title: Unsupervised Learning of Categories from Sets of Partially Matching Image Features

abstract: We present a method to automatically learn object categories from unlabeled images. Each image is represented by an unordered set of local features, and all sets are embedded into a space where they cluster according to their partial-match feature correspondences. After efficiently computing the pairwise affinities between the input images in this space, a spectral clustering technique is used to recover the primary groupings among the images. We introduce an efficient means of refining these groupings according to intra-cluster statistics over the subsets of features selected by the partial matches between the images, and based on an optional, variable amount of user supervision. We compute the consistent subsets of feature correspondences within a grouping to infer category feature masks. The output of the algorithm is a partition of the data into a set of learned categories, and a set of classifiers trained from these ranked partitions that can recognize the categories in novel images.


title: A Dynamic Rationalization of Distance Rationalizability

abstract: Distance rationalizability is an intuitive paradigm for developing and studying voting rules: given a notion of consensus and a distance function on preference profiles, a rationalizable voting rule selects an alternative that is closest to being a consensus winner. Despite its appeal, distance rationalizability faces the challenge of connecting the chosen distance measure and consensus notion to an operational measure of social desirability. We tackle this issue via the decision-theoretic framework of dynamic social choice, in which a social choice Markov decision process (MDP) models the dynamics of voter preferences in response to winner selection. We show that, for a prominent class of distance functions, one can construct a social choice MDP, with natural preference dynamics and rewards, such that a voting rule is (votewise) rationalizable with respect to the unanimity consensus for a given distance function iff it is a (deterministic) optimal policy in the MDP. This provides an alternative rationale for distance rationalizability, demonstrating the equivalence of rationalizable voting rules in a static sense and winner selection to maximize societal utility in a dynamic process.


title: Real-time classification of dance gestures from skeleton animation

abstract: We present a real-time gesture classification system for skeletal wireframe motion. Its key components include an angular representation of the skeleton designed for recognition robustness under noisy input, a cascaded correlation-based classifier for multivariate time-series data, and a distance metric based on dynamic time-warping to evaluate the difference in motion between an acquired gesture and an oracle for the matching gesture. While the first and last tools are generic in nature and could be applied to any gesture-matching scenario, the classifier is conceived based on the assumption that the input motion adheres to a known, canonical time-base: a musical beat. On a benchmark comprising 28 gesture classes, hundreds of gesture instances recorded using the XBOX Kinect platform and performed by dozens of subjects for each gesture class, our classifier has an average accuracy of 96:9%, for approximately 4-second skeletal motion recordings. This accuracy is remarkable given the input noise from the real-time depth sensor.


title: Multitier reactive abstractions

abstract: Distributed applications are traditionally developed using separate modules for each component in the distributed system, which can even be written in different programming languages. Those modules react on events such as user input, which are produced by other modules, and may in turn produce new events to be handled by different modules. Thus, most distributed applications are reactive in nature. Distributed event-based data flow makes it is hard to reason about the system and therefore makes the development of distributed systems challenging. In this paper, we present language abstractions for distributed reactive programming easing the development of such applications and supporting various distributed architectures.


title: A survey on sentiment detection of reviews

abstract: The sentiment detection of texts has been witnessed a booming interest in recent years, due to the increased availability of online reviews in digital form and the ensuing need to organize them. Till to now, there are mainly four different problems predominating in this research community, namely, subjectivity classification, word sentiment classification, document sentiment classification and opinion extraction. In fact, there are inherent relations between them. Subjectivity classification can prevent the sentiment classifier from considering irrelevant or even potentially misleading text. Document sentiment classification and opinion extraction have often involved word sentiment classification techniques. This survey discusses related issues and main approaches to these problems.


title: How Does Disagreement Benefit Co-teaching?

abstract: Learning with noisy labels is one of the most important question in weakly-supervised learning domain. Classical approaches focus on adding the regularization or estimating the noise transition matrix. However, either a regularization bias is permanently introduced, or the noise transition matrix is hard to be estimated accurately. In this paper, following a novel path to train on small-loss samples, we propose a robust learning paradigm called Co-teaching+. This paradigm naturally bridges "Update by Disagreement" strategy with Co-teaching that trains two deep neural networks, thus consists of disagreement-update step and cross-update step. In disagreement-update step, two networks predicts all data first, and feeds forward prediction disagreement data only. Then, in cross-update step, each network selects its small-loss data from such disagreement data, but back propagates the small-loss data by its peer network and updates itself parameters. Empirical results on noisy versions of MNIST, CIFAR-10 and NEWS demonstrate that Co-teaching+ is much superior to the state-of-the-art methods in the robustness of trained deep models.


title: Mapping Mel sub-band energies using Deep belief network for robust speech recognition

abstract: Sub-band speech processing is well-known in robust speech recognition. On the other hand, in recent years, deep neural networks (DNNs) have been widely used in speech recognition for acoustic modeling and also feature extraction and transformation. In this paper, we propose to use deep belief network (DBN) as a post-processing method for de-noising in Mel sub-band level where we enhance logarithm of Mel filter bank energies (LMFBs). In addition, we use DBN for extracting tandem features (posterior probability of phones occurrence) from de-noised LMFBs (obtained from previous stage) to obtain more robust and discriminative features. The final robust feature vector consists of de-noised LMFBs concatenated to mentioned tandem features. Evaluation results on Aurora2 database show that the proposed feature vector performs better than similar and conventional techniques, where it increases recognition accuracy in average by 55.12% in comparison to LMFBs.


Brand community


title: Inhibition of mTOR induces autophagy and reduces toxicity of polyglutamine expansions in fly and mouse models of Huntington disease

abstract: Huntington disease is one of nine inherited neurodegenerative disorders caused by a polyglutamine tract expansion. Expanded polyglutamine proteins accumulate abnormally in intracellular aggregates. Here we show that mammalian target of rapamycin (mTOR) is sequestered in polyglutamine aggregates in cell models, transgenic mice and human brains. Sequestration of mTOR impairs its kinase activity and induces autophagy, a key clearance pathway for mutant huntingtin fragments. This protects against polyglutamine toxicity, as the specific mTOR inhibitor rapamycin attenuates huntingtin accumulation and cell death in cell models of Huntington disease, and inhibition of autophagy has the converse effects. Furthermore, rapamycin protects against neurodegeneration in a fly model of Huntington disease, and the rapamycin analog CCI-779 improved performance on four different behavioral tasks and decreased aggregate formation in a mouse model of Huntington disease. Our data provide proof-of-principle for the potential of inducing autophagy to treat Huntington disease.


title: Efficient and Effective Single-Document Summarizations and A Word-Embedding Measurement of Quality

abstract: Our task is to generate an effective summary for a given document with specific realtime requirements. We use the softplus function to enhance keyword rankings to favor important sentences, based on which we present a number of summarization algorithms using various keyword extraction and topic clustering methods. We show that our algorithms meet the realtime requirements and yield the best ROUGE recall scores on DUC-02 over all previously-known algorithms. We show that our algorithms meet the realtime requirements and yield the best ROUGE recall scores on DUC-02 over all previously-known algorithms. To evaluate the quality of summaries without human-generated benchmarks, we define a measure called WESM based on word-embedding using Word Mover's Distance. We show that the orderings of the ROUGE and WESM scores of our algorithms are highly comparable, suggesting that WESM may serve as a viable alternative for measuring the quality of a summary.


title: Snake Charmer: Physically Enabling Virtual Objects

abstract: Augmented and virtual reality have the potential of being indistinguishable from the real world. Holographic displays, including head mounted units, support this vision by creating rich stereoscopic scenes, with objects that appear to float in thin air - often within arm's reach. However, one has but to reach out and grasp nothing but air to destroy the suspension of disbelief. Snake-charmer is an attempt to provide physical form to virtual objects by revisiting the concept of Robotic Graphics or Encountered-type Haptic interfaces with current commodity hardware. By means of a robotic arm, Snake-charmer brings physicality to a virtual scene and explores what it means to truly interact with an object. We go beyond texture and position simulation and explore what it means to have a physical presence inside a virtual scene. We demonstrate how to render surface characteristics beyond texture and position, including temperature; how to physically move objects; and how objects can physically interact with the user's hand. We analyze our implementation, present the performance characteristics, and provide guidance for the construction of future physical renderers.


title: A 22.5-to-32-Gb/s 3.2-pJ/b Referenceless Baud-Rate Digital CDR With DFE and CTLE in 28-nm CMOS

abstract: This paper presents a referenceless baud-rate clock and data recovery (CDR) incorporated with a continuous-time linear equalizer (CTLE) and one-tap decision feedback equalizer (DFE) to achieve data rates from 22.5 to 32 Gb/s across a channel with Nyquist loss ranging from 10.1 to 14.8 dB. The referenceless CDR includes a proposed frequency acquisition scheme that consists of two parts: frequency detection and frequency correction. Frequency detection is achieved by examining rising and falling data waveforms to detect discrepancies between the data rate and the locally recovered clock frequency. Frequency correction uses digitally adjustable asymmetry of the proposed adjustable baud-rate phase detector to correct any frequency error. The receiver is implemented in the TSMC 28-nm CMOS process with an analog front end consisting of a CTLE, sampling comparators, a digitally controlled oscillator, and a digital back end consisting of synthesized digital CDR logic. The open-loop frequency detector range is measured to be 39%. The closed-loop CDR capture range is measured to be 34%, limited by test equipment. The proposed frequency acquisition scheme improves the measured CDR capture range by up to $227\times $ . At 32 Gb/s, the entire receiver consumes 102.04 mW, achieving energy consumption below 3.19 pJ/b.


title: Combining words and speech prosody for automatic topic segmentation

abstract: We present a probabilistic model that uses both prosodic and lexical cues for the automatic segmentation of speech into topic units. The approach combines hidden Markov models, statistical language models, and prosody-based decision trees. Lexical information is obtained from a speech recognizer, and prosodic features are extracted automatically from speech waveforms. We evaluate our approach on the Broadcast News corpus, using standard evaluation metrics. Results show that the prosodic model alone outperforms the word-based segmentation method. Furthermore, we achieve an additional reduction in error by combining the prosodic and wordbased knowledge sources.


title: Effective Estimation of Driver Drowsiness Based on Eye Status Detection and Analysis

abstract: One of the primary causes of traffic accidents is the loss of alertness of the drivers involved. In several cases it has been found to be due to a driver falling asleep on the wheel or becoming too drowsy. Accidents caused due to driver drowsiness are difficult to prevent by traffic laws. Often, the drivers are not mindful of their lowered state of senses and the dangerous repercussions it may have and they continue driving. In some expensive vehicles, high tech cameras have been installed that seek to monitor the alertness levels of the drivers but they are not cost effective and cannot reach out to the masses. In this paper, we have outlined the design of a very simple and economical system which deals with this issue. In this system, the eye status of the driver is monitored, recorded and analyzed. The difference in the blinking pattern of an alert-driver and a drowsy-driver helps us to determine the level of alertness of the driver. If the alertness level falls below a certain threshold, an alarm is raised to warn the driver and a red beacon, attached to the rear of the vehicle, is flashed to warn other drivers. The system has been successfully tested and found to be extremely effective.


title: P2P group management systems: A conceptual analysis

abstract: Peer-to-Peer (P2P) networks are becoming eminent platforms for both distributed computing and interpersonal communication. Their role in contemporary multimedia content delivery and communication systems is strong, as witnessed by many popular applications and services. Groups in P2P systems can originate from the relations between humans, or they can be defined with purely technical criteria such as proximity. In this article, we present a conceptual analysis of P2P group management systems. We illustrate how groups are formed using different P2P system architectures, and analyze the advantages and disadvantages of using each P2P system architecture for implementing P2P group management. The evaluation criteria in the analysis are performance, robustness, fairness, suitability for battery-powered devices, scalability, and security. The outcome of the analysis facilitates the selection of an appropriate P2P system architecture for implementing P2P group management in both further research and prototype development.


Using Data Mining Techniques for Sentiment Shifter Identification.


title: Textual Modelling Embedded into Graphical

abstract: Although labelled graphical, many modelling languages represent important model parts as structured text. We benefit from sophisticated text editors when we use programming languages, but we neglect the same technology when we edit the textual parts of graphical models. Recent advances in generative engineering of textual model editors make the development of such sophisticated text editors practical, even for the smallest textual constructs of graphical languages. In this paper, we present techniques to embed textual model editors into graphical model editors and prove our approach for EMF-based textual editors and graphical editors created with GMF.


Beta Adrenergic Signaling: A Targetable Regulator of Angiosarcoma and Hemangiosarcoma


Learning to Identify Sentence Parallelism in Student Essays


title: Sensor fusion of depth camera and ultrasound data for obstacle detection and robot navigation

abstract: Depth cameras have gained much popularity in robotics in recent years. The Microsoft Kinect camera enables a mobile robot to do essential tasks like localization and navigation. Unfortunately, such structured light cameras also suffer from limitations. Exposing them to direct sunlight renders them blind, and transparent objects like glass windows can not be detected. This is a problem for the task of obstacle detection, where false negative measurements must be avoided. At the same time, ultrasound sensors have been studied by the robotic research community for decades. While they have lost attention with the advent of laser scanners and cameras, they remain successful for special applications due to their robustness and simplicity. In this paper we argue that depth cameras and ultrasound sensors extend each other very well. Ultrasound sensors are able to correct the problems inherent to camera-based sensors. We present a sensor fusion algorithm that merges depth camera data and ultrasound measurements using an occupancy grid approach. We validated the algorithm using obstacles in multiple scenarios.


title: Cross-scene crowd counting via deep convolutional neural networks

abstract: Cross-scene crowd counting is a challenging task where no laborious data annotation is required for counting people in new target surveillance crowd scenes unseen in the training set. The performance of most existing crowd counting methods drops significantly when they are applied to an unseen scene. To address this problem, we propose a deep convolutional neural network (CNN) for crowd counting, and it is trained alternatively with two related learning objectives, crowd density and crowd count. This proposed switchable learning approach is able to obtain better local optimum for both objectives. To handle an unseen target crowd scene, we present a data-driven method to finetune the trained CNN model for the target scene. A new dataset including 108 crowd scenes with nearly 200,000 head annotations is introduced to better evaluate the accuracy of cross-scene crowd counting methods. Extensive experiments on the proposed and another two existing datasets demonstrate the effectiveness and reliability of our approach.


title: Semi-Automatically Mapping Structured Sources into the Semantic Web ⋆

abstract: Linked data continues to grow at a rapid rate, but a limitation of a lot of the data that is being published is the lack of a semantic description. There are tools, such as D2R, that allow a user to quickly convert a database into RDF, but these tools do not provide a way to easily map the data into an existing ontology. This paper presents a semi-automatic approach to map structured sources to ontologies in order to build semantic descriptions (source models). Since the precise mapping is sometimes ambiguous, we also provide a graphical user interface that allows a user to interactively refine the models. The resulting source models can then be used to convert data into RDF with respect to a given ontology or to define a SPARQL end point that can be queried with respect to an ontology. We evaluated the overall approach on a variety of sources and show that it can be used to quickly build source models with minimal user interaction.


title: Realizing Ultra-Massive MIMO (1024×1024) communication in the (0.06–10) Terahertz band

abstract: Abstract The increasing demand for higher bandwidth and higher speed wireless communication motivates the exploration of higher frequency bands. The Terahertz (THz) band (0.06–10 THz) is envisioned as one of the key players to meet the demand for such higher bandwidth and data rates. However, the available bandwidth at THz frequencies comes with the cost of a much higher propagation loss. Due to the power limitations of compact solid-state THz transceivers, this results in very short communication distances of approximately one meter. In this paper, the concept of Ultra-Massive Multiple Input Multiple Output (UM MIMO) communication is introduced as a way to increase the communication distance and the achievable capacity of THz-band communication networks. The very small size of THz plasmonic nano-antennas, which leverage the properties of nanomaterials and metamaterials, enables the development of very large plasmonic arrays in very small footprints. For frequencies in the 0.06–1 THz range, metamaterials enable the design of plasmonic antenna arrays with hundreds of elements in a few square centimeters (e.g., 144 elements in 1 cm 2 at 60 GHz). In the 1–10 THz band, graphene-based plasmonic nano-antenna arrays with thousands of elements can be embedded in a few square millimeters (e.g., 1024 elements in 1 mm 2 at 1 THz). The resulting arrays can be utilized both in transmission and in reception (e.g., 1024 × 1024 UM MIMO at 1 THz) to support different modes, from razor-sharp UM beamforming to UM spatial multiplexing, as well as multi-band communication schemes. After introducing the main properties of plasmonic nano-antenna arrays, the working modes of UM MIMO are presented, and preliminary results are provided to highlight the potential of this paradigm. Finally, open challenges and potential solutions to enable UM MIMO communication are described.


title: Robust camera self-calibration from monocular images of Manhattan worlds

abstract: We focus on the detection of orthogonal vanishing points using line segments extracted from a single view, and using these for camera self-calibration. Recent methods view this problem as a two-stage process. Vanishing points are extracted through line segment clustering and subsequently likely orthogonal candidates are selected for calibration. Unfortunately, such an approach is easily distracted by the presence of clutter. Furthermore, geometric constraints imposed by the camera and scene orthogonality are not enforced during detection, leading to inaccurate results which are often inadmissible for calibration. To overcome these limitations, we present a RANSAC-based approach using a minimal solution for estimating three orthogonal vanishing points and focal length from a set of four lines, aligned with either two or three orthogonal directions. In addition, we propose to refine the estimates using an efficient and robust Maximum Likelihood Estimator. Extensive experiments on standard datasets show that our contributions result in significant improvements over the state-of-the-art.


title: Controlled experiments on the web: survey and practical guide

abstract: The web provides an unprecedented opportunity to evaluate ideas quickly using controlled experiments, also called randomized experiments, A/B tests (and their generalizations), split tests, Control/Treatment tests, MultiVariable Tests (MVT) and parallel flights. Controlled experiments embody the best scientific design for establishing a causal relationship between changes and their influence on user-observable behavior. We provide a practical guide to conducting online experiments, where end-users can help guide the development of features. Our experience indicates that significant learning and return-on-investment (ROI) are seen when development teams listen to their customers, not to the Highest Paid Person's Opinion (HiPPO). We provide several examples of controlled experiments with surprising results. We review the important ingredients of running controlled experiments, and discuss their limitations (both technical and organizational). We focus on several areas that are critical to experimentation, including statistical power, sample size, and techniques for variance reduction. We describe common architectures for experimentation systems and analyze their advantages and disadvantages. We evaluate randomization and hashing techniques, which we show are not as simple in practice as is often assumed. Controlled experiments typically generate large amounts of data, which can be analyzed using data mining techniques to gain deeper understanding of the factors influencing the outcome of interest, leading to new hypotheses and creating a virtuous cycle of improvements. Organizations that embrace controlled experiments with clear evaluation criteria can evolve their systems with automated optimizations and real-time analyses. Based on our extensive practical experience with multiple systems and organizations, we share key lessons that will help practitioners in running trustworthy controlled experiments.


title: Authorized Public Auditing of Dynamic Big Data Storage on Cloud with Efficient Verifiable Fine-Grained Updates

abstract: Cloud computing opens a new era in IT as it can provide various elastic and scalable IT services in a pay-as-you-go fashion, where its users can reduce the huge capital investments in their own IT infrastructure. In this philosophy, users of cloud storage services no longer physically maintain direct control over their data, which makes data security one of the major concerns of using cloud. Existing research work already allows data integrity to be verified without possession of the actual data file. When the verification is done by a trusted third party, this verification process is also called data auditing, and this third party is called an auditor. However, such schemes in existence suffer from several common drawbacks. First, a necessary authorization/authentication process is missing between the auditor and cloud service provider, i.e., anyone can challenge the cloud service provider for a proof of integrity of certain file, which potentially puts the quality of the so-called ‘auditing-as-a-service’ at risk; Second, although some of the recent work based on BLS signature can already support fully dynamic data updates over fixed-size data blocks, they only support updates with fixed-sized blocks as basic unit, which we call coarse-grained updates. As a result, every small update will cause re-computation and updating of the authenticator for an entire file block, which in turn causes higher storage and communication overheads. In this paper, we provide a formal analysis for possible types of fine-grained data updates and propose a scheme that can fully support authorized auditing and fine-grained update requests. Based on our scheme, we also propose an enhancement that can dramatically reduce communication overheads for verifying small updates. Theoretical analysis and experimental results demonstrate that our scheme can offer not only enhanced security and flexibility, but also significantly lower overhead for big data applications with a large number of frequent small updates, such as applications in social media and business transactions.


title: DESIGN ANALYSIS OF HIGH GAIN WIDEBAND L-PROBE FED MICROSTRIP PATCH ANTENNA

abstract: A new high gain wideband L-probe fed inverted EE-H shaped slotted (LEE-H) microstrip patch antenna is presented in this paper. The design adopts contemporary techniques; L-probe feeding, inverted patch structure with air-fllled dielectric, and EE-H shaped patch. The integration of these techniques leads to a new patch antenna with a low proflle as well as useful operational features, as the broadband and high gain. The measured result showed satisfactory performance with achievable impedance bandwidth of 21.15% at 10dB return loss (VSWR • 2) and a maximum gain of 9.5dBi. The antenna exhibits stable radiation pattern in the entire operating band.


title: Reduction of Inductive Crosstalk Using Quadrupole Inductors

abstract: Interference due to inductor crosstalk is a growing concern in modern RFICs where inductors are placed in close proximity. A quadrupole inductor is explored as a method to reduce inductive crosstalk. A quadrupole inductor and standard inductor are compared with respect to inductance, quality factor, and area. Then, physics-based calculations are corroborated with simulation and measurement to predict crosstalk reduction as a function of position. Measurements verify up to a 31 dB reduction in crosstalk. Finally, phase noise measurements of voltage-controlled oscillators show that the quadrupole inductor can be used in circuits without negatively impacting performance.


title: A Team Formation and Project-based Learning Support Service for Social Learning Networks

abstract: The Internet affords new approaches to learning. Geographically dispersed self- directed learners can learn in computer-supported communities, forming social learning networks. However, self-directed learners can suffer from a lack of continuous motivation. And surprisingly, social learning networks do not readily support effective, coherence-creating and motivating learning settings. It is argued that providing project-based learning opportunities and team formation services can help overcome these shortcomings. A review of existing team formation tools evidences that a new design for team formation and the initiation of project- based learning is required before these can be supported in social learning networks. A design is proposed which identifies 'knowledge', 'personality' and 'preferences' as categories in which data is needed to form teams, and it specifies how the required data are gathered and assessed. The design defines rules deduced from team formation principles from prior team formation research to optimise team formations towards increased productivity, creative solutions or higher learning outcomes. The rules are implemented in three team formation expressions each calculating one of the desired team formations. The expressions are deployed on a set of test data, demonstrating the effectiveness of the team formation service design. The article includes a discussion of the results and provides indications for future research.


Linux Containers : Why They ’ re in Your Future and What Has to Happen First


The INTERSPEECH 2018 Computational Paralinguistics Challenge: Atypical and Self-Assessed Affect, Crying and Heart Beats


title: Binder migration during drying of pigment coatings

abstract: The major experimental findings on binder migration are summarised and the two major theories are discussed and their weaknesses are identified. A new theory is proposed. It is based on the rheological phenomenon of wall effect, ie surface binder enrichment, relative to pigment, occurs during application so that such enrichment exists in the wet coating layer from the very beginning of its existence (...)


title: Research on the Forecast of Shared Bicycle Rental Demand Based on Spark Machine Learning Framework

abstract: In recent years, the shared bicycle project has developed rapidly. In use of shared bicycles, a great deal of user riding information is recorded. How to extract effective knowledge from these vast amounts of information, how to use this knowledge to improve the shared bicycle system, and how to improve the user experience, are problems to solve. Citi Bike is selected as the research target. Data on Citi Bike’s user historical behavior, weather information, and holiday information are collected from three different sources, and converted into appropriate formats for model training. Spark MLlib is used to construct three different predictive models, advantages and disadvantages of different forecasting models are compared. Some techniques are used to enhance the accuracy of random forests model. The experimental results show that the root mean square error RMSE of the final model is reduced from 305.458 to 243.346.


title: CACTI-3DD: Architecture-level modeling for 3D die-stacked DRAM main memory

abstract: Emerging 3D die-stacked DRAM technology is one of the most promising solutions for future memory architectures to satisfy the ever-increasing demands on performance, power, and cost. This paper introduces CACTI-3DD, the first architecture-level integrated power, area, and timing modeling framework for 3D die-stacked off-chip DRAM main memory. CACTI-3DD includes TSV models, improves models for 2D off-chip DRAM main memory over current versions of CACTI, and includes 3D integration models that enable the analysis of a full spectrum of 3D DRAM designs from coarse-grained rank-level 3D stacking to bank-level 3D stacking. CACTI-3DD enables an in-depth study of architecture-level tradeoffs of power, area, and timing for 3D die-stacked DRAM designs. We demonstrate the utility of CACTI-3DD in analyzing design trade-offs of emerging 3D die-stacked DRAM main memories. We find that a coarse-grained 3D DRAM design that stacks canonical DRAM dies can only achieve marginal benefits in power, area, and timing compared to the original 2D design. To fully leverage the huge internal bandwidth of TSVs, DRAM dies must be re-architected, and system implications must be considered when building 3D DRAMs with redesigned 2D planar DRAM dies. Our results show that the 3D DRAM with re-architected DRAM dies achieves significant improvements in power and timing compared to the coarse-grained 3D die-stacked DRAM.


title: Utilizing a NoSQL Data Store for Scalable Log Analysis

abstract: A potential problem for persisting large volume of data logs with a conventional relational database is that loading massive logs produced at high rates is not fast enough due to the strong consistency model and high cost of indexing. As a possible alternative, a modern NoSQL data store, which sacrifices transactional consistency to achieve higher performance and scalability, can be utilized. In this paper, we investigate to what degree a state-of-the-art NoSQL database can achieve high performance persisting and fundamental analyses of large-scale data logs from real world applications. For the evaluation, a state-of-the-art NoSQL database, MongoDB, is compared with a relational DBMS from a major commercial vendor and with a popular open source relational DBMS. MongoDB is chosen as it provides both primary and secondary indexing compared to other popular NoSQL systems. These indexing techniques are essential for scalable processing of queries over large scale data logs. To explore the impact of parallelism on query execution, sharding was investigated for MongoDB. Our results revealed that relaxing the consistency did not provide substantial performance enhancement in persisting large-scale data logs for any of the systems. However, for high-performance loading and analysis of data logs, MongoDB is shown to be a viable alternative compared to relational databases for queries where the choice of an optimal execution plan is not critical.


title: Automated Negotiations: A Survey of the State of the Art

abstract: This paper provides a definition of automated negotiation within electronic commerce. It outlines two barriers to automated negotiation, the ontology issue and the strategy problem. State of the art overviews are given of automated negotiation, specifically Negotiation Support Systems, intelligent agents, the auction mechanism, and online marketspaces. Both academic research and currently functional systems are covered, and several World Wide Web addresses are given for readers who wish to investigate further on their own. 1 1 While every attempt is made to provide current URL locations, the Web changes more quickly than print media can ever capture. Hence, some of the URLs may not be current or correct by the time this article appears. We will try to keep our Negotiation Project web site, http://haas.berkeley.edu/~citm/nego-proj.html, current with respect to these addresses.


title: Survey on sketch based image retrieval methods

abstract: In many applications critical roles are played by face sketch-photo synthesis, such as law enforcement and digital entertainment. Several image based retrieval systems are text based, content based and sketch based. Image retrieval methods which use sketch content as input are referred to as Sketch Based Image Retrieval systems. In several ways an image can be retrieved from the database using user queries as input. One of the efficient methods and popular methods for retrieval is as Sketch Based Image Retrieval which is not necessary to have a high skill to draw the query sketch. Paper reviews the various sketch based image retrieval methods used in image processing and a comparison of all these methods is also done. The retrieval system using sketches can be effective and essential in our day to day life such as Medical diagnosis, digital library, search engines, crime prevention, photo sharing sites, geographical information, and sensing remote systems.


title: The effects of medial ulnar collateral ligament reconstruction on Major League pitching performance

abstract: Background Medial ulnar collateral ligament (MUCL) reconstruction is commonly performed on Major League Baseball (MLB) pitchers. Previous studies have reported that most pitchers return to presurgical statistical performance levels after MUCL reconstruction. Methods Pitching performance data—specifically, earned run average (ERA), walks and hits per inning pitched (WHIP), winning percentage, and innings pitched—were acquired for 168 MLB pitchers who had undergone MUCL reconstruction. These data were averaged over the 3 years before surgery and the 3 years after surgery and also acquired from 178 age-matched, uninjured MLB pitchers. Results Of the pitchers who had MUCL reconstruction surgery, 87% returned to MLB pitching. However, compared with presurgical data, pitching performance declined in terms of ERA ( P = .001), WHIP ( P = .011), and innings pitched ( P = .026). Pitching performance also declined in the season before the surgery compared with previous years (ERA, P = .014; WHIP, P = .036; innings pitched, P P = .004). Compared with age-matched control pitchers, the MUCL reconstruction pitchers had significantly more major league experience at the same age ( P Conclusion MUCL reconstruction allows most players to return to pitching at the major league level. However, after MUCL reconstruction, there is a statistically significant decline in pitching performance. There appears to be a statistically significant decline in pitching performance the year before reconstructive surgery, and this decline is also a risk factor for requiring surgery. In addition, there is an increased risk of MUCL reconstruction for pitchers who enter the major leagues at a younger age.


title: Integrated Circuits and Electrode Interfaces for Noninvasive Physiological Monitoring

abstract: This paper presents an overview of the fundamentals and state of the-art in noninvasive physiological monitoring instrumentation with a focus on electrode and optrode interfaces to the body, and micropower-integrated circuit design for unobtrusive wearable applications. Since the electrode/optrode-body interface is a performance limiting factor in noninvasive monitoring systems, practical interface configurations are offered for biopotential acquisition, electrode-tissue impedance measurement, and optical biosignal sensing. A systematic approach to instrumentation amplifier (IA) design using CMOS transistors operating in weak inversion is shown to offer high energy and noise efficiency. Practical methodologies to obviate 1/f noise, counteract electrode offset drift, improve common-mode rejection ratio, and obtain subhertz high-pass cutoff are illustrated with a survey of the state-of-the-art IAs. Furthermore, fundamental principles and state-of-the-art technologies for electrode-tissue impedance measurement, photoplethysmography, functional near-infrared spectroscopy, and signal coding and quantization are reviewed, with additional guidelines for overall power management including wireless transmission. Examples are presented of practical dry-contact and noncontact cardiac, respiratory, muscle and brain monitoring systems, and their clinical applications.


title: Ultrastructure and composition of Call-Exner bodies in bovine follicles

abstract: Call-Exner bodies are present in ovarian follicles of a range of species including human and rabbit, and in a range of human ovarian tumors. We have also found structures resembling Call-Exner bodies in bovine preantral and small antral follicles. Hematoxylin and eosin staining of single sections of bovine ovaries has shown that 30% of preantral follicles with more than one layer of granulosa cells and 45% of small (less than 650 microns) antral follicles have at least one Call-Exner body composed of a spherical eosinophilic region surrounded by a rosette of granulosa cells. Alcian blue stains the spherical eosinophilic region of the Call-Exner bodies. Electron microscopy has demonstrated that some Call-Exner bodies contain large aggregates of convoluted basal lamina, whereas others also contain regions of unassembled basal-lamina-like material. Individual chains of the basal lamina components type IV collagen (alpha 1 to alpha 5) and laminin (alpha 1, beta 2 and delta 1) have been immunolocalized to Call-Exner bodies in sections of fresh-frozen ovaries. Bovine Call-Exner bodies are presumably analogous to Call-Exner bodies in other species but are predominantly found in preantral and small antral follicles, rather than large antral follicles. With follicular development, the basal laminae of Call-Exner bodies change in their apparent ratio of type IV collagen to laminin, similar to changes observed in the follicular basal lamina, suggesting that these structures have a common cellular origin.


title: Novel resource allocation algorithms to performance and energy efficiency in cloud computing

abstract: The rapid growth in demand for computational power has led to a shift to the cloud computing model established by large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy. Cloud providers must ensure that their service delivery is flexible to meet various consumer requirements. However, to support green computing, cloud providers also need to minimize the cloud infrastructure energy consumption while conducting the service delivery. In this paper, for cloud environments, a novel QoS-aware VMs consolidation approach is proposed that adopts a method based on resource utilization history of virtual machines. Proposed algorithms have been implemented and evaluated using CloudSim simulator. Simulation results show improvement in QoS metrics and energy consumption as well as demonstrate that there is a trade-off between energy consumption and quality of service in the cloud environment.


title: A Top-Down Approach to Achieving Performance Predictability in Database Systems

abstract: While much of the research on transaction processing has focused on improving overall performance in terms of throughput and mean latency, surprisingly less attention has been given to performance predictability: how often individual transactions exhibit execution latency far from the mean. Performance predictability is increasingly important when transactions lie on the critical path of latency-sensitive applications, enterprise software, or interactive web services. In this paper, we focus on understanding and mitigating the sources of performance unpredictability in today's transactional databases. We conduct the first quantitative study of major sources of variance in MySQL, Postgres (two of the largest and most popular open-source products on the market), and VoltDB (a non-conventional database). We carry out our study with a tool called TProfiler that, given the source code of a database system and programmer annotations indicating the start and end of a transaction, is able to identify the dominant sources of variance in transaction latency. Based on our findings, we investigate alternative algorithms, implementations, and tuning strategies to reduce latency variance without compromising mean latency or throughput. Most notably, we propose a new lock scheduling algorithm, called Variance-Aware Transaction Scheduling (VATS), and a lazy buffer pool replacement policy. In particular, our modified MySQL exhibits significantly lower variance and 99th percentile latencies by up to 5.6× and 6.3×, respectively. Our proposal has been welcomed by the open-source community, and our VATS algorithm has already been adopted as of MySQL's 5.7.17 release (and been made the default scheduling policy in MariaDB).


title: Prospects for repellent in pest control: current developments and future challenges

abstract: The overall interest in environmentally safe pest control methods and the rise of insecticide resistance in pest populations have prompted medical and agricultural entomology research on insect repellents in recent years. However, conducting research on repellent is challenging for several reasons: (1) the different repellent phenomena are not well defined; (2) it is difficult to test for and quantify repellent; (3) the physiological mechanisms are poorly known; (4) the field efficacy appears to be highly variable. Here, we identified five different types of repellent: expellency, irritancy, deterrency, odor masking and visual masking, and described behavioral bioassays to differentiate between them. Although these categories are currently defined by their behavioral response to different stimuli, we suggest new definitions based on their mechanism of action. We put forward three main hypotheses on the physiological mechanism: (1) a dose effect that modifies the behavior, (2) a repellent mechanism with specific receptors, or (3) inhibition of the transduction of neural information.


title: Screen-Camera Calibration using a Spherical Mirror

abstract: Developments in the consumer market have indicated that the average user of a personal computer is likely to also own a webcam. With the emergence of this new user group will come a new set of applications, which will require a user-friendly way to calibrate the position of the camera with respect to the location of the screen. This paper presents a fully automatic method to calibrate a screen-camera setup, using a single moving spherical mirror. Unlike other methods, our algorithm needs no user intervention other then moving around a spherical mirror. In addition, if the user provides the algorithm with the exact radius of the sphere in millimeters, the scale of the computed solution is uniquely defined.


title: Scale-Invariant Local Descriptor for Event Recognition in 1D Sensor Signals

abstract: In this paper, we introduce a shape-based, time-scale invariant feature descriptor for 1-D sensor signals. The timescale invariance of the feature allows us to use feature from one training event to describe events of the same semantic class which may take place over varying time scales such as walking slow and walking fast. Therefore it requires less training set. The descriptor takes advantage of the invariant location detection in the scale space theory and employs a high level shape encoding scheme to capture invariant local features of events. Based on this descriptor, a scale-invariant classifier with “R” metric (SIC-R) is designed to recognize multi-scale events of human activities. The R metric combines the number of matches of keypoint in scale space with the Dynamic Time Warping score. SIC-R is tested on various types of 1-D sensors data from passive infrared, accelerometer and seismic sensors with more than 90% classification accuracy.


title: The self-fulfilling prophecy in close relationships

abstract: The authors hypothesized a self-fulfilling prophecy wherein rejection expectancies lead people to behave in ways that elicit rejection from their dating partners. The hypothesis was tested in 2 studies of conflict in couples: (a) a longitudinal field study where couples provided daily-diary reports and (b) a lab study involving behavioral observations. Results from the field study showed that high rejection-sensitive (HRS) people's relationships were more likely to break up than those of low rejection-sensitive (LRS) people. Conflict processes that contribute to relationship erosion were revealed for HRS women but not for HRS men. Following naturally occurring relationship conflicts, HRS women's partners were more rejecting than were LRS women's partners. The lab study showed that HRS women's negative behavior during conflictual discussions helped explain their partners' more rejecting postconflict responses.


title: Shield: vulnerability-driven network filters for preventing known vulnerability exploits

abstract: Software patching has not been effective as a first-line defense against large-scale worm attacks, even when patches have long been available for their corresponding vulnerabilities. Generally, people have been reluctant to patch their systems immediately, because patches are perceived to be unreliable and disruptive to apply. To address this problem, we propose a first-line worm defense in the network stack, using shields -- vulnerability-specific, exploit-generic network filters installed in end systems once a vulnerability is discovered, but before a patch is applied. These filters examine the incoming or outgoing traffic of vulnerable applications, and correct traffic that exploits vulnerabilities. Shields are less disruptive to install and uninstall, easier to test for bad side effects, and hence more reliable than traditional software patches. Further, shields are resilient to polymorphic or metamorphic variations of exploits [43].In this paper, we show that this concept is feasible by describing a prototype Shield framework implementation that filters traffic above the transport layer. We have designed a safe and restrictive language to describe vulnerabilities as partial state machines of the vulnerable application. The expressiveness of the language has been verified by encoding the signatures of several known vulnerabilites. Our evaluation provides evidence of Shield's low false positive rate and small impact on application throughput. An examination of a sample set of known vulnerabilities suggests that Shield could be used to prevent exploitation of a substantial fraction of the most dangerous ones.


Quantum Computing : A High-Level Overview


title: Multi-label classification using boolean matrix decomposition

abstract: This paper introduces a new multi-label classifier based on Boolean matrix decomposition. Boolean matrix decomposition is used to extract, from the full label matrix, latent labels representing useful Boolean combinations of the original labels. Base level models predict latent labels, which are subsequently transformed into the actual labels by Boolean matrix multiplication with the second matrix from the decomposition. The new method is tested on six publicly available datasets with varying numbers of labels. The experimental evaluation shows that the new method works particularly well on datasets with a large number of labels and strong dependencies among them.


title: A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers

abstract: Weight pruning methods for deep neural networks (DNNs) have been investigated recently, but prior work in this area is mainly heuristic, iterative pruning, thereby lacking guarantees on the weight reduction ratio and convergence time. To mitigate these limitations, we present a systematic weight pruning framework of DNNs using the alternating direction method of multipliers (ADMM). We first formulate the weight pruning problem of DNNs as a nonconvex optimization problem with combinatorial constraints specifying the sparsity requirements, and then adopt the ADMM framework for systematic weight pruning. By using ADMM, the original nonconvex optimization problem is decomposed into two subproblems that are solved iteratively. One of these subproblems can be solved using stochastic gradient descent, the other can be solved analytically. Besides, our method achieves a fast convergence rate. The weight pruning results are very promising and consistently outperform the prior work. On the LeNet-5 model for the MNIST data set, we achieve 71.2 times weight reduction without accuracy loss. On the AlexNet model for the ImageNet data set, we achieve 21 times weight reduction without accuracy loss. When we focus on the convolutional layer pruning for computation reductions, we can reduce the total computation by five times compared with the prior work (achieving a total of 13.4 times weight reduction in convolutional layers). Our models and codes are released at https://github.com/KaiqiZhang/admm-pruning


title: Methodology for customer relationship management

abstract: Customer relationship management (CRM) is a customer-focused business strategy that dynamically integrates sales, marketing and customer care service in order to create and add value for the company and its customers.This change towards a customer-focused strategy is leading to a strong demand for CRM solutions by companies. However, in spite of companies' interest in this new management model, many CRM implementations fail. One of the main reasons for this lack of success is that the existing methodologies being used to approach a CRM project are not adequate, since they do not satisfactorily integrate and complement the strategic and technological aspects of CRM.This paper describes a formal methodology for directing the process of developing and implementing a CRM System that considers and integrates various aspects, such as defining a customer strategy, re-engineering customer-oriented business processes, human resources management, the computer system, management of change and continuous improvement.


title: Faster, Cheaper, Better – a Hybridization Methodology to Develop Linear Algebra Software for

abstract: In this chapter, we present a hybridization methodology for the development of linear algebra software for GPUs. The methodology is successfully used in MAGMA – a new generation of linear algebra libraries, similar in functionality to LAPACK, but extended for hybrid, GPU-based systems. Algorithms of interest are split into computational tasks. The tasks' execution is scheduled over the computational components of a hybrid system of multicore CPUs with GPU accelerators using StarPU – a runtime system for accelerator-based multicore architectures. StarPU enables to express parallelism through sequential-like code and schedules the different tasks over the hybrid processing units. The productivity becomes then fast and cheap as the development is high level, using existing software infrastructure. Moreover, the resulting hybrid algorithms are better performance-wise than corresponding homogeneous algorithms designed exclusively for either GPUs or homogeneous multicore CPUs.


title: Online Adaptive Passive-Aggressive Methods for Non-Negative Matrix Factorization and Its Applications

abstract: This paper aims to investigate efficient and scalable machine learning algorithms for resolving Non-negative Matrix Factorization (NMF), which is important for many real-world applications, particularly for collaborative filtering and recommender systems. Unlike traditional batch learning methods, a recently proposed online learning technique named "NN-PA" tackles NMF by applying the popular Passive-Aggressive (PA) online learning, and found promising results. Despite its simplicity and high efficiency, NN-PA falls short in at least two critical limitations: (i) it only exploits the first-order information and thus may converge slowly especially at the beginning of online learning tasks; (ii) it is sensitive to some key parameters which are often difficult to be tuned manually, particularly in a practical online learning system. In this work, we present a novel family of online Adaptive Passive-Aggressive (APA) learning algorithms for NMF, named "NN-APA", which overcomes two critical limitations of NN-PA by (i) exploiting second-order information to enhance PA in making more informative updates at each iteration; and (ii) achieving the parameter auto-selection by exploring the idea of online learning with expert advice in deciding the optimal combination of the key parameters in NMF. We theoretically analyze the regret bounds of the proposed method and show its advantage over the state-of-the-art NN-PA method, and further validate the efficacy and scalability of the proposed technique through an extensive set of experiments on a variety of large-scale real recommender systems datasets.


title: The TIPSTER SUMMAC Text Summarization Evaluation

abstract: The TIPSTER Text Summarization Evaluation (SUMMAC) has established definitively that automatic text summarization is very effective in relevance assessment tasks. Summaries as short as 17% of full text length sped up decision-making by almost a factor of 2 with no statistically significant degradation in F-score accuracy. SUMMAC has also introduced a new intrinsic method for automated evaluation of informative summaries.


title: Driver fatigue detection based on saccadic eye movements

abstract: The correct determination of driver's level of fatigue has been of vital importance for the safety of driving. There are various methods, such as analyzing facial expression, eyelid activity, and head movements to assess the fatigue level of drivers. This paper describes the design and prototype implementation of a driver fatigue level determination system based on detection of saccadic eye movements. Driver's eye movement speed is used to assess driver's fatigue level. The information about eyes is obtained via infrared led camera device. Movements of pupils were recorded in two driving scenarios with different traffic density. In the first scenario, the traffic density was set to low while the second scenario was based on high density and aggressive traffic. Based on the movements of pupils, the data on saccadic eye movement was analyzed to determine fatigue level of the driver. Acceleration, speed, and size of pupils at both traffic scenarios were compared with data mining techniques, such as segmentation adaptive peak, entropy, and data distribution analyses. Significantly different levels of fatigue were found between the tired and vigorous driver for the different types of scenarios.


title: Contextual Semantic Parsing using Crowdsourced Spatial Descriptions

abstract: We describe a contextual parser for the Robot Commands Treebank, a new crowdsourced resource. In contrast to previous semantic parsers that select the most-probable parse, we consider the different problem of parsing using additional situational context to disambiguate between different readings of a sentence. We show that multiple semantic analyses can be searched using dynamic programming via interaction with a spatial planner, to guide the parsing process. We are able to parse sentences in near linear-time by ruling out analyses early on that are incompatible with spatial context. We report a 34% upper bound on accuracy, as our planner correctly processes spatial context for 3,394 out of 10,000 sentences. However, our parser achieves a 96.53% exact-match score for parsing within the subset of sentences recognized by the planner, compared to 82.14% for a non-contextual parser.


MICROSTRIP PATCH ANTENNA FOR DUAL BAND OPERATION


title: Perceptron learning for reuse prediction

abstract: The disparity between last-level cache and memory latencies motivates the search for efficient cache management policies. Recent work in predicting reuse of cache blocks enables optimizations that significantly improve cache performance and efficiency. However, the accuracy of the prediction mechanisms limits the scope of optimization. This paper proposes perceptron learning for reuse prediction. The proposed predictor greatly improves accuracy over previous work. For multi- programmed workloads, the average false positive rate of the proposed predictor is 3.2%, while sampling dead block prediction (SDBP) and signature-based hit prediction (SHiP) yield false positive rates above 7%. The improvement in accuracy translates directly into performance. For single-thread workloads and a 4MB lastlevel cache, reuse prediction with perceptron learning enables a replacement and bypass optimization to achieve a geometric mean speedup of 6.1%, compared with 3.8% for SHiP and 3.5% for SDBP on the SPEC CPU 2006 benchmarks. On a memory-intensive subset of SPEC, perceptron learning yields 18.3% speedup, versus 10.5% for SHiP and 7.7% for SDBP. For multi- programmed workloads and a 16MB cache, the proposed technique doubles the efficiency of the cache over LRU and yields a geometric mean normalized weighted speedup of 7.4%, compared with 4.4% for SHiP and 4.2% for SDBP.


title: Entity Comparison in RDF Graphs

abstract: In many applications, there is an increasing need for the new types of RDF data analysis that are not covered by standard reasoning tasks such as SPARQL query answering. One such important analysis task is entity comparison, i.e., determining what are similarities and differences between two given entities in an RDF graph. For instance, in an RDF graph about drugs, we may want to compare Metamizole and Ibuprofen and automatically find out that they are similar in that they are both analgesics but, in contrast to Metamizole, Ibuprofen also has a considerable anti-inflammatory effect. Entity comparison is a widely used functionality available in many information systems, such as universities or product comparison websites. However, comparison is typically domain-specific and depends on a fixed set of aspects to compare. In this paper, we propose a formal framework for domain-independent entity comparison over RDF graphs. We model similarities and differences between entities as SPARQL queries satisfying certain additional properties, and propose algorithms for computing them.


title: High-Order Distance-Based Multiview Stochastic Learning in Image Classification

abstract: How do we find all images in a larger set of images which have a specific content? Or estimate the position of a specific object relative to the camera? Image classification methods, like support vector machine (supervised) and transductive support vector machine (semi-supervised), are invaluable tools for the applications of content-based image retrieval, pose estimation, and optical character recognition. However, these methods only can handle the images represented by single feature. In many cases, different features (or multiview data) can be obtained, and how to efficiently utilize them is a challenge. It is inappropriate for the traditionally concatenating schema to link features of different views into a long vector. The reason is each view has its specific statistical property and physical interpretation. In this paper, we propose a high-order distance-based multiview stochastic learning (HD-MSL) method for image classification. HD-MSL effectively combines varied features into a unified representation and integrates the labeling information based on a probabilistic framework. In comparison with the existing strategies, our approach adopts the high-order distance obtained from the hypergraph to replace pairwise distance in estimating the probability matrix of data distribution. In addition, the proposed approach can automatically learn a combination coefficient for each view, which plays an important role in utilizing the complementary information of multiview data. An alternative optimization is designed to solve the objective functions of HD-MSL and obtain different views on coefficients and classification scores simultaneously. Experiments on two real world datasets demonstrate the effectiveness of HD-MSL in image classification.


title: Speeding up feature selection: A deep-inspired network pruning algorithm

abstract: To address the high-dimensionality of big data, numerous iterative algorithms have been introduced including least absolute shrinkage selection operator (Lasso) and iteratively sure independent screening (ISIS). However, the iterative nature of these algorithms renders the computational cost of retraining the learning model impractical. We take advantage of this key observation to propose a novel non-iterative algorithm inspired by deep neural network input reconstruction. Our proposed technique provides faster feature selection by training the model once. This is achieved by exploiting the cross entropy error between the input and the estimates. Simulation studies support our approach on several real world datasets. For rigor, a comparative analysis of the computational complexity is also provided to assert the advantage of our approach.


title: EM Algorithms for PCA and SPCA

abstract: I present an expectation-maximization (EM) algorithm for principal component analysis (PCA). The algorithm allows a few eigenvectors and eigenvalues to be extracted from large collections of high dimensional data. It is computationally very efficient in space and time. It also naturally accommodates missing information. I also introduce a new variant of PCA called sensible principal component analysis (SPCA) which defines a proper density model in the data space. Learning for SPCA is also done with an EM algorithm. I report results on synthetic and real data showing that these EM algorithms correctly and efficiently find the leading eigenvectors of the covariance of datasets in a few iterations using up to hundreds of thousands of datapoints in thousands of dimensions.


title: Epigenetic regulation of vitamin D 24-hydroxylase/CYP24A1 in human prostate cancer.

abstract: Calcitriol, a regulator of calcium homeostasis with antitumor properties, is degraded by the product of the CYP24A1 gene, which is downregulated in human prostate cancer by unknown mechanisms. We found that CYP24A1 expression is inversely correlated with promoter DNA methylation in prostate cancer cell lines. Treatment with the DNA methyltransferase inhibitor 5-aza-2'-deoxycytidine (DAC) activates CYP24A1 expression in prostate cancer cells. In vitro methylation of the CYP24A1 promoter represses its promoter activity. Furthermore, inhibition of histone deacetylases by trichostatin A (TSA) enhances the expression of CYP24A1 in prostate cancer cells. Quantitative chromatin immunoprecipitation-PCR (ChIP-qPCR) reveals that specific histone modifications are associated with the CYP24A1 promoter region. Treatment with TSA increases H3K9ac and H3K4me2 and simultaneously decreases H3K9me2 at the CYP24A1 promoter. ChIP-qPCR assay reveals that treatment with DAC and TSA increases the recruitment of vitamin D receptor to the CYP24A1 promoter. Reverse transcriptase-PCR analysis of paired human prostate samples revealed that CYP24A1 expression is downregulated in prostate malignant lesions compared with adjacent histologically benign lesions. Bisulfite pyrosequencing shows that CYP24A1 gene is hypermethylated in malignant lesions compared with matched benign lesions. Our findings indicate that repression of CYP24A1 gene expression in human prostate cancer cells is mediated in part by promoter DNA methylation and repressive histone modifications.


title: 6F2 buried wordline DRAM cell for 40nm and beyond

abstract: We present a 46 nm 6F2 buried word-line (bWL) DRAM technology, enabling the smallest cell size of 0.013 mum2 published to date. The TiN/ W buried word-line is built below the Si surface, forming a low resistive interconnect and the metal gate of the array transistors. We demonstrate high array device on-current, small parameter variability, high reliability and small parasitic capacitances, resulting in an excellent array performance. The array device can be scaled down to 30 nm without compromising its performance.


title: Action Search: Spotting Actions in Videos and Its Application to Temporal Action Localization

abstract: State-of-the-art temporal action detectors inefficiently search the entire video for specific actions. Despite the encouraging progress these methods achieve, it is crucial to design automated approaches that only explore parts of the video which are the most relevant to the actions being searched for. To address this need, we propose the new problem of action spotting in video, which we define as finding a specific action in a video while observing a small portion of that video. Inspired by the observation that humans are extremely efficient and accurate in spotting and finding action instances in video, we propose Action Search, a novel Recurrent Neural Network approach that mimics the way humans spot actions. Moreover, to address the absence of data recording the behavior of human annotators, we put forward the Human Searches dataset, which compiles the search sequences employed by human annotators spotting actions in the AVA and THUMOS14 datasets. We consider temporal action localization as an application of the action spotting problem. Experiments on the THUMOS14 dataset reveal that our model is not only able to explore the video efficiently (observing on average 17.3% of the video) but it also accurately finds human activities with 30.8% mAP.


Emotion Analysis from Text : A Survey


title: Replication, falsification, and the crisis of confidence in social psychology

abstract: The (latest) “crisis in confidence” in social psychology has generated much heated discussion about the importance of replication, including how such replication should be carried out as well as interpreted by scholars in the field. What does it mean if a replication attempt “fails”—does it mean that the original results, or the theory that predicted them, have been falsified? And how should “failed” replications affect our belief in the validity of the original research? In this paper, we consider the “replication” debate from a historical and philosophical perspective, and provide a conceptual analysis of both replication and falsification as they pertain to this important discussion. Along the way, we introduce a Bayesian framework for assessing “failed” replications in terms of how they should affect our confidence in purported findings.


title: Modeling and Predicting Cyber Hacking Breaches

abstract: Analyzing cyber incident data sets is an important method for deepening our understanding of the evolution of the threat situation. This is a relatively new research topic, and many studies remain to be done. In this paper, we report a statistical analysis of a breach incident data set corresponding to 12 years (2005–2017) of cyber hacking activities that include malware attacks. We show that, in contrast to the findings reported in the literature, both hacking breach incident inter-arrival times and breach sizes should be modeled by stochastic processes, rather than by distributions because they exhibit autocorrelations. Then, we propose particular stochastic process models to, respectively, fit the inter-arrival times and the breach sizes. We also show that these models can predict the inter-arrival times and the breach sizes. In order to get deeper insights into the evolution of hacking breach incidents, we conduct both qualitative and quantitative trend analyses on the data set. We draw a set of cybersecurity insights, including that the threat of cyber hacks is indeed getting worse in terms of their frequency, but not in terms of the magnitude of their damage.


title: Bacterial Pathogens and Community Composition in Advanced Sewage Treatment Systems Revealed by Metagenomics Analysis Based on High-Throughput Sequencing

abstract: This study used 454 pyrosequencing, Illumina high-throughput sequencing and metagenomic analysis to investigate bacterial pathogens and their potential virulence in a sewage treatment plant (STP) applying both conventional and advanced treatment processes. Pyrosequencing and Illumina sequencing consistently demonstrated that Arcobacter genus occupied over 43.42% of total abundance of potential pathogens in the STP. At species level, potential pathogens Arcobacter butzleri, Aeromonas hydrophila and Klebsiella pneumonia dominated in raw sewage, which was also confirmed by quantitative real time PCR. Illumina sequencing also revealed prevalence of various types of pathogenicity islands and virulence proteins in the STP. Most of the potential pathogens and virulence factors were eliminated in the STP, and the removal efficiency mainly depended on oxidation ditch. Compared with sand filtration, magnetic resin seemed to have higher removals in most of the potential pathogens and virulence factors. However, presence of the residual A. butzleri in the final effluent still deserves more concerns. The findings indicate that sewage acts as an important source of environmental pathogens, but STPs can effectively control their spread in the environment. Joint use of the high-throughput sequencing technologies is considered a reliable method for deep and comprehensive overview of environmental bacterial virulence.


title: The bleak future of nand flash memory

abstract: In recent years, flash-based SSDs have grown enormously both in capacity and popularity. In high-performance enterprise storage applications, accelerating adoption of SSDs is predicated on the ability of manufacturers to deliver performance that far exceeds disks while closing the gap in cost per gigabyte. However, while flash density continues to improve, other metrics such as a reliability, endurance, and performance are all declining. As a result, building larger-capacity flash-based SSDs that are reliable enough to be useful in enterprise settings and high-performance enough to justify their cost will become challenging. ::: ::: In this work, we present our empirical data collected from 45 flash chips from 6 manufacturers and examine the performance trends for these raw flash devices as flash scales down in feature size. We use this analysis to predict the performance and cost characteristics of future SSDs. We show that future gains in density will come at significant drops in performance and reliability. As a result, SSD manufacturers and users will face a tough choice in trading off between cost, performance, capacity and reliability.


title: Holter monitoring to detect silent atrial fibrillation in high-risk subjects: the Perugia General Practitioner Study

abstract: Atrial fibrillation (AF) is diagnosed for the first time in about 5 % of patients admitted for acute ischemic stroke. Advanced aged and arterial hypertension are risk factors for AF. We evaluated the prevalence of silent AF in subjects with advanced age and systemic arterial hypertension. Subjects of both gender, aged 65 years or more with systemic arterial hypertension were randomly identified from the patient lists of the participating general practitioners in the Perugia area, in Italy. Study subjects underwent baseline 12-lead ECG and, if this did not show AF, 48-h Holter monitoring was performed. AF was known and confirmed by 12-lead ECG in 4 out of the 308 evaluated subjects (1.3 %). Baseline 12-lead ECG showed no cases of silent AF. Holter monitoring was performed in 300 subjects, mean age 70 ± 4. Twenty-six recordings were not evaluable for the presence of artifacts; therefore, 274 subjects were included in the analysis. Holter monitoring showed AF in 27 out of 274 subjects (10 %; 95 % confidence interval 6.4–13.5 %); AF was longer than 30 s in four of the subjects. In 56 additional subjects, Holter monitoring revealed excessive supraventricular ectopic activity (20 %; 95 % confidence interval 15.3–24.7 %). Holter monitoring was able to detect silent AF in about 10 % of subjects aged 65 or above with systemic arterial hypertension. The risk of stroke associated with screened silent AF should be carefully evaluated.


title: Potential applications of thermoelectric waste heat recovery in the automotive industry

abstract: Several proposed applications of thermoelectric (TE) waste heat recovery devices in the automotive industry are reviewed. To assess the feasibility of these applications at a vehicle level, the effect of electrical load and weight on fuel economy for a series of cars and trucks was investigated. These results will help us to identify the appropriate vehicle platforms for TE waste heat recovery, and to establish a set of requirements for an automotive TE waste heat recovery subsystem. The key to the realization of this technology is still the continued development of new materials with increased TE efficiency.


title: Control method of robot suit HAL working as operator's muscle using biological and dynamical information

abstract: For assisting human motion, assistive devices working as muscles would be useful. A robot suit HAL (hybrid assistive limb) has been developed as an assistive device for lower limbs. Human can appropriately produce muscle contraction torque and control joint viscoelasticity by muscle effort such as co-contraction. Thus, to implement functions equivalent to human muscles using HAL, it is necessary to control viscoelasticity of HAL as well as to produce torque in accordance with operator's intention. Therefore the purpose of this study is to propose a control method of HAL using biological and motion information. In this method, HAL produces torque corresponding to muscle contraction torque by referring to the myoelectricity that is biological information to control operator's muscles. In addition, the viscoelasticities of HAL are adjusted in proportion to operator's viscoelasticity that is estimated from motion information by using an on-line parameter identification method. To evaluate the effectiveness of the proposed method, the method was applied to a swinging motion of a lower leg. When this method was applied, HAL could work like operator's muscles in the swinging motion, and as a consequence, the muscle activities of the operator were reduced. As a result of this experiment, we confirmed the effectiveness of the proposed method.


title: Autophagy wins the 2016 Nobel Prize in Physiology or Medicine: Breakthroughs in baker's yeast fuel advances in biomedical research

abstract: Abstract Autophagy is an ancient pathway in which parts of eukaryotic cells are self-digested within the lysosome or vacuole. This process has been studied for the past seven decades; however, we are only beginning to gain a molecular understanding of the key steps required for autophagy. Originally characterized as a hormonal and starvation response, we now know that autophagy has a much broader role in biology, including organellar remodeling, protein and organelle quality control, prevention of genotoxic stress, tumor suppression, pathogen elimination, regulation of immunity and inflammation, maternal DNA inheritance, metabolism, and cellular survival. Although autophagy is usually a degradative pathway, it also participates in biosynthetic and secretory processes. Given that autophagy has a fundamental role in many essential cellular functions, it is not surprising that autophagic dysfunction is associated with a wide range of human diseases. Genetic studies in various fungi, particularly Saccharomyces cerevisiae, provided the key initial breakthrough that led to an explosion of research on the basic mechanisms and the physiological connections of autophagy to health and disease. The Nobel Committee has recognized this breakthrough by the awarding of the 2016 Nobel Prize in Physiology or Medicine for research in autophagy.


title: The influence of clothing on first impressions : Rapid and positive responses to minor changes in male attire

abstract: Purpose – Clothing communicates information about the wearer and first impressions can be heavily influenced by the messages conveyed by attire. The purpose of this paper is to investigate the effect of minor changes in clothing on the perception of a male model, in the absence of facial information with limited time exposure.Design/methodology/approach – In an on‐line study, 274 participants rated four images on five dimensions (confidence, success, trustworthiness, salary and flexibility). The man was depicted wearing a bespoke (made‐to‐measure) and a regular (off‐the‐peg) suit, which differed only in minor details. Participants saw the faceless images for a maximum five seconds.Findings – The man was rated more positively on all attributes apart from trustworthiness when pictured in the bespoke suit. The earnings of participants also played a role in perception, with higher earners giving lower ratings to both suit types.Practical implications – Minor clothing manipulations can give rise to significant...


title: Pattern and Cluster Mining on Text Data

abstract: Due to heavy use of electronics devices nowadays most of the information is available in electronic format and a substantial portion of information is stored as text such as in news articles, technical papers, books, digital libraries, email messages, blogs, and web pages. Mining the knowledge like pattern finding or clustering of similar kind of words is one of the important issues nowadays. This paper focuses on mining the important information from the text data. This paper uses the stories data set from project Guttenbergs William Shakespeare stories dataset for experimental study. R is used as Text Mining and statistical analysis tool in Ubuntu 12.04 LTS Linux Operating System. Frequent pattern mining is used to find the frequent terms, appeared in the documents and word Association among two or more words is measured at a given threshold value. Our algorithm uses cosine similarity in order to measure the distance between the words before clustering. The algorithm may be use to find the similarity between stories, news, emails. In this paper k-means and hierarchical agglomerative clustering algorithm is used to form the cluster.


title: 6-Shogaol reduced chronic inflammatory response in the knees of rats treated with complete Freund's adjuvant

abstract: Background ::: 6-Shogaol is one of the major compounds in the ginger rhizome that may contribute to its anti-inflammatory properties. Confirmation of this contribution was sought in this study in Sprague- Dawley rats (200–250 g) treated with a single injection (0.5 ml of 1 mg/ml) of a commercial preparation of complete Freund's Adjuvant (CFA) to induce monoarthritis in the right knee over a period of 28 days. During this development of arthritis, each rat received a daily oral dose of either peanut oil (0.2 ml-control) or 6-shogaol (6.2 mg/Kg in 0.2 ml peanut oil).


title: Two-stage multi-intent detection for spoken language understanding

abstract: This paper presents a system to detect multiple intents (MIs) in an input sentence when only single-intent (SI)-labeled training data are available. To solve the problem, this paper categorizes input sentences into three types and uses a two-stage approach in which each stage attempts to detect MIs in different types of sentences. In the first stage, the system generates MI hypotheses based on conjunctions in the input sentence, then evaluates the hypotheses and then selects the best one that satisfies specified conditions. In the second stage, the system applies sequence labeling to mark intents on the input sentence. The sequence labeling model is trained based on SI-labeled training data. In experiments, the proposed two-stage MI detection method reduced errors for written and spoken input by 20.54 and 17.34 % respectively.


Rapid Manufacture of Novel Variable Impedance Robots


title: Second-order Optimization Method for Large Mini-batch: Training ResNet-50 on ImageNet in 35 Epochs.

abstract: Large-scale distributed training of deep neural networks suffer from the generalization gap caused by the increase in the effective mini-batch size. Previous approaches try to solve this problem by varying the learning rate and batch size over epochs and layers, or some ad hoc modification of the batch normalization. We propose an alternative approach using a second-order optimization method that shows similar generalization capability to first-order methods, but converges faster and can handle larger mini-batches. To test our method on a benchmark where highly optimized first-order methods are available as references, we train ResNet-50 on ImageNet. We converged to 75% Top-1 validation accuracy in 35 epochs for mini-batch sizes under 16,384, and achieved 75% even with a mini-batch size of 131,072, which took 100 epochs.


title: Calculation of power and sample size with bounded outcome scores

abstract: The two-sample Wilcoxon rank sum test is the most popular non-parametric test for the comparison of two samples when the underlying distributions are not normal. Although the underlying distributions need not be known in detail to calculate the null distribution of the test statistic, parametric assumptions are often made to determine the power of the test or the sample size. We encountered difficulties with this approach in the planning of a recent clinical trial in stroke patients. It is shown that, for power and sample size estimation, it can be dangerous to apply the classical formulae routinely, especially with outcome scores having a U-shaped or a J-shaped distribution. As an example we have taken the Barthel index, a quality-of-life outcome measure in stroke patients. Further, we have investigated alternative methods by means of Monte Carlo simulation. The distributional characteristics of the estimated powers were compared. Our findings suggest more appropriate computer software is necessary for the calculation of power and sample size when efficacy is measured by a non-parametric method.


title: Targeting Business Users with Decision Table Classifiers

abstract: Business users and analysts commonly use spreadsheets and 2D plots to analyze and understand their data. On-line Analytical Processing (OLAP) provides these users with added flexibility in pivoting data around different attributes and drilling up and down the multi-dimensional cube of aggregations. Machine learning researchers, however, have concentrated on hypothesis spaces that are foreign to most users: hyper-planes (Perceptrons), neural networks, Bayesian networks, decision trees, nearest neighbors, etc. In this paper we advocate the use of decision table classifiers that are easy for line-of-business users to understand. We describe several variants of algorithms for learning decision tables, compare their performance, and describe a visualization mechanism that we have implemented in MineSet. The performance of decision tables is comparable to other known algorithms, such as C4.5/C5.0, yet the resulting classifiers use fewer attributes and are more comprehensible.


title: Large gain linear series-fed microstrip antenna arrays at Ka and C bands

abstract: In this paper, the design of a series-fed microstrip antenna array is discussed and its design challenge of limited gain is explained. It discusses that the phase between the patches of the array plays a significant role in overcoming the challenge. A 23 element series fed linear array at Ka-band gives gain of 19dBi with beam in broadside direction and has SLL better than −15dB. A scaled down version at C-band has been designed and fabricated. A 7-element series fed antenna array yielded measured gain of 15.1 dBi at 5.79 GHz. This design can be extended for planar antenna arrays to obtain higher gain.


title: Analyzing an automotive testing process with evidence-based software engineering

abstract: Context: Evidence-based software engineering (EBSE) provides a process for solving practical problems based on a rigorous research approach. The primary focus so far was on mapping and aggregating evidence through systematic reviews. Objectives: We extend existing work on evidence-based software engineering by using the EBSE process in an industrial case to help an organization to improve its automotive testing process. With this we contribute in (1) providing experiences on using evidence based processes to analyze a real world automotive test process and (2) provide evidence of challenges and related solutions for automotive software testing processes. Methods: In this study we perform an in-depth investigation of an automotive test process using an extended EBSE process including case study research (gain an understanding of practical questions to define a research scope), systematic literature review (identify solutions through systematic literature), and value stream mapping (map out an improved automotive test process based on the current situation and improvement suggestions identified). These are followed by reflections on the EBSE process used. Results: In the first step of the EBSE process we identified 10 challenge areas with a total of 26 individual challenges. For 15 out of those 26 challenges our domain specific systematic literature review identified solutions. Based on the input from the challenges and the solutions, we created a value stream map of the current and future process. Conclusions: Overall, we found that the evidence-based process as presented in this study helps in technology transfer of research results to industry, but at the same time some challenges lie ahead (e.g. koping systematic reviews to focus more on concrete industry problems, and understanding strategies of conducting EBSE with respect to effort and quality of the evidence). (C) 2013 Elsevier B.V. All rights reserved. (Less)


title: Personalization versus Privacy: An Empirical Examination of the Online Consumer's Dilemma

abstract: Personalization refers to the tailoring of products and purchase experience to the tastes of individual consumers based upon their personal and preference information. Recent advances in information acquisition and processing technologies have allowed online vendors to offer varieties of web-based personalization that not only increases switching costs, but also serves as important means of acquiring valuable customer information. However, investments in online personalization may be severely undermined if consumers do not use these services due to privacy concerns. In the absence of any empirical evidence that seeks to understand this consumer dilemma, our research develops a parsimonious model to predict consumers' usage of online personalization as a result of the tradeoff between their value for personalization and concern for privacy. In addition to this tradeoff, we find that a consumer's intent to use personalization services is positively influenced by her trust in the vendor. Our findings suggest that: 1. online vendors can improve their abilities to acquire and use customer information through trust building activities; 2. it is of critical importance that vendors understand and evaluate the different values consumers may place in enjoying various types of personalization.


title: EnclaveDB: A Secure Database Using SGX

abstract: We propose EnclaveDB, a database engine that guarantees confidentiality, integrity, and freshness for data and queries. EnclaveDB guarantees these properties even when the database administrator is malicious, when an attacker has compromised the operating system or the hypervisor, and when the database runs in an untrusted host in the cloud. EnclaveDB achieves this by placing sensitive data (tables, indexes and other metadata) in enclaves protected by trusted hardware (such as Intel SGX). EnclaveDB has a small trusted computing base, which includes an in-memory storage and query engine, a transaction manager and pre-compiled stored procedures. A key component of EnclaveDB is an efficient protocol for checking integrity and freshness of the database log. The protocol supports concurrent, asynchronous appends and truncation, and requires minimal synchronization between threads. Our experiments using standard database benchmarks and a performance model that simulates large enclaves show that EnclaveDB achieves strong security with low overhead (up to 40% for TPC-C) compared to an industry strength in-memory database engine.


title: Application of the Laminar Navier–Stokes Equations for Solving 2D and 3D Pathfinding Problems with Static and Dynamic Spatial Constraints: Implementation and Validation in Comsol Multiphysics

abstract: Pathfinding problems consist in determining the optimal shortest path, or at least one path, between two points in the space. In this paper, we propose a particular approach, based on methods used in computational fluid dynamics, that intends to solve such problems. In particular, we reformulate pathfinding problems as the motion of a viscous fluid via the use of the laminar Navier–Stokes equations completed with suitable boundary conditions corresponding to some characteristics of the considered problem: position of the initial and final points, a-priori information of the terrain, One-way routes and dynamic spatial configuration. Then, we propose and validate a numerical implementation of this methodology by using Comsol Multiphysics (i.e., a finite element methods software) and by considering various experiments. We compare the obtained results with those returned by a classical pathfinding algorithm. Finally, we perform a sensitivity analysis of the proposed algorithms with respect to some key parameters.


title: Customer revisit intention to restaurants: Evidence from online reviews

abstract: One of the generally recognized marketing principles is that retaining customers is more profitable than winning prospective customers. Therefore, how to retain existing customers and improve their repeat purchases is an important consideration for practitioners to gain profit. The purpose of this study is to investigate factors influencing customer revisit intention to restaurants by analyzing online reviews. We used regression analysis to analyze quantitative scores of 10,136 restaurant reviews collected from an online life community in China, and found that food quality, price and value, service quality, and atmosphere are the antecedents of restaurant customers' revisit intention, and that restaurant type moderates the effect of customer satisfaction with service quality, atmosphere, and price and value on revisit intention. We also used text mining technology to identify detailed evaluation indicators in each dimension and explore customers' evaluation behavior characteristics. We found that food quality and, price and value have four indicators while service quality and atmosphere have two indicators. The results are useful for restaurant operators to take effective actions to attract more customers to revisit.


title: Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

abstract: Spectrum scarcity due to inefficient utilisation has ignited a plethora of dynamic spectrum access solutions to accommodate the expanding demand for future wireless networks. Dynamic spectrum access systems allow secondary users to utilise spectrum bands owned by primary users if the resulting interference is kept below a pre-designated threshold. Primary and secondary user spectrum occupancy patterns determine if minimum interference and seamless communications can be guaranteed. Thus, spectrum occupancy prediction is a key component of an optimised dynamic spectrum access system. Spectrum occupancy prediction recently received significant attention in the wireless communications literature. Nevertheless, a single consolidated literature source on statistical spectrum occupancy prediction is not yet available in the open literature. Our main contribution in this paper is to provide a statistical prediction classification framework to categorise and assess current spectrum occupancy models. An overview of statistical sequential prediction is presented first. This statistical background is used to analyse current techniques for spectrum occupancy prediction. This review also extends spectrum occupancy prediction to include cooperative prediction. Finally, theoretical and implementation challenges are discussed.


title: Faster command selection on tablets with FastTap

abstract: Touch-based tablet UIs provide few shortcut mechanisms for rapid command selection; as a result, command selection on tablets often requires slow traversal of menus. We developed a new selection technique for multi-touch tablets, called FastTap, that uses thumb-and-finger touches to show and choose from a spatially-stable grid-based overlay interface. FastTap allows novices to view and inspect the full interface, but once item locations are known, FastTap allows people to select commands with a single quick thumb-and-finger tap. The interface helps users develop expertise, since the motor actions carried out as a novice rehearse the expert behavior. A controlled study showed that FastTap was significantly faster (by 33% per selection overall) than marking menus, both for novices and experts, and without reduction in accuracy or subjective preference. Our work introduces a new and efficient selection mechanism that supports rapid command execution on touch tablets, for both novices and experts.


SmartFarm : Improving Agriculture Sustainability Using Modern Information Technology


title: Face detection using generic eye template matching

abstract: Face identification and detection has become very popular, interesting and wide field of current research area. As there are several algorithms for face detection exist but none of the algorithms globally detect all sorts of human faces among the different colors and intensities in a given picture. In this paper, a novel method for face detection technique has been described. ::: ::: Here, the centers of both the eyes are detected using generic eye template matching method. After detecting the center of both the eyes, the corresponding face bounding box is determined. The ::: experimental results have shown that the proposed algorithm is able to accomplish successfully proper detection and to mark the exact face and eye region in the given image.


Bibliometric Information Retrieval System (BIRS): A Web Search Interface Utilizing Bibliometric Research Results


A Fast Kernel for Attributed Graphs.


title: The Effect of Focal Loss in Semantic Segmentation of High Resolution Aerial Image

abstract: The semantic segmentation of High Resolution Remote Sensing (HRRS) images is the fundamental research area of the earth observation. Convolutional Neural Network (CNN), which has achieved superior performance in computer vision task, is also useful for semantic segmentation of HRRS images. In this work, focal loss is used instead of cross-entropy loss in training of CNN to handle the imbalance in training data. To evaluate the effect of focal loss, we train SegNet and FCN with focal loss and confirm improvement in accuracy in ISPRS 2D Semantic Labeling Contest dataset, especially when $\gamma$ is 0.5 in SegNet.


title: Coded aperture compressive temporal imaging

abstract: We use mechanical translation of a coded aperture for code division multiple access compression of video. We discuss the compressed video’s temporal resolution and present experimental results for reconstructions of > 10 frames of temporal data per coded snapshot.