searching on a large time series data set is an important but non- trivial problem. In this work, we propose a new approach to improve the quality of similarity search on time series data by combining symbolic aggregate approximation (SAX) and piecewise linear approximation. The approach consists of three steps: transforming real valued time series sequences to symbolic strings via SAX, pattern matching on the symbolic strings and a post-processing via Piecewise Linear Approximation.


title: Soft-Switching Bidirectional DC/DC Converter with a LC Series Resonant Circuit

abstract: In this paper, a soft-switching bidirectional dc/dc converter with a LC series resonant circuit is proposed. The proposed converter has been obtained by adding LC series resonant tank in the conventional bidirectional dc/dc converter. The proposed topology performs soft switching at both buck and boost operations. Through the theoretical boost and buck mode analysis, zero voltage switching operation is explained. Moreover, characteristics of the proposed topology are verified with simulation and experimental results.


title: Example-Based Object Detection in Images by Components

abstract: We present a general example-based framework for detecting objects in static images by components. The technique is demonstrated by developing a system that locates people in cluttered scenes. The system is structured with four distinct example-based detectors that are trained to separately find the four components of the human body: the head, legs, left arm, and right arm. After ensuring that these components are present in the proper geometric configuration, a second example-based classifier combines the results of the component detectors to classify a pattern as either a "person" or a "nonperson." We call this type of hierarchical architecture, in which learning occurs at multiple stages, an adaptive combination of classifiers (ACC). We present results that show that this system performs significantly better than a similar full-body person detector. This suggests that the improvement in performance is due to the component-based approach and the ACC data classification architecture. The algorithm is also more robust than the full-body person detection method in that it is capable of locating partially occluded views of people and people whose body parts have little contrast with the background.


title: The effects of filtered video on awareness and privacy

abstract: Video-based media spaces are designed to support casual interaction between intimate collaborators. Yet transmitting video is fraught with privacy concerns. Some researchers suggest that the video stream be filtered to mask out potentially sensitive information. While a variety of filtering techniques exist, they have not been evaluated for how well they safeguard privacy. In this paper, we analyze how a blur and a pixelize video filter might impact both awareness and privacy in a media space. Each filter is considered at nine different levels of fidelity, ranging from heavily applied filter levels that mask almost all information, to lightly applied filters that reveal almost everything. We examined how well observers of several filtered video scenes extract particular awareness cues: the number of actors; their posture (moving, standing, seated); their gender; the visible objects (basic to detailed); and how available people look (their busyness, seriousness and approachability). We also examined the privacy-preserving potential of each filter level in the context of common workplace activities. Our results suggest that the blur filter, and to a lesser extent the pixelize filter, have a level suitable for providing awareness information while safeguarding privacy.


Word Embeddings, Analogies, and Machine Learning: Beyond king - man + woman = queen


title: Algorithms for Learning Kernels Based on Centered Alignment

abstract: This paper presents new and effective algorithms for learning kernels. In particular, as shown by our empirical results, these algorithms consistently outperform the so-called uniform combination solution that has proven to be difficult to improve upon in the past, as well as other algorithms for learning kernels based on convex combinations of base kernels in both classification and regression. Our algorithms are based on the notion of centered alignment which is used as a similarity measure between kernels or kernel matrices. We present a number of novel algorithmic, theoretical, and empirical results for learning kernels based on our notion of centered alignment. In particular, we describe efficient algorithms for learning a maximum alignment kernel by showing that the problem can be reduced to a simple QP and discuss a one-stage algorithm for learning both a kernel and a hypothesis based on that kernel using an alignment-based regularization. Our theoretical results include a novel concentration bound for centered alignment between kernel matrices, the proof of the existence of effective predictors for kernels with high alignment, both for classification and for regression, and the proof of stability-based generalization bounds for a broad family of algorithms for learning kernels based on centered alignment. We also report the results of experiments with our centered alignment-based algorithms in both classification and regression.


title: Crystal structures of the {011}, {610}, and {010}growth sectors in brewsterite

abstract: Abstract Crystal structures were determined by single-crystal X-ray methods for the {011}, {610}, and {010} growth sectors of brewsterite from Strontian, Scotland. Refinements Rw = 4.3-6.6%) showed that all three growth sectors are triclinic and that their structures differ slightly.


title: Putting Question-Answering Systems into Practice: Transfer Learning for Efficient Domain Customization

abstract: Traditional information retrieval (such as that offered by web search engines) impedes users with information overload from extensive result pages and the need to manually locate the desired information therein. Conversely, question-answering systems change how humans interact with information systems: users can now ask specific questions and obtain a tailored answer - both conveniently in natural language. Despite obvious benefits, their use is often limited to an academic context, largely because of expensive domain customizations, which means that the performance in domain-specific applications often fails to meet expectations. This paper presents cost-efficient remedies: a selection mechanism increases the precision of document retrieval and a fused approach to transfer learning is proposed in order to improve the performance of answer extraction. Here knowledge is inductively transferred from a related, yet different, tasks to the domain-specific application, while accounting for potential differences in the sample sizes across both tasks. The resulting performance is demonstrated with an actual use case from a finance company, where fewer than 400 question-answer pairs had to be annotated in order to yield significant performance gains. As a direct implication to management, this presents a promising path to better leveraging of knowledge stored in information systems.


title: On the Vital Areas of Intrusion Detection Systems in Wireless Sensor Networks

abstract: This paper surveys recently proposed works on Intrusion Detection Systems (IDS) in WSNs, and presents a comprehensive classification of various IDS approaches according to their employed detection techniques. The three main categories explored in this paper are anomaly detection, misuse detection, and specification-based detection protocols. We give a description of existing security attacks in WSNs and the corresponding proposed IDS protocols to tackle those attacks. We analyze the works with respect to the network structure of WSNs. In addition, we highlight various critical shortcomings that IDSs currently have and define future research tracks for IDSs in wireless sensor networks. Though a few restricted survey works on this topic have already been done, we feel that there is a great need of performing a detailed and comprehensive study on the vital aspects so that the IDS in WSN could be analyzed from all the `need-to-know' angles. Thus, the paper's main aim is to include the most recent advancements in this area as well as to predict the future course of research so that the general as well as expert readers could be greatly benefited.


title: Convolutional neural networks (CNN) based human fall detection on Body Sensor Networks (BSN) sensor data

abstract: According to the World Health Organization, around 28–35% of people aged 65 and older fall each year. This number increases to around 32–42% for people over 70 years old. For this reason, this research targets the exploration of the role of Convolutional Neural Networks(CNN) in human fall detection. There are a number of current solutions related to fall detection; however, remain low detection accuracy. Although CNN has proven a powerful technique for image recognition problems, and the CNN library in Matlab was designed to work with either images or matrices, this research explored how to apply CNN to streaming sensor data, collected from Body Sensor Networks (BSN), in order to improve the fall detection accuracy. The idea of this research is that given the stream data sets as input, we converted them into images before applying CNN. The final accuracy result achieved is, to the best of our knowledge, the highest compared to other proposed methods: 92.3%.


title: Image2Mesh: A Learning Framework for Single Image 3D Reconstruction

abstract: One challenge that remains open in 3D deep learning is how to efficiently represent 3D data to feed deep networks. Recent works have relied on volumetric or point cloud representations, but such approaches suffer from a number of issues such as computational complexity, unordered data, and lack of finer geometry. This paper demonstrates that a mesh representation (i.e. vertices and faces to form polygonal surfaces) is able to capture fine-grained geometry for 3D reconstruction tasks. A mesh however is also unstructured data similar to point clouds. We address this problem by proposing a learning framework to infer the parameters of a compact mesh representation rather than learning from the mesh itself. This compact representation encodes a mesh using free-form deformation and a sparse linear combination of models allowing us to reconstruct 3D meshes from single images. In contrast to prior work, we do not rely on silhouettes and landmarks to perform 3D reconstruction. We evaluate our method on synthetic and real-world datasets with very promising results. Our framework efficiently reconstructs 3D objects in a low-dimensional way while preserving its important geometrical aspects.


title: SME e-procurement adoption in Hong Kong - the roles of power, trust and value

abstract: The objective of this research is to test a model of electronic procurement (e-procurement) adoption behavior by small- to medium-sized enterprises (SME). We investigate the phenomenon from the perspective of buyer-seller relationship and argue that, value of e-procurement adoption, trust on supplier, trust on IT, power of e-procurement and power of suppliers are factors leading to SME's adoption behavior. Research findings from the four case studies provide some evidence that the former four factors have significant effect on SME's e-procurement adoption behavior when the last one does not. This study contributes to a better understanding of how and why SME in Hong Kong employ e-procurement using a snapshot approach. Practical and theoretical implications are also provided.


GENERATIVE ADVERSARIAL NETWORKS FOR IMAGE STEGANOGRAPHY


title: Inverse reinforcement learning from summary data

abstract: Inverse reinforcement learning (IRL) aims to explain observed strategic behavior by fitting reinforcement learning models to behavioral data. However, traditional IRL methods are only applicable when the observations are in the form of state-action paths. This assumption may not hold in many real-world modeling settings, where only partial or summarized observations are available. In general, we may assume that there is a summarizing function $$\sigma $$ σ , which acts as a filter between us and the true state-action paths that constitute the demonstration. Some initial approaches to extending IRL to such situations have been presented, but with very specific assumptions about the structure of $$\sigma $$ σ , such as that only certain state observations are missing. This paper instead focuses on the most general case of the problem, where no assumptions are made about the summarizing function, except that it can be evaluated. We demonstrate that inference is still possible. The paper presents exact and approximate inference algorithms that allow full posterior inference, which is particularly important for assessing parameter uncertainty in this challenging inference situation. Empirical scalability is demonstrated to reasonably sized problems, and practical applicability is demonstrated by estimating the posterior for a cognitive science RL model based on an observed user’s task completion time only.


title: The 4-source photometric stereo technique for three-dimensional surfaces in the presence of highlights and shadows

abstract: We present an algorithm for separating the local gradient information and Lambertian color by using 4-source color photometric stereo in the presence of highlights and shadows. We assume that the surface reflectance can be approximated by the sum of a Lambertian and a specular component. The conventional photometric method is generalized for color images. Shadows and highlights in the input images are detected using either spectral or directional cues and excluded from the recovery process, thus giving more reliable estimates of local surface parameters.


title: Regression from patch-kernel

abstract: In this paper, we present a patch-based regression framework for addressing the human age and head pose estimation problems. Firstly, each image is encoded as an ensemble of orderless coordinate patches, the global distribution of which is described by Gaussian mixture models (GMM), and then each image is further expressed as a specific distribution model by Maximum a Posteriori adaptation from the global GMM. Then the patch-kernel is designed for characterizing the Kullback-Leibler divergence between the derived models for any two images, and its discriminating power is further enhanced by a weak learning process, called inter-modality similarity synchronization. Finally, kernel regression is employed for ultimate human age or head pose estimation. These three stages are complementary to each other, and jointly minimize the regression error. The effectiveness of this regression framework is validated by three experiments: 1) on the YAMAHA aging database, our solution brings a more than 50% reduction in age estimation error compared with the best reported results; 2) on the FG-NET aging database, our solution based on raw image features performs even better than the state-of-the-art algorithms which require fine face alignment for extracting warped appearance features; and 3) on the CHIL head pose database, our solution significantly outperforms the best one reported in the CLEAR07 evaluation.


title: Quantum Key Distribution Protocols: A Review

abstract: Quantum key distribution (QKD) provides a way for distribution of secure key in at least two parties which they initially share. And there are many protocols for providing a secure key i.e. BB84 protocol, SARG04 protocol, E91 protocol and many more. In this paper all the concerned protocols that share a secret key is explained and comparative study of all protocols shown.


title: Ontologies and the semantic web

abstract: How ontologies provide the semantics, as explained here with the help of Harry Potter and his owl Hedwig.


IEEE Robotics & Automation Magazine Review of Actuators with Passive Adjustable Compliance/Controllable Stiffness for Robotic Applications


title: Encryption using reconfigurable reversible logic gate and its simulation in FPGAs

abstract: Recently an approach to encryption/decryption based on using reversible logic circuits has been proposed. The reason for this is that conventional microelectronic technologies are reaching their limits. On the other hand, reversible logic circuits can decrease energy dissipation theoretically to zero. This paper presents a solution to designing encryption schemes based entirely on reversible logic. In our solution a building block of an encryption scheme is a cascade of 4-input reversible gates. In this way the building block can perform any reversible 4-variable function. For this purpose a reconfigurable reversible gate has been proposed. The design of such a reconfigurable gate built from standard reversible gates, i.e. NOT, CNOT, Toffoli and Fredkin gates, is presented. In the paper a complete scheme for encryption/decryption of 8-bit data is described using VHDL language and its quantum cost is calculated. Simulation and verification of this scheme in FPGAs conclude the paper.


title: Microfluidics-Based Biochips: Technology Issues, Implementation Platforms, and Design-Automation Challenges

abstract: Microfluidics-based biochips are soon expected to revolutionize clinical diagnosis, deoxyribonucleic acid (DNA) sequencing, and other laboratory procedures involving molecular biology. In contrast to continuous-flow systems that rely on permanently etched microchannels, micropumps, and microvalves, digital microfluidics offers a scalable system architecture and dynamic reconfigurability; groups of unit cells in a microfluidics array can be reconfigured to change their functionality during the concurrent execution of a set of bioassays. As more bioassays are executed concurrently on a biochip, system integration and design complexity are expected to increase dramatically. This paper presents an overview of an integrated system-level design methodology that attempts to address key issues in the synthesis, testing and reconfiguration of digital microfluidics-based biochips. Different actuation mechanisms for microfluidics-based biochips, and associated design-automation trends and challenges are also discussed. The proposed top-down design-automation approach is expected to relieve biochip users from the burden of manual optimization of bioassays, time-consuming hardware design, and costly testing and maintenance procedures, and it will facilitate the integration of fluidic components with a microelectronic component in next-generation systems-on-chips (SOCs).


title: Car-Rec: A real time car recognition system

abstract: Recent advances in computer vision have significantly reduced the difficulty of object classification and recognition. Robust feature detector and descriptor algorithms are particularly useful, forming the basis for many recognition and classification applications. These algorithms have been used in divergent bag-of-words and structural matching approaches. This work demonstrates a recognition application, based upon the SURF feature descriptor algorithm, which fuses bag-of-words and structural verification techniques. The resulting system is applied to the domain of car recognition and achieves accurate (> 90%) and real-time performance when searching databases containing thousands of images.


title: Trends in indigenous foodborne disease and deaths, England and Wales: 1992 to 2000

abstract: Background: Commitment to food safety is evidenced by high profile governmental initiatives around the globe. To measure progress towards targets, policy makers need to know the baseline from which they started. Aim: To describe the burden (mortality, morbidity, new presentations to general practice, hospital admissions, and hospital occupancy) and trends of indigenous foodborne disease (IFD) in England and Wales between 1992 and 2000. Methods: Routinely available surveillance data, special survey data, and hospital episode statistics were collated and arithmetic employed to estimate the burden and trends of IFD in England and Wales. Adjustments were made for underascertainment of disease through national surveillance and for foreign travel. The final estimates were compared with those from the USA. Results: In 1995 there were an estimated 2 365 909 cases, 21 138 hospital admissions, and 718 deaths in England and Wales due to IFD. By 2000 this had fallen to 1 338 772 cases, 20 759 hospital admissions, and 480 deaths. In terms of disease burden the most important pathogens were campylobacters, salmonellas, Clostridium perfringens , verocytotoxin producing Escherichia coli (VTEC) O157, and Listeria monocytogenes . The ratio of food related illness in the USA to IFD in England and Wales in 2000 was 57:1. Taking into account population rates, this ratio fell to 11:1 and converged when aetiology and disease severity were considered. Conclusion: Reducing IFD in England and Wales means tackling campylobacter. Lowering mortality rates however also requires better control and prevention of salmonellas, Cl perfringens , L monocytogenes , and VTEC O157.


title: Adaptation of Renal Function in Heart Failure

abstract: Congestive heart failure is the only major cardiovascular disease with an increasing incidence and prevalence in industrialized countries. Despite considerable progress in the clinical management of heart failure during the last 20 years, the prognosis is still worse than in many common types of cancer. The kidney is the main organ affected when cardiac function is compromised. In addition, the kidney significantly contributes to the development of the clinical syndrome of heart failure. Specific hemodynamic and neurohormonal abnormalities define the pathophysiology, clinical presentation, and prognosis of this disorder. In this setting, the kidney plays a dual role: the activation of the renin-angiotensin-aldosterone system and the regulation of sodium and water excretion. The kidney is generally intact in heart failure, but extrarenal stimuli alter its function to a point where mechanisms that are initially homeostatic become maladaptive. In this review article, the mechanisms involved in renal adaptati...


title: An Omnidirectional High-Gain Antenna Element for TD-SCDMA Base Station

abstract: A novel printed collinear dipole array antenna element is presented. The antenna comprises 8 back-to-back dipole arrays with balanced microstrip as its feed line. It works in the TD-SCDMA band with a good omnidirectional radiation pattern (with variations less than 1 dB) in the horizontal plane and a high gain level of over 9.5dBi. Simulated results obtained by Ansoft HFSS are given.


title: SDN/NFV-Based Mobile Packet Core Network Architectures: A Survey

abstract: The emergence of two new technologies, namely, software defined networking (SDN) and network function virtualization (NFV), have radically changed the development of network functions and the evolution of network architectures. These two technologies bring to mobile operators the promises of reducing costs, enhancing network flexibility and scalability, and shortening the time-to-market of new applications and services. With the advent of SDN and NFV and their offered benefits, the mobile operators are gradually changing the way how they architect their mobile networks to cope with ever-increasing growth of data traffic, massive number of new devices and network accesses, and to pave the way toward the upcoming fifth generation networking. This survey aims at providing a comprehensive survey of state-of-the-art research work, which leverages SDN and NFV into the most recent mobile packet core network architecture, evolved packet core. The research work is categorized into smaller groups according to a proposed four-dimensional taxonomy reflecting the: 1) architectural approach, 2) technology adoption, 3) functional implementation, and 4) deployment strategy. Thereafter, the research work is exhaustively compared based on the proposed taxonomy and some added attributes and criteria. Finally, this survey identifies and discusses some major challenges and open issues, such as scalability and reliability, optimal resource scheduling and allocation, management and orchestration, and network sharing and slicing that raise from the taxonomy and comparison tables that need to be further investigated and explored.


title: 48 V to 12 V soft-switching interleaved buck and boost converter for mild-hybrid vehicle application

abstract: 48 V to 12 V Soft-Switching Interleaved Buck and Boost Converter for mild-hybrid vehicle is proposed. The converter is consist of interleaved buck and boost converter with auxiliary inductor. Resonance between auxiliary capacitors and inductor is used for soft-switching. Therefore, reduction of the passive component size and switching loss can be implemented. Also, optimal design of the auxiliary components is suggested for satisfaction of soft switching. The converter and design method are verified in simulation.


title: Random Walk Inference and Learning in A Large Scale Knowledge Base

abstract: We consider the problem of performing learning and inference in a large scale knowledge base containing imperfect knowledge with incomplete coverage. We show that a soft inference procedure based on a combination of constrained, weighted, random walks through the knowledge base graph can be used to reliably infer new beliefs for the knowledge base. More specifically, we show that the system can learn to infer different target relations by tuning the weights associated with random walks that follow different paths through the graph, using a version of the Path Ranking Algorithm (Lao and Cohen, 2010b). We apply this approach to a knowledge base of approximately 500,000 beliefs extracted imperfectly from the web by NELL, a never-ending language learner (Carlson et al., 2010). This new system improves significantly over NELL's earlier Horn-clause learning and inference method: it obtains nearly double the precision at rank 100, and the new learning method is also applicable to many more inference tasks.


title: A Comparison Of Document, Sentence, And Term Event Spaces

abstract: The trend in information retrieval systems is from document to sub-document retrieval, such as sentences in a summarization system and words or phrases in question-answering system. Despite this trend, systems continue to model language at a document level using the inverse document frequency (IDF). In this paper, we compare and contrast IDF with inverse sentence frequency (ISF) and inverse term frequency (ITF). A direct comparison reveals that all language models are highly correlated; however, the average ISF and ITF values are 5.5 and 10.4 higher than IDF. All language models appeared to follow a power law distribution with a slope coefficient of 1.6 for documents and 1.7 for sentences and terms. We conclude with an analysis of IDF stability with respect to random, journal, and section partitions of the 100,830 full-text scientific articles in our experimental corpus.


title: Wideband, Low-Profile Patch Array Antenna With Corporate Stacked Microstrip and Substrate Integrated Waveguide Feeding Structure

abstract: In this communication, a corporate stacked microstrip and substrate integrated waveguide (SIW) feeding structure is reported to be used to broaden the impedance bandwidth of a $4 \times 4$ patch array antenna. The proposed array antenna is based on a multilayer printed circuit board structure containing two dielectric substrates and four copper cladding layers. The radiating elements, which consist of slim rectangular patches with surrounding U-shaped parasitic patches, are located on the top layer. Every four radiation elements are grouped together as a $2 \times 2$ subarray and fed by a microstrip power divider on the next copper layer through metalized blind vias. Four such subarrays are corporate-fed by an SIW feeding network underneath. The design process and analysis of the array antenna are discussed. A prototype of the proposed array antenna is fabricated and measured, showing a good agreement between the simulation and measurement, thus validating the correctness of the design. The measured results indicate that the proposed array antenna exhibits a wide $\vert \text {S}_{11}\vert dB bandwidth of 17.7%, i.e., 25.3–30.2 GHz, a peak gain of 16.4 dBi, a high radiation efficiency above 80%, and a good orthogonal polarization discrimination of higher than 30 dB. In addition, the use of low-profile substrate in the SIW feeding network makes this array antenna easier to be integrated directly with millimeter-wave front-end integrated circuits. The demonstrated array antenna can be a good candidate for various Ka -band wireless applications, such as 5G, satellite communications and so on.


title: Automatic Legal Document Analysis: Improving the Results of Information Extraction Processes Using an Ontology

abstract: Information Extraction (IE) is a pervasive task in the industry that allows to obtain automatically structured data from documents in natural language. Current software systems focused on this activity are able to extract a large percentage of the required information, but they do not usually focus on the quality of the extracted data. In this paper we present an approach focused on validating and improving the quality of the results of an IE system. Our proposal is based on the use of ontologies which store domain knowledge, and which we leverage to detect and solve consistency errors in the extracted data. We have implemented our approach to run against the output of the AIS system, an IE system specialized in analyzing legal documents and we have tested it using a real dataset. Preliminary results confirm the interest of our approach.


title: Multi-View Representation Learning: A Survey from Shallow Methods to Deep Methods

abstract: Recently, multi-view representation learning has become a rapidly growing direction in machine learning and data mining areas. This paper introduces several principles for multi-view representation learning: correlation, consensus, and complementarity principles. Consequently, we first review the representative methods and theories of multi-view representation learning based on correlation principle, especially on canonical correlation analysis (CCA) and its several extensions. Then from the viewpoint of consensus and complementarity principles we investigate the advancement of multi-view representation learning that ranges from shallow methods including multi-modal topic learning, multi-view sparse coding, and multi-view latent space Markov networks, to deep methods including multi-modal restricted Boltzmann machines, multi-modal autoencoders, and multi-modal recurrent neural networks. Further, we also provide an important perspective from manifold alignment for multi-view representation learning. Overall, this survey aims to provide an insightful overview of theoretical basis and state-of-the-art developments in the field of multi-view representation learning and to help researchers find the most appropriate tools for particular applications.


title: The Claw-Wheel transformable hybrid robot with reliable stair climbing and high maneuverability

abstract: This paper presents a novel Claw-Wheel transformable hybrid robot, which can transform between the claw mode and the wheel mode for the investigation of various environment. The transformation process is done by simply folding or unfolding the front body through the active transformation mechanism. The claw mode driven by four unique claws is used to overcome rough terrain, especially steep stairs. The wheel mode driven by two driving wheels is used for moving on flat terrain without oscillations. For enhancing the stability during climbing steep stairs in the claw mode, we extend the body length to make the robot stride three steps. Compared with the robot designed for striding two steps, the COM projection of the robot will be assured to lie in the support polygon. Furthermore, the difference of the required torques between the front and rear motors could be narrowed down. In the wheel mode, the kinematics of specific differential drive is analyzed. The switching control strategy between the angle-tracking control and the speed control is employed to form complete wheels and follow a specified path. The claws in stance could drive the robot on the desired path by following the respective velocities differentially. To form the driving wheels, the claws in flight track the claws in stance in 90° phase difference by the angle-tracking control. The switching control strategy with yaw feedback make the robot track the desired orientation more correctly. The results from simulations and experiments are presented and discussed.


title: Secure and Energy-Efficient Disjoint Multipath Routing for WSNs

abstract: Recent advances in microelectromechanical system (MEMS) technology have boosted the deployment of wireless sensor networks (WSNs). Limited by the energy storage capability of sensor nodes, it is crucial to jointly consider security and energy efficiency in data collection of WSNs. The disjoint multipath routing scheme with secret sharing is widely recognized as one of the effective routing strategies to ensure the safety of information. This kind of scheme transforms each packet into several shares to enhance the security of transmission. However, in many-to-one WSNs, shares have high probability to traverse through the same link and to be intercepted by adversaries. In this paper, we formulate the secret-sharing-based multipath routing problem as an optimization problem. Our objective aims at maximizing both network security and lifetime, subject to the energy constraints. To this end, a three-phase disjoint routing scheme called the Security and Energy-efficient Disjoint Route (SEDR) is proposed. Based on the secret-sharing algorithm, the SEDR scheme dispersively and randomly delivers shares all over the network in the first two phases and then transmits these shares to the sink node. Both theoretical and simulation results demonstrate that our proposed scheme has significant improvement in network security under both scenarios of single and multiple black holes without reducing the network lifetime.


title: The FERET database and evaluation procedure for face-recognition algorithms

abstract: Abstract The Face Recognition Technology (FERET) program database is a large database of facial images, divided into development and sequestered portions. The development portion is made available to researchers, and the sequestered portion is reserved for testing facerecognition algorithms. The FERET evaluation procedure is an independently administered test of face-recognition algorithms. The test was designed to: (1) allow a direct comparison between different algorithms, (2) identify the most promising approaches, (3) assess the state of the art in face recognition, (4) identify future directions of research, and (5) advance the state of the art in face recognition.


On the Gravity Recommendation System


title: Avatar locomotion in crowd simulation

abstract: This paper presents an Animation Planning Mediator (APM) designed to synthesize animations efficiently for virtual characters in real time crowd simulation. From a set of animation clips, the APM selects the most appropriate and modifies the skeletal configuration of each character to satisfy desired constraints (e.g. eliminating foot-sliding or restricting upper body torsion), while still providing natural looking animations. We use a hardware accelerated character animation library to blend animations increasing the number of possible locomotion types. The APM allows the crowd simulation module to maintain control of path planning, collision avoidance and response. A key advantage of our approach is that the APM can be integrated with any crowd simulator working in continuous space. We show visual results achieved in real time for several hundreds of agents, as well as the quantitative ac-curacy.


title: Diffusion Independent Semi-Bandit Influence Maximization

abstract: We consider \emph{influence maximization} (IM) in social networks, which is the problem of maximizing the number of users that become aware of a product by selecting a set of "seed" users to expose the product to. While prior work assumes a known model of information diffusion, we propose a parametrization in terms of pairwise reachability which makes our framework agnostic to the underlying diffusion model. We give a corresponding monotone, submodular surrogate function, and show that it is a good approximation to the original IM objective. We also consider the case of a new marketer looking to exploit an existing social network, while simultaneously learning the factors governing information propagation. For this, we propose a pairwise-influence semi-bandit feedback model and develop a LinUCB-based bandit algorithm. Our model-independent regret analysis shows that our bound on the cumulative regret has a better (as compared to previous work) dependence on the size of the network. By using the graph Laplacian eigenbasis to construct features, we describe a practical LinUCB implementation. Experimental evaluation suggests that our framework is robust to the underlying diffusion model and can efficiently learn a near-optimal solution.


Lew hardy's third myth: A matter of perspective


title: Texting, reading, and other daily habits associated with adolescents’ literacy levels

abstract: The level of functional illiteracy in the North American context, among others, appears to be reaching epidemic levels. The necessary linguistic and intellectual tools required to adequately function in our complex, highly-literate societies are not being sufficiently developed in many of our homes or schools. The findings from the present study are consistent with some general trends found in the research literature connected to practices potentially influencing the development of literacy in youth. This correlational, quantitative study showed that text-messaging practices did not appear to be significantly associated with literacy in this adolescent population sample; reading in the traditional sense, on the other hand, exhibited more positive links with literacy attainment than all the other practices considered. These findings, similarly to previous research, suggest that the type of reading that is occurring while texting is substantially different, in terms of its associations with literacy, from more traditional forms of reading. Yet while the amount of time spent by young people on digital practices such as texting is rising rapidly, the amount of time spent reading, in the traditional sense, remains low. Rather than focusing on potential technological solutions, increasing the quantity and complexity level of more traditional forms of reading in adolescence appears to be a more prudent strategy for enhancing functional literacy in society.


title: Compiler-based prefetching for recursive data structures

abstract: Software-controlled data prefetching offers the potential for bridging the ever-increasing speed gap between the memory subsystem and today's high-performance processors. While prefetching has enjoyed considerable success in array-based numeric codes, its potential in pointer-based applications has remained largely unexplored. This paper investigates compiler-based prefetching for pointer-based applications---in particular, those containing recursive data structures. We identify the fundamental problem in prefetching pointer-based data structures and propose a guideline for devising successful prefetching schemes. Based on this guideline, we design three prefetching schemes, we automate the most widely applicable scheme (greedy prefetching) in an optimizing research compiler, and we evaluate the performance of all three schemes on a modern superscalar processor similar to the MIPS R10000. Our results demonstrate that compiler-inserted prefetching can significantly improve the execution speed of pointer-based codes---as much as 45% for the applications we study. In addition, the more sophisticated algorithms (which we currently perform by hand, but which might be implemented in future compilers) can improve performance by as much as twofold. Compared with the only other compiler-based pointer prefetching scheme in the literature, our algorithms offer substantially better performance by avoiding unnecessary overhead and hiding more latency.


title: Persuasive Robotics: The influence of robot gender on human behavior

abstract: Persuasive Robotics is the study of persuasion as it applies to human-robot interaction (HRI). Persuasion can be generally defined as an attempt to change another's beliefs or behavior. The act of influencing others is fundamental to nearly every type of social interaction. Any agent desiring to seamlessly operate in a social manner will need to incorporate this type of core human behavior. As in human interaction, myriad aspects of a humanoid robot's appearance and behavior can significantly alter its persuasiveness - this work will focus on one particular factor: gender. In the current study, run at the Museum of Science in Boston, subjects interacted with a humanoid robot whose gender was varied. After a short interaction and persuasive appeal, subjects responded to a donation request made by the robot, and subsequently completed a post-study questionnaire. Findings showed that men were more likely to donate money to the female robot, while women showed little preference. Subjects also tended to rate the robot of the opposite sex as more credible, trustworthy, and engaging. In the case of trust and engagement the effect was much stronger between male subjects and the female robot. These results demonstrate the importance of considering robot and human gender in the design of HRI.


Multi-Sided Platforms 1


title: What questions are raised during lab-work?

abstract: During lab-work students are expected to link observed data, often represented by graphs, to either theoretical models, or to the real world that they are exploring. In recent research some of the problems have been examined, e.g. that students do not generalize in a scientifically epistemic manner and that they do not make links between the object/event world and the theory/model world. That epistemology of science has to be taught explicitly is also a question discussed in research. In this paper we present some questions that students ask during lab-work, and try to compare these questions to the ones teachers expect them to raise and answer during the lab-sessions. We also try to find out which questions the students do not answer, and possibly what changes in the instructions could lead the students to ask relevant questions. We can see that most of the questions, whether asked or answered, are related to different representations in the theory/model world, and that very few questions or answers seem to relate to the connection between theory/model and object/event world.


title: Control of parallel connected inverters in stand-alone AC supply systems

abstract: A scheme for controlling parallel-connected inverters in a standalone AC supply system is presented. This scheme is suitable for control of inverters in distributed source environments such as in isolated AC systems, large and distributed uninterruptible power supply (UPS) systems, photovoltaic systems connected to AC grids, and low-voltage DC power transmission meshes. A key feature of the control scheme is that it uses feedback of only those variables that can be measured locally at the inverter and does not need communication of control signals between the inverters. This is essential for the operation of large AC systems, where distances between inverters make communication impractical. It is also important in high-reliability UPS systems where system operation can be maintained in the face of a communication breakdown. Real and reactive power sharing between inverters can be achieved by controlling two independent quantities: the power angle and the fundamental inverter voltage magnitude. Simulation results are presented. >


title: Constrained Hidden Markov Models

abstract: By thinking of each state in a hidden Markov model as corresponding to some spatial region of a fictitious topology space it is possible to naturally define neighbouring states as those which are connected in that space. The transition matrix can then be constrained to allow transitions only between neighbours; this means that all valid state sequences correspond to connected paths in the topology space. I show how such constrained HMMs can learn to discover underlying structure in complex sequences of high dimensional data, and apply them to the problem of recovering mouth movements from acoustics in continuous speech.


title: Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis

abstract: Abstract Electroencephalography (EEG) is an important tool for studying the human brain activity and epileptic processes in particular. EEG signals provide important information about epileptogenic networks that must be analyzed and understood before the initiation of therapeutic procedures. Very small variations in EEG signals depict a definite type of brain abnormality. The challenge is to design and develop signal processing algorithms which extract this subtle information and use it for diagnosis, monitoring and treatment of patients with epilepsy. This paper presents a review of wavelet techniques for computer-aided seizure detection and epilepsy diagnosis with an emphasis on research reported during the past decade. A multiparadigm approach based on the integration of wavelets, nonlinear dynamics and chaos theory, and neural networks advanced by Adeli and associates is the most effective method for automated EEG-based diagnosis of epilepsy.


title: Semantic Binary Codes

abstract: Fast Image Retrieval is required for many applications like Image Search and Shopping, especially for large datasets. Hashing addresses this problem by learning compact binary codes for images and using them as direct addresses into hash tables. In practice, using binary codes as addresses does not guarantee fast retrieval, as similar images are not mapped to the same binary code(address). We address this problem by presenting an efficient supervised hashing method that aims to explicitly map all images from the same class to a unique binary code to obtain fast retrieval. We refer to the binary codes of the images as 'Semantic Binary Codes' and the unique code for all same class images as 'Class Binary Code'. We formulate this intuitive objective 'directly' by minimizing the squared error criterion between the semantic binary codes and the corresponding class binary codes. We further propose a Deep Semantic Binary Code model that utilizes the class binary codes and show that we significantly outperform the state-of-the-art. We also propose a new class-based Hamming metric that dramatically reduces the retrieval times for larger databases and also improves the performance of the method by large margins.


title: Towards temporally-coherent video matting

abstract: Extracting temporally-coherent alpha mattes in video is an important but challenging problem in post-production. Previous video matting systems are highly sensitive to initial conditions and image noise, thus cannot reliably produce stable alpha mattes without temporal jitter. In this paper we propose an improved video matting system which contains two new components: (1) an accurate trimap propagation mechanism for setting up the initial matting conditions in a temporally-coherent way; and (2) a temporal matte filter which can improve the temporal coherence of the mattes while maintaining the matte structures on individual frames. Experimental results show that compared with previous methods, the two new components lead to alpha mattes with better temporal coherence.


title: Formal worst-case performance analysis of time-sensitive Ethernet with frame preemption

abstract: One of the key challenges in future Ethernet-based automotive and industrial networks is the low-latency transport of time-critical data. To date, Ethernet frames are sent non-preemptively. This introduces a major source of delay, as, in the worst-case, a latency-critical frame might be blocked by a frame of lower priority, which started transmission just before the latency-critical frame. The upcoming IEEE 802.3br standard will introduce Ethernet frame preemption to address this problem. While high-priority traffic benefits from preemption, lower-priority (yet still latency-sensitive) traffic experiences a certain overhead, impacting its timing behavior. In this paper, we present a formal timing analysis for Ethernet to derive worst-case latency bounds under preemption. We use a realistic automotive Ethernet setup to analyze the worst-case performance of standard Ethernet and Ethernet TSN under preemption and also compare our results to non-preemptive implementations of these standards.


title: Open Source Software-Defined Radio: A survey on GNUradio and its applications

abstract: This report analyzes the feasibility of using a Software Defined Radio solution for research purposes. We focused on the open source GNUradio project and studied its suitability for reproducing and analyzing some widespread wireless protocols, such as IEEE 802.11, Bluetooth, IEEE 802.15.4, and GSM. We found that the use of GNUradio with the Universal Software Radio Peripheral can help researchers in avoiding the closed source firmwares/drivers of commercial chipsets by providing a full customizability at physical and datalink layers. On the other hand, software radios are not always capable of correctly reproducing operations previously done in the hardware domain. This leads to several limitations with widespread standards. In this report we try to provide a picture of such limitations and the current status of the GNUradio framework. This work has been supported by the Telecommunications Research Center Vienna (ftw.) project N0. Ftw is supported by the Austrian Government and by the City of Vienna within the competence center program COMET. This work would not have been possible without the precious information contained in the official GNUradio mailing list archive. A special thanks goes to all the active participants.


title: Visualization and Visual Modelling for Strategic Analysis and Problem-Solving

abstract: Strategic problem-solving is a complicated task that requires processing of large amount of information using theoretical knowledge and practical experience. Effective problem-solving requires fast and accurate comprehension and analysis of the issues surrounding the problem. There are several tools, techniques and frameworks that support strategic analysis and decision-making. This paper develops a framework to clarify the relationship between visualization and modelling, and offers a classification scheme for visualization and modelling tools and techniques with a perspective on strategic problem-solving.


title: EVA²: Exploiting Temporal Redundancy in Live Computer Vision

abstract: Hardware support for deep convolutional neural networks (CNNs) is critical to advanced computer vision in mobile and embedded devices. Current designs, however, accelerate generic CNNs; they do not exploit the unique characteristics of real-time vision. We propose to use the temporal redundancy in natural video to avoid unnecessary computation on most frames. A new algorithm, activation motion compensation, detects changes in the visual input and incrementally updates a previously-computed output. The technique takes inspiration from video compression and applies well-known motion estimation techniques to adapt to visual changes. We use an adaptive key frame rate to control the trade-off between efficiency and vision quality as the input changes. We implement the technique in hardware as an extension to existing state-of-the-art CNN accelerator designs. The new unit reduces the average energy per frame by 54.2%, 61.7%, and 87.6% for three CNNs with less than 1% loss in vision accuracy.


title: Cooperative body-area-communications: Enhancing coexistence without coordination between networks

abstract: In this paper, coexistence of multiple mobile wireless body area networks (WBANs), where there is no coordination between WBANs, is investigated for the case where the WBAN-of-interest employs cooperative communications. A decode-and-forward protocol with two dual-hop links, two relays and selection combining (SC) at the hub (or gateway device) is chosen for the WBAN-of-interest. A suitable time-division-multiple-access (TDMA) scheme is used, enabling intra-network and inter-network operation, to allocate slots for each Tx/Rx link packet transmission. Realistic channel models are employed with various amounts of shadowing, small-scale fading and white noise introduced between WBANs. For the WBAN-of-interest, many hours of measured channel gain data is employed to emulate the channel for this WBAN. It is found that the chosen cooperative communications provides significantly better co-channel interference mitigation than single-link star topology WBAN communications in a mobile, dynamic, scenario, hence the signal-to-interference-plus-noise ratio (SINR) for 10% outage probability at the hub is greatly improved by up-to 12 dB. It is also demonstrated that the location of the hub, given three typical locations, has significant impact on the performance of the cooperative WBAN communications.


title: Fully integrated planar magnetics for primary-parallel isolated boost converter

abstract: A highly efficient planar integrated magnetic (PIM) design approach for primary-parallel isolated boost converters is presented. All magnetic components in the converter, including two input inductors and two transformers with primary-parallel and secondary-series windings, are integrated into an E-I-E-core geometry, reducing the total ferrite volume and core loss. The transformer windings are symmetrically distributed into the outer legs of E-cores, and the inductor windings are wound on the center legs of E-cores with air gaps. Therefore, the inductor and the transformer can be operated independently. Due to the low-reluctance path provided by the shared I-core, the two input inductors can be integrated independently, and also, the two transformers can be partially coupled to each other. Detailed characteristics of the integrated structure have been studied in this paper. AC losses in the windings and the leakage inductance of the transformer are kept low by interleaving the primary and secondary turns of the transformers substantially. Because of the combination of inductors and transformers, the maximum output power capability of the fully integrated module needs to be investigated. Winding loss, core loss, and switching loss of MOSFETs are analyzed in-depth in this work as well. To verify the validity of the design approach, a 2-kW prototype converter with two primary power stages is implemented for fuel-cell-fed traction applications with 20-50-V input and 400-V output. An efficiency of 95.9% can be achieved during 1.5-kW nominal operating conditions. Experimental comparisons between the PIM module and three separated cases have illustrated that the PIM module has advantages of lower footprint and higher efficiencies.


title: Immediate core buildup of endodontically treated teeth: the rest of the seal.

abstract: One primary objective of endodontic therapy is to establish an adequate seal with the root canal filling material. The placement of an immediate amalgam buildup at obturation, in conjunction with rubber dam isolation, allows an endodontic seal to be extended from the foramen to the cavosurface margin. As a result of this immediate buildup, the length of the seal and the longevity of the endodontic treatment are presumably extended. This article describes an effective means of performing the immediate core buildup of endodontically treated teeth.


title: Experiments on bar formation in a straight flume: 1. Uniform sediment

abstract: Laboratory experiments have been carried out in a large laboratory flume using a nearly uniform sand and under controlled steady flow conditions such as to ensure the development of alternate bars on a ripple and/or dune-covered bed. The interaction between small-scale and large-scale bed forms is found to enhance a modulation in time and in space of the bar pattern. Moreover, the flow depth decrease experienced by the flow field when approaching bar fronts, leading to a progressive reduction of the dimensions of small-scale bed forms, may influence appreciably the overall flow resistance. Experimental values of bar wavelength and of bar celerity are compared with the theoretical estimates obtained from a model developed within the classical framework of linear stability analysis. In particular, the model accounts for the local variability of friction coefficient and water level, for the secondary helical flow effects, and for the influence of longitudinal slope on sediment transport. Quantitatively satisfactory predictions of bar wavelength appear to be possible in spite of the fact that in various runs a correct estimation of flow resistance and flow discharge appears to be relatively difficult.