title: A 20 W/Channel Class-D Amplifier With Near-Zero Common-Mode Radiated Emissions

abstract: A class-D amplifier that employs a new modulation scheme and associated output stage to achieve true filter-less operation is presented. It uses a new type of BD modulation that keeps the output common-mode constant, thereby removing a major contributor to radiated emissions, typically an issue for class-D amplifiers. The amplifier meets the FCC class B standard for radiated emissions without any LC filtering. It can accomplish this without any degradation to audio performance and while retaining high efficiency. THD+N is 0.19% at 1 kHz while supplying 5 W to an 8 Ohm load from a 12 V supply. Efficiency is 90% while providing 10 W under the same supply and load conditions. The new output stage occupies 1.8 mm2 per channel using the high voltage devices of a 0.25 ?m BCD process.


title: Design and Control of Modular Multilevel Converters for Battery Electric Vehicles

abstract: New advanced power conversion systems play an essential role in the extension of range and life of batteries. This paper proposes a new modular multilevel converter with embedded electrochemical cells that achieves very low cell unbalancing without traditional balancing circuits and a negligible harmonic content of the output currents. In this new topology, the cells are connected in series by means of half-bridge converters, allowing high flexibility for the discharge and recharge of the battery. The converter features a cell balancing control that operates on each individual arm of the converter to equalize the state of charge of the cells. The paper shows that the proposed control does not affect the symmetry of the three-phase voltage output, even for significantly unbalanced cells. The viability of the proposed converter for battery electric vehicles and the effectiveness of the cell balancing control are confirmed by numerical simulations and experiments on a kilowatt-size prototype.


title: Achieving scalability and expressiveness in an Internet-scale event notification service

abstract: This paper describes the design of S IENA, an Internet-scale event notification middleware service for distributed event-based applications deployed over wide-area networks. S IENA is responsible for selecting the notifications that are of interest to clients (as expressed in client subscriptions) and then delivering those notifications to the clients via access points. The key design challenge for S IENA is maximizing expressiveness in the selection mechanism without sacrificing scalability of the delivery mechanism. This paper focuses on those aspects of the design of S IENA that fundamentally impact scalability and expressiveness. In particular, we describe S IENA's data model for notifications, the covering relations that formally define the semantics of the data model, the distributed architectures we have studied for S IENA's implementation, and the processing strategies we developed to exploit the covering relations for optimizing the routing of notifications.


title: Assessment of maintainability metrics for object-oriented software system

abstract: Many organizations assess the maintainability of software systems before they are deployed. Object-oriented design has been shown to be a useful technique to develop and deliver quality software. Objectoriented metrics can be used to assess the maintainability of a software system. Various software metrics and models have been developed and described. This paper provides a review of this literature and the related state-of-the-art. It also proposes a maintainability model that is based on the analysis of the relationship between object-oriented metrics and maintainability.


title: CREDO: A military decision-support system based on credal networks

abstract: A software tool especially designed for military domains to create and query decision-support systems is presented. Credal networks, which are Bayesian networks whose parameters have the freedom to vary in convex sets, are used to model the relations among the system variables. A novel elicitation procedure of these sets, which allows the military experts to report their knowledge by purely qualitative judgements, is proposed. Two high-level fusion procedures to cope with multiple experts in this framework are also derived. All these features are supported by the software and demonstrated in an application to space security tested during the last NATO multinational experiment.


title: Distant Supervision for Cancer Pathway Extraction from Text

abstract: s by classifying candidate sentences with co-occurring protein pairs. Note that we do not know the gold annotation for the positive examples used in training, nor could the learner memorize them since no protein-specific features are in use. Training took five minutes and extraction took 30 minutes using 900 cores. This PubMed-scale extraction yields 1,491,373 regulation instances, with 838,255 unique relation triples. To assess the quality of the extraction, we sampled 300 extractions and manually annotated them. Table 3 summarizes our findings. Among the 300 sample extractions, 56 have wrong protein annotation, which is not surprising given that protein mentions are often highly ambiguous. Of the remaining 244 instances, 75 are correct, giving an end-to-end precision of 25% and a precision of 31% assuming gold protein annotation. Among the errors, 46 are actually correct regulation events, but in 21 of them the sign (positive regulation or negative regulation) is wrong or the sentence is ambiguous about it, and in the remaining 25 the causality direction is reversed. With this sample precision, we estimate that distant supervision from PID yields about 372,000 correct extractions, and 210,000 unique relation triples, which is an order of magnitude larger than PID. These results are promising and testify to the feasibility of this direction. Of course, there is still much room for improvement. Protein errors occur in about one fifth of extractions. Currently, protein extraction does not benefit from distant supervision and is done separately from event extraction. Joint learning of protein and event extraction with distant supervision could potentially produce large improvement in both tasks. Relation errors often occur for two proteins in paths that are in conjunction, as in the example in Table 3, which might potentially be avoided with better filtering criteria. Finally, distant supervision can be used in Table 4. Top ten most studied cancer types, along with the top ten genes for each type, both in the number of unique pathway extractions. Cancer Relations Top ten most studied genes Breast 27988 TP53, ESR1, MYBL2, BRCA1, ZFP36, EGFR, ZFP, ESR2, EGF, AKT Prostate 10981 CBX8, SLC22A3, AR, KLK3, EPHB2, TP53, TDRD7, NPEPPS, AKT, SERPINB6 Lung 9423 EGFR, TP53, KRAS, VEGFA, CASP, MMP2, AKT, EPHB2, CDH1, CEACAM5 Liver 8438 TP53, EPHB6, HCCS, AFP, MYLIP, CCL2, VEGFA, RELA, NFKB1, NA Colon 6092 TP53, APC, CTNNB1, AOM, TMED7, RELA, EGFG, NFKB1, SRC, PTGS2 Colorectal 5381 TP53, CTNNB1, APC, CEACAM5, KRAS, EGFR, MSI, CASP, VEGFA, PTGS2 Pancreatic 5178 INS, KRAS, EGFR, VEGFA, CEACAM5, TNF, AURKA, MIA, RELA, NFKB1 Ovarian 4331 LPA, TP53, EGFR, VEGFA, AKT, MMP, MUC16, BRCA1, BARX2, IFNG Skin 4324 SERPINB3, TP53, KRT13, NFKB1, RELA, CD4, VIM, TNF, IL2, CD68 Brain 3988 RIPK1, CCDC88A, TP53, CSF2, NCAM1, AFP, MGMT, EGFR, ELAVL1, AKT Table 5. Top ten most studied cancer types, along with top genes ranked by association score. Cancer Top ten most significantly associated genes Breast ESR1, BRCA1, ESR2, BRCA2, PGR, CYP19A1, ERBB2, EGF, IGF1, KRAS Prostate CBX8, KLK3, NPEPPS, AR, DYNLL1, SERPINB6, ERG, DPT, TMPRSS2, FOLH1 Lung EGFR, KRAS, ALK, PCSK9, CBX8, ARCN1, KLK3, BRCA1, F7, TTF1 Liver HCCS, EPHB6, AFP, TRIM26, HSPG2, ADAM17, LDLR, DNLZ, ALB, CCL15 Colon AOM, DLD, CEACAM5, DDX53, APC, CTNNB1, GAST, PPARG, WNT16, SELE Colorectal KRAS, APC, MSI2, CTNNB1, MSI1, CEACAM5, MRC1, BRAF, FAP, MLH1 Pancreatic INS, GCG, MIA, SST, CCK, KRAS, ZGLP1, PDX1, PRSS27, SMAD4 Ovarian BRCA2, MUC16, BRCA1, LPA, DIRAS3, BRCA3, ARID1A, HEY1, GNRHR, ABCB1 Skin SERPINB3, TNFRSF8, COL1A1, CMM, MLANA, EGFR, MC1R, CPD, CD8A, MCC Brain GFAP, MGMT, IDH1, MS, SMS, NEFH, KIAA1549, CSF2, GSC, NAA60 an integrative loop for eCuration:23 distant supervision produces initial extractions, eCurators then verify them via an online interface such as Literome,20 fixing errors by a click of buttons, which is much more efficient than annotation from scratch. The feedback could be fed back into distant supervision via online learning and used to continuously improve extraction quality. Active learning can also be incorporated by prioritizing the least confident extractions for annotator verification. 3.3. Cancer Pathway Analysis With cancer contexts identified by MeSH terms (Section 2.6) and PubMed extractions produced from distant supervision (Section 3.2), we conducted an exploratory analysis to survey the research landscape and findings on cancer pathways. Cancer Pathway Research Among the 1.5 million pathway extractions, 150,379 occur in the context of cancer, or about 10%; among the 838 thousand unique pathway relations, 108,373 occur in the context of cancer, or about 13%. Table 4 shows the top ten most studied cancer types, along with the top ten genes for each type, both in the number of unique pathway relations found in our extraction. Not surprisingly, many well-known cancer genes are in the list, such as TP53 and EGFR. Overall, the top ten most studied genes for cancers are: TP53, EGFR, VEGFA, CBX8, ESR1, SLC22A3, AKT, MYLIP, EGF, EPHB2. For non-cancer context: INS, TNF, CA2, TCF, CD4, TP53, IRF6, EPHB2, CALM3, CASP. Nasopharyngeal Prostatic Breast Ovarian Thyroid Kidney Pituitary Liver Experimental Liver Bone Lung Skin Pancreatic Colorectal Colonic Hereditary Nonpolyposis Colorectal Stomach Urinary Bladder Adrenal Gland Mouth Laryngeal Thymus Parathyroid Salivary Gland IN S EG FR KR AS BR C A2 BR C A1 PG R C YP 19 A1 ES R 2 ES R 1 R B1 KL K3 C BX 8 PR L G H 1 N G F AR ER G D PT DY N LL 1


title: Long-Term Follow-Up Radiologic and Clinical Evaluation of Cylindrical Cage for Anterior Interbody Fusion in Degenerative Cervical Disc Disease

abstract: Objective ::: Various procedures have been introduced for anterior interbody fusion in degenerative cervical disc disease including plate systems with autologous iliac bone, carbon cages, and cylindrical cages. However, except for plate systems, the long-term results of other methods have not been established. In the present study, we evaluated radiologic findings for cylindrical cervical cages over long-term follow up periods.


title: Competitive influence maximization in social networks

abstract: Social networks often serve as a medium for the diffusion of ideas or innovations. An individual's decision whether to adopt a product or innovation will be highly dependent on the choices made by the individual's peers or neighbors in the social network. In this work, we study the game of innovation diffusion with multiple competing innovations such as when multiple companies market competing products using viral marketing. Our first contribution is a natural and mathematically tractable model for the diffusion of multiple innovations in a network. We give a (1-1/e) approximation algorithm for computing the best response to an opponent's strategy, and prove that the "price of competition" of this game is at most 2. We also discuss "first mover" strategies which try to maximize the expected diffusion against perfect competition. Finally, we give an FPTAS for the problem of maximizing the influence of a single player when the underlying graph is a tree.


title: Adaptive shape prior in graph cut segmentation

abstract: In this paper, we propose a novel method to adaptively apply shape prior in graph cut segmentation. By incorporating shape priors in an adaptive way, we introduce a robust way to harness shape prior in graph cut segmentation. Since traditional graph cut approaches with shape prior may fail in cases where parameters for shape prior term are not set appropriately, incorporation of shape priors adaptively within this framework mitigates these problems. To address this issue, we propose to adaptively apply shape prior based on a shape probability map, defined to reflect the need of shape prior at each location of an image. We show that the proposed method can be easily applied to existing algorithms of graph cut segmentation with shape prior, such as level set based shape prior method, and star shape prior graph cut. We validate our method in various types of images corrupted by significant noise and intensity inhomogeneities. Convincing results are obtained.


title: Learning to Shift the Polarity of Words for Sentiment Classification

abstract: We propose a machine learning based method of sentiment classification of sentences using word-level polarity. The polarities of words in a sentence are not always the same as that of the sentence, because there can be polarity-shifters such as negation expressions. The proposed method models the polarity-shifters. Our model can be trained in two different ways: word-wise and sentence-wise learning. In sentence-wise learning, the model can be trained so that the prediction of sentence polarities should be accurate. The model can also combined with features used in previous work such as bag-of-words and n-grams. We empirically show that our method improves the performance of sentiment classification of sentences especially when we have only small amount of training data.


title: Unsupervised and Efficient Vocabulary Expansion for Recurrent Neural Network Language Models in ASR

abstract: In automatic speech recognition (ASR) systems, recurrent neural network language models (RNNLM) are used to rescore a word lattice or N-best hypotheses list. Due to the expensive training, the RNNLM's vocabulary set accommodates only small shortlist of most frequent words. This leads to suboptimal performance if an input speech contains many out-of-shortlist (OOS) words. An effective solution is to increase the shortlist size and retrain the entire network which is highly inefficient. Therefore, we propose an efficient method to expand the shortlist set of a pretrained RNNLM without incurring expensive retraining and using additional training data. Our method exploits the structure of RNNLM which can be decoupled into three parts: input projection layer, middle layers, and output projection layer. Specifically, our method expands the word embedding matrices in projection layers and keeps the middle layers unchanged. In this approach, the functionality of the pretrained RNNLM will be correctly maintained as long as OOS words are properly modeled in two embedding spaces. We propose to model the OOS words by borrowing linguistic knowledge from appropriate in-shortlist words. Additionally, we propose to generate the list of OOS words to expand vocabulary in unsupervised manner by automatically extracting them from ASR output.


Analytical and practical analysis of switched-capacitor dc-dc converters


title: A Novel Automatic Facial Expression Recognition Method Based on AAM

abstract: This paper proposes anovel method to recognize facial expression through ActiveAppearance Model (AAM)to extract facial regions based on Facial Action CodingSystem (FACS). Itis composed of three parts: extractionof facial regions based on AAM,extraction of facial featuresby Gabor wavelettransformation, and expressionrecognition through Support Vector Machines (SVMs).AAM has better performance thanother methodsin eliminations of the influenceof different facialregion size, head pose and lighting condition and thus can effectively increase the recognitionaccuracy. Therefore it is usedto extract facial regions before extracting features by Gabor wavelettransformation. Finally, SVMsis appliedto recognize expression for its advantage of solvingthe problems of small sample size and overfitting. The feasibility and effectiveness of this method are evaluated and verified by experiments, and satisfactoryresults are achieved.


title: A New Low-Light Image Enhancement Algorithm Using Camera Response Model

abstract: Low-light images are not conducive to human observation and computer vision algorithms due to their low visibility. To solve this problem, many image enhancement techniques have been proposed. However, existing techniques inevitably introduce color and lightness distortion when increasing visibility. To lower the distortion, we propose a novel enhancement method using the response characteristics of cameras. First, we investigate the relationship between two images with different exposures to obtain an accurate camera response model. Then we borrow the illumination estimation techniques to estimate the exposure ratio map. Finally, we use our camera response model to adjust each pixel to its desired exposure according to the estimated exposure ratio map. Experiments show that our method can obtain enhancement results with less color and lightness distortion compared to several state-of-the-art methods.


title: Assessing wine quality using a decision tree

abstract: Even though wine-drinkers generally agree that wines may be ranked by quality, wine-tasting is famously subjective. There have been many attempts to construct a more methodical approach to the assessment of wines. We propose a method of assessing wine quality using a decision tree, and test it against the wine-quality dataset from the UC Irvine Machine Learning Repository. Results are 60% in agreement with traditional assessment techniques.


title: Managing work-life boundaries with mobile technologies: An interpretive study of mobile work practices

abstract: Purpose – The purpose of this paper is to explore the role that mobile technologies play in mobile workers’ efforts to manage the boundaries between work and non-work domains. Previous theories of work-life boundary management frame boundary management strategies as a range between the segmentation and integration of work-life domains, but fail to provide a satisfactory account of technology’s role. Design/methodology/approach – The authors apply the concept of affordances, defined as the relationship between users’ abilities and features of mobile technology, in two field studies of a total of 25 mobile workers who used a variety of mobile devices and services. Findings – The results demonstrate that the material features of mobile technologies offer five specific affordances that mobile workers use in managing work-life boundaries: mobility, connectedness, interoperability, identifiability and personalization. These affordances persist in their influence across time, despite their connection to differen...


title: A pediatric robotic thumb exoskeleton for at-home rehabilitation: The Isolated Orthosis for Thumb Actuation (IOTA)

abstract: In this paper, we present the design of a thumb exoskeleton for pediatric at-home rehabilitation. Pediatric disorders, such as cerebral palsy (CP) and stroke, can result in thumb in palm deformity greatly limiting hand function. This not only limits children's ability to perform activities of daily living but also limits important motor skill development. Specifically, the device, dubbed IOTA (Isolated Orthosis for Thumb Actuation) is a 2-DOF thumb exoskeleton that can actuate the carpometacarpal (CMC) and metacarpalphalangeal (MCP) joints through ranges of motion required for activities of daily living. The device consists of a lightweight hand-mounted mechanism that can be custom secured and aligned to the wearer. The mechanism is actuated via flexible cables that connect to a portable control box. Embedded encoders and bend sensors monitor the two degrees of freedom of the thumb and flexion/extension of the wrist. Using this platform, a number of control modes can be implemented that will enable the device to be intuitively controlled by a patient to assist with opposition grasp, fine motor control, and ultimately facilitate motor recovery. We envision this at-home device augmenting the current in-clinic therapy and enabling tele-rehabilitation where a clinician can remotely monitor a patient's usage and performance.


title: A 22 nm 15-Core Enterprise Xeon® Processor Family

abstract: This paper describes a 4.3B transistors, 15-cores, 30-threads enterprise Xeon® processor with a 37.5 MB shared L3 cache implemented in a 22 nm 9M Hi-K metal gate tri-gate process. A modular floorplan methodology enables easy chops to 10 and 6 cores. Multiple clock and voltage domains are used to reduce power consumption. The clock distribution uses a single PLL per column to save power and minimize deskew crossing points. Integrated PCIe Gen3 and Quick Path Interconnect® (QPI) ports operate at 8GT/s. The 4-channel memory interface supports both 1866 MT/s DDR3 and a new memory buffer interface running at 2667 MT/s on the same pins. The core, cache and I/O recovery techniques improve manufacturing yields and enable multiple product flavors from the same silicon die.


title: Forecasting user visits for online display advertising

abstract: Online display advertising is a multi-billion dollar industry where advertisers promote their products to users by having publishers display their advertisements on popular Web pages. An important problem in online advertising is how to forecast the number of user visits for a Web page during a particular period of time. Prior research addressed the problem by using traditional time-series forecasting techniques on historical data of user visits; (e.g., via a single regression model built for forecasting based on historical data for all Web pages) and did not fully explore the fact that different types of Web pages and different time stamps have different patterns of user visits. In this paper, we propose a series of probabilistic latent class models to automatically learn the underlying user visit patterns among multiple Web pages and multiple time stamps. The last (and the most effective) proposed model identifies latent groups/classes of (i) Web pages and (ii) time stamps with similar user visit patterns, and learns a specialized forecast model for each latent Web page and time stamp class. Compared with a single regression model as well as several other baselines, the proposed latent class model approach has the capability of differentiating the importance of different types of information across different classes of Web pages and time stamps, and therefore has much better modeling flexibility. An extensive set of experiments along with detailed analysis carried out on real-world data from Yahoo! demonstrates the advantage of the proposed latent class models in forecasting online user visits in online display advertising.


title: Anticipating human actions for collaboration in the presence of task and sensor uncertainty

abstract: A representation for structured activities is developed that allows a robot to probabilistically infer which task actions a human is currently performing and to predict which future actions will be executed and when they will occur. The goal is to enable a robot to anticipate collaborative actions in the presence of uncertain sensing and task ambiguity. The system can represent multi-path tasks where the task variations may contain partially ordered actions or even optional actions that may be skipped altogether. The task is represented by an AND-OR tree structure from which a probabilistic graphical model is constructed. Inference methods for that model are derived that support a planning and execution system for the robot which attempts to minimize a cost function based upon expected human idle time. We demonstrate the theory in both simulation and actual human-robot performance of a two-waybranch assembly task. In particular we show that the inference model can robustly anticipate the actions of the human even in the presence of unreliable or noisy detections because of its integration of all its sensing information along with knowledge of task structure.


title: Using the Triangle Inequality to Accelerate k-Means

abstract: The k-means algorithm is by far the most widely used method for discovering clusters in data. We show how to accelerate it dramatically, while still always computing exactly the same result as the standard algorithm. The accelerated algorithm avoids unnecessary distance calculations by applying the triangle inequality in two different ways, and by keeping track of lower and upper bounds for distances between points and centers. Experiments show that the new algorithm is effective for datasets with up to 1000 dimensions, and becomes more and more effective as the number k of clusters increases. For k ≥ 20 it is many times faster than the best previously known accelerated k-means method.


title: Using neural networks in reliability prediction

abstract: It is shown that neural network reliability growth models have a significant advantage over analytic models in that they require only failure history as input and not assumptions about either the development environment or external parameters. Using the failure history, the neural-network model automatically develops its own internal model of the failure process and predicts future failures. Because it adjusts model complexity to match the complexity of the failure history, it can be more accurate than some commonly used analytic models. Results with actual testing and debugging data which suggest that neural-network models are better at endpoint predictions than analytic models are presented. >


title: A new optimization method: Dolphin echolocation

abstract: Nature has provided inspiration for most of the man-made technologies. Scientists believe that dolphins are the second to humans in smartness and intelligence. Echolocation is the biological sonar used by dolphins and several kinds of other animals for navigation and hunting in various environments. This ability of dolphins is mimicked in this paper to develop a new optimization method. There are different meta-heuristic optimization methods, but in most of these algorithms parameter tuning takes a considerable time of the user, persuading the scientists to develop ideas to improve these methods. Studies have shown that meta-heuristic algorithms have certain governing rules and knowing these rules helps to get better results. Dolphin echolocation takes advantages of these rules and outperforms many existing optimization methods, while it has few parameters to be set. The new approach leads to excellent results with low computational efforts.


title: Retrieval practice over the long term: Should spacing be expanding or equal-interval?

abstract: If multiple opportunities are available to review to-be-learned material, should a review occur soon after initial study and recur at progressively expanding intervals, or should the reviews occur at equal intervals? Landauer and Bjork (1978) argued for the superiority of expanding intervals, whereas more recent research has often failed to find any advantage. However, these prior studies have generally compared expanding versus equal-interval training within a single session, and have assessed effects only upon a single final test. We argue that a more generally important goal would be to maintain high average performance over a considerable period of training. For the learning of foreign vocabulary spread over four weeks, we found that expanding retrieval practice (i.e., sessions separated by increasing numbers of days) produced recall equivalent to that from equal-interval practice on a final test given eight weeks after training. However, the expanding schedule yielded much higher average recallability over the whole training period.


title: Design and evaluation of dynamic optimizations for a Java just-in-time compiler

abstract: The high performance implementation of Java Virtual Machines (JVM) and Just-In-Time (JIT) compilers is directed toward employing a dynamic compilation system on the basis of online runtime profile information. The trade-off between the compilation overhead and performance benefit is a crucial issue for such a system. This article describes the design and implementation of a dynamic optimization framework in a production-level Java JIT compiler, together with two techniques for profile-directed optimizations: method inlining and code specialization. Our approach is to employ a mixed mode interpreter and a three-level optimizing compiler, supporting level-1 to level-3 optimizations, each of which has a different set of trade-offs between compilation overhead and execution speed. A lightweight sampling profiler operates continuously during the entire period while applications are running to monitor the programs' hot spots. Detailed information on runtime behavior can be collected by dynamically generating instrumentation code that is installed to and uninstalled from the specified recompilation target code. Value profiling with this instrumentation mechanism allows fully automatic profile-directed method inlining and code specialization to be performed on the basis of call site information or specific parameter values at the higher optimization levels. The experimental results show that our approach offers high performance and low compilation overhead in both program startup and steady state measurements in comparison to the previous systems. The two profile-directed optimization techniques contribute significant portions of the improvements.


title: The Google effect in doctoral theses

abstract: It is often said that successive generations of researchers face an increasing educational burden due to knowledge accumulation. On the other hand, technological advancement over time can improve the productivity of researchers and even change their cognitive processes. This paper presents a longitudinal study (2004–2011) of citation behavior in doctoral theses at the Massachusetts Institute of Technology’s Department of Electrical Engineering and Computer Science. It is found that the number of references cited has increased over the years. At the same time, there has been a decrease in the length of time in the doctoral program and a relative constancy in the culture of the department. This suggests that students are more productive in facing an increased knowledge burden, and indeed seem to encode prior literature as transactive memory to a greater extent, as evidenced by the greater use of older literature.


title: Large-scale integration of hybrid method digital subscriber loops

abstract: This paper reports on studies concerning the feasibility of large-scale integrated realization of the circuits needed to provide hybrid-mode full-duplex digital transmission at 80 kbits/s or higher rates over standard local telephone loops. Alternative means of achieving the required 60 dB or so of echo cancellation have been studied in detail. The conclusion is that a combination of analog and digital circuit techniques permit practical MOSLSI realization of the complete modem, including filters, echo canceller, timing recovery, and A/D and D/A converters, without need for external circuit elements, trimming, or adjustments. The preferred system configuration has been evaluated by means of analysis, simulation, and laboratory and field measurements. A complete full duplex system, including an experimental NMOS integrated circuit echo canceller, was built and tested. Measurements showed a bit error rate lower than l0^{-8} with line attenuation up to 40 dB, operating at 80 kbits/s. We conclude that a fully integrated MOSLSI ciruit to implement all functions for a hybrid-mode digital local loop is entirely feasible.


title: Rate-optimal graphon estimation

abstract: Network analysis is becoming one of the most active research areas in statistics. Significant advances have been made recently on developing theories, methodologies and algorithms for analyzing networks. However, there has been little fundamental study on optimal estimation. In this paper, we establish optimal rate of convergence for graphon estimation. For the stochastic block model with $k$ clusters, we show that the optimal rate under the mean squared error is $n^{-1}\log k+k^2/n^2$. The minimax upper bound improves the existing results in literature through a technique of solving a quadratic equation. When $k\leq\sqrt{n\log n}$, as the number of the cluster $k$ grows, the minimax rate grows slowly with only a logarithmic order $n^{-1}\log k$. A key step to establish the lower bound is to construct a novel subset of the parameter space and then apply Fano's lemma, from which we see a clear distinction of the nonparametric graphon estimation problem from classical nonparametric regression, due to the lack of identifiability of the order of nodes in exchangeable random graph models. As an immediate application, we consider nonparametric graphon estimation in a H\"{o}lder class with smoothness $\alpha$. When the smoothness $\alpha\geq1$, the optimal rate of convergence is $n^{-1}\log n$, independent of $\alpha$, while for $\alpha\in(0,1)$, the rate is $n^{-2\alpha/(\alpha+1)}$, which is, to our surprise, identical to the classical nonparametric rate.


title: Discovering Structure in the Universe of Attribute Names

abstract: Recently, search engines have invested significant effort to answering entity--attribute queries from structured data, but have focused mostly on queries for frequent attributes. In parallel, several research efforts have demonstrated that there is a long tail of attributes, often thousands per class of entities, that are of interest to users. Researchers are beginning to leverage these new collections of attributes to expand the ontologies that power search engines and to recognize entity--attribute queries. Because of the sheer number of potential attributes, such tasks require us to impose some structure on this long and heavy tail of attributes. This paper introduces the problem of organizing the attributes by expressing the compositional structure of their names as a rule-based grammar. These rules offer a compact and rich semantic interpretation of multi-word attributes, while generalizing from the observed attributes to new unseen ones. The paper describes an unsupervised learning method to generate such a grammar automatically from a large set of attribute names. Experiments show that our method can discover a precise grammar over 100,000 attributes of {\sc Countries} while providing a 40-fold compaction over the attribute names. Furthermore, our grammar enables us to increase the precision of attributes from 47\% to more than 90\% with only a minimal curation effort. Thus, our approach provides an efficient and scalable way to expand ontologies with attributes of user interest.


title: Adversarially Tuned Scene Generation

abstract: Generalization performance of trained computer vision (CV) systems that use computer graphics (CG) generated data is not yet effective due to the concept of domain-shift between virtual and real data. Although simulated data augmented with a few real-world samples has been shown to mitigate domain shift and improve transferability of trained models, guiding or bootstrapping the virtual data generation with the distributions learnt from target real world domain is desired, especially in the fields where annotating even few real images is laborious (such as semantic labeling, optical flow, and intrinsic images etc.). In order to address this problem in an unsupervised manner, our work combines recent advances in CG, which aims at generating stochastic scene layouts using large collections of 3D object models, and generative adversarial training, which aims at training generative models by measuring discrepancy between generated and real data in terms of their separability in the space of a deep discriminatively-trained classifier. Our method uses iterative estimation of the posterior density of prior distributions for a generative graphical model. This is done within a rejection sampling framework. Initially, we assume uniform distributions as priors over parameters of a scene described by a generative graphical model. As iterations proceed the uniform prior distributions are updated sequentially to distributions that are closer to the unknown distributions of target data. We demonstrate the utility of adversarially tuned scene generation on two real world benchmark datasets (CityScapes and CamVid) for traffic scene semantic labeling with a deep convolutional net (DeepLab). We obtained performance improvements by 2.28 and 3.14 points on the IoU metric between the DeepLab models trained on simulated sets prepared from the scene generation models before and after tuning to CityScapes and CamVid respectively.


title: Towards Multi-Agent Communication-Based Language Learning

abstract: We propose an interactive multimodal framework for language learning. Instead of being passively exposed to large amounts of natural text, our learners (implemented as feed-forward neural networks) engage in cooperative referential games starting from a tabula rasa setup, and thus develop their own language from the need to communicate in order to succeed at the game. Preliminary experiments provide promising results, but also suggest that it is important to ensure that agents trained in this way do not develop an adhoc communication code only effective for the game they are playing


title: Differential Impairment of Remembering the Past and Imagining Novel Events after Thalamic Lesions

abstract: Vividly remembering the past and imagining the future (mental time travel) seem to rely on common neural substrates and mental time travel impairments in patients with brain lesions seem to encompass both temporal domains. However, because future thinking-or more generally imagining novel events-involves the recombination of stored elements into a new event, it requires additional resources that are not shared by episodic memory. We aimed to demonstrate this asymmetry in an event generation task administered to two patients with lesions in the medial dorsal thalamus. Because of the dense connection with pFC, this nucleus of the thalamus is implicated in executive aspects of memory (strategic retrieval), which are presumably more important for future thinking than for episodic memory. Compared with groups of healthy matched control participants, both patients could only produce novel events with extensive help of the experimenter (prompting) in the absence of episodic memory problems. Impairments were most pronounced for imagining personal fictitious and impersonal events. More precisely, the patients' descriptions of novel events lacked content and spatio-temporal relations. The observed impairment is unlikely to trace back to disturbances in self-projection, scene construction, or time concept and could be explained by a recombination deficit. Thus, although memory and the imagination of novel events are tightly linked, they also partly rely on different processes.


title: Design and development of a glove for post-stroke hand rehabilitation

abstract: Numerous endeavors have been made in creating automated post-stroke hand rehabilitation and assistive devices over the last decade. An extensive variety of actuators have been investigated in directing the patients' finger to maneuver in a natural path. Despite the fact that their execution is considered satisfactory for some typical undertakings, there is ample room for improvement by applying late advances in lightweight, soft and flexible bending actuator. This paper presents an innovative design of a skinny rehabilitation glove by using a bi-directional soft pneumatic bending actuator. It describes the prototyping of a complete hand rehabilitation system, including the design of glove, control kit and user interface. Some preliminary test results demonstrate good repeatability and controllability of the prototype. Attributed to its light weight, low profile, anthropomorphic form, satisfactory output force and angular displacement in both contraction and extension, it shows a pragmatic solution to an automatic robot-aided post-stroke hand rehabilitation system.


title: Highly secure image steganography algorithm using curvelet transform and DCT encryption

abstract: This paper presents a highly secure data hiding system (a.k.a. Steganography) in a cover image using the low frequency Curvelet domain. The contribution of the suggested technique is its high security, because it is using four secret keys (encryption key, two shuffling keys, data hiding key) and using only the low frequency component of Curvelet domain. The use of low frequency component of Curvelet transform in steganography provides a number of advantages compared to other techniques such as: 1) Computation time reduction and, 2) Curvelet transform are designed to handle curves discontinuities using only a small number of coefficients, so hiding in the low frequency components will not affect edges coefficients, which produces better stego object quality.


title: High-Speed Parallel Decimal Multiplication with Redundant Internal Encodings

abstract: The decimal multiplication is one of the most important decimal arithmetic operations which have a growing demand in the area of commercial, financial, and scientific computing. In this paper, we propose a parallel decimal multiplication algorithm with three components, which are a partial product generation, a partial product reduction, and a final digit-set conversion. First, a redundant number system is applied to recode not only the multiplier, but also multiples of the multiplicand in signed-digit (SD) numbers. Furthermore, we present a multioperand SD addition algorithm to reduce the partial product array. Finally, a digit-set conversion algorithm with a hybrid prefix network to decrease the number of the logic gates on the critical path is discussed. An analysis of the timing delay and an HDL model synthesized under 90 nm technology show that by considering the tradeoff of designs among three components, the overall delay of the proposed 16 × 16-digit multiplier takes about 11 percent less timing delay with 2 percent less area compared to the current fastest design.


title: Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks.

abstract: Convolutional neural networks (CNNs) have recently been applied to the optical flow estimation problem. As training the CNNs requires sufficiently large ground truth training data, existing approaches resort to synthetic, unrealistic datasets. On the other hand, unsupervised methods are capable of leveraging real-world videos for training where the ground truth flow fields are not available. These methods, however, rely on the fundamental assumptions of brightness constancy and spatial smoothness priors which do not hold near motion boundaries. In this paper, we propose to exploit unlabeled videos for semi-supervised learning of optical flow with a Generative Adversarial Network. Our key insight is that the adversarial loss can capture the structural patterns of flow warp errors without making explicit assumptions. Extensive experiments on benchmark datasets demonstrate that the proposed semi-supervised algorithm performs favorably against purely supervised and semi-supervised learning schemes.


title: Multi-Level Chireix Outphasing GaN MMIC PA

abstract: This paper presents the design and performance of a 9.7 GHz GaN MMIC Chireix outphasing PA with discrete supply modulation. This architecture is referred to as Multi-Level Chireix Outphasing (ML- CO). The internal PAs include class-F harmonic terminations, while the Chireix combiner provides the fundamental frequency load modulation. A peak output power of 37 dBm (5W) is achieved, while a peak ηtot of 60.2 % is reached at 35.7 dBm. The average total efficiency calculated for a 6 dB PAR QPSK signal is 48.1%, a 15.5 point improvement over the constant supply case.


title: Anti-falling tree climbing mechanism optimization

abstract: This paper presents an improved anti-falling tree climbing mechanism. A study and comparison was made on an original tree climbing robotic system published in reference. This optimized robotic system only relies on one stepper to achieve the anti-falling and anti-jamming functionality under either static or dynamic situations. This improved design feature reduces the total robot weight and enhances the control system robustness. Moreover, to achieve the robot spiral climbing morphology and decrease the servomotor rotational frictional forces, a special servomotor module and a bearing supporting mechanism are designed and implemented. The optimized mechanism design of the climbing robot is modelled in SolidWorks and the climbing process is simulated using SimMechanics. The simulation outcome of the initial and improved design shows that the new design has higher climbing speed and lighter weight. Physical prototype experiment matches with the simulation outcome and verifies the feasibility and effectiveness of this redesigned tree climbing mechanism.


title: Optimal design of a permanent magnet spring

abstract: This paper describes the optimization and the performance evaluation of a Permanent Magnet spring. A dedicated semi-analytical model of the device is developed allowing for fast and accurate evaluation of the force vs. displacement characteristic of an arbitrary arrangement of coaxial ring-shaped PMs. This model is used to define the cost function to find the optimal PMs configuration which realizes a magnetic spring with a given characteristic. A stochastic, evolutionary, global optimization algorithm, based on a modified version of the self-organizing map is used. Once the optimal configuration is defined, the electromagnetic damping obtained by properly positioned conductive materials, will be investigated.


title: Offline and online evaluation of news recommender systems at swissinfo.ch

abstract: We report on the live evaluation of various news recommender systems conducted on the website swissinfo.ch. We demonstrate that there is a major difference between offline and online accuracy evaluations. In an offline setting, recommending most popular stories is the best strategy, while in a live environment this strategy is the poorest. For online setting, context-tree recommender systems which profile the users in real-time improve the click-through rate by up to 35%. The visit length also increases by a factor of 2.5. Our experience holds important lessons for the evaluation of recommender systems with offline data as well as for the use of the click-through rate as a performance indicator.


Smart Transmitters and Receivers for Underwater Free-Space Optical Communication – A Review


title: Using Second-Order Power Analysis to Attack DPA Resistant Software

abstract: Under a simple power leakage model based on Hamming weight, a software implementation of a data-whitening routine is shown to be vulnerable to a first-order Differential Power Analysis (DPA) attack. This routine is modified to resist the first-order DPA attack, but is subsequently shown to be vulnerable to a second-order DPA attack. A second-order DPA attack that is optimal under certain assumptions is also proposed. Experimental results in an ST16 smartcard confirm the practicality of the first and second-order DPA attacks.


title: Query Expansion using Wikipedia and DBpedia

abstract: In this paper, we describe our query expansion approach submitted for the Semantic Enrichment task in Cultural Heritage in CLEF (CHiC) 2012. Our approach makes use of an external knowledge base such as Wikipedia and DBpedia. It consists of two major steps, concept candidates generation from knowledge bases and the selection of K-best related concepts. For selecting the K-best concepts, we ranked them according to their semantic relatedness with the query. We used Wikipedia-based Explicit Semantic Analysis to calculate the semantic relatedness scores. We evaluate our approach on 25 queries from the CHiC Semantic Enrichment dataset.


title: Consistent Optimization for Single-Shot Object Detection

abstract: We present consistent optimization for single stage object detection. Previous works of single stage object detectors usually rely on the regular, dense sampled anchors to generate hypothesis for the optimization of the model. Through an examination of the behavior of the detector, we observe that the misalignment between the optimization target and inference configurations has hindered the performance improvement. We propose to bride this gap by consistent optimization, which is an extension of the traditional single stage detector's optimization strategy. Consistent optimization focuses on matching the training hypotheses and the inference quality by utilizing of the refined anchors during training. To evaluate its effectiveness, we conduct various design choices based on the state-of-the-art RetinaNet detector. We demonstrate it is the consistent optimization, not the architecture design, that yields the performance boosts. Consistent optimization is nearly cost-free, and achieves stable performance gains independent of the model capacities or input scales. Specifically, utilizing consistent optimization improves RetinaNet from 39.1 AP to 40.1 AP on COCO dataset without any bells or whistles, which surpasses the accuracy of all existing state-of-the-art one-stage detectors when adopting ResNet-101 as backbone. The code will be made available.


title: Design and analysis of axial flux permanent magnet motor for electric bicycles with hybrid stator core

abstract: This paper proposes a new axial flux permanent magnet (AFPM) motor, whose stator is composed of laminated steel and soft magnetic composite (SMC) material. Axial flux machines are considered a preferred solution compared to radial flux machines for electric vehicles and traction applications because of its high torque density and thin disk-shaped structure. However, the manufacturing cost of axial flux machines is greater than radial flux machines due to its inherent structure. The hybrid construction of the stator core presented in this paper reduces the manufacturing complexity and fabrication cost in axial flux machines. The proposed AFPM is designed to be utilized in electric bicycles replacing the currently used radial flux brushless DC (BLDC) motor. The performance characteristics of the developed AFPM are analyzed using the 3D Finite Element Analysis (FEA) to verify the advantages of the proposed model. However, the torque ripple of the basic model of novel AFPM was high, which was reduced by adjusting rotor pole arc width (PAW) ratio, to make it viable for the commercial application. Finally, a prototype of the proposed motor was manufactured and tested in the laboratory.


title: User activities outlier detection system using principal component analysis and fuzzy rule-based system

abstract: In this paper, a user activities outlier detection system is introduced. The proposed system is implemented in a smart home environment equipped with appropriate sensory devices. An activity outlier detection system consist of a two-stage integration of Principal Component Analysis (PCA) and Fuzzy Rule-Based System (FRBS). In the first stage, the Hamming distance is used to measure the distances between the activities. PCA is then applied to the distance measures to find two indices of Hotelling's T2 and Squared Prediction Error (SPE). In the second stage of the process, the calculated indices are provided as inputs to FRBSs to model them heuristically. They are used to identify the outliers and classify them. Three case studies are reported to demonstrate the effectiveness of the proposed system. The proposed system successfully identifies the outliers and helps in distinguishing between the normal and abnormal behaviour patterns of the Activities of Daily Living (ADL).


title: Comparative analysis of features based machine learning approaches for phishing detection

abstract: Machine learning based anti-phishing techniques are based on various features extracted from different sources. These features differentiate a phishing website from a legitimate one. Features are taken from various sources like URL, page content, search engine, digital certificate, website traffic, etc, of a website to detect it as a phishing or non-phishing. The websites are declared as phishing sites if the heuristic design of the websites matches with the predefined rules. The accuracy of the anti-phishing solution depends on features set, training data and machine learning algorithm. This paper presents a comprehensive analysis of Phishing attacks, their exploitation, some of the recent machine learning based approaches for phishing detection and their comparative study. It provides a better understanding of the phishing problem, current solution space in machine learning domain, and scope of future research to deal with Phishing attacks efficiently using machine learning based approaches.


title: Detecting affect from non-stylised body motions

abstract: In this paper we present a novel framework for analysing non-stylised motion in order to detect implicitly communicated affect. Our approach makes use of a segmentation technique which can divide complex motions into a set of automatically derived motion primitives. The parsed motion is then analysed in terms of dynamic features which are shown to encode affective information. In order to adapt our algorithm to personal movement idiosyncrasies we developed a new approach for deriving unbiased motion features. We have evaluated our approach using a comprehensive database of affectively performed motions. The results show that removing personal movement bias can have a significant benefit for automated affect recognition from body motion. The resulting recognition rate is similar to that of humans who took part in a comparable psychological experiment.


title: A Three-Stage Text Normalization Strategy for Mandarin Text-to-Speech Systems

abstract: Text normalization is an important component in mandarin Text-to-Speech system. This paper develops a taxonomy of Non-Standard Words (NSW's) based on a Large-scale Chinese corpus and proposes a three-stage text normalization strategy: Finite State Automata (FSA) for initial classification, Maximum Entropy (ME) Classifier & Rules for further classification and General Rules for standard word conversion. The three-stage approach achieves Precision of 96.02% in experiments, 5.21% higher than that of simple rule based approach and 2.21% higher than that of simple machine learning method. Experiments results show that the approach of three-stage disambiguation strategy for text normalization makes considerable improvement, and works well in real TTS system.


title: Integer Programming Formulation of Traveling Salesman Problems

abstract: It has been observed by many people that a striking number of quite diverse mathematical problems can be formulated as problems in integer programming, that is, linear programming problems in which some or all of the variables are required to assume integral values. This fact is rendered quite interesting by recent research on such problems, notably by R. E. Gomory [2, 3], which gives promise of yielding efficient computational techniques for their solution. The present paper provides yet another example of the versatility of integer programming as a mathematical modeling device by representing a generalization of the well-known “Travelling Salesman Problem” in integer programming terms. The authors have developed several such models, of which the one presented here is the most efficient in terms of generality, number of variables, and number of constraints. This model is due to the second author [4] and was presented briefly at the Symposium on Combinatorial Problems held at Princeton University, April 1960, sponsored by SIAM and IBM. The problem treated is: (1) A salesman is required to visit each of <italic>n</italic> cities, indexed by 1, ··· , <italic>n</italic>. He leaves from a “base city” indexed by 0, visits each of the <italic>n</italic> other cities exactly once, and returns to city 0. During his travels he must return to 0 exactly <italic>t</italic> times, including his final return (here <italic>t</italic> may be allowed to vary), and he must visit no more than <italic>p</italic> cities in one tour. (By a tour we mean a succession of visits to cities without stopping at city 0.) It is required to find such an itinerary which minimizes the total distance traveled by the salesman. ::: Note that if <italic>t</italic> is fixed, then for the problem to have a solution we must have <italic>tp</italic> ≧ <italic>n</italic>. For <italic>t</italic> = 1, <italic>p</italic> ≧ <italic>n</italic>, we have the standard traveling salesman problem. ::: Let <italic>d<subscrpt>ij</subscrpt></italic> (<italic>i</italic> ≠ <italic>j</italic> = 0, 1, ··· , <italic>n</italic>) be the distance covered in traveling from city <italic>i</italic> to city <italic>j</italic>. The following integer programming problem will be shown to be equivalent to (1): (2) Minimize the linear form ∑<subscrpt>0≦<italic>i</italic>≠<italic>j</italic>≦<italic>n</italic></subscrpt>∑ <italic>d<subscrpt>ij</subscrpt>x<subscrpt>ij</subscrpt></italic> over the set determined by the relations ∑<supscrpt><italic>n</italic></supscrpt><subscrpt><italic>i</italic>=0<italic>i</italic>≠<italic>j</italic></subscrpt> <italic>x<subscrpt>ij</subscrpt></italic> = 1 (<italic>j</italic> = 1, ··· , <italic>n</italic>) ∑<supscrpt><italic>n</italic></supscrpt><subscrpt><italic>j</italic>=0<italic>j</italic>≠<italic>i</italic></subscrpt> <italic>x<subscrpt>ij</subscrpt></italic> = 1 (<italic>i</italic> = 1, ··· , <italic>n</italic>) <italic>u<subscrpt>i</subscrpt></italic> - <italic>u<subscrpt>j</subscrpt></italic> + <italic>px<subscrpt>ij</subscrpt></italic> ≦ <italic>p</italic> - 1 (1 ≦ <italic>i</italic> ≠ <italic>j</italic> ≦ <italic>n</italic>) where the <italic>x<subscrpt>ij</subscrpt></italic> are non-negative integers and the <italic>u<subscrpt>i</subscrpt></italic> (<italic>i</italic> = 1, …, <italic>n</italic>) are arbitrary real numbers. (We shall see that it is permissible to restrict the <italic>u<subscrpt>i</subscrpt></italic> to be non-negative integers as well.) ::: If <italic>t</italic> is fixed it is necessary to add the additional relation: ∑<supscrpt><italic>n</italic></supscrpt><subscrpt><italic>u</italic>=1</subscrpt> <italic>x</italic><subscrpt><italic>i</italic>0</subscrpt> = <italic>t</italic> Note that the constraints require that <italic>x<subscrpt>ij</subscrpt></italic> = 0 or 1, so that a natural correspondence between these two problems exists if the <italic>x<subscrpt>ij</subscrpt></italic> are interpreted as follows: The salesman proceeds from city <italic>i</italic> to city <italic>j</italic> if and only if <italic>x<subscrpt>ij</subscrpt></italic> = 1. Under this correspondence the form to be minimized in (2) is the total distance to be traveled by the salesman in (1), so the burden of proof is to show that the two feasible sets correspond; i.e., a feasible solution to (2) has <italic>x<subscrpt>ij</subscrpt></italic> which do define a legitimate itinerary in (1), and, conversely a legitimate itinerary in (1) defines <italic>x<subscrpt>ij</subscrpt></italic>, which, together with appropriate <italic>u<subscrpt>i</subscrpt></italic>, satisfy the constraints of (2). ::: Consider a feasible solution to (2). ::: The number of returns to city 0 is given by ∑<supscrpt><italic>n</italic></supscrpt><subscrpt><italic>i</italic>=1</subscrpt> <italic>x</italic><subscrpt><italic>i</italic>0</subscrpt>. The constraints of the form ∑ <italic>x<subscrpt>ij</subscrpt></italic> = 1, all <italic>x<subscrpt>ij</subscrpt></italic> non-negative integers, represent the conditions that each city (other than zero) is visited exactly once. The <italic>u<subscrpt>i</subscrpt></italic> play a role similar to node potentials in a network and the inequalities involving them serve to eliminate tours that do not begin and end at city 0 and tours that visit more than <italic>p</italic> cities. Consider any <italic>x</italic><subscrpt><italic>r</italic><subscrpt>0</subscrpt><italic>r</italic><subscrpt>1</subscrpt></subscrpt> = 1 (<italic>r</italic><subscrpt>1</subscrpt> ≠ 0). There exists a unique <italic>r</italic><subscrpt>2</subscrpt> such that <italic>x</italic><subscrpt><italic>r</italic><subscrpt>1</subscrpt><italic>r</italic><subscrpt>2</subscrpt></subscrpt> = 1. Unless <italic>r</italic><subscrpt>2</subscrpt> = 0, there is a unique <italic>r</italic><subscrpt>3</subscrpt> with <italic>x</italic><subscrpt><italic>r</italic><subscrpt>2</subscrpt><italic>r</italic><subscrpt>3</subscrpt></subscrpt> = 1. We proceed in this fashion until some <italic>r<subscrpt>j</subscrpt></italic> = 0. This must happen since the alternative is that at some point we reach an <italic>r<subscrpt>k</subscrpt></italic> = <italic>r<subscrpt>j</subscrpt></italic>, <italic>j</italic> + 1 < <italic>k</italic>. ::: Since none of the <italic>r</italic>'s are zero we have <italic>u<subscrpt>r<subscrpt>i</subscrpt></subscrpt></italic> - <italic>u</italic><subscrpt><italic>r</italic><subscrpt><italic>i</italic> + 1</subscrpt></subscrpt> + <italic>px</italic><subscrpt><italic>r<subscrpt>i</subscrpt></italic><italic>r</italic><subscrpt><italic>i</italic> + 1</subscrpt></subscrpt> ≦ <italic>p</italic> - 1 or <italic>u<subscrpt>r<subscrpt>i</subscrpt></subscrpt></italic> - <italic>u</italic><subscrpt><italic>r</italic><subscrpt><italic>i</italic> + 1</subscrpt></subscrpt> ≦ - 1. Summing from <italic>i</italic> = <italic>j</italic> to <italic>k</italic> - 1, we have <italic>u<subscrpt>r<subscrpt>j</subscrpt></subscrpt></italic> - <italic>u<subscrpt>r<subscrpt>k</subscrpt></subscrpt></italic> = 0 ≦ <italic>j</italic> + 1 - <italic>k</italic>, which is a contradiction. Thus all tours include city 0. It remains to observe that no tours is of length greater than <italic>p</italic>. Suppose such a tour exists, <italic>x</italic><subscrpt>0<italic>r</italic><subscrpt>1</subscrpt></subscrpt> , <italic>x</italic><subscrpt><italic>r</italic><subscrpt>1</subscrpt><italic>r</italic><subscrpt>2</subscrpt></subscrpt> , ·&middot·· , <italic>x</italic><subscrpt><italic>r<subscrpt>p</subscrpt>r</italic><subscrpt><italic>p</italic>+1</subscrpt></subscrpt> = 1 with all <italic>r<subscrpt>i</subscrpt></italic> ≠ 0. Then, as before, <italic>u</italic><subscrpt><italic>r</italic>1</subscrpt> - <italic>u</italic><subscrpt><italic>r</italic><subscrpt><italic>p</italic>+1</subscrpt></subscrpt> ≦ - <italic>p</italic> or <italic>u</italic><subscrpt><italic>r</italic><subscrpt><italic>p</italic>+1</subscrpt></subscrpt> - <italic>u</italic><subscrpt><italic>r</italic><subscrpt>1</subscrpt></subscrpt> ≧ <italic>p</italic>. ::: But we have <italic>u</italic><subscrpt><italic>r</italic><subscrpt><italic>p</italic>+1</subscrpt></subscrpt> - <italic>u</italic><subscrpt><italic>r</italic><subscrpt>1</subscrpt></subscrpt> + <italic>px</italic><subscrpt><italic>r</italic><subscrpt><italic>p</italic>+1</subscrpt><italic>r</italic><subscrpt>1</subscrpt></subscrpt> ≦ <italic>p</italic> - 1 or <italic>u</italic><subscrpt><italic>r</italic><subscrpt><italic>p</italic>+1</subscrpt></subscrpt> - <italic>u</italic><subscrpt><italic>r</italic><subscrpt>1</subscrpt></subscrpt> ≦ <italic>p</italic> (1 - <italic>x</italic><subscrpt><italic>r</italic><subscrpt><italic>p</italic>+1</subscrpt><italic>r</italic><subscrpt>1</subscrpt></subscrpt>) - 1 ≦ <italic>p</italic> - 1, which is a contradiction. ::: Conversely, if the <italic>x<subscrpt>ij</subscrpt></italic> correspond to a legitimate itinerary, it is clear that the <italic>u<subscrpt>i</subscrpt></italic> can be adjusted so that <italic>u<subscrpt>i</subscrpt></italic> = <italic>j</italic> if city <italic>i</italic> is the <italic>j</italic>th city visited in the tour which includes city <italic>i</italic>, for we then have <italic>u<subscrpt>i</subscrpt></italic> - <italic>u<subscrpt>j</subscrpt></italic> = - 1 if <italic>x<subscrpt>ij</subscrpt></italic> = 1, and always <italic>u<subscrpt>i</subscrpt></italic> - <italic>u<subscrpt>j</subscrpt></italic> ≦ <italic>p</italic> - 1. ::: The above integer program involves <italic>n</italic><supscrpt>2</supscrpt> + <italic>n</italic> constraints (if <italic>t</italic> is not fixed) in <italic>n</italic><supscrpt>2</supscrpt> + 2<italic>n</italic> variables. Since the inequality form of constraint is fundamental for integer programming calculations, one may eliminate 2<italic>n</italic> variables, say the <italic>x</italic><subscrpt><italic>i</italic>0</subscrpt> and <italic>x</italic><subscrpt>0<italic>j</italic></subscrpt>, by means of the equation constraints and produce


title: The real story of stuxnet

abstract: The paper discusses how Kaspersky Lab tracked down the malware that stymied Iran's nuclear-fuel enrichment program.


title: Evaluating the role of risk networks on risk identification, classification and emergence

abstract: Modern society heavily relies on strongly connected, socio-technical systems. As a result, distinct risks threatening the operation of individual systems can no longer be treated in isolation. Consequently, risk experts are actively seeking for ways to relax the risk independence assumption that undermines typical risk management models. Prominent work has advocated the use of risk networks as a way forward. Yet, the inevitable biases introduced during the generation of these survey-based risk networks limit our ability to examine their topology, and in turn challenge the utility of the very notion of a risk network. To alleviate these concerns, we proposed an alternative methodology for generating weighted risk networks. We subsequently applied this methodology to an empirical dataset of financial data. This paper reports our findings on the study of the topology of the resulting risk network. We observed a modular topology, and reasoned on its use as a robust risk classification framework. Using these modules, we highlight a tendency of specialization during the risk identification process, with some firms being solely focused on a subset of the available risk classes. Finally, we considered the independent and systemic impact of some risks and attributed possible mismatches to their emerging nature.


Generating processors from specifications of instruction sets


title: Genome editing with engineered zinc finger nucleases

abstract: Reverse genetics in model organisms such as Drosophila melanogaster, Arabidopsis thaliana, zebrafish and rats, efficient genome engineering in human embryonic stem and induced pluripotent stem cells, targeted integration in crop plants, and HIV resistance in immune cells - this broad range of outcomes has resulted from the application of the same core technology: targeted genome cleavage by engineered, sequence-specific zinc finger nucleases followed by gene modification during subsequent repair. Such 'genome editing' is now established in human cells and a number of model organisms, thus opening the door to a range of new experimental and therapeutic possibilities.


title: Clinical practice guidelines for diagnosis of autism spectrum disorder in adults and children in the UK: a narrative review

abstract: Research suggests that diagnostic procedures for Autism Spectrum Disorder are not consistent across practice and that diagnostic rates can be affected by contextual and social drivers. The purpose of this review was to consider how the content of clinical practice guidelines shapes diagnoses of Autism Spectrum Disorder in the UK; and investigate where, within those guidelines, social factors and influences are considered. We electronically searched multiple databases (NICE Evidence Base; TRIP; Social Policy and Practice; US National Guidelines Clearinghouse; HMIC; The Cochrane Library; Embase; Global health; Ovid; PsychARTICLES; PsychINFO) and relevant web sources (government, professional and regional NHS websites) for clinical practice guidelines. We extracted details of key diagnostic elements such as assessment process and diagnostic tools. A qualitative narrative analysis was conducted to identify social factors and influences. Twenty-one documents were found and analysed. Guidelines varied in recommendations for use of diagnostic tools and assessment procedures. Although multidisciplinary assessment was identified as the ‘ideal’ assessment, some guidelines suggested in practice one experienced healthcare professional was sufficient. Social factors in operational, interactional and contextual areas added complexity to guidelines but there were few concrete recommendations as to how these factors should be operationalized for best diagnostic outcomes. Although individual guidelines appeared to present a coherent and systematic assessment process, they varied enough in their recommendations to make the choices available to healthcare professionals particularly complex and confusing. We recommend a more explicit acknowledgement of social factors in clinical practice guidelines with advice about how they should be managed and operationalised to enable more consistency of practice and transparency for those coming for diagnosis.


title: A low phase-noise microwave oscillator using a substrate integrated waveguide resonator based on complementary split ring resonator

abstract: In this paper, a low phase-noise oscillator using a complementary split ring resonator (CSRR) based on a substrate integrated waveguide (SIW) has been proposed and developed for microwave applications. The substrate integrated concept is applied to implement an oscillator circuit. The unloaded Q factor of the SIW cavity is increased by loading a complementary split ring resonator (CSRR). A detailed explanation for generation of the resonant frequency has been illuminated. It is shown that the low phase-noise oscillator is developed by taking advantage of the high Q factor of the CSRR-loaded SIW structure. An unloaded Q-factor (Q U ) of the designed resonator is 1960. Measured results show that the oscillation frequency is located at 9.3 GHz. The amplitude of output power is able to deliver 11.3 dBm and phase-noise of −122.3 dBc/Hz at a 1-MHz offset. The implementation of this type oscillator can be easily realized and appropriate for low cost microwave and millimeter-wave applications.


title: Tour-guide robot

abstract: Museums, castles, and aquariums are some of the many tourist sites visited once exploring a new place. However, visiting a tourist site without the aid of a tourist guide; some would feel lost and confused. In this work, we introduce the implementation of a tour-guide robot using Kinect technology to ease the process of tourist guides. The robot will replace the current human guide. The robot will follow the tourists wherever they go, avoiding obstacles in its way, and provide information about the place once the tourists have given it the command to do so. The robot detects different objects, thus providing information about them, using RFID tags. In addition, these tags will help identifying the robot's location with the use of a monitoring website. In Kuwait, the lack of tourist's guides is common and the goal of this work is to give the tourists a smooth and pleasant experience at the touristic areas.


title: Survey of STT-MRAM Cell Design Strategies: Taxonomy and Sense Amplifier Tradeoffs for Resiliency

abstract: Spin-Transfer Torque Random Access Memory (STT-MRAM) has been explored as a post-CMOS technology for embedded and data storage applications seeking non-volatility, near-zero standby energy, and high density. Towards attaining these objectives for practical implementations, various techniques to mitigate the specific reliability challenges associated with STT-MRAM elements are surveyed, classified, and assessed in this article. Cost and suitability metrics assessed include the area of nanomagmetic and CMOS components per bit, access time and complexity, sense margin, and energy or power consumption costs versus resiliency benefits. Solutions to the reliability issues identified are addressed within a taxonomy created to categorize the current and future approaches to reliable STT-MRAM designs. A variety of destructive and non-destructive sensing schemes are assessed for process variation tolerance, read disturbance reduction, sense margin, and write polarization asymmetry compensation. The highest resiliency strategies deliver a sensing margin above 300mV while incurring low power and energy consumption on the order of picojoules and microwatts, respectively, and attaining read sense latency of a few nanoseconds down to hundreds of picoseconds for non-destructive and destructive sensing schemes, respectively.


title: Building a Low Latency, Highly Associative DRAM Cache with the Buffered Way Predictor

abstract: The emerging die-stacked DRAM technology allows computer architects to design a last-level cache (LLC) with high memory bandwidth and large capacity. There are four key requirements for DRAM cache design: minimizing on-chip tag storage overhead, optimizing access latency, improving hit rate, and reducing off-chip traffic. These requirements seem mutually incompatible. For example, to reduce the tag storage overhead, the recent proposed LH-cache co-locates tags and data in the same DRAM cache row, and the Alloy Cache proposed to alloy data and tags in the same cache line in a direct-mapped design. However, these ideas either require significant tag lookup latency or sacrifice hit rate for hit latency. To optimize all four key requirements, we propose the Buffered Way Predictor (BWP). The BWP predicts the way ID of a DRAM cache request with high accuracy and coverage, allowing data and tag to be fetched back to back. Thus, the read latency for the data can be completely hidden so that DRAM cache hitting requests have low access latency. The BWP technique is designed for highly associative block-based DRAM caches and achieves a low miss rate and low off-chip traffic. Our evaluation with multi-programmed workloads and a 128MB DRAM cache shows that a 128KB BWP achieves a 76.2% hit rate. The BWP improves performance by 8.8% and 12.3% compared to LH-cache and Alloy Cache, respectively.


title: Review On Detection of Suspicious Transaction In Anti-Money Laundering Using Data Mining Framework

abstract: Data mining, “the extraction of hidden predictive information from large databases”, is a powerful new technology with great potential to help for detecting the Anti-money Laundering. To detect suspicious money laundering transaction in the real world financial is a critical task. Database is take important role to store various kinds of useful and meaningful information. Data mining technique like Decision tree is easily extract the information from large dataset. For detection of Anti Money laundering, Data mining is used extensively, because of limited scalability, adaptability and validity. The main motivation for detecting money laundering used in real time application like classification, clustering, neural network, machine learning, prediction etc.In this review paper we study on various Data mining techniques and Anti Money laundering detection methods. And based on that we decide that the clustering techniques are the best techniques for detecting anti-money laundering.


title: Simple Question Answering by Attentive Convolutional Neural Network

abstract: This work focuses on answering single-relation factoid questions over Freebase. Each question can acquire the answer from a single fact of form (subject, predicate, object) in Freebase. This task, simple question answering (SimpleQA), can be addressed via a two-step pipeline: entity linking and fact selection. In fact selection, we match the subject entity in a fact candidate with the entity mention in the question by a character-level convolutional neural network (char-CNN), and match the predicate in that fact with the question by a word-level CNN (word-CNN). This work makes two main contributions. (i) A simple and effective entity linker over Freebase is proposed. Our entity linker outperforms the state-of-the-art entity linker over SimpleQA task. (ii) A novel attentive maxpooling is stacked over word-CNN, so that the predicate representation can be matched with the predicate-focused question representation more effectively. Experiments show that our system sets new state-of-the-art in this task.


title: SArF map: Visualizing software architecture from feature and layer viewpoints

abstract: To facilitate understanding the architecture of a software system, we developed SArF Map technique that visualizes software architecture from feature and layer viewpoints using a city metaphor. SArF Map visualizes implicit software features using our previous study, SArF dependency-based software clustering algorithm. Since features are high-level abstraction units of software, a generated map can be directly used for high-level decision making such as reuse and also for communications between developers and non-developer stakeholders. In SArF Map, each feature is visualized as a city block, and classes in the feature are laid out as buildings reflecting their software layer. Relevance between features is represented as streets. Dependency links are visualized lucidly. Through open source and industrial case studies, we show that the architecture of the target systems can be easily overviewed and that the quality of their packaging designs can be quickly assessed.


title: Gossip-based cooperative caching for mobile applications in mobile wireless networks

abstract: Cooperative caching is an efficient way to improve the performance of data access in mobile wireless networks, by cache nodes selecting different data items in their limited storage in order to reduce total access delay. With more demands on sharing a video or other data, especially for mobile applications in an Internet-based Mobile Ad Hoc Network, considering the relations among data items in cooperative caching becomes more important than before. However, most of the existing works do not consider these inherent relations among data items, such as the logical, temporal, or spatial relations. In this paper, we present a novel solution, Gossip-based Cooperative Caching (GosCC) to address the cache placement problem, and consider the sequential relation among data items. Each mobile node stores the IDs of data items cached locally and the ID of the data item in use into its progress report. Each mobile node also makes use of these progress reports to determine whether a data item should be cached locally. These progress reports are propagated within the network in a gossip-based way. To improve the user experience, GosCC aims to provide users with an uninterrupted data access service. Simulation results show that GosCC achieves better performance than Benefit-based Data Caching and HybridCache, in terms of average interruption intervals and average interruption times, while sacrificing message cost to a certain degree.


title: JS: Expectancy-Value Theory of Achievement Motivation

abstract: Abstract We discuss the expectancy–value theory of motivation, focusing on an expectancy–value model developed and researched by Eccles, Wigfield, and their colleagues. Definitions of crucial constructs in the model, including ability beliefs, expectancies for success, and the components of subjective task values, are provided. These definitions are compared to those of related constructs, including self-efficacy, intrinsic and extrinsic motivation, and interest. Research is reviewed dealing with two issues: (1) change in children's and adolescents' ability beliefs, expectancies for success, and subjective values, and (2) relations of children's and adolescents' ability-expectancy beliefs and subjective task values to their performance and choice of activities.


title: A novel interlaced chirp sequence radar concept with range-Doppler processing for automotive applications

abstract: In this paper, we introduce a novel concept for primary frequency-modulated continuous-wave (FMCW) radar. The approach is based on a phase locked loop-controlled interlaced chirp sequence (ICS) waveform and, in contrast to basic range-Doppler processing, it enables higher target velocities to be detected. It is thus very suitable for automotive applications. The interlaced ramps in the system are generated by two separate frequency synthesizers. These are combined by an RF switch to suppress transients caused by oscillator overshoot and to avoid incoherencies due to programming times of the phase locked loop (PLL) ICs. A prototype radar system was realized in K-Band. Promising test results bode well for other applications.


title: An Exemplar-Based CRF for Multi-instance Object Segmentation

abstract: We address the problem of joint detection and segmentation of multiple object instances in an image, a key step towards scene understanding. Inspired by data-driven methods, we propose an exemplar-based approach to the task of instance segmentation, in which a set of reference image/shape masks is used to find multiple objects. We design a novel CRF framework that jointly models object appearance, shape deformation, and object occlusion. To tackle the challenging MAP inference problem, we derive an alternating procedure that interleaves object segmentation and shape/appearance adaptation. We evaluate our method on two datasets with instance labels and show promising results.


title: Applying Software Metrics to evaluate Business Process Models

abstract: In this paper, we define a set of metrics for the evaluation of conceptual models of business processes. The proposal supposes the adaptation and extension of the FMESP framework (Framework for the Modeling and Evaluation of Software Processes). This adaptation can be carried out thanks to the similarities that exist between both types of processes (software and business). FMESP includes a set of metrics, which provide the quantitative basis necessary to find out the maintainability of the software process models. This proposal has been used as the starting point in proposing a set of metrics for the evaluation of the complexity of business process models defined by BPMN (Business Process Modeling Notation). Moreover, the groups of metrics of FMESP have been extended. This is because the models of business processes represented in BPMN include quite a number of aspects of interest in this domain which are not considered in software processes modelled with SPEM (Software Process Engineering Metamodel).


title: The exit chart - introduction to extrinsic information transfer in iterative processing

abstract: The extrinsic information transfer (EXIT) chart pioneered by Stephan ten Brink is introduced in a tutorial way for readers not yet familiar with this powerful technique which is used to analyze the behavior of iterative so-called turbo techniques. Simple examples and preliminary analytical results are given as well as some typical applications.


title: DISCO Nets: DISsimilarity COefficient Networks

abstract: We present a new type of probabilistic model which we call DISsimilarity COefficient Networks (DISCO Nets). DISCO Nets allow us to efficiently sample from a posterior distribution parametrised by a neural network. During training, DISCO Nets are learned by minimising the dissimilarity coefficient between the true distribution and the estimated distribution. This allows us to tailor the training to the loss related to the task at hand. We empirically show that (i) by modeling uncertainty on the output value, DISCO Nets outperform equivalent non-probabilistic predictive networks and (ii) DISCO Nets accurately model the uncertainty of the output, outperforming existing probabilistic models based on deep neural networks.


title: The moderating role of socio-demographics on smartphone adoption

abstract: This study investigated the extent to which the perceived usefulness, perceived ease of use, perceived enjoyment and social influence of smartphones affected word of mouth, as well as the moderating role of customer socio-demographic characteristics. Data were collected through a cross-sectional survey of 481 South Korean smartphone consumers. The results indicated that perceived usefulness, perceived ease of use and social influence affected word of mouth. Moreover, hierarchical regression analyses confirmed that the effect of perceived enjoyment on word of mouth was moderated by customer gender, age and education. Finally, the relationship between perceived ease of use and word of mouth was moderated by gender. These results suggest the need to design targeted smartphone marketing strategies that consider users' gender, age and education.


title: Robust subspace segmentation by simultaneously learning data representations and their affinity matrix

abstract: The goal of subspace segmentation is to partition a set of data drawn from a union of subspace into their underlying subspaces. The performance of spectral clustering based approaches heavily depends on learned data affinity matrices, which are usually constructed either directly from the raw data or from their computed representations. In this paper, we propose a novel method to simultaneously learn the representations of data and the affinity matrix of representation in a unified optimization framework. A novel Augmented Lagrangian Multiplier based algorithm is designed to effectively and efficiently seek the optimal solution of the problem. The experimental results on both synthetic and real data demonstrate the efficacy of the proposed method and its superior performance over the state-of-the-art alternatives.


title: Understanding Philosophy in a Nurse’s World: What, Where and Why?

abstract: Concerns raised about the relevance of philosophy to the work of nurses have prompted us to examine the importance of philosophy in professional nursing. We introduce diverse understandings of what philosophy is, provide an overview of analytical and continental philosophies, and describe how philosophy is central to the work of nurses, educators and researchers. The discussion is situated in what some scholars view as a growing milieu of anti-intellectualism, instrumentalism, and neoliberalism. A three-pronged framework of philosophy is presented to help demonstrate the pervasiveness and magnitude of philosophy in nursing practice today. Philosophy helps nurses to think more critically and reflect on how their own values influence their practice and way of being. A better understanding of the importance of philosophy in the nurses' world is not only relevant but vital to our discipline and professional practice. A broader understanding of the interdependence of practical and philosophical matters in professional nursing is needed.


title: Deep neural networks for improved, impromptu trajectory tracking of quadrotors

abstract: Trajectory tracking control for quadrotors is important for applications ranging from surveying and inspection, to film making. However, designing and tuning classical controllers, such as proportional-integral-derivative (PID) controllers, to achieve high tracking precision can be time-consuming and difficult, due to hidden dynamics and other non-idealities. The Deep Neural Network (DNN), with its superior capability of approximating abstract, nonlinear functions, proposes a novel approach for enhancing trajectory tracking control. This paper presents a DNN-based algorithm as an add-on module that improves the tracking performance of a classical feedback controller. Given a desired trajectory, the DNNs provide a tailored reference input to the controller based on their gained experience. The input aims to achieve a unity map between the desired and the output trajectory. The motivation for this work is an interactive “fly-as-you-draw” application, in which a user draws a trajectory on a mobile device, and a quadrotor instantly flies that trajectory with the DNN-enhanced control system. Experimental results demonstrate that the proposed approach improves the tracking precision for user-drawn trajectories after the DNNs are trained on selected periodic trajectories, suggesting the method's potential in real-world applications. Tracking errors are reduced by around 40–50% for both training and testing trajectories from users, highlighting the DNNs' capability of generalizing knowledge.


title: A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions

abstract: The fifth generation (5G) wireless network technology is to be standardized by 2020, where main goals are to improve capacity, reliability, and energy efficiency, while reducing latency and massively increasing connection density. An integral part of 5G is the capability to transmit touch perception type real-time communication empowered by applicable robotics and haptics equipment at the network edge. In this regard, we need drastic changes in network architecture including core and radio access network (RAN) for achieving end-to-end latency on the order of 1 ms. In this paper, we present a detailed survey on the emerging technologies to achieve low latency communications considering three different solution domains: 1) RAN; 2) core network; and 3) caching. We also present a general overview of major 5G cellular network elements such as software defined network, network function virtualization, caching, and mobile edge computing capable of meeting latency and other 5G requirements.


title: Development of Fire alarm system using Raspberry Pi and Arduino Uno

abstract: The proposed Fire alarm system is a real-time monitoring system that detects the presence of smoke in the air due to fire and captures images via a camera installed inside a room when a fire occurs. The embedded systems used to develop this fire alarm system are Raspberry Pi and Arduino Uno. The key feature of the system is the ability to remotely send an alert when a fire is detected. When the presence of smoke is detected, the system will display an image of the room state in a web page. The system will need the user confirmation to report the event to the Firefighter using Short Message Service (SMS). The advantage of using this system is it will reduce the possibility of false alert reported to the Firefighter. The camera will only capture an image, so this system will consume a little storage and power. Keywords-component; Raspberry Pi; Arduino Uno; fire; alarm; webserver


Tutoring for SAT-Math with Wayang Outpost


title: NewsCube: delivering multiple aspects of news to mitigate media bias

abstract: The bias in the news media is an inherent flaw of the news production process. The resulting bias often causes a sharp increase in political polarization and in the cost of conflict on social issues such as Iraq war. It is very difficult, if not impossible, for readers to have penetrating views on realities against such bias. This paper presents NewsCube, a novel Internet news service aiming at mitigating the effect of media bias. NewsCube automatically creates and promptly provides readers with multiple classified viewpoints on a news event of interest. As such, it effectively helps readers understand a fact from a plural of viewpoints and formulate their own, more balanced viewpoints. While media bias problem has been studied extensively in communications and social sciences, our work is the first to develop a news service as a solution and study its effect. We discuss the effect of the service through various user studies.


A Brain Computer Interface based Humanoid Robot Control System


title: Collaborative expression representation using peak expression and intra class variation face images for practical subject-independent emotion recognition in videos

abstract: This paper proposes a facial expression recognition (FER) method in videos. The proposed method automatically selects the peak expression face from a video sequence using closeness of the face to the neutral expression. The severely non-frontal faces and poorly aligned faces are discarded in advance to eliminate their negative effects on the peak expression face selection and FER. To reduce the effect of the facial identity in the feature extraction, we compute difference information between the peak expression face and its intra class variation (ICV) face. An ICV face is generated by combining the training faces of an expression class and looks similar to the peak expression face in identity. Because the difference information is defined as the distances of locally pooled texture features between the two faces, the feature extraction is robust to face rotation and mis-alignment. Results show that the proposed method is practical with videos containing spontaneous facial expressions and pose variations. We propose a facial expression recognition method in practical videos.We propose a method for selecting the peak expression face from a video.We propose a robust subject-independent feature extraction.The feature extraction normalizes facial identity of peak expression face.The proposed method is feasible for spontaneous facial expression.


title: Joint Semantic Synthesis and Morphological Analysis of the Derived Word

abstract: Much like sentences are composed of words, words themselves are composed of smaller units. For example, the English word questionably can be analyzed as question+able+ly. However, this structural decomposition of the word does not directly give us a semantic representation of the word's meaning. Since morphology obeys the principle of compositionality, the semantics of the word can be systematically derived from the meaning of its parts. In this work, we propose a novel probabilistic model of word formation that captures both the analysis of a word w into its constituents segments and the synthesis of the meaning of w from the meanings of those segments. Our model jointly learns to segment words into morphemes and compose distributional semantic vectors of those morphemes. We experiment with the model on English CELEX data and German DerivBase (Zeller et al., 2013) data. We show that jointly modeling semantics increases both segmentation accuracy and morpheme F1 by between 3% and 5%. Additionally, we investigate different models of vector composition, showing that recurrent neural networks yield an improvement over simple additive models. Finally, we study the degree to which the representations correspond to a linguist's notion of morphological productivity.


Measuring the efficiency of decision making units


Overview of NLP-TEA 2016 Shared Task for Chinese Grammatical Error Diagnosis.


title: A Bayesian Filtering Algorithm for Gaussian Mixture Models

abstract: A Bayesian filtering algorithm is developed for a class of state-space systems that can be modelled via Gaussian mixtures. In general, the exact solution to this filtering problem involves an exponential growth in the number of mixture terms and this is handled here by utilising a Gaussian mixture reduction step after both the time and measurement updates. In addition, a square-root implementation of the unified algorithm is presented and this algorithm is profiled on several simulated systems. This includes the state estimation for two non-linear systems that are strictly outside the class considered in this paper.


title: The INSuRE Project: CAE-Rs Collaborate to Engage Students in Cybersecurity Research

abstract: Since fall 2012, several National Centers of Academic Excellence in Cyber Defense Research (CAE-Rs) fielded a collaborative course to engage students in solving applied cybersecurity research problems. We describe our experiences with this Information Security Research and Education (INSuRE) research collaborative. We explain how we conducted our project-based research course, give examples of student projects, and discuss the outcomes and lessons learned.


title: Online Acquisition of Digital Forensic Evidence

abstract: Providing the ability to any law enforcement officer to remotely transfer an image from any suspect computer directly to a forensic laboratory for analysis, can only help to greatly reduce the time wasted by forensic investigators in conducting on-site collection of computer equipment. RAFT (Remote Acquisition Forensic Tool) is a system designed to facilitate forensic investigators by remotely gathering digital evidence. This is achieved through the implementation of a secure, verifiable client/server imaging architecture. The RAFT system is designed to be relatively easy to use, requiring minimal technical knowledge on behalf of the user. One of the key focuses of RAFT is to ensure that the evidence it gathers remotely is court admissible. This is achieved by ensuring that the image taken using RAFT is verified to be identical to the original evidence on a suspect computer.


title: Automatic Human Animation for Non-Humanoid 3D Characters

abstract: In this paper, we propose a system, that automatically transfers human body motion captured from an ordinary video camera to an unknown 3D character mesh. In our system, no manual intervention is required for specifying the internal skeletal structure or defining how the mesh surfaces deform. A sparse graph is generated from the input polygons based on their connectivity and geometric distributions. To estimate articulated body parts in the video, a progressive particle filter is used for identifying correspondences. We anticipate our proposed system can bring animation to a new audience with a more intuitive user interface.


Cognitive Adaptations for Social Exchange


title: Fast localization for emergency monitoring and rescue in disaster scenarios based on WSN

abstract: Natural disasters, such as earthquake, typhoon and flood, have caused great loss of lives and property each year, which makes emergency monitoring and rescue an imperative problem to be addressed. In this paper, we designed a novel monitoring and rescue system based on wireless sensor network (WSN) for disaster scenarios, which combines environment monitoring, information transmission, and emergency localization. In our system, fast localization through WSN is a crucial technique for searching and rescuing. We proposed an improved weighted centroid algorithm with adaptive error correction to increase localization accuracy of sensor networks with sparse anchors. This method based on received signal strength indication (RSSI) is low cost, expandable, easy to implement, and suitable for disaster rescues.