title: Multidirectional sprints and small-sided games training effect on agility and change of direction abilities in youth soccer.

abstract: The aim of this study was to compare the training effects of a small-sided game (SSG) and multidirectional sprint intervention on agility and change of direction (COD) ability in young male soccer players. Thirty-six soccer players (age: 14.2 ± 0.9 years; height: 167.2 ± 5.7 cm; body mass: 54.1 ± 6.3 kg, body fat: 12.5 ± 2.2%) participated in a short-term (6 weeks) randomized parallel fully controlled training study, with pre-to-post measurements. Players were randomly assigned to 2 experimental groups: training with preplanned COD drills (CODG, n = 12) or using SSGs (SSGG, n = 12) and to a control group (CONG, n = 12). Pre- and post-training players completed a test battery involving linear sprinting (15- and 30-m sprint), COD sprinting (COD: 15 m, ball: 15 m, 10-8-8-10 m, zigzag: 20 m), reactive agility test (RAT, RAT-ball), and vertical and horizontal jumping (countermovement jump and 5-jump, respectively). A significant (p <= 0.05) group × time effect was detected for all variables in CODG and SSGG. Improvements in sprint, agility without ball, COD, and jumping performances, were higher in CODG than in the other groups. The SSGG improved significantly more (p <= 0.05) than other groups in agility tests with the ball. The CONG showed significant improvements (p <= 0.05) on linear sprinting over a distance longer than 10 m and in all the agility and COD tests used in this study. It is concluded that in young male soccer players, agility can be improved either using purpose-built SSG or preplanned COD sprints. However, the use of specifically designed SSG may provide superior results in match-relevant variables.


title: Analysis of error recovery schemes for networks on chips

abstract: In this article, we discuss design constraints to characterize efficient error recovery mechanisms for the NoC design environment. We explore error control mechanisms at the data link and network layers and present the schemes' architectural details. We investigate the energy efficiency, error protection efficiency, and performance impact of various error recovery mechanisms.


title: Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework

abstract: Survival analysis/time-to-event models are extremely useful as they can help companies predict when a customer will buy a product, churn or default on a loan, and therefore help them improve their ROI. In this paper, we introduce a new method to calculate survival functions using the Multi-Task Logistic Regression (MTLR) model as its base and a deep learning architecture as its core. Based on the Concordance index (C-index) and Brier score, this method outperforms the MTLR in all the experiments disclosed in this paper as well as the Cox Proportional Hazard (CoxPH) model when nonlinear dependencies are found.


title: Application-based collision avoidance in wireless sensor networks

abstract: Wireless sensor networks are characterized by collections of small, low-power nodes that collect information about the physical world. Concurrent transmissions caused by the well-known hidden terminal problem result in collisions and packet corruption. Since corrupted packets must be retransmitted, collisions add an additional burden to the already energy constrained system. We present an application-based approach to collision avoidance. We propose two specific algorithms; the first one follows TCP's congestion avoidance algorithm and adjusts the transmission rate when a collision occurs, while the second one shifts packet transmission times to minimize collisions. We evaluated both algorithms through simulations and our results show that our approach can reduce the number of collision-induced retransmissions by a factor of 8 and the energy consumption by up to 50%.


title: Tensor Fusion Network for Multimodal Sentiment Analysis

abstract: Multimodal sentiment analysis is an increasingly popular research area, which extends the conventional language-based definition of sentiment analysis to a multimodal setup where other relevant modalities accompany language. In this paper, we pose the problem of multimodal sentiment analysis as modeling intra-modality and inter-modality dynamics. We introduce a novel model, termed Tensor Fusion Network, which learns both such dynamics end-to-end. The proposed approach is tailored for the volatile nature of spoken language in online videos as well as accompanying gestures and voice. In the experiments, our model outperforms state-of-the-art approaches for both multimodal and unimodal sentiment analysis.


title: Commercial applications of natural language processing

abstract: Vast quantities of text are becoming available in electronic form, ranging from published documents (e.g., electronic dictionaries, encyclopedias, libraries and archives for information retrieval services), to private databases (e.g., marketing information, legal records, medical histories), to personal email and faxes. Online information services are reaching mainstream computer users. There were over 15 million Internet users in 1993, and projections are for 30 million in 1997. With media attention reaching all-time highs, hardly a day goes by without a new article on the National Information Infrastructure, digital libraries, networked services, digital convergence or intelligent agents. This attention is moving natural language processing along the critical path for all kinds of novel applications.


title: Analysis of various full-adder circuits in cadence

abstract: The Adder is the important part in any processor/controller design. Till date there are a plenty of 1-bit full-adder circuits which have been proposed and designed. In this paper we have a analytic and comparative description of various full adder circuits, considering various constraints like power consumption, speed of operation and area. The circuits are designed in the virtuoso platform, using cadence tool with the available GPDK - 45nm kit. The Fulladder circuits with the most 28 transistor to the one with only 6 transistors are successfully designed, simulated and compared for various parameters like power consumption, speed of operation(delay) and area (transistor count), and finally concluded the best designs, that suite for the particular specifications.


title: Curvelet-based geodesic snakes for image segmentation with multiple objects

abstract: Curvelet transform is a multiscale and multidirectional geometric wavelet transform, which is an optimal sparse representation of edges and contours of objects. In this paper, a curvelet-based geodesic snake (CGS) is proposed for image segmentation of multiple objects. By producing the edge map of objects by curvelet thresholding instead of simple gradient methods, the proposed method shows great promises to recognize edges of multiple objects with weak edges and strong noises. In addition, we design several rules to quantitatively compare the segmentation accuracy.


title: A routing protocol for socially selfish delay tolerant networks

abstract: Existing routing algorithms for Delay Tolerant Networks (DTNs) assume that nodes are willing to forward packets for others. In the real world, however, most people are socially selfish; i.e., they are willing to forward packets for nodes with whom they have social ties but not others, and such willingness varies with the strength of the social tie. Following the philosophy of design for user, we propose a Social Selfishness Aware Routing (SSAR) algorithm to cope with user selfishness and provide good routing performance in an efficient way. To select an effective forwarding node, SSAR considers both users' willingness to forward and their contact opportunity, and derives a metric with mathematical modeling and machine learning techniques to measure the forwarding capability of the mobile nodes. Moreover, SSAR formulates the data forwarding process as a Multiple Knapsack Problem with Assignment Restrictions (MKPAR) to satisfy user demands for selfishness and performance. Trace-driven simulations show that SSAR allows users to maintain selfishness and achieves good routing performance with low transmission cost.


title: A Markov Modulated Characterization of Packetized Voice and Data Traffic and Related Statistical Multiplexer Performance

abstract: We study the performance of a statistical multiplexer whose inputs consist of a superposition of packetized voice sources and data. The performance analysis predicts voice packet delay distributions, which usually have a stringent requirement, as well as data packet delay distributions. The superposition is approximated by a correlated Markov modulated Poisson process (MMPP), which is chosen such that several of its statistical characteristics identically match those of the superposition. Matrix analytic methods are then used to evaluate system performance measures. In particular, we obtain moments of voice and data delay distributions and queue length distributions. We also obtain Laplace-Stieitjes transforms of the voice and data packet delay distributions, which are numerically inverted to evaluate tails of delay distributions. It is shown how the matrix analytic methodology can incorporate practical system considerations such as finite buffers and a class of overload control mechanisms discussed in the literature. Comparisons with simulation show the methods to be accurate. The numerical results for the tails of the voice packet delay distribution show the dramatic effect of traffic variability and correlations on performance.


title: Robust Spectral Inference for Joint Stochastic Matrix Factorization

abstract: Spectral inference provides fast algorithms and provable optimality for latent topic analysis. But for real data these algorithms require additional ad-hoc heuristics, and even then often produce unusable results. We explain this poor performance by casting the problem of topic inference in the framework of Joint Stochastic Matrix Factorization (JSMF) and showing that previous methods violate the theoretical conditions necessary for a good solution to exist. We then propose a novel rectification method that learns high quality topics and their interactions even on small, noisy data. This method achieves results comparable to probabilistic techniques in several domains while maintaining scalability and provable optimality.


title: A Comparison between Image Metadata and Visual Features to Classify Social Media Images

abstract: Image classification is needed to build image search and recommender systems. This paper applies classification techniques on two properties of the social media images: metadata of the images and their pixel intensities. Naive Bayes is applied on the tags and captions extracted from the images while a Convolutional Neural Network is trained for classifying images based on pixel values. Our dataset consists of more than 33,000 Flickr images segregated into 18 classes. Naive Bayes is applied by converting the metadata into a feature vectors. The Convolutional Neural Network is made up of three convolutional layers and three fully connected layers. On comparison, it was observed that analyzing the metadata associated with an image rather than the intensities provided better classification.


title: Resolving "unfinished business": Efficacy of experiential therapy using empty-chair dialogue.

abstract: In this study, 34 clients with unresolved feelings related to a significant other were randomly assigned to either experiential therapy using a Gestalt empty-chair dialogue intervention or an attention-placebo condition. The latter was a psychoeducational group offering information about «unfinished business.» Treatment outcomes were evaluated before and after the treatment period in each condition and at 4 months and 1 year after the experiential therapy. Outcome instruments targeted general symptomotology, interpersonal distress, target complaints, unfinished business resolution, and perceptions of self and other in the unfinished business relationship. Results indicated that experiential therapy achieved clinically meaningful gains for most clients and significantly greater improvement than the psychoeducational group on all outcome measures. Treatment gains for the experiential therapy group were maintained at follow-up


title: Cryptanalysis of Microsoft's point-to-point tunneling protocol (PPTP)

abstract: The Point-to-Point Tunneling Protocol (PPTP) is used to secure PPP connections over TCP/IP links. In this paper we analyze Microsoft’s Windows NT implementation of PPTP. We show how to break both the challenge/response authentication protocol (Microsoft CHAP) and the RC4 encryption protocol (MPPE), as well as how to attack the control channel in Microsoft’s implementation. These attacks do not necessarily break PPTP, but only Microsoft’s implementation of the protocol.


title: Real-time driver's eye state detection

abstract: Driver's eye state is a key factor for identifying the driver's visual attention. This paper proposes a real-time system to detect the driver's eye state. The system combines several basic image processing techniques which can achieve better performance than other single arithmetic. And we contrive different methods to detect the eye state in the daytime and at night. Firstly, the system combines the skin-color matching & vertical projection in the daytime and applies the intensity adjusting & vertical projection at night to detect and track the face region, and then validates it by geometric symmetry of face; secondly, we apply the horizontal projection to detect the eye position and validate it by some facial features; Lastly, a new complexity function is employed to judge the eye state, and the frequency of eye closure is registered. The method in our paper can detect whether the eyes are close more efficient than many other methods and it is more real-time with less computation. The experiment results show that this method is a robust and real-time one.


title: Optimum beamforming using transmit antenna arrays

abstract: Transmit beamforming is a powerful means of increasing capacity in systems in which the transmitter is equipped with an antenna array, especially in systems in which receive beamforming is not available, as is typical in the base-to-mobile downlink in a cellular communication system. In this paper, the problem of transmit beamforming is formulated as one of minimizing the power radiated by the base station, subject to satisfying quality of service requirements at the mobiles. For a single cell system, a global minimum is shown to exist, and an iterative algorithm that converges to it is provided. The solution extends to accommodate receive beamforming at the mobiles. It also extends to provide a mechanism for space-time transmit filtering, which exploits the differences between the temporal as well as the spatial channels between the base station and different mobiles. Finally, a system with multiple source-destination pairs (e.g., in an ad hoc network) is considered. An iterative algorithm that outputs a convergent sequence of feasible points with monotonically decreasing costs is provided for this case.


title: Vehicle routing through deadlock-free policy for cooperative traffic control in a network of intersections: Reservation and congestion

abstract: This paper considers a network of intersections, traversed by autonomous vehicles. Vehicles negotiate the right-of-way before traversing each intersection. The cooperative traffic management at intersections allows high traffic performance at a single intersection. This paper extends the issue of the traffic control efficiency to a network of intersections. It proposes a hierarchical architecture of the traffic control for preventing traffic from deadlock into a grid of intersections. Then, it discusses policies for routing vehicles in the network. Three policies are considered: shortest path, reservation and congestion avoidance. Furthermore, three traffic contexts are considered and simulated. Since, the traffic management at intersections is adapted to the currently observed vehicles, then the simulation shows that it is required to define new policy that considers the overall network performance rather than optimizing the route of each vehicle according to a given criteria. Indeed, reorienting vehicles according to a given policy can increase the number of conflicts and risks of cycles.


Making Sense of Regulations with SBVR


title: Travel time estimation of a path using sparse trajectories

abstract: In this paper, we propose a citywide and real-time model for estimating the travel time of any path (represented as a sequence of connected road segments) in real time in a city, based on the GPS trajectories of vehicles received in current time slots and over a period of history as well as map data sources. Though this is a strategically important task in many traffic monitoring and routing systems, the problem has not been well solved yet given the following three challenges. The first is the data sparsity problem, i.e., many road segments may not be traveled by any GPS-equipped vehicles in present time slot. In most cases, we cannot find a trajectory exactly traversing a query path either. Second, for the fragment of a path with trajectories, they are multiple ways of using (or combining) the trajectories to estimate the corresponding travel time. Finding an optimal combination is a challenging problem, subject to a tradeoff between the length of a path and the number of trajectories traversing the path (i.e., support). Third, we need to instantly answer users' queries which may occur in any part of a given city. This calls for an efficient, scalable and effective solution that can enable a citywide and real-time travel time estimation. To address these challenges, we model different drivers' travel times on different road segments in different time slots with a three dimension tensor. Combined with geospatial, temporal and historical contexts learned from trajectories and map data, we fill in the tensor's missing values through a context-aware tensor decomposition approach. We then devise and prove an object function to model the aforementioned tradeoff, with which we find the most optimal concatenation of trajectories for an estimate through a dynamic programming solution. In addition, we propose using frequent trajectory patterns (mined from historical trajectories) to scale down the candidates of concatenation and a suffix-tree-based index to manage the trajectories received in the present time slot. We evaluate our method based on extensive experiments, using GPS trajectories generated by more than 32,000 taxis over a period of two months. The results demonstrate the effectiveness, efficiency and scalability of our method beyond baseline approaches.


title: Behaviour-Driven Development of Foundational UML Components

abstract: Behaviour-Driven Development (BDD) focuses all development activities on the delivery of behaviours - what a system should do, described such that developers and domain experts speak the same language. BDD frameworks allow users to represent the required system behaviour as executable user stories and the acceptance criteria as executable scenarios attached to user stories. In this paper we define a UML profile that allows users to create executable Foundational UML (fUML) stories and scenarios. In order to easily construct scenarios we introduce a BDD model library which contains fUML activities for testing equalities and inclusions. We also present an Eclipse-based development tool that supports a BDD approach for developing fUML components. The tool provides developers a concrete syntax for defining executable scenarios, and automatically updates the project status based on verified delivered behaviorus.


title: ABSTRACT Automated Essay Scoring

abstract: The impacts of computers on writing have been widely studied for three decades. Even basic computers functions, i.e. word processing, have been of great assistance to writers in modifying their essays. The research on Automated Essay Scoring (AES) has revealed that computers have the capacity to function as a more effective cognitive tool (Attali, 2004). AES is defined as the computer technology that evaluates and scores the written prose (Shermis & Barrera, 2002; Shermis & Burstein, 2003; Shermis, Raymat, & Barrera, 2003). Revision and feedback are essential aspects of the writing process. Students need to receive feedback in order to increase their writing quality. However, responding to student papers can be a burden for teachers. Particularly if they have large number of students and if they assign frequent writing assignments, providing individual feedback to student essays might be quite time consuming. AES systems can be very useful because they can provide the student with a score as well as feedback within seconds (Page, 2003). Four types of AES systems, which are widely used by testing companies, universities, and public schools: Project Essay Grader (PEG), Intelligent Essay Assessor (IEA), E-rater, and IntelliMetric. AES is a developing technology. Many AES systems are used to overcome time, cost, and generalizability issues in writing assessment. The accuracy and reliability of these systems have been proven to be high. The search for excellence in machine scoring of essays is continuing and numerous studies are being conducted to improve the effectiveness of the AES systems.


title: The Complementarity of Information Technology Infrastructure and E-Commerce Capability : A Resource-Based Assessment of Their Business Value

abstract: This study seeks to assess the business value of e-commerce capability and information technology (IT) infrastructure in the context of electronic business at the firm level. Grounded in the IT business-value literature and enhanced by the resource-based theory of the firm, we developed a research framework in which both the main effects and the interaction effects of e-commerce and IT on firm performance were tested. Within this theoretical framework, we formulated several hypotheses. We then developed a multidimensional e-commerce capability construct, and after establishing its validity and reliability, tested the hypotheses with empirical data from 114 companies in the retail industry. Controlling for variations of firm size and subindustry effects, our empirical analysis found a strong positive interaction effect between IT infrastructure and e-commerce capability. This suggests that their complementarity positively contributes to firm performance in terms of sales per employee, inventory turnover, and cost reduction. The results are consistent with the resource-based theory, and provide empirical evidence to the complementary synergy between front-end e-commerce capability and back-end IT infrastructure. Combined together, they become more effective in producing business value. Yet the value of this synergy has not been recognized in the IT payoff literature. The "productivity paradox" observed in various studies has been attributed to variation in methods and measures, yet we offer an additional explanation: ignoring complementarities in business value measurement implies that the impact of IT was seriously underestimated. Our results emphasized the integration of resources as a feasible path to e-commerce value--companies need to enhance the integration between front-end e-commerce capability and back-end IT infrastructure in order to reap the benefits of e-commerce investments.


title: The application of small unmanned aerial systems for precision agriculture: a review

abstract: Precision agriculture (PA) is the application of geospatial techniques and sensors (e.g., geographic information systems, remote sensing, GPS) to identify variations in the field and to deal with them using alternative strategies. In particular, high-resolution satellite imagery is now more commonly used to study these variations for crop and soil conditions. However, the availability and the often prohibitive costs of such imagery would suggest an alternative product for this particular application in PA. Specifically, images taken by low altitude remote sensing platforms, or small unmanned aerial systems (UAS), are shown to be a potential alternative given their low cost of operation in environmental monitoring, high spatial and temporal resolution, and their high flexibility in image acquisition programming. Not surprisingly, there have been several recent studies in the application of UAS imagery for PA. The results of these studies would indicate that, to provide a reliable end product to farmers, advances in platform design, production, standardization of image georeferencing and mosaicing, and information extraction workflow are required. Moreover, it is suggested that such endeavors should involve the farmer, particularly in the process of field design, image acquisition, image interpretation and analysis.


title: Efficacy of Topical Combination of 0.25% Finasteride and 3% Minoxidil Versus 3% Minoxidil Solution in Female Pattern Hair Loss: A Randomized, Double-Blind, Controlled Study

abstract: Background ::: The relationship between female pattern hair loss (FPHL) and androgenic hormones is not well established, but some evidence indicates oral finasteride may be efficacious in FPHL. Use of a topical formulation has been proposed to minimize unwanted effects.


title: Semantic Pose Using Deep Networks Trained on Synthetic RGB-D

abstract: In this work we address the problem of indoor scene understanding from RGB-D images. Specifically, we propose to find instances of common furniture classes, their spatial extent, and their pose with respect to generalized class models. To accomplish this, we use a deep, wide, multi-output convolutional neural network (CNN) that predicts class, pose, and location of possible objects simultaneously. To overcome the lack of large annotated RGB-D training sets (especially those with pose), we use an on-the-fly rendering pipeline that generates realistic cluttered room scenes in parallel to training. We then perform transfer learning on the relatively small amount of publicly available annotated RGB-D data, and find that our model is able to successfully annotate even highly challenging real scenes. Importantly, our trained network is able to understand noisy and sparse observations of highly cluttered scenes with a remarkable degree of accuracy, inferring class and pose from a very limited set of cues. Additionally, our neural network is only moderately deep and computes class, pose and position in tandem, so the overall run-time is significantly faster than existing methods, estimating all output parameters simultaneously in parallel.


title: Human-machine-interaction in the industry 4.0 era

abstract: The development of Industry 4.0 will be accompanied by changing tasks and demands for the human in the factory. As the most flexible entity in cyber-physical production systems, workers will be faced with a large variety of jobs ranging from specification and monitoring to verification of production strategies. Through technological support it is guaranteed that workers can realize their full potential and adopt the role of strategic decision-makers and flexible problem-solvers. The use of established interaction technologies and metaphors from the consumer goods market seems to be promising. This paper demonstrates solutions for the technological assistance of workers, which implement the representation of a cyber-physical world and the therein occurring interactions in the form of intelligent user interfaces. Besides technological means, the paper points out the requirement for adequate qualification strategies, which will create the required, inter-disciplinary understanding for Industry 4.0.


'No More Credit Score': Employer Credit Check Bans and Signal Substitution


title: Cyber–Physical Systems: A Perspective at the Centennial

abstract: Cyber-physical systems (CPSs) are the next generation of engineered systems in which computing, communication, and control technologies are tightly integrated. Research on CPSs is fundamentally important for engineered systems in many important application domains such as transportation, energy, and medical systems. We overview CPS research from both a historical point of view in terms of technologies developed for early generations of control systems, as well as recent results on CPSs in many relevant research domains such as networked control, hybrid systems, real-time computing, real-time networking, wireless sensor networks, security, and model-driven development. We outline the potential for CPSs in many societally important application domains.


title: A 4.2 ps Time-Interval RMS Resolution Time-to-Digital Converter Using a Bin Decimation Method in an UltraScale FPGA

abstract: The common solution for a field programmable gate array (FPGA)-based time-to-digital converter (TDC) is constructing a tapped delay line (TDL) for time interpolation to yield a sub-clock time resolution. The granularity and uniformity of the delay elements of TDL determine the TDC time resolution. In this paper, we propose a dual-sampling TDL architecture and a bin decimation method that could make the delay elements as small and uniform as possible, so that the implemented TDCs can achieve a high time resolution beyond the intrinsic cell delay. Two identical full hardware-based TDCs were implemented in a Xilinx UltraScale FPGA for performance evaluation. For fixed time intervals in the range from 0 to 440 ns, the average time-interval RMS resolution is measured by the two TDCs with 4.2 ps, thus the timestamp resolution of single TDC is derived as 2.97 ps. The maximum hit rate of the TDC is as high as half the system clock rate of FPGA, namely 250 MHz in our demo prototype. Because the conventional online bin-by-bin calibration is not needed, the implementation of the proposed TDC is straightforward and relatively resource-saving.


title: The Impact of Node Selfishness on Multicasting in Delay Tolerant Networks

abstract: Due to the uncertainty of transmission opportunities between mobile nodes, delay tolerant networks (DTNs) exploit the opportunistic forwarding mechanism. This mechanism requires nodes to forward messages in a cooperative and selfish way. However, in the real word, most of the nodes exhibit selfish behaviors, such as individual and social selfishness. In this paper, we are the first to investigate how the selfish behaviors of nodes affect the performance of DTN multicast. We consider two typical multicast relaying schemes, namely, two-hop relaying and epidemic relaying, and study their performance in terms of average message transmission delay and transmission cost. Specifically, we model the message delivery process under selfish behaviors by a 3-D continuous time Markov chain; under this model, we derive closed-form formulas for the message transmission delay and cost. Then, we evaluate the accuracy of the proposed Markov chain model by comparing the theoretical results with the simulation results obtained by simulating the message dissemination under both two-hop and epidemic relaying with different network sizes and mobility models. Our study shows that different selfish behaviors may have different impacts on different performance metrics. In addition, selfish behaviors influence epidemic relaying more than two-hop relaying. Furthermore, our results show that the performance of multicast with selfish nodes depends on the multicast group size.


title: Overview of Steroidogenic Enzymes in the Pathway from Cholesterol to Active Steroid Hormones

abstract: Significant advances have taken place in our knowledge of the enzymes involved in steroid hormone biosynthesis since the last comprehensive review in 1988. Major developments include the cloning, identification, and characterization of multiple isoforms of 3beta-hydroxysteroid dehydrogenase, which play a critical role in the biosynthesis of all steroid hormones and 17beta-hydroxysteroid dehydrogenase where specific isoforms are essential for the final step in active steroid hormone biosynthesis. Advances have taken place in our understanding of the unique manner that determines tissue-specific expression of P450aromatase through the utilization of alternative promoters. In recent years, evidence has been obtained for the expression of steroidogenic enzymes in the nervous system and in cardiac tissue, indicating that these tissues may be involved in the biosynthesis of steroid hormones acting in an autocrine or paracrine manner. This review presents a detailed description of the enzymes involved in the biosynthesis of active steroid hormones, with emphasis on the human and mouse enzymes and their expression in gonads, adrenal glands, and placenta.


title: Fun and usable: augmented reality instructions in a hospital setting

abstract: The differences between Augmented Reality (AR) systems and computer display based systems create a need for a different approach than "traditional" HCI to the design and development of AR systems. This paper presents theoretical and empirical work which uses holistic approach compared to traditional guidelines in human computer interaction. The paper includes a usability study where AR was used for giving instructions to professional users in a hospital. The theoretical stance of Cognitive Systems Engineering is suggested as a possible approach to the design of AR system. The analysis show that the users in the context of medical care are positive towards AR systems as a technology and as a tool for instructions. This indicates that AR technology may become an accepted part of every day work in such a context.


title: Dictionary-Guided Editing Networks for Paraphrase Generation

abstract: An intuitive way for a human to write paraphrase sentences is to replace words or phrases in the original sentence with their corresponding synonyms and make necessary changes to ensure the new sentences are fluent and grammatically correct. We propose a novel approach to modeling the process with dictionary-guided editing networks which effectively conduct rewriting on the source sentence to generate paraphrase sentences. It jointly learns the selection of the appropriate word level and phrase level paraphrase pairs in the context of the original sentence from an off-the-shelf dictionary as well as the generation of fluent natural language sentences. Specifically, the system retrieves a set of word level and phrase level araphrased pairs derived from the Paraphrase Database (PPDB) for the original sentence, which is used to guide the decision of which the words might be deleted or inserted with the soft attention mechanism under the sequence-to-sequence framework. We conduct experiments on two benchmark datasets for paraphrase generation, namely the MSCOCO and Quora dataset. The evaluation results demonstrate that our dictionary-guided editing networks outperforms the baseline methods.


title: Secure and Practical Identity-Based Encryption

abstract: A variant of Waters' identity-based encryption scheme with a much smaller system parameters size (only a few kilobytes) is presented. It is shown that this variant is semantically secure against passive adversaries in the standard model. In essence, the new variant divides Waters' system parameters size by a factor l at the cost of (negligibly) reducing security by l bits. The construction yields a fully secure practical identity-based encryption scheme.


title: Counting Graphlets: Space vs Time

abstract: Counting graphlets is a well-studied problem in graph mining and social network analysis. Recently, several papers explored very simple and natural approaches based on Monte Carlo sampling of Markov Chains (MC), and reported encouraging results. We show, perhaps surprisingly, that this approach is outperformed by a carefully engineered version of color coding (CC) [1], a sophisticated algorithmic technique that we extend to the case of graphlet sampling and for which we prove strong statistical guarantees. Our computational experiments on graphs with millions of nodes show CC to be more accurate than MC. Furthermore, we formally show that the mixing time of the MC approach is too high in general, even when the input graph has high conductance. All this comes at a price however. While MC is very efficient in terms of space, CC's memory requirements become demanding when the size of the input graph and that of the graphlets grow. And yet, our experiments show that a careful implementation of CC can push the limits of the state of the art, both in terms of the size of the input graph and of that of the graphlets.


title: Practical and Secure Substring Search

abstract: In this paper we address the problem of outsourcing sensitive strings while still providing the functionality of substring searches. While security is one important aspect that requires careful system design, the practical application of the solution depends on feasible processing time and integration efforts into existing systems. That is, searchable symmetric encryption (SSE) allows queries on encrypted data but makes common indexing techniques used in database management systems for fast query processing impossible. As a result, the overhead for deploying such functional and secure encryption schemes into database systems while maintaining acceptable processing time requires carefully designed special purpose index structures. Such structures are not available on common database systems but require individual modifications depending on the deployed SSE scheme. Our technique transforms the problem of secure substring search into range queries that can be answered efficiently and in a privacy-preserving way on common database systems without further modifications using frequency-hiding order-preserving encryption. We evaluated our prototype implementation deployed in a real-world scenario, including the consideration of network latency, we demonstrate the practicability of our scheme with 98.3 ms search time for 10,000 indexed emails. Further, we provide a practical security evaluation of this transformation based on the bucketing attack that is the best known published attack against this kind of property-preserving encryption.


title: Edge-Based Adaptation for a 1 IIR + 1 Discrete-Time Tap DFE Converging in $5~\mu$ s

abstract: A 16 Gb/s 1-tap Infinite impulse response (IIR) + 1-tap discrete-time (DT) decision feedback equalizer (DFE) with integrated clock recovery and adaptation is demonstrated in 28 nm FD-SOI CMOS. Using a CMOS phase rotator, 0.7 unit interval (UI) high-frequency jitter tolerance is achieved when operating mesochronously, and over 0.4 UI operating plesiochronously. The half-rate architecture includes a novel 2:1 multiplexer to reduce delay in the IIR feedback path. With a 28 dB loss channel, a BER below $10^{-12}$ is measured over a 0.32 UI timing window with a TX swing of 0.8 Vpp-diff. Using a 2 Vpp-diff TX swing, a 30 dB loss channel has a measured BER below $10^{-12}$ over a 0.3 UI timing window. A novel edge-based algorithm adapts both IIR and DT equalizer coefficients using the same high-speed circuitry and signals required for clock recovery. The algorithm utilizes all transitions to inform the adaptation of all equalizer coefficients, thereby providing faster convergence than previously-reported algorithms which await specific patterns. Moreover, the adaptation freezes automatically unless a diverse set of data patterns is received, thereby making the algorithm robust in the presence of poorly-conditioned data. The adaptive DFE converges within $5~\mu \text{s}$ .


title: Robot operating systems: Bridging the gap between human and robot

abstract: A robot operating system (ROS) is a collection of programs which allow a user to easily control the mobile operations of a robot. This paper describes research conducted on sixteen different ROSs to determine which one will most accommodate future Trinity undergraduates for use in further robotics research. The goal of this research is to reduce this list of 16 ROSs to a single ROS that can be used by Trinity undergraduates with limited programming experience to perform simple robotic motion tasks. First, a detailed list of criteria describing the ideal ROS was created. The list of ROSs was narrowed down to a single ROS that best fit these criteria. This ROS is called Player/Stage. Next, Player/Stage was tested to ensure the validity of the research performed. In these tests, a robot's mobility and sensors were controlled by a user via Player/Stage. This ROS excelled in both the mobility tests and the sensor tests, and also proved simple to navigate and manage.


title: Evolutionary architecture search for deep multitask networks

abstract: Multitask learning, i.e. learning several tasks at once with the same neural network, can improve performance in each of the tasks. Designing deep neural network architectures for multitask learning is a challenge: There are many ways to tie the tasks together, and the design choices matter. The size and complexity of this problem exceeds human design ability, making it a compelling domain for evolutionary optimization. Using the existing state of the art soft ordering architecture as the starting point, methods for evolving the modules of this architecture and for evolving the overall topology or routing between modules are evaluated in this paper. A synergetic approach of evolving custom routings with evolved, shared modules for each task is found to be very powerful, significantly improving the state of the art in the Omniglot multitask, multialphabet character recognition domain. This result demonstrates how evolution can be instrumental in advancing deep neural network and complex system design in general.


title: Theory of mind and psychopathy: can psychopathic individuals read the ‘language of the eyes’?

abstract: Abstract There have been suggestions that Theory of Mind (ToM) impairment might lead to aggressive behaviour and psychopathy. Psychopathic and matched non-psychopathic individuals, as defined by the Hare Psychopathy Checklist [The Hare Psychopath Checklist-Revised, 1991] completed the ‘Reading the Mind in the Eyes’ ToM Test [Journal of Child Psychology and Psychiatry, 1997;38:813]. This test requires the self-paced identification of mental states from photographs of the eye region alone. Results indicated that the psychopathic individuals did not present with any generalised impairment in ToM. The data are discussed with reference to the putative neural system mediating performance on this task and models of psychopathy.


title: Designing an ensemble classifier over subspace classifiers using iterative convergence routine

abstract: There can be multiple classifiers for a given data set. One way to generate multiple classifiers is to use subspaces of the attribute sets. In this paper, we generate subspace classifiers by an iterative convergence routine to build an ensemble classifier. Experimental evaluation covers the cases of both labelled and unlabelled (blind) data separately. We evaluate our approach on many benchmark UC Irvine datasets to assess the robustness of our approach with varying induced noise levels. We explicitly compare and present the utility of the clusterings generated for classification using several diverse clustering dissimilarity metrics. Results show that our ensemble classifier is a more robust classifier in comparison to different multi-class classification approaches.


title: Multi-Domain Adaptation for SMT Using Multi-Task Learning

abstract: Domain adaptation for SMT usually adapts models to an individual specific domain. However, it often lacks some correlation among different domains where common knowledge could be shared to improve the overall translation quality. In this paper, we propose a novel multi-domain adaptation approach for SMT using Multi-Task Learning (MTL), with in-domain models tailored for each specific domain and a general-domain model shared by different domains. The parameters of these models are tuned jointly via MTL so that they can learn general knowledge more accurately and exploit domain knowledge better. Our experiments on a largescale English-to-Chinese translation task validate that the MTL-based adaptation approach significantly and consistently improves the translation quality compared to a non-adapted baseline. Furthermore, it also outperforms the individual adaptation of each specific domain.


title: The effect of sound-based intervention on children with sensory processing disorders and visual-motor delays.

abstract: This study investigated the effects of a sensory diet and therapeutic listening intervention program, directed by an occupational therapist and implemented by parents, on children with sensory processing disorders (SPD) and visual�motor delays. A convenience sample was used of 10 participants, ages 5 to 11 years, with SPD and visual�motor delays. In the first phase, each participant completed a 4-week sensory diet program, then an 8 week therapeutic-listening and sensory diet program. The Sensory Profile was completed by the participants� parents before and after both study phases. The Draw-A-Person test, Developmental Test of Visual Motor Integration (VMI), and Evaluation Tool of Children�s Handwriting (ETCH) were administered before and after each phase. Over 12 weeks, the participants exhibited significant improvement on the Sensory Profile, increasing a mean of 71 points. Parents reported improvements in their children�s behaviors related to sensory processing. Scores on the VMI visual and ETCH legibility scales also improved more during the therapeutic listening phase. Therapeutic listening combined with a sensory diet appears effective in improving behaviors related to sensory processing in children with SPD and visual�motor impairments


title: Control of single-stage single-phase PV inverter

abstract: In this paper the issue of control strategies for single-stage photovoltaic (PV) inverter is addressed. Two different current controllers have been implemented and an experimental comparison between them has been made. A complete control structure for the single-phase PV system is also presented. The main elements of the PV control structure are: - a maximum power point tracker (MPPT) algorithm using the incremental conductance method; - a synchronization method using the phase-locked-loop (PLL), based on delay; the input power control using the dc voltage controller and power feed-forward; - and the grid current controller implemented in two different ways, using the classical proportional integral (PI) and the novel proportional resonant (PR) controllers. The control strategy was tested experimentally on 1.5 kW PV inverter


title: Capturing Feature-Level Irregularity in Disease Progression Modeling

abstract: Disease progression modeling (DPM) analyzes patients' electronic medical records (EMR) to predict the health state of patients, which facilitates accurate prognosis, early detection and treatment of chronic diseases. However, EMR are irregular because patients visit hospital irregularly based on the need of treatment. For each visit, they are typically given different diagnoses, prescribed various medications and lab tests. Consequently, EMR exhibit irregularity at the feature level. To handle this issue, we propose a model based on the Gated Recurrent Unit by decaying the effect of previous records using fine-grained feature-level time span information, and learn the decaying parameters for different features to take into account their different behaviours like decaying speeds under irregularity. Extensive experimental results in both an Alzheimer's disease dataset and a chronic kidney disease dataset demonstrate that our proposed model of capturing feature-level irregularity can effectively improve the accuracy of DPM.


title: Structure-Mapping in Metaphor Comprehension

abstract: Metaphor has a double life. It can be described as a directional process in which a stable, familiar base domain provides inferential structure to a less clearly specified target. But metaphor is also described as a process of finding commonalities, an inherently symmetric process. In this second view, both concepts may be altered by the metaphorical comparison. Whereas most theories of metaphor capture one of these aspects, we offer a model based on structure-mapping that captures both sides of metaphor processing. This predicts (a) an initial processing stage of symmetric alignment; and (b) a later directional phase in which inferences are projected to the target. To test these claims, we collected comprehensibility judgments for forward (e.g., ‘‘A rumor is a virus’’) and reversed (‘‘A virus is a rumor’’) metaphors at early and late stages of processing, using a deadline procedure. We found an advantage for the forward direction late in processing, but no directional preference early in processing. Implications for metaphor theory are discussed.


title: Quadrature broadband phase shift generation using passive RC polyphase filter for RF front-end

abstract: This paper reviews the state of the art of a polyphase complex filter for RF front-end low-IF transceivers applications. We then propose a multi-stage polyphase filter design to generate a quadrature I/Q signal to achieve a wideband precision quadrature phase shift with a constant 90 ° phase difference for self-interference cancellation circuit for full duplex radio. The number of the stages determines the bandwidth requirement of the channel. An increase of 87% in bandwidth is attained when our design is implemented in multi-stage from 2 to an extended 6 stages. A 4-stage polyphase filter achieves 2.3 GHz bandwidth.


title: TALL: Temporal Activity Localization via Language Query

abstract: This paper focuses on temporal localization of actions in untrimmed videos. Existing methods typically train classifiers for a pre-defined list of actions and apply them in a sliding window fashion. However, activities in the wild consist of a wide combination of actors, actions and objects; it is difficult to design a proper activity list that meets users' needs. We propose to localize activities by natural language queries. Temporal Activity Localization via Language (TALL) is challenging as it requires: (1) suitable design of text and video representations to allow cross-modal matching of actions and language queries; (2) ability to locate actions accurately given features from sliding windows of limited granularity. We propose a novel Cross-modal Temporal Regression Localizer (CTRL) to jointly model text query and video clips, output alignment scores and action boundary regression results for candidate clips. For evaluation, we adopt TaCoS dataset, and build a new dataset for this task on top of Charades by adding sentence temporal annotations, called Charades-STA. We also build complex sentence queries in Charades-STA for test. Experimental results show that CTRL outperforms previous methods significantly on both datasets.


title: A PAC-Bayesian Tutorial with A Dropout Bound

abstract: This tutorial gives a concise overview of existing PAC-Bayesian theory focusing on three generalization bounds. The first is an Occam bound which handles rules with finite precision parameters and which states that generalization loss is near training loss when the number of bits needed to write the rule is small compared to the sample size. The second is a PAC-Bayesian bound providing a generalization guarantee for posterior distributions rather than for individual rules. The PAC-Bayesian bound naturally handles infinite precision rule parameters, $L_2$ regularization, {\em provides a bound for dropout training}, and defines a natural notion of a single distinguished PAC-Bayesian posterior distribution. The third bound is a training-variance bound --- a kind of bias-variance analysis but with bias replaced by expected training loss. The training-variance bound dominates the other bounds but is more difficult to interpret. It seems to suggest variance reduction methods such as bagging and may ultimately provide a more meaningful analysis of dropouts.


title: Optical and kinetic properties of the dusty plasma in radiofrequency discharge

abstract: Dependence of diffusion coefficient of dusty particles in dusty plasma was experimentally obtained. A proportional increase of diffusion of the dust particles with the increase of the discharge power was experimentally found. The pair correlation functions of dusty particles were also obtained from the experiments and the change of dust cloud structure with the increase of the discharge power was observed. The influence of the dust particles on the spectrum of plasma glow was investigated using spatial spectrometer. It was shown that upon introduction of dust particles into the plasma bulk the overall intensity of the plasma glow decreases.


title: Systems Engineering: Architecture and Design

abstract: System architectures elements of systems architecture design of systems synthesis and analysis in systems design.


title: Development of Novel High Density System Integration Solutions in FOWLP-Complex and Thin Wafer-Level SiP and Wafer-Level 3D Packages

abstract: Expanding FOWLP (Fan-Out Wafer-Level Packaging) from mainly 2D single or multi die solutions to 3D stacked multi-die solutions with SMDs integration, is of crucial importance to meet the requirements arising from new markets such as IoT/IoE and Wearables. This drives the development of new capabilities and technology breakthroughs in the current FOWLP process. One of the most hailed capabilities of FOWLP is the heterogeneous high-density system integration in a package. Wafer Level System-in-Package (WLSiP) already integrates active dies, passive components and even already-packaged components, in a wide range of geometries and materials. Vertical interconnections enable FO-based WL3D solutions, thru Package-on-Package (PoP) assembly. The nature of FOWLP, being a substrate-less technology and using thin-film re-distribution layers, makes the package itself an active interposer. This concept allows very thin packages and PoP solutions, with excellent electrical and thermal behaviour compared to other packaging technologies. To accomplish the vertical package interconnect, or Thru Package Vias (TPV), required for package front to backside connections and 3D assembly, pre-formed vias solution was developed as the concept of choice at NANIUM for lower IO density and package body thickness from 200 to 400um. To allow the process on very thin Fan-Out wafers and, on the last stage, the double side RDL process to complete PoP solution, dedicated Temporary Wafer Bonding (TWB) and Debonding solution for FOWLP were developed and tested. This paper presents the approaches used to effectively enable FOWLP-based WLSIP and WL3D products: Pre-formed via solutions in three build-up options, from process development to reliability result, Wafer front-to-back RDL alignment solutions for high-accuracy 3D package, FOWLP TWB solution for WL3D/ PoP products, and stack-up/ stack-down solution for the final PoP implementation, when there is no space for additional die inside the WLSiP or due to the need to simplify routing complexity and reduce number of RDL's. Several demonstrators are built to demonstrate the above mentioned features, from a very thin,


title: Ferroelectric Lumped Element Filter/Switch for Microwave Applications

abstract: This paper describes the experimental performance of a ferroelectric lumped element filter/switch grown on silicon substrate. The filtering/switching operation is based on the dc electric field controlled dielectric constant of a Ba0.25Sr0.75TiO3 (BST) thin-film. The switch is made by changing the cut-off frequency of a low-pass filter. 18 dB isolation has been demonstrated under 15 V bias at 25 GHz with great consistency being observed among different samples. The properties of the BST thin-film has been extracted by comparing the electromagnetic full-wave simulation with the experimental measurements.


title: Beyond Local Reasoning for Stereo Confidence Estimation with Deep Learning

abstract: Confidence measures for stereo gained popularity in recent years due to their improved capability to detect outliers and the increasing number of applications exploiting these cues. In this field, convolutional neural networks achieved top-performance compared to other known techniques in the literature by processing local information to tell disparity assignments from outliers. Despite this outstanding achievements, all approaches rely on clues extracted with small receptive fields thus ignoring most of the overall image content. Therefore, in this paper, we propose to exploit nearby and farther clues available from image and disparity domains to obtain a more accurate confidence estimation. While local information is very effective for detecting high frequency patterns, it lacks insights from farther regions in the scene. On the other hand, enlarging the receptive field allows to include clues from farther regions but produces smoother uncertainty estimation, not particularly accurate when dealing with high frequency patterns. For these reasons, we propose in this paper a multi-stage cascaded network to combine the best of the two worlds. Extensive experiments on three datasets using three popular stereo algorithms prove that the proposed framework outperforms state-of-the-art confidence estimation techniques.


title: A Collection of Software Engineering Challenges for Big Data System Development

abstract: In recent years, the development of systems for processing and analyzing large amounts of data (so-called Big Data) has become an important sub-discipline of software engineering. However, to date there exits no comprehensive summary of the specific idiosyncrasies and challenges that the development of Big Data systems imposes on software engineers. With this paper, we aim to provide a first step towards filling this gap based on our collective experience from industry and academic projects as well as from consulting and initial literature reviews. The main contribution of our work is a concise summary of 26 challenges in engineering Big Data systems, collected and consolidated by means of a systematic identification process. The aim is to make practitioners more aware of common challenges and to offer researchers a solid baseline for identifying novel software engineering research directions.


title: Comparative fluorescence analysis of the bovine sperm using IVA-520 (anti-CD46 antibody) and lectins: probable localisation of CD46 on bovine sperm membrane

abstract: Membrane cofactor protein (CD46) is complement regulatory protein with probable function in the reproduction process. Expression of CD46 on human, mice, rat and guinea pig spermatozoa is restricted to the inner acrosomal membrane. In spite of the presence of anti-sperm antibodies and other potential complement activating agents in follicular fluid, CD46 is not expressed on the plasma membrane of spermatozoa as the other complement regulatory proteins (DAF and CD59) in human. Using dual immunofluorescence labelling with mAb IVA-520 (anti-bovine CD46) and various lectins with different binding pattern or monoclonal antibody ACR.4, targeted against intra-acrosomal protein, we excluded the expression of CD46 on the inner acrosomal membrane as well as in the acrosomal content but, we suggested the localization of this molecule on the outer acrosomal membrane and possibly on the plasma membrane of bovine sperm.


title: ATH: Auto-Tuning HBase’s Configuration via Ensemble Learning

abstract: HBase is a distributed database management system and is becoming increasingly popular for applications that need fast random access to a large amount of data. However, it has a number of performance-critical configuration parameters, which may interact with each other in a complex way, making manually tuning them for optimal performance extremely difficult. In this paper, we propose a novel approach to auto-tune the configuration parameters for a given HBase application, called Auto-Tuning HBase (ATH). The key is an accurate performance model with low cost, which takes configuration parameters as inputs. To this end, we systematically explore different modeling techniques and decide to employ an ensemble learning algorithm to build the performance model. Subsequently, we leverage genetic algorithm to search the optimal configuration parameters for the application by using the performance model. As such, ATH can quickly as well as automatically identify a set of configuration parameter values to make the performance of the application optimal. We validate ATH in a cluster with ten nodes by using five typical applications from Yahoo! Cloud Serving Benchmark. The experimental results show that ATH can improve throughput by 41% on average and up to 97% compared with the default configurations. At the same time, the latency of HBase operations is reduced by 11.3% on average and up to 57%.


title: Using ontology-based semantic similarity to facilitate the article screening process for systematic reviews

abstract: Abstract Objective Systematic Reviews (SRs) are utilized to summarize evidence from high quality studies and are considered the preferred source of evidence-based practice (EBP). However, conducting SRs can be time and labor intensive due to the high cost of article screening. In previous studies, we demonstrated utilizing established (lexical) article relationships to facilitate the identification of relevant articles in an efficient and effective manner. Here we propose to enhance article relationships with background semantic knowledge derived from Unified Medical Language System (UMLS) concepts and ontologies. Methods We developed a pipelined semantic concepts representation process to represent articles from an SR into an optimized and enriched semantic space of UMLS concepts. Throughout the process, we leveraged concepts and concept relations encoded in biomedical ontologies (SNOMED-CT and MeSH) within the UMLS framework to prompt concept features of each article. Article relationships (similarities) were established and represented as a semantic article network, which was readily applied to assist with the article screening process. We incorporated the concept of active learning to simulate an interactive article recommendation process, and evaluated the performance on 15 completed SRs. We used work saved over sampling at 95% recall (WSS95) as the performance measure. Results We compared the WSS95 performance of our ontology-based semantic approach to existing lexical feature approaches and corpus-based semantic approaches, and found that we had better WSS95 in most SRs. We also had the highest average WSS95 of 43.81% and the highest total WSS95 of 657.18%. Conclusion We demonstrated using ontology-based semantics to facilitate the identification of relevant articles for SRs. Effective concepts and concept relations derived from UMLS ontologies can be utilized to establish article semantic relationships. Our approach provided a promising performance and can easily apply to any SR topics in the biomedical domain with generalizability.


title: Nonlinear and cooperative control of multiple hovercraft with input constraints

abstract: In this paper, we introduce an approach for distributed nonlinear control of multiple hovercraft-type underactuated vehicles with bounded and unidirectional inputs. First, a bounded nonlinear controller is given for stabilization and tracking of a single vehicle, using a cascade backstepping method. Then, this controller is combined with a distributed gradient-based control for multi-vehicle formation stabilization using formation potential functions previously constructed. The vehicles are used in the Caltech Multi-Vehicle Wireless Testbed (MVWT). We provide simulation and experimental results for stabilization and tracking of a single vehicle, and a simulation of stabilization of a six-vehicle formation, demonstrating that in all cases the control bounds and the control objective are satisfied.


title: Strabismus Recognition Using Eye-Tracking Data and Convolutional Neural Networks

abstract: Strabismus is one of the most common vision diseases that would cause amblyopia and even permanent vision loss. Timely diagnosis is crucial for well treating strabismus. In contrast to manual diagnosis, automatic recognition can significantly reduce labor cost and increase diagnosis efficiency. In this paper, we propose to recognize strabismus using eye-tracking data and convolutional neural networks. In particular, an eye tracker is first exploited to record a subject’s eye movements. A gaze deviation (GaDe) image is then proposed to characterize the subject’s eye-tracking data according to the accuracies of gaze points. The GaDe image is fed to a convolutional neural network (CNN) that has been trained on a large image database called ImageNet. The outputs of the full connection layers of the CNN are used as the GaDe image’s features for strabismus recognition. A dataset containing eye-tracking data of both strabismic subjects and normal subjects is established for experiments. Experimental results demonstrate that the natural image features can be well transferred to represent eye-tracking data, and strabismus can be effectively recognized by our proposed method.


title: Generalized Alpha-Beta Divergences and Their Application to Robust Nonnegative Matrix Factorization

abstract: We propose a class of multiplicative algorithms for Nonnegative Matrix Factorization (NMF) which are robust with respect to noise and outliers. To achieve this, we formulate a new family generalized divergences referred to as the Alpha-Beta-divergences (AB-divergences), which are parameterized by the two tuning parameters, alpha and beta, and smoothly connect the fundamental Alpha-, Beta- and Gamma-divergences. By adjusting these tuning parameters, we show that a wide range of standard and new divergences can be obtained. The corresponding learning algorithms for NMF are shown to integrate and generalize many existing ones, including the Lee-Seung, ISRA (Image Space Reconstruction Algorithm), EMML (Expectation Maximization Maximum Likelihood), Alpha-NMF, and Beta-NMF. Owing to more degrees of freedom in tuning the parameters, the proposed family of AB-multiplicative NMF algorithms is shown to improve robustness with respect to noise and outliers. The analysis illuminates the links of between AB-divergence and other divergences, especially Gamma- and Itakura-Saito divergences.


title: Augur: Mining Human Behaviors from Fiction to Power Interactive Systems

abstract: From smart homes that prepare coffee when we wake, to phones that know not to interrupt us during important conversations, our collective visions of HCI imagine a future in which computers understand a broad range of human behaviors. Today our systems fall short of these visions, however, because this range of behaviors is too large for designers or programmers to capture manually. In this paper, we instead demonstrate it is possible to mine a broad knowledge base of human behavior by analyzing more than one billion words of modern fiction. Our resulting knowledge base, Augur, trains vector models that can predict many thousands of user activities from surrounding objects in modern contexts: for example, whether a user may be eating food, meeting with a friend, or taking a selfie. Augur uses these predictions to identify actions that people commonly take on objects in the world and estimate a user's future activities given their current situation. We demonstrate Augur-powered, activity-based systems such as a phone that silences itself when the odds of you answering it are low, and a dynamic music player that adjusts to your present activity. A field deployment of an Augur-powered wearable camera resulted in 96% recall and 71% precision on its unsupervised predictions of common daily activities. A second evaluation where human judges rated the system's predictions over a broad set of input images found that 94% were rated sensible.


title: Efficient Analysis and Design of Novel SIW Leaky-Wave Antenna

abstract: A novel transverse equivalent network is developed in this letter to efficiently analyze a recently proposed leaky-wave antenna in substrate integrated waveguide (SIW) technology. For this purpose, precise modeling of the SIW posts for any distance between vias is essential to obtain accurate results. A detailed parametric study is performed resulting in leaky-mode dispersion curves as a function of the main geometrical dimensions of the antenna. Finally, design curves that directly provide the requested dimensions to synthesize the desired scanning response and leakage rate are reported and validated with experiments.


title: Comparing the Effectiveness of Penetration Testing and Static Code Analysis on the Detection of SQL Injection Vulnerabilities in Web Services

abstract: Web services are becoming business-critical components that must provide a non-vulnerable interface to the client applications. However, previous research and practice show that many web services are deployed with critical vulnerabilities. SQL Injection vulnerabilities are particularly relevant, as web services frequently access a relational database using SQL commands. Penetration testing and static code analysis are two well-know techniques often used for the detection of security vulnerabilities. In this work we compare how effective these two techniques are on the detection of SQL Injection vulnerabilities in web services code. To understand the strengths and limitations of these techniques, we used several commercial and open source tools to detect vulnerabilities in a set of vulnerable services. Results suggest that, in general, static code analyzers are able to detect more SQL Injection vulnerabilities than penetration testing tools. Another key observation is that tools implementing the same detection approach frequently detect different vulnerabilities. Finally, many tools provide a low coverage and a high false positives rate, making them a bad option for programmers.


title: SSRI augmentation of antipsychotic alters expression of GABAA receptor and related genes in PMC of schizophrenia patients

abstract: Clinical studies have shown that negative symptoms of schizophrenia unresponsive to antipsychotic given alone can improve after augmentation with SSRI antidepressant. Laboratory investigations into the mechanism of this synergism showed that co-administration of SSRI and antipsychotic produces changes in GABAA receptor and related systems, which differ from the effects of each drug alone. To examine the clinical relevance of these findings, the current study examined the effects of SSRI augmentation treatment on GABAA receptor and related systems in schizophrenia patients. Schizophrenia patients with high levels of negative symptoms unresponsive to antipsychotic treatment received add-on fluvoxamine (100 mg/d). Blood was taken before and 1, 3 and 6 wk after adding fluvoxamine and peripheral mono- nuclear cells (PMC) isolated. RNA encoding for GABAAb3, 5-HT2A, and 5-HT7 receptors, PKCb2, and brain-derived neurotrophic factor (BDNF) was assayed with real-time RT-PCR. Plasma BDNF protein was assayed using ELISA. Clinical symptoms were assessed with validated rating scales. We found significant increase in mRNA encoding for GABAAb3 and 5-HT2A, 5-HT7 receptors and BDNF and a reduction in PKCb2 mRNA. Plasma BDNF protein concentrations were increased. There were significant correlations among the genes. Clinical symptoms improved significantly. mRNA expression of PKCb2, 5-HT2A and 5-HT7 showed significant associations with clinical symptoms. Combined SSRI+antipsychotic treatment is associated with changes in GABAA receptor and in related signalling systems in patients. These changes may be part of the mechanism of clinically effective drug action and may prove to be biomarkers of pharmacological response.


title: Enhanced Channel Estimation and Codebook Design for Millimeter-Wave Communication

abstract: The existing channel estimation methods for millimeter-wave communications, e.g., hierarchical search and compressed sensing, either acquire only one single multipath component (MPC) or require considerably high training overhead. To realize fast yet accurate channel estimation, we propose a multipath decomposition and recovery approach in this paper. The proposed approach has two stages. In the first stage, instead of directly searching the real MPCs, we decompose each real MPC into several virtual MPCs and acquire the virtual MPCs by using the hierarchical search based on a normal-resolution codebook. Then, in the second stage, the real MPCs are recovered from the acquired virtual MPCs in the first stage, which turns out to be a sparse reconstruction problem, where the size of the dictionary matrix is greatly reduced by exploiting the results of the virtual multipath acquisition. Moreover, to make the proposed approach applicable for both analog and hybrid beamforming/combining devices with strict constant-modulus constraint, we particularly design a codebook for the hierarchical search by using an enhanced subarray technique, and the codebook is also applicable in other hierarchical search methods. Performance comparisons show that the proposed approach achieves a superior tradeoff between estimation performance and training penalty over the state-of-the-art alternatives.


title: A 28-GHz CMOS Broadband Single-Path Power Amplifier with 17.4-dBm P1dB for 5G Phased-Array

abstract: This paper reports a fully integrated broadband and linear power amplifier (PA) for 5G phased-array. Weakly-and strongly-coupled transformers are compared and analyzed in detail. The output strongly-coupled transformer is designed to transfer maximum power. The inter-stage weakly-coupled transformer is optimized to broaden the bandwidth. Besides, linearity is highly improved by operating the PA in deep class AB region. Designed and implemented in 65-nm CMOS process with 1V supply, the two-stage PA delivers a maximum small-signal gain of 19 dB. Maximum 1-dB compressed power (P 1dB ) of 17.4 dBm and saturated output power (P SAT ) of 18 dBm are measured at 28 GHz. The power-added efficiency (P AE ) at P 1dB is 26.5%. The measured P 1dB is above 16 dBm from 23 to 32 GHz, covering potential 5G bands worldwide around 28 GHz.


title: Human Body Posture Classification by a Neural Fuzzy Network and Home Care System Application

abstract: A new classification approach for human body postures based on a neural fuzzy network is proposed in this paper, and the approach is applied to detect emergencies that are caused by accidental falls. Four main body postures are used for posture classification, including standing, bending, sitting, and lying. After the human body is segmented from the background, the classification features are extracted from the silhouette. The body silhouette is projected onto horizontal and vertical axes, and then, a discrete Fourier transform is applied to each projected histogram. Magnitudes of significant Fourier transform coefficients together with the silhouette length-width ratio are used as features. The classifier is designed by a neural fuzzy network. The four postures can be classified with high accuracy according to experimental results. Classification results are also applicable to home care emergency detection of a person who suddenly falls and remains in the lying posture for a period of time due to experiments that were performed.


title: Linking Design-Time and Run-Time: A Graph-Based Uniform Workflow Provenance Model

abstract: Workflow is an important way to mashup reusable software services to create value-added data analytics services. Workflow provenance is core to understand how services and workflows behaved in the past, which knowledge can be used to provide a better recommendation. Existing workflow provenance management systems handle various types of provenance separately. A typical data science exploration scenario, however, calls for an integrated view of provenance and seamless transition among different types of provenance. In this paper, a graph-based, uniform provenance model is proposed to link together design-time and run-time provenance, by combining retrospective provenance, prospective provenance, and evolution provenance. Such a unified provenance model will not only facilitate workflow mining and exploration, but also facilitate workflow interoperability. The model is formalized into colored Petri nets for verification and monitoring management. A SQL-like query language is developed, which supports basic queries, recursive queries, and cross-provenance queries. To verify the effectiveness of our model, A web-based, collaborative workflow prototyping system is developed as a proof-of-concept. Experiments have been conducted to evaluate the effectiveness of the proposed SQL-like graph query against SQL query.


title: On the processing of regular and irregular forms of verbs and nouns: evidence from neuropsychology

abstract: Following acquired brain damage, a native English speaking patient (AW) encountered problems accessing phonology in speech production, while her ability to access word meaning appeared to be intact. In a series of tasks, AW was presented either with a verb, and was asked to produce its past tense or past participle (walk → “walked”), or with a noun, and was asked to produce its plural (glove → “gloves”). A stark dissociation was found: while AW responded accurately with regular forms of verbs (walked) and nouns (gloves), performance was significantly less accurate with irregular forms (found; children). The appearance of a selective deficit for irregular forms in conditions of impaired lexical access is in line with dual-mechanism accounts, which proposes that irregular forms are specified in the lexicon whereas regular forms are computed via rule-based mechanisms. In contrast, AW's data are problematic for connectionist accounts that do not posit separate mechanisms for processing regular and irregular forms, including the connectionist model recently proposed by Joanisse and Seidenberg (ProceedingsoftheNationalAcademyofSciencesUSA96 (1999) 7592) which successfully simulated a variety of earlier neuropsychological findings. Analyses of AW's responses shed light on further details of the representation and processing of regular and irregular inflected forms.


title: The Ripple Protocol Consensus Algorithm

abstract: While several consensus algorithms exist for the Byzantine Generals Problem, specifically as it pertains to distributed payment systems, many suffer from high latency induced by the requirement that all nodes within the network communicate synchronously. In this work, we present a novel consensus algorithm that circumvents this requirement by utilizing collectively-trusted subnetworks within the larger network. We show that the “trust” required of these subnetworks is in fact minimal and can be further reduced with principled choice of the member nodes. In addition, we show that minimal connectivity is required to maintain agreement throughout the whole network. The result is a low-latency consensus algorithm which still maintains robustness in the face of Byzantine failures. We present this algorithm in its embodiment in the Ripple Protocol.


title: PRDX6 Exacerbates Dopaminergic Neurodegeneration in a MPTP Mouse Model of Parkinson’s Disease

abstract: Peroxiredoxin 6 (PRDX6) is a bifunctional protein with both glutathione peroxidase (GPx) and calcium-independent phospholipase A2 (iPLA2) activities. Expression of PRDX6 has been detected in human Parkinson's disease (PD) and dementia patients. However, no study has described PRDX6 function in the dopaminergic neurodegeneration in PD. Herein, we investigated the effects of PRDX6 on 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced dopaminergic neurodegeneration using PRDX6 transgenic (Tg) mice. Immunohistochemistry (IHC) and Western blot data for tyrosine hydroxylase (TH) showed that PRDX6 Tg mice had much higher loss of dopaminergic neurons by MPTP administration compared to non-Tg mice, as well as there was much higher behavioral impairment and astrocyte activation in PRDX6 Tg mice. MPTP-induced GPx activity was not different between PRDX6 Tg mice and non-Tg mice, which is accompanied by hyperoxidation of PRDX6. While iPLA2 activity was increased in PRDX6 Tg mice followed by an increase in the level of ROS and 4-hydroxynonenal (4-HNE). Intriguingly, the expression pattern of PRDX6 showed similar distribution and co-localization with astrocytes, but not neuron in the mouse and human brain. Furthermore, we demonstrated that iPLA2 activity of PRDX6 induced astrocytic activation followed by increased proinflammatory cytokines (TNF-α and IL1-β), 4-HNE, and PRDX6 hyperoxidation in primary cultured astrocytes. Our findings provide novel insights for PRDX6 function on nigrostriatal dopaminergic neuronal system, and we suggest that PRDX6 has an important role in dopaminergic neurodegeneration of PD.


title: Measuring Infant Visual Acuity with Gaze Tracker Monitored Visual Fixation.

abstract: PURPOSE ::: To validate a method of measuring grating acuity with remote gaze tracking (GT) against a current clinical test of visual acuity (VA), the Teller Acuity Cards (TACs), as part of the development of an automated VA test for infants. ::: ::: ::: METHODS ::: Visual acuity for computer-generated horizontal square-wave gratings was determined from relative fixation time on a grating area compared with the background. In experiment 1, binocular VA was based on eye movements with a GT in 15 uncorrected myopic adults and compared with VA measured with subjective responses with the same stimuli and with the TACs. In experiment 2, binocular VA was determined in 19 typically developing infants aged 3 to 11 months on two visits with both the GT and TACs. ::: ::: ::: RESULTS ::: In adults, the mean difference between VA measured by the GT and TACs was 0.01 log cycles per degree (cpd) and the 95% limits of agreement were 0.11. One hundred percent of GT VA results were within 0.5 octave of the TACs' VAs. The mean difference between the GT and TACs for infants was 0.17 log cpd on both the first and second visit (95% limits of agreement, 0.42 and 0.47, respectively). The mean difference between test and retest for infant GT VA was 0.06 log cpd, and limits of agreement for repeatability were 0.48 log cpd. In infants, both the TACs and the GT had a reliability of 89% within less than or equal to 1 octave between visits. Gaze tracking VA improved with age and is in agreement with published norms. ::: ::: ::: CONCLUSIONS ::: The agreement between the TACs and GT in adults and infants validates the method of measuring grating acuity with the remote GT. These results demonstrate its potential for an automated test of infant VA.


title: From neural sentence summarization to headline generation: a coarse-to-fine approach

abstract: Headline generation is a task of abstractive text summarization, and previously suffers from the immaturity of natural language generation techniques. Recent success of neural sentence summarization models shows the capacity of generating informative, fluent headlines conditioned on selected recapitulative sentences. In this paper, we investigate the extension of sentence summarization models to the document headline generation task. The challenge is that extending the sentence summarization model to consider more document information will mostly confuse the model and hurt the performance. In this paper, we propose a coarse-to-fine approach, which first identifies the important sentences of a document using document summarization techniques, and then exploits a multi-sentence summarization model with hierarchical attention to leverage the important sentences for headline generation. Experimental results on a large real dataset demonstrate the proposed approach significantly improves the performance of neural sentence summarization models on the headline generation task.


title: A Two-Level Classification Approach for Detecting Clickbait Posts using Text-Based Features

abstract: The emergence of social media as news sources has led to the rise of clickbait posts attempting to attract users to click on article links without informing them on the actual article content. This paper presents our efforts to create a clickbait detector inspired by fake news detection algorithms, and our submission to the Clickbait Challenge 2017. The detector is based almost exclusively on text-based features taken from previous work on clickbait detection, our own work on fake post detection, and features we designed specifically for the challenge. We use a two-level classification approach, combining the outputs of 65 first-level classifiers in a second-level feature vector. We present our exploratory results with individual features and their combinations, taken from the post text and the target article title, as well as feature selection. While our own blind tests with the dataset led to an F-score of 0.63, our final evaluation in the Challenge only achieved an F-score of 0.43. We explore the possible causes of this, and lay out potential future steps to achieve more successful results.


title: --> Psychology, Community & Health pch.psychopen.eu | 2182-438X Empirical Articles Psychometric Properties of the Portuguese Version of the Treatment Self-Regulation Questionnaire for Physical Activity (TSRQ- PA)

abstract: Aim : The aim of the present study was to analyze the reliability and factorial validity of the Portuguese Version of the Treatment Self-Regulation Questionnaire for physical activity (TSRQ-PA). Method: In this study, 148 healthy adults (Mean age = 44.70, 96.6% women) and 89 chronic fatigue patients (Mean age = 47.39, 97.8% women) filled out questionnaires related to behaviour regulation style (TSRQ – PA) and physical activity. Results : The confirmatory factor analysis adjustment indices of a two-factor structure (Autonomous Regulation scale and Controlled Regulation scale) of the TSRQ-PA were satisfactory and internal consistency estimates were acceptable for both factors. A higher degree of autonomous behaviour regulation was significantly associated with higher levels of physical activity. Conclusion : These findings provide support for the validity and reliability of the TSRQ – PA for measuring behaviour regulation style for engaging in physical activity, in different settings.


title: Identification and control of dynamical systems using neural networks

abstract: It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis is on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are interconnected in novel configurations, and hence there is a real need to study them in a unified fashion. Simulation results reveal that the identification and adaptive control schemes suggested are practically feasible. Basic concepts and definitions are introduced throughout, and theoretical questions that have to be addressed are also described. >


title: Semantic embedding space for zero-shot action recognition

abstract: The number of categories for action recognition is growing rapidly. It is thus becoming increasingly hard to collect sufficient training data to learn conventional models for each category. This issue may be ameliorated by the increasingly popular “zero-shot learning” (ZSL) paradigm. In this framework a mapping is constructed between visual features and a human interpretable semantic description of each category, allowing categories to be recognised in the absence of any training data. Existing ZSL studies focus primarily on image data, and attribute-based semantic representations. In this paper, we address zero-shot recognition in contemporary video action recognition tasks, using semantic word vector space as the common space to embed videos and category labels. This is more challenging because the mapping between the semantic space and space-time features of videos containing complex actions is more complex and harder to learn. We demonstrate that a simple self-training and data augmentation strategy can significantly improve the efficacy of this mapping. Experiments on human action datasets including HMDB51 and UCF101 demonstrate that our approach achieves the state-of-the-art zero-shot action recognition performance.


title: Contributions of psychology to the understanding and treatment of people with chronic pain: Why it matters to ALL psychologists.

abstract: Chronic pain is a prevalent problem with significant costs to individuals, significant others, and society. In this article, which introduces the American Psychologist special issue on chronic pain, we provide an overview of the seminal contributions made by psychologists to our current understanding of this important problem. We also describe the primary treatments that have been developed based on psychological principles and models of pain, many of which have demonstrated efficacy for reducing pain and its impact on psychological and physical functioning. The article ends with an enumeration of directions for future research and clinical practice. We believe that the chronicle of psychology's role in improving our understanding and treatment of pain provides a model for how psychologists can have a significant influence on many fields, and that the models and approaches developed for understanding and treating pain may be of use to psychologists working in other areas. Thus, we think that chronic pain is an important area of study that offers insights about translational research for ALL psychologists.


An 8-day-old infant with poor perfusion and diaphoresis


title: Higher order learning with graphs

abstract: Recently there has been considerable interest in learning with higher order relations (i.e., three-way or higher) in the unsupervised and semi-supervised settings. Hypergraphs and tensors have been proposed as the natural way of representing these relations and their corresponding algebra as the natural tools for operating on them. In this paper we argue that hypergraphs are not a natural representation for higher order relations, indeed pairwise as well as higher order relations can be handled using graphs. We show that various formulations of the semi-supervised and the unsupervised learning problem on hypergraphs result in the same graph theoretic problem and can be analyzed using existing tools.


title: Authorship Analysis and Identification Techniques: A Review

abstract: in data mining are increasing over the time. Current world is of internet and everything is available over internet, which leads to criminal and malicious activity. So the identity of available content is now a need. Available content is always in the form of text data. Authorship analysis is the statistical study of linguistic and computational characteristics of the written documents of individuals. This paper describes review of various methods for authorship analysis and identification for a set of provided text. Surely research in authorship analysis and identification will continue and even increase over decades. In this article, we put our vision of future authorship analysis and identification with high performance and solution for behavioral feature extraction from set of text documents. adapted for the automatic analysis of the text. A Source Code (programming code) Author Profiles (SCAP) represents a new, highly accurate approach to source code authorship identification. Another section, text categorization based on keywords that may appear uniquely, may dual sequences like computer+science, genetic+algorithm etc. In discriminative syntactic tree approach, there is direct mining from a given set of syntactic trees. Later part of this paper is organized in research work done, challenges in feature extraction and classification.


title: The unreasonable effectiveness of the forget gate

abstract: Given the success of the gated recurrent unit, a natural question is whether all the gates of the long short-term memory (LSTM) network are necessary. Previous research has shown that the forget gate is one of the most important gates in the LSTM. Here we show that a forget-gate-only version of the LSTM with chrono-initialized biases, not only provides computational savings but outperforms the standard LSTM on multiple benchmark datasets and competes with some of the best contemporary models. Our proposed network, the JANET, achieves accuracies of 99% and 92.5% on the MNIST and pMNIST datasets, outperforming the standard LSTM which yields accuracies of 98.5% and 91%.


title: Finding Synonyms Using Automatic Word Alignment And Measures Of Distributional Similarity

abstract: There have been many proposals to extract semantically related words using measures of distributional similarity, but these typically are not able to distinguish between synonyms and other types of semantically related words such as antonyms, (co)hyponyms and hypernyms. We present a method based on automatic word alignment of parallel corpora consisting of documents translated into multiple languages and compare our method with a monolingual syntax-based method. The approach that uses aligned multilingual data to extract synonyms shows much higher precision and recall scores for the task of synonym extraction than the monolingual syntax-based approach.


title: Compact coplanar stripline-fed folded strip dipole antenna for millimeter energy combining

abstract: A compact folded dipole antenna for millimeter-wave (MMW) energy harvesting is proposed in this paper. The antenna consists of two folded arms excited by a coplanar stripline (CPS). A coplanar waveguide (CPW) to coplanar stripline (CPS) transformer is introduced for wide band operation. The antenna radiates from 33 GHz to 41 GHz with fractional bandwidth about 21.6%. The proposed antenna shows good radiation characteristics and low VSWR, lower than 2, as well as average antenna gain is around 5 dBi over the whole frequency range. The proposed dipole antenna shows about 49% length reduction. The simulated results using both Ansoft HFSS and CST Microwave Studio show a very good agreement between them.


title: Noninvasive Cuffless Estimation of Blood Pressure from Pulse Arrival Time and Heart Rate with Adaptive Calibration

abstract: We study the problem of noninvasively estimating Blood Pressure (BP) without using a cuff, which is attractive for continuous monitoring of BP over Body Area Networks. It has been shown that the Pulse Arrival Time (PAT) measured as the delay between the ECG peak and a point in the finger PPG waveform can be used to estimate systolic and diastolic BP. Our aim is to evaluate the performance of such a method using the available MIMIC database, while at the same time improve the performance of existing techniques. We propose an algorithm to estimate BP from a combination of PAT and heart rate, showing improvement over PAT alone. We also show how the method achieves recalibration using an RLS adaptive algorithm. Finally, we address the use case of ECG and PPG sensors wirelessly communicating to an aggregator and study the effect of skew and jitter on BP estimation.


title: Automatic generation and analysis of physics-based puzzle games

abstract: In this paper we present a method for the automatic generation of content for the physics-based puzzle game Cut The Rope. An evolutionary game generator is implemented which evolves the design of levels based on a context-free grammar. We present various measures for analyzing the expressivity of the generator and visualizing the space of content covered. We further perform an experiment on evolving playable content of the game and present and analyze the results obtained.


title: Representing Discourse Coherence: A Corpus-Based Study

abstract: This article aims to present a set of discourse structure relations that are easy to code and to develop criteria for an appropriate data structure for representing these relations. Discourse structure here refers to informational relations that hold between sentences in a discourse. The set of discourse relations introduced here is based on Hobbs (1985).We present a method for annotating discourse coherence structures that we used to manually annotate a database of 135 texts from the Wall Street Journal and the AP Newswire. All texts were independently annotated by two annotators. Kappa values of greater than 0.8 indicated good interannotator agreement.We furthermore present evidence that trees are not a descriptively adequate data structure for representing discourse structure: In coherence structures of naturally occurring texts, we found many different kinds of crossed dependencies, as well as many nodes with multiple parents. The claims are supported by statistical results from our hand-annotated database of 135 texts.


title: Semi-Supervised Boosting for Multi-Class Classification

abstract: Most semi-supervised learning algorithms have been designed for binary classification, and are extended to multi-class classification by approaches such as one-against-the-rest. The main shortcoming of these approaches is that they are unable to exploit the fact that each example is only assigned to one class. Additional problems with extending semi-supervised binary classifiers to multi-class problems include imbalanced classification and different output scales of different binary classifiers. We propose a semi-supervised boosting framework, termed Multi-Class Semi-Supervised Boosting (MCSSB) , that directly solves the semi-supervised multi-class learning problem. Compared to the existing semi-supervised boosting methods, the proposed framework is advantageous in that it exploits both classification confidence and similarities among examples when deciding the pseudo-labels for unlabeled examples. Empirical study with a number of UCI datasets shows that the proposed MCSSB algorithm performs better than the state-of-the-art boosting algorithms for semi-supervised learning.