title: Convolutional Gaussian Processes

abstract: We present a practical way of introducing convolutional structure into Gaussian processes, making them more suited to high-dimensional inputs like images. The main contribution of our work is the construction of an inter-domain inducing point approximation that is well-tailored to the convolutional kernel. This allows us to gain the generalisation benefit of a convolutional kernel, together with fast but accurate posterior inference. We investigate several variations of the convolutional kernel, and apply it to MNIST and CIFAR-10, which have both been known to be challenging for Gaussian processes. We also show how the marginal likelihood can be used to find an optimal weighting between convolutional and RBF kernels to further improve performance. We hope that this illustration of the usefulness of a marginal likelihood will help automate discovering architectures in larger models.


A Game with Many Purposes : Improving Information Retrieval Through Pleasurable Competition


title: A Fast Distance-Based Algorithm to Detect Outliers

abstract: A fast distance-based algorithm for outlier detection will be proposed. It was found that the proposed algorithm reduced the number of distance calculations compared to the nested- loop algorithm. Test results were performed on different well-known data sets. The results showed that the proposed algorithm gave a reasonable amount of CPU time saving.


title: Heterogeneity of popular boys: antisocial and prosocial configurations.

abstract: This study examined subtypes of popular 4th-6th grade boys (N = 452). Popular-prosocial (model) and popular-antisocial (tough) configurations were identified by means of teacher ratings and compared with peer and self-assessments and social centrality measures. Peers perceived model boys as cool, athletic, leaders, cooperative, studious, not shy, and nonaggressive. Peers perceived tough boys as cool, athletic, and antisocial. Model boys saw themselves as nonaggressive and academically competent. Tough boys saw themselves as popular, aggressive, and physically competent. Tough boys were disproportionately African American, particularly when African Americans were a minority in their classrooms. Model and tough boys were overrepresented at nuclear social centrality levels. These findings suggest that highly aggressive boys can be among the most popular and socially connected children in elementary classrooms. The move from conceptualizing unpopular children as a uniform group to recognizing distinct configurations of unpopular children has been a key area of progress in the study of children's peer


title: Electronic speaking system for speech impaired people: Speak up

abstract: Sign Language is the only way of communication for speech impaired people. But general people can't understand the sign language so it becomes difficult for a speech impaired person to communicate with them. In this project an electronic speaking system was developed to ease the communication process of speech impaired people. A glove was developed which consists of five flex sensors. When a gesture is made with the glove, the change in resistance of flex sensors fed into the Arduino Nano and specific prerecorded audio command for that gesture is played from SD card through speaker and the text command for that gesture is displayed on the LCD. There are four gestures that are designed for user input so that user can play his/her chosen audio commands using those gestures. This device not only helps a speech impaired person to communicate with a normal person via audio commands but also helps him/her to communicate with a hearing impaired person by displaying the text commands on the LCD.


title: Potential Risk of Cross-Infection by Tourniquets: A Need for Effective Control Practices in Pakistan

abstract: Background: Tourniquets used repeatedly on patients for blood sampling are a potential source of nosocomial infections. They harbor numerous microorganisms, including methicillin‑resistant Staphylococcus aureus (MRSA). The aim of this study was to investigate tourniquets for the presence of microorganisms and to ascertain the infection control practices of health care workers. Methods: A cross‑sectional study was carried out in 2012 on 100 samples of tourniquets collected from public and private sector hospitals in Karachi, Pakistan. The samples were cultured, and pathogenic microorganisms were identified and tested for methicillin resistance. A questionnaire was administered simultaneously to 100 health care workers who had used the tourniquets. Descriptive data are represented as frequencies and percentages. Ethical considerations were taken into account. Results: The total colonization rate was 51%, with no bacterial growth in 17/40 and 32/60 samples from public and private sector hospitals, respectively. S. aureus was isolated from 12 (42%) private sector hospital samples and 10 (43%) public sector hospital samples. Although MRSA was found in more samples from public than private sector hospitals, the difference was not statistically significant. Nevertheless, 90% of all elastic and 41% of all rubber tourniquets harbored microorganisms ( P < 0.001). Although 96% of health care workers agreed that hospital staff and fomites can transmit infection, none identified tourniquets as a potential source. When asked whether tourniquets appeared clean before use, 66% agreed, and only 25% considered that tourniquets should be washed or cleaned before use. Conclusions: Tourniquets are a potential reservoir and vehicle for the spread of nosocomial infections, including MRSA. Health care workers have inadequate knowledge about infection control procedures and personal hygiene for disinfecting reusable items. Keywords: Fomite, health care worker, infection, nosocomial infection, tourniquet


title: Challenges in Chinese knowledge graph construction

abstract: The automatic construction of large-scale knowledge graphs has received much attention from both academia and industry in the past few years. Notable knowledge graph systems include Google Knowledge Graph, DBPedia, YAGO, NELL, Probase and many others. Knowledge graph organizes the information in a structured way by explicitly describing the relations among entities. Since entity identification and relation extraction are highly depending on language itself, data sources largely determine the way the data are processed, relations are extracted, and ultimately how knowledge graphs are formed, which deeply involves the analysis of lexicon, syntax and semantics of the content. Currently, much progress has been made for knowledge graphs in English language. In this paper, we discuss the challenges facing Chinese knowledge graph construction because Chinese is significantly different from English in various linguistic perspectives. Specifically, we analyze the challenges from three aspects: data sources, taxonomy derivation and knowledge extraction. We also present our insights in addressing these challenges.


title: ChemBank: a small-molecule screening and cheminformatics resource database

abstract: ChemBank (http://chembank.broad.harvard.edu/) is a public, web-based informatics environment developed through a collaboration between the Chemical Biology Program and Platform at the Broad Institute of Harvard and MIT. This knowledge environment includes freely available data derived from small molecules and small-molecule screens and resources for studying these data. ChemBank is unique among small-molecule databases in its dedication to the storage of raw screening data, its rigorous definition of screening experiments in terms of statistical hypothesis testing, and its metadata-based organization of screening experiments into projects involving collections of related assays. ChemBank stores an increasingly varied set of measurements derived from cells and other biological assay systems treated with small molecules. Analysis tools are available and are continuously being developed that allow the relationships between small molecules, cell measurements, and cell states to be studied. Currently, ChemBank stores information on hundreds of thousands of small molecules and hundreds of biomedically relevant assays that have been performed at the Broad Institute by collaborators from the worldwide research community. The goal of ChemBank is to provide life scientists unfettered access to biomedically relevant data and tools heretofore available primarily in the private sector.


title: Reduction of THD in Diode Clamped Multilevel Inverter

abstract: Conventional two-level pulse width modulation (PWM) inverters generate high dv/dt, high frequency common mode voltages and introduces harmonics which is very harmful in electric drives applications. It may damage motor bearings, conducted electromagnetic interferences, and malfunctioning of electronic equipments. Multilevel inverter (MLI) technology has emerged recently as a very important alternative in the area of high-power medium-voltage energy control. Neutral-point- clamped (NPC) inverters are the most widely used topology of multilevel inverters in high-power applications (several megawatts). This paper presents the most relevant control and modulation method developed for inverters to reduce total harmonic distortion (THD): multilevel sinusoidal pulse width modulation (SPWM). More relevant applications of these converters are laminators, conveyor belts, and unified power- flow controllers. Simulation results obtained in Matlab/Simulink confirms the effectiveness with negligible THD.


title: Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach

abstract: Markov chain Monte Carlo (MCMC) methods are often deemed far too computationally intensive to be of any practical use for large datasets. This paper describes a methodology that aims to scale up the Metropolis-Hastings (MH) algorithm in this context. We propose an approximate implementation of the accept/reject step of MH that only requires evaluating the likelihood of a random subset of the data, yet is guaranteed to coincide with the accept/reject step based on the full dataset with a probability superior to a user-specified tolerance level. This adaptive subsampling technique is an alternative to the recent approach developed in (Korattikara et al., 2014), and it allows us to establish rigorously that the resulting approximate MH algorithm samples from a perturbed version of the target distribution of interest, whose total variation distance to this very target is controlled explicitly. We explore the benefits and limitations of this scheme on several examples.


title: Quality Control in Crowdsourcing: A Survey of Quality Attributes, Assessment Techniques and Assurance Actions

abstract: Crowdsourcing enables one to leverage on the intelligence and wisdom of potentially large groups of individuals toward solving problems. Common problems approached with crowdsourcing are labeling images, translating or transcribing text, providing opinions or ideas, and similar—all tasks that computers are not good at or where they may even fail altogether. The introduction of humans into computations and/or everyday work, however, also poses critical, novel challenges in terms of quality control, as the crowd is typically composed of people with unknown and very diverse abilities, skills, interests, personal objectives, and technological resources. This survey studies quality in the context of crowdsourcing along several dimensions, so as to define and characterize it and to understand the current state of the art. Specifically, this survey derives a quality model for crowdsourcing tasks, identifies the methods and techniques that can be used to assess the attributes of the model, and the actions and strategies that help prevent and mitigate quality problems. An analysis of how these features are supported by the state of the art further identifies open issues and informs an outlook on hot future research directions.


Database Encryption


title: Recent Development of Open-Source Speech Recognition Engine Julius

abstract: Julius is an open-source large-vocabulary speech recognition software used for both academic research and industrial applications. It executes real-time speech recognition of a 60k-word dictation task on low-spec PCs with small footprint, and even on embedded devices. Julius supports standard language models such as statistical N-gram model and rule-based grammars, as well as Hidden Markov Model (HMM) as an acoustic model. One can build a speech recognition system of his own purpose, or can integrate the speech recognition capability to a variety of applications using Julius. This article describes an overview of Julius, major features and specifications, and summarizes the developments conducted in the recent years.


title: A MOEA/D approach to exploit the crucial structure of convolution kernels

abstract: Deep convolutional neural networks achieved remarkably performance on computer vision tasks. However, they are always over-parameterized and computationally intensive to deploy on computing platforms with limited hardware resources. In this paper, a MOEA/D based pruning approach is proposed. We exploit the crucial structure of convolution kernels using MOEA/D based pruning approach and find 88% computational redundancy in convolutional layers of LeNet-5 without any accuracy loss. Networks are encoded as chromosomes in continues or discrete spaces. A multi-objective evolutionary algorithm, MOEA/D, is employed to evolve multiple convolutional neural networks simultaneously subject to accuracy and computational cost as two conflicting objectives. Several experiments are conducted on three classical image classification tasks. Statistical results demonstrate that our proposed method is both effective and superior to other methods.


title: YCSB and TPC-H: Big Data and Decision Support Benchmarks

abstract: In computing, a benchmark is the result of running a set of computer programs or a computer program, in order to assess relative performance by running a series of standard tests. By doing this, researchers highlight the characteristics of certain systems and are able to rank the system against the rest. On the other hand, BigData is a hot topic. It not only deals with large amounts of data sets and the procedures and tools used to analyze and manipulate them, but also to a computational turn in research and thought. At the same time, Decision Support applications are related to Big Data as they need to deal with large datasets. In this paper we describe two of the most popular benchmarks, one representing Decision Support Systems (TPC-H), and the other represents the Big Data class (YCSB - Yahoo Cloud Serving Benchmark).


title: Hierarchical Haptic Manipulation for Complex Skill Learning

abstract: In complex manipulation scenarios (e.g. tasks requiring complex interaction of two hands or in-hand manipulation), generalization is a hard problem. Current methods still either require a substantial amount of (supervised) training data and / or strong assumptions on both the environment and the task. In this paradigm, controllers solving these tasks tend to be complex. We propose a paradigm of maintaining simpler controllers solving the task in a small number of specific situations. In order to generalize to novel situations, the robot transforms the environment from novel situations into a situation where the solution of the task is already known. Our solution to this problem is to play with objects and use previously trained skills (basis skills). These skills can either be used for estimating or for changing the current state of the environment and are organized in skill hierarchies. The approach is evaluated in complex pick-and-place scenarios that involve complex manipulation. We further show that these skills can be learned by autonomous playing.


title: A Discourse Commitment-Based Framework for Recognizing Textual Entailment

abstract: In this paper, we introduce a new framework for recognizing textual entailment which depends on extraction of the set of publicly-held beliefs -- known as discourse commitments -- that can be ascribed to the author of a text or a hypothesis. Once a set of commitments have been extracted from a t-h pair, the task of recognizing textual entailment is reduced to the identification of the commitments from a t which support the inference of the h. Promising results were achieved: our system correctly identified more than 80% of examples from the RTE-3 Test Set correctly, without the need for additional sources of training data or other web-based resources.


title: Branding 2.0 - Designing an Information System to build a Brand based on User-generated Content

abstract: This work proposes a new branding process that is inspired by and builds on the fundamental ideas behind the term Web 2.0. The paper shows how information systems can be designed to create value throughout the branding process by collecting and distributing user-generated content that supports physical-virtual artifacts. The paper follows the design-science research guidelines provided by Hevner et al. (2004). It introduces the model of Branding 2.0 and a real-world instantiation. Related cases are analyzed and compared to the instantiation to refine the model and extract implications for designing future branding applications. Finally, a note on possible business models demonstrates options for an implementation scenario.


Toward a Web-based Speech Corpus for Algerian Arabic Dialectal Varieties


title: Bringing Modern Hierarchical Memory Systems Into Focus: A study of architecture and workload factors on system performance

abstract: The increasing size of workloads has led to the development of new technologies and architectures that are intended to help address the capacity limitations of DRAM main memories. The proposed solutions fall into two categories: those that re-engineer Flash-based SSDs to further improve storage system performance and those that incorporate non-volatile technology into a Hybrid main memory system. These developments have blurred the line between the storage and memory systems. In this paper, we examine the differences between these two approaches to gain insight into the types of applications and memory technologies that benefit the most from these different architectural approaches. In particular this work utilizes full system simulation to examine the impact of workload randomness on system performance, the impact of backing store latency on system performance, and how the different implementations utilize system resources differently. We find that the software overhead incurred by storage based implementations can account for almost 50% of the overall access latency. As a result, backing store technologies that have an access latency up to 25 microseconds tend to perform better when implemented as part of the main memory system. We also see that high degrees of random access can exacerbate the software overhead problem and lead to large performance advantages for the Hybrid main memory approach. Meanwhile, the page replacement algorithm utilized by the OS in the storage approach results in considerably better performance on highly sequential workloads at the cost of greater pressure on the cache.


title: Fall Classification by Machine Learning Using Mobile Phones

abstract: Fall prevention is a critical component of health care; falls are a common source of injury in the elderly and are associated with significant levels of mortality and morbidity. Automatically detecting falls can allow rapid response to potential emergencies; in addition, knowing the cause or manner of a fall can be beneficial for prevention studies or a more tailored emergency response. The purpose of this study is to demonstrate techniques to not only reliably detect a fall but also to automatically classify the type. We asked 15 subjects to simulate four different types of falls–left and right lateral, forward trips, and backward slips–while wearing mobile phones and previously validated, dedicated accelerometers. Nine subjects also wore the devices for ten days, to provide data for comparison with the simulated falls. We applied five machine learning classifiers to a large time-series feature set to detect falls. Support vector machines and regularized logistic regression were able to identify a fall with 98% accuracy and classify the type of fall with 99% accuracy. This work demonstrates how current machine learning approaches can simplify data collection for prevention in fall-related research as well as improve rapid response to potential injuries due to falls.


Update: American Public Opinion on the Death Penalty - It's Getting Personal (Symposium: How the Death Penalty Works: Empirical Studies of the Modern Capital Sentencing System)


title: Substitute Teacher Networks: Learning with Almost No Supervision

abstract: Learning through experience is time-consuming, inefficient and often bad for your cortisol levels. To address this problem, a number of recently proposed teacher-student methods have demonstrated the benefits of private tuition, in which a single model learns from an ensemble of more experienced tutors. Unfortunately, the cost of such supervision restricts good representations to a privileged minority. Unsupervised learning can be used to lower tuition fees, but runs the risk of producing networks that require extracurriculum learning to strengthen their CVs and create their own LinkedIn profiles. Inspired by the logo on a promotional stress ball at a local recruitment fair, we make the following three contributions. First, we propose a novel almost no supervision training algorithm that is effective, yet highly scalable in the number of student networks being supervised, ensuring that education remains affordable. Second, we demonstrate our approach on a typical use case: learning to bake, developing a method that tastily surpasses the current state of the art. Finally, we provide a rigorous quantitive analysis of our method, proving that we have access to a calculator. Our work calls into question the long-held dogma that life is the best teacher.


title: Fake Identities in Social Media: A Case Study on the Sustainability of the Facebook Business Model

abstract: Social networks such as Facebook, Twitter and Google+ have attracted millions of users in the last years. One of the most widely used social networks, Facebook, recently had an initial public offering (IPO) in May 2012, which was among the biggest in Internet technology. Forprofit and nonprofit organizations primarily use such platforms for target-oriented advertising and large-scale marketing campaigns. Social networks have attracted worldwide attention because of their potential to address millions of users and possible future customers. The potential of social networks is often misused by malicious users who extract sensitive private information of unaware users. One of the most common ways of performing a large-scale data harvesting attack is the use of fake profiles, where malicious users present themselves in profiles impersonating fictitious or real persons. The main goal of this research is to evaluate the implications of fake user profiles on Facebook. To do so, we established a comprehensive data harvesting attack, the social engineering experiment, and analyzed the interactions between fake profiles and regular users to eventually undermine the Facebook business model. Furthermore, privacy considerations are analyzed using focus groups. As a result of our work, we provided a set of countermeasures to increase the awareness of users.


title: A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection

abstract: In this paper, we study a novel approach for named entity recognition (NER) and mention detection in natural language processing. Instead of treating NER as a sequence labelling problem, we propose a new local detection approach, which rely on the recent fixed-size ordinally forgetting encoding (FOFE) method to fully encode each sentence fragment and its left/right contexts into a fixed-size representation. Afterwards, a simple feedforward neural network is used to reject or predict entity label for each individual fragment. The proposed method has been evaluated in several popular NER and mention detection tasks, including the CoNLL 2003 NER task and TAC-KBP2015 and TAC-KBP2016 Tri-lingual Entity Discovery and Linking (EDL) tasks. Our methods have yielded pretty strong performance in all of these examined tasks. This local detection approach has shown many advantages over the traditional sequence labelling methods.


title: Music of the 7Ts: Predicting and Decoding Multivoxel fMRI Responses with Acoustic, Schematic, and Categorical Music Features

abstract: Underlying the experience of listening to music are parallel streams of auditory, categorical, and schematic qualia, whose representations and cortical organization remain largely unresolved. We collected high-field (7T) fMRI data in a music listening task, and analyzed the data using multivariate decoding and stimulus-encoding models. Twenty subjects participated in the experiment, which measured BOLD responses evoked by naturalistic listening to 25 music clips from five genres. Our first analysis applied machine classification to the multivoxel patterns that were evoked in temporal cortex. Results yielded above-chance levels for both stimulus identification and genre classification–cross-validated by holding out data from multiple of the stimuli during model training and then testing decoding performance on the held-out data. Genre model misclassifications were significantly correlated with those in a corresponding behavioral music categorization task, supporting the hypothesis that geometric properties of multivoxel pattern spaces underlie observed musical behavior. A second analysis employed a spherical searchlight regression analysis which predicted multivoxel pattern responses to music features representing melody and harmony across a large area of cortex. The resulting prediction-accuracy maps yielded significant clusters in the temporal, frontal, parietal, and occipital lobes, as well as in the parahippocampal gyrus and the cerebellum. These maps provide evidence in support of our hypothesis that geometric properties of music cognition are neurally encoded as multivoxel representational spaces. The maps also reveal a cortical topography that differentially encodes categorical and absolute-pitch information in distributed and overlapping networks, with smaller specialized regions that encode tonal music information in relative-pitch representations.


title: Security and Results of a Large-Scale

abstract: This paper presents the design and discusses the results of a secured high-interaction honeypot. The challenge is to have a honeypot that welcomes attackers, allows userland malicious activities but prevents system corruption. The honeypot must authorize real malicious activities. It must ease the analysis of those activities. A clustered honeypot is proposed for two kinds of hosts. The first class prevents a system corruption and never has to be reinstalled. The second class assumes a system corruption but an easy reinstallation is available. Various off-the-shelf security tools are deployed to detect a corruption and to ease analysis. Moreover, host and network information enable a full analysis for complex scenario of attacks. The solution is totally based on open source software and has been validated over two years. A complete analysis is provided using the collected events and alarms. First, different types of malicious activities are easily reconstructed. Second, correlation of alarms enables us to compare the efficiency of various off-the-shelf security tools. Third, a correlation eases a complete analysis for the host and network activities. Finally, complete examples of attacks are explained. Ongoing works focus on recognition of complex malicious activities using a correlation grid and on distributed analysis.


title: C-OPT: Coverage-Aware Trajectory Optimization Under Uncertainty

abstract: We introduce a new problem of continuous, coverage-aware trajectory optimization under localization and sensing uncertainty. In this problem, the goal is to plan a path from a start state to a goal state that maximizes the coverage of a user-specified region while minimizing the control costs of the robot and the probability of collision with the environment. We present a principled method for quantifying the coverage sensing uncertainty of the robot. We use this sensing uncertainty along with the uncertainty in robot localization to develop C-OPT, a coverage-optimization algorithm which optimizes trajectories over belief-space to find locally optimal coverage paths. We highlight the applicability of our approach in multiple simulated scenarios inspired by surveillance, UAV crop analysis, and search-and-rescue tasks. We also present a case study on a physical, differential-drive robot. We also provide quantitative and qualitative analysis of the paths generated by our approach.


title: Resistance training with soy vs whey protein supplements in hyperlipidemic males

abstract: BackgroundMost individuals at risk for developing cardiovascular disease (CVD) can reduce risk factors through diet and exercise before resorting to drug treatment. The effect of a combination of resistance training with vegetable-based (soy) versus animal-based (whey) protein supplementation on CVD risk reduction has received little study. The study's purpose was to examine the effects of 12 weeks of resistance exercise training with soy versus whey protein supplementation on strength gains, body composition and serum lipid changes in overweight, hyperlipidemic men.MethodsTwenty-eight overweight, male subjects (BMI 25–30) with serum cholesterol >200 mg/dl were randomly divided into 3 groups (placebo (n = 9), and soy (n = 9) or whey (n = 10) supplementation) and participated in supervised resistance training for 12 weeks. Supplements were provided in a double blind fashion.ResultsAll 3 groups had significant gains in strength, averaging 47% in all major muscle groups and significant increases in fat free mass (2.6%), with no difference among groups. Percent body fat and waist-to-hip ratio decreased significantly in all 3 groups an average of 8% and 2%, respectively, with no difference among groups. Total serum cholesterol decreased significantly, again with no difference among groups.ConclusionParticipation in a 12 week resistance exercise training program significantly increased strength and improved both body composition and serum cholesterol in overweight, hypercholesterolemic men with no added benefit from protein supplementation.


title: Deep LAC: Deep localization, alignment and classification for fine-grained recognition

abstract: We propose a fine-grained recognition system that incorporates part localization, alignment, and classification in one deep neural network. This is a nontrivial process, as the input to the classification module should be functions that enable back-propagation in constructing the solver. Our major contribution is to propose a valve linkage function (VLF) for back-propagation chaining and form our deep localization, alignment and classification (LAC) system. The VLF can adaptively compromise the errors of classification and alignment when training the LAC model. It in turn helps update localization. The performance on fine-grained object data bears out the effectiveness of our LAC system.


title: Fraud detection using self-organizing map visualizing the user profiles

abstract: We propose a fraud detection method based on the user accounts visualization and threshold-type detection. The visualization technique employed in our approach is the Self-Organizing Map (SOM). Since the SOM technique in its original form visualizes only the vectors, and the user accounts are represented in our work as the matrices storing a collection of records reflecting the user sequential activities, we propose a method of the matrices visualization on the SOM grid, which constitutes the main contribution of this paper. Furthermore, we propose a method of the detection threshold setting on the basis of the SOM U-matrix. The results of the conducted experimental study on real data in three different research fields confirm the advantages and effectiveness of the proposed approach.


title: Single-Phase Inverter Scheme for Permanent Magnet Synchronous Motor Drive with Resonant Capacitor

abstract: This paper proposes a method of driving a permanent magnet synchronous motor (PMSM) with a single-phase inverter and a capacitor. The proposed system combines variable speed and load control capability of the inverter and phase shift capability of the capacitor to provide high power density. In the system, stator voltages can be boosted above the dc voltage by resonance of stator windings and capacitor. Due to wider available voltage range, the proposed system provides greater maximum power than the conventional three-phase drive. Also, it has wider operating range and reduced torque ripple than existing single-phase drives as power imbalance due to single-phase excitation is mitigated by capacitor. In this paper, operating characteristics and operable area of the proposed drive are analyzed, and a torque control method is proposed to minimize copper losses. The feasibility of the proposed PMSM drive and control method is verified by experiments with a 3.5kW PMSM.


title: Learning Implicit Fields for Generative Shape Modeling

abstract: We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes. An implicit field assigns a value to each point in 3D space, so that a shape can be extracted as an iso-surface. IM-NET is trained to perform this assignment by means of a binary classifier. Specifically, it takes a point coordinate, along with a feature vector encoding a shape, and outputs a value which indicates whether the point is outside the shape or not. By replacing conventional decoders by our implicit decoder for representation learning (via IM-AE) and shape generation (via IM-GAN), we demonstrate superior results for tasks such as generative shape modeling, interpolation, and single-view 3D reconstruction, particularly in terms of visual quality. Code and supplementary material are available at https://github.com/czq142857/implicit-decoder.


title: Image Classification using Random Forests and Ferns

abstract: We explore the problem of classifying images by the object categories they contain in the case of a large number of object categories. To this end we combine three ingredients: (i) shape and appearance representations that support spatial pyramid matching over a region of interest. This generalizes the representation of Lazebnik et al., (2006) from an image to a region of interest (ROI), and from appearance (visual words) alone to appearance and local shape (edge distributions); (ii) automatic selection of the regions of interest in training. This provides a method of inhibiting background clutter and adding invariance to the object instance 's position; and (iii) the use of random forests (and random ferns) as a multi-way classifier. The advantage of such classifiers (over multi-way SVM for example) is the ease of training and testing. Results are reported for classification of the Caltech-101 and Caltech-256 data sets. We compare the performance of the random forest/ferns classifier with a benchmark multi-way SVM classifier. It is shown that selecting the ROI adds about 5% to the performance and, together with the other improvements, the result is about a 10% improvement over the state of the art for Caltech-256.


title: Generative Compression

abstract: Traditional image and video compression algorithms rely on hand-crafted encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the data being compressed. Here we describe the concept of generative compression, the compression of data using generative models, and show its potential to produce more accurate and visually pleasing reconstructions at much deeper compression levels for both image and video data. We also demonstrate that generative compression is orders-of-magnitude more resilient to bit error rates (e.g. from noisy wireless channels) than traditional variable-length entropy coding schemes.


title: A user-guided approach to program analysis

abstract: Program analysis tools often produce undesirable output due to various approximations. We present an approach and a system EUGENE that allows user feedback to guide such approximations towards producing the desired output. We formulate the problem of user-guided program analysis in terms of solving a combination of hard rules and soft rules: hard rules capture soundness while soft rules capture degrees of approximations and preferences of users. Our technique solves the rules using an off-the-shelf solver in a manner that is sound (satisfies all hard rules), optimal (maximally satisfies soft rules), and scales to real-world analyses and programs. We evaluate EUGENE on two different analyses with labeled output on a suite of seven Java programs of size 131–198 KLOC. We also report upon a user study involving nine users who employ EUGENE to guide an information-flow analysis on three Java micro-benchmarks. In our experiments, EUGENE significantly reduces misclassified reports upon providing limited amounts of feedback.


title: Stromule extension along microtubules coordinated with actin-mediated anchoring guides perinuclear chloroplast movement during innate immunity

abstract: Dynamic tubular extensions from chloroplasts called stromules have recently been shown to connect with nuclei and function during innate immunity. We demonstrate that stromules extend along microtubules (MTs) and MT organization directly affects stromule dynamics since stabilization of MTs chemically or genetically increases stromule numbers and length. Although actin filaments (AFs) are not required for stromule extension, they provide anchor points for stromules. Interestingly, there is a strong correlation between the direction of stromules from chloroplasts and the direction of chloroplast movement. Stromule-directed chloroplast movement was observed in steady-state conditions without immune induction, suggesting it is a general function of stromules in epidermal cells. Our results show that MTs and AFs may facilitate perinuclear clustering of chloroplasts during an innate immune response. We propose a model in which stromules extend along MTs and connect to AF anchor points surrounding nuclei, facilitating stromule-directed movement of chloroplasts to nuclei during innate immunity.


title: Pharmacodynamics and Bactericidal Activity of Moxifloxacin in Experimental Escherichia coli Meningitis

abstract: Moxifloxacin, an 8-methoxyquinolone with broad-spectrum activity in vitro, was studied in the rabbit model of Escherichia coli meningitis. The purposes of this study were to evaluate the bactericidal effectiveness and the pharmacodynamic profile of moxifloxacin in cerebrospinal fluid (CSF) and to compare the bactericidal activity with that of ceftriaxone and meropenem therapy. After induction of meningitis, animals were given single doses of 10, 20, and 40 mg/kg or divided-dose regimens of 5, 10, and 20 mg/kg twice, separated by 6 h. After single doses, the penetration of moxifloxacin into purulent CSF, measured as percentage of the area under the concentration-time curve (AUC) in CSF relative to the AUC in plasma, was approximately 50%. After single doses of 10, 20, and 40 mg/kg, the maximum CSF concentration ( C max ) values were 1.8, 4.2, and 4.9 μg/ml, respectively; the AUC values (total drug) were 13.4, 25.4, and 27.1 μg/ml · h, respectively, and the half-life values ( t ½ ) were 6.7, 6.6, and 4.7 h, respectively. The bacterial killing in CSF for moxifloxacin, calculated as the Δlog 10 CFU per milliliter per hour, at 3, 6, and 12 h after single doses of 10, 20, and 40 mg/kg were −5.70, −6.62, and −7.02; −7.37, −7.37, and −6.87; and −6.62, −6.62, and −6.62, respectively, whereas those of ceftriaxone and meropenem were −4.18, −5.24, and −4.43, and −3.64, −3.59, and −4.12, respectively. The CSF pharmacodynamic indices of AUC/MBC and C max /MBC were interrelated ( r = 0.81); there was less correlation with T > MBC ( r = 0.74). In this model, therapy with moxifloxacin appears to be at least as effective as ceftriaxone and more effective than meropenem therapy in eradicating E. coli from CSF.


title: Endoscopic Findings and Clinicopathologic Characteristics of Ischemic Colitis: A Report of 85 Cases

abstract: Ischemic colitis is the most common type of intestinal ischemia and has a clinical spectrum of injury that ranges from mild and transient ischemia to acute fulminant colitis. The aim of this study was to explore endoscopic findings and clinicopathologic characteristics of ischemic colitis and be accurate enough to avoid missed diagnosis or misdiagnosis. A retrospective analysis was undertaken of endoscopy findings and clinicopathologic characteristics of 85 cases of ischemic colitis from March 2005 to April 2008 in the endoscopy center of our hospital. All cases underwent colonoscopy with biopsy within 2 weeks of the onset of symptoms, and all specimens with forceps were stained with hematoxylin–eosin and observed under light microscopy. Of the 85 cases of ischemic colitis (24 men and 61 women, average age 61.36 ± 14.49 years old, range 29–84), 71 were over 50 years of age. These cases were associated with the basal diseases such as hypertension, cardiovascular disorders, diabetes, and hematological diseases as well as a history of abdominal operation. The clinical features usually presented with sudden onset of abdominal pain, diarrhea, and hematochezia. Ischemic lesions were located mainly in the left colon with segmental form (only descending colon affected 16%, only splenic flexure 14%, and only sigmoid colon 23%). The 85 patients consisted of the non-gangrenous type (82), which were composed of reversible IC (76) and chronic IC (6), and the gangrenous type (3). Endoscopic appearance of the transient ischemic colitis consisted of petechial hemorrhages, edematous and fragile mucosa, segmental erythema, scattered erosion, longitudinal ulcerations, and sharply defined segment of involvement. Ischemic colitis of stricture was characterized by full-thickness mucosa, lumens stricture, and diseased haustrations. The mucosa of gangrenous colitis with cyanotic and pseudopolyps was endoscopically observed as well. Clinicopathologic characteristics showed mucosal inflammation accompanied by erosion, granulation tissue hyperplasia and gland atrophy, lamina propria hemorrhage, and macrophages with hemosiderin pigmentation in submucosa in particular. Although endoscopy findings and clinicopathologic characteristics of ischemic colitis are nonspecific, colonoscopy with biopsy plays a vital role in the early diagnosis of ischemic colitis.


Show, Reward and Tell: Automatic Generation of Narrative Paragraph from Photo Stream by Adversarial Training


title: Least-squares optimisation of numerical inversion of Laplace transforms

abstract: A scheme for computing optimal values of the constants occurring in the numerical inversion of Laplace transforms is developed. The scheme employs the techniques of linear least-squares theory and is used to compute real-valued constants.


title: Development of extensible open information extraction

abstract: Open information extraction is used to extract information from open-domain documents such as articles in the Internet. Most extraction techniques have common extraction flow starting from collecting documents, preprocessing documents, extracting informations, postprocessing extracted relations and evaluation of extracted relations. Implementation of a new technique requires the user to build the whole process from scratch. Consequently, implementation becomes a complex and time-consuming process. Therefore, a prototype acting as a framework to abstract and split the whole extraction processes into multiple separate processes implemented as components or plugins will allow users to reuse already existing extraction subprocess.


title: High Voc in (Cu,Ag)(In,Ga)Se2 Solar Cells

abstract: In this contribution, we show that silver substitution for copper in Cu(In,Ga)Se2 (CIGS) to form (Ag,Cu)(In,Ga)Se2 (ACIGS) leads to a reduction of the voltage loss expressed as Eg/q – V oc. This, in turn, leads to higher device efficiencies as compared to similar CIGS devices without Ag. We report $V_{{\rm{oc}}}$ at 814 mV at a conversion efficiency of 21% for our best ACIGS device with 20% of the group I element consisting of silver. Comparing ACIGS and CIGS devices with the same Ga/(Ga + In) ratio, the ACIGS devices exhibit about 0.05 eV higher bandgap. Alkali postdeposition treatment with KF leads to improvements in efficiency both for CIGS and ACIGS, but we find that the dose of KF needed for optimum device for ACIGS is 10–20% of the dose used for CIGS.


title: How does EMDR work?

abstract: Eye movement desensitisation and reprocessing (EMDR) is an effective treatment for alleviating trauma symptoms, and the positive effects of this treatment have been scientifically confirmed under wellcontrolled conditions. This has provided an opportunity to explore how EMDR works. The present paper reports on the findings of a long series of experiments that disproved the hypothesis that eye movements or other ‘dual tasks’ are unnecessary. These experiments also disproved the idea that ‘bilateral stimulation’ is needed; moving the eyes up and down produces the same effect as horizontal eye movement, and so do tasks that require no eye movement at all. However, it is important that the dual task taxes working memory. Several predictions can be derived from the working memory explanation for eye movements in EMDR. These seem to hold up extremely well in critical experimental tests, and create a solid explanation on how eye movements work. This paper discusses the implications that this theory and the empirical findings may have for the EMDR technique.


title: Dual-Frequency Microwave-Enhanced Infrared Thermography

abstract: In this paper, we propose to extend the Microwave Enhanced Infrared Thermography (MEIT) method for landmine detection using two excitation microwave frequencies. We present the results of a detailed modeling effort. The extended method has the potential for detecting buried objects under rough surfaces at depths greater than with the original single frequency concept. Our result shows that the two-frequency technique minimizes the clutter introduced by an irregular surface, and can lead to better detection of buried objects. A 2-D computational model of this method has been developed to simulate real-world landmine detection. Receiver Operating Characteristic (ROC) curves were generated to evaluate the performance of this new method. Finally we demonstrate improvements achieved in detection depth under rough surfaces, compared to the single-frequency MEIT technology. The theoretical results obtained so far are presently being used to plan laboratory and field experiments for validation of our assumptions.


title: POTEL: low cost realtime virtual pottery maker simulator

abstract: In this paper we introduce an affordable system to model virtual pottery without the need to hold any device to interact with environment, just the user's plain hands. The system uses an Oculus VR for visualizing the world, a Leap Motion for interaction with the environment and an optional Arduino with celphone vibrators for haptic feedback. We do realtime clay deformation by extruding / compressing triangle vertices on a radius of influence and interact with the world by pressing buttons with the user's index finger. Finally the system can also 3D print the final pottery creation if the player wants.


The Impact Of Accounting System On The Performance Of Small And Medium Scale Enterprises In Nigeria – A Survey Of SME ’ s In Oyo State-Nigeria


title: Bid evaluation behavior in online procurement auctions involving technical and business experts

abstract: Multi-attribute reverse auction-based procurement has been widely used by large organizations. The attributes of the auctioned objects are usually divided into two groups: technical and business attributes. They are reviewed and scored by technical and business experts who act as referees in the bid evaluation process. To analyze their bid evaluation behavior, we built a model for a multi-attribute reverse auction. With correlations between the bid evaluations of the different referee groups across the attributes, the bid evaluation problem is not the usual multi-attribute decision-making problem. We assess the cause-effect relationship that is present, and show that antagonism between referee groups tends to grow over time. We tested how this works with data from simulated auctions. To diminish the potential for antagonism between the two referee groups, we propose a modified bid evaluation mechanism. We also conducted role-playing experiments involving the referee behaviors as a means for assessing the proposed mechanism. Our results suggest that the modified bid evaluation mechanism is beneficial.


title: The Art of Prolog: Advanced Programming Techniques

abstract: Part 1 Logic programs: basic constructs database programming recursive programming the computation model of logic programs theory of logic programs. Part 2 The Prolog language: pure Prolog programming in pure Prolog arithmetic structure inspection meta-logical predicates cuts and negation extra-logical predicates program development. Part 3 Advanced Prolog programming techniques: nondeterministic programming incomplete data structures second-order programming interpreters program transformation logic grammars search techniques. Part 4 Applications: game-playing programs a credit evaluation expert system an equation solver a compiler. Appendix: operators.


title: CAD/Graphics 2013: Interactive shape co-segmentation via label propagation

abstract: In this paper, we present an interactive approach for shape co-segmentation via label propagation. Our intuitive approach is able to produce error-free results and is very effective at handling out-of-sample data. Specifically, we start by over-segmenting a set of shapes into primitive patches. Then, we allow the users to assign labels to some patches and propagate the label information from these patches to the unlabeled ones. We iterate the last two steps until the error-free consistent segmentations are obtained. Additionally, we provide an inductive extension of our framework, which effectively addresses the out-of-sample data. The experimental results demonstrate the effectiveness of our approach.


title: The design and control of a therapeutic exercise robot for lower limb rehabilitation: Physiotherabot

abstract: Abstract This study explains the design and control of three degrees of freedom therapeutic exercise robot (Physiotherabot) for the lower limbs of a patient who needs rehabilitation after a spinal cord injury (SCI), stroke, muscle disorder, or a surgical operation. In order to control this robot, a “Human–Machine Interface” with a rule-based control structure was developed. The robot manipulator (RM) can perform all active and passive exercises as well as learn specific exercise motions and perform them without the physiotherapist (PT) through the Human–Machine Interface. Furthermore, if a patient reacts against the robot manipulator during the exercise, the robot manipulator can change the position according to feedback data. Thus, the robot manipulator can serve as both therapeutic exercise equipment and as a physiotherapist in terms of motion capability. Experiments carried out on healthy subjects have demonstrated that the RM can perform the necessary exercise movements as well as imitate the manual exercises performed by the PT.


title: Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments

abstract: We present a self-supervised approach to ignoring “distractors” in camera images for the purposes of robustly estimating vehicle motion in cluttered urban environments. We leverage offline multi-session mapping approaches to automatically generate a per-pixel ephemerality mask and depth map for each input image, which we use to train a deep convolutional network. At run-time we use the predicted ephemerality and depth as an input to a monocular visual odometry (VO) pipeline, using either sparse features or dense photometric matching. Our approach yields metric-scale VO using only a single camera and can recover the correct egomotion even when 90% of the image is obscured by dynamic, independently moving objects. We evaluate our robust VO methods on more than 400km of driving from the Oxford RobotCar Dataset and demonstrate reduced odometry drift and significantly improved egomotion estimation in the presence of large moving vehicles in urban traffic.


title: An optimized pipelined architecture of SHA-256 hash function

abstract: Real time applications of digital communication systems are rapidly increasing. Due to this there is a huge demand for high level of security. In cryptographic algorithms, SHA-256 has become an integral part in many applications. A hardware implementation of the SHA-256 hash algorithm is physically separate from the main processor and hence, it has more security and higher performance than the software implementation. An execution of a hash algorithm on FPGAs is convenient, as it is flexible and easily upgradable. However, implementation of this algorithm on hardware has been challenging, due to the demand of high processing speed. In this paper, an optimized pipelined architecture of SHA-256 hash function has been implemented in hardware HDL Verilog language and synthesized in Xilinx Virtex-4 FPGA. The compressor and expander block of hash function are modified. Carry skip adder is also used to improve the performance of the architecture. The obtained result shows a significant improvement in the performance of the proposed SHA-256 algorithm and it is compared with existing various architectures. Its maximum clock frequency is 170.75 MHz, throughput of 1344.98 Mbps and an improved efficiency of 2.2 Mbps/ slice.


title: Cryptanalysis of the F-FCSR stream cipher family

abstract: This paper focuses on F-FCSR, a new family of stream ciphers proposed by Arnault and Berger at FSE 2005. It uses a non-linear primitive called the Feedback with Carry Shift Register (FCSR) as a building block. Its security relies on some properties of the 2-adic numbers. The F-FCSR family contains several stream ciphers, each of them proposing different features. First, we show a resynchronization attack that breaks algorithms in the family that support initialization vectors. The attack requires at most 2 16 chosen IV's and a little offline processing to recover the full secret key. We have implemented it with success on a standard PC. Secondly, we show a time/memory/data trade-off attack which breaks several algorithms in the F-FCSR family, even when initialization vectors are not supported. Its complexity ranges from 2 64 to 2 80 operations (depending on which algorithm in the family we consider), while the internal state has size 196 bits at least. Therefore this attack is better than generic attacks.


title: Word processing in Parkinson's disease is impaired for action verbs but not for concrete nouns

abstract: Recent studies have demonstrated that processing of action words recruits cortical motor regions that are also involved in the planning and execution of the actions words refer to. The functional role of these regions in word understanding remains, however, to be clarified. The present study investigates this issue by examining the impact of Parkinson's disease (PD) on lexical decision performance for action words, relative to concrete nouns, in a masked priming paradigm. Priming effects for the two word categories were measured in non-demented PD patients off and on dopaminergic treatment, and in healthy participants. Our results revealed that although overall performances did not differ between verbs and nouns, priming effects showed a clear dissociation between word categories. While priming for concrete nouns was not affected by Levodopa intake, it dissociated as a function of treatment for action verbs. No priming was actually obtained for action verbs in PD patients off dopaminergic treatment. Following Levodopa intake, this deficit recovered, however, because priming effects for verbs became comparable to those for concrete nouns and similar to performance of healthy participants. Overall, this study thus brings compelling evidence that processing lexico-semantic information about action words depends on the integrity of the motor system.


title: Detecting Encrypted Traffic: A Machine Learning Approach

abstract: Detecting encrypted traffic is increasingly important for deep packet inspection (DPI) to improve the performance of intrusion detection systems. We propose a machine learning approach with several randomness tests to achieve high accuracy detection of encrypted traffic while requiring low overhead incurred by the detection procedure. To demonstrate how effective the proposed approach is, the performance of four classification methods (Naive Bayesian, Support Vector Machine, CART and AdaBoost) are explored. Our recommendation is to use CART which is not only capable of achieving an accuracy of 99.9% but also up to about 2.9 times more efficient than the second best candidate (Naive Bayesian).


Enhanced Fraud Miner : Credit Card Fraud Detection using Clustering Data Mining Techniques


title: Introduction to Robotics mechanics and Control

abstract: 1. Introduction. 2. Spatial Descriptions and Transformations. 3. Manipulator Kinematics. 4. Inverse Manipulator Kinematics. 5. Jacobians: Velocities and Static Forces. 6. Manipulator Dynamics. 7. Trajectory Generation. 8. Manipulator Mechanism Design. 9. Linear Control of Manipulators. 10. Nonlinear Control of Manipulators. 11. Force Control of Manipulators. 12. Robot Programming Languages and Systems. 13. Off-Line Programming Systems.


title: Digital signature and authentication mechanisms using new customized hash function for cognitive radio networks

abstract: Wireless technologies, applications and services have been witnessed a rapid growth in the past few years, due to this development, spectrum scarcity and shortage has become a major concern, several spectrum portions of the static allocated licensed bands are under-utilized. Cognitive radio networks (CRNs) are considered to be the most promising solution in improving the spectrum utilization and solving the spectrum scarcity by providing licensed spectrum portions to unlicensed users, however due to nature of these networks, CRNs are exposed to different types of threats and attacks from different malicious users, which can affect the network availability and performance, in this paper a proposed authentication mechanisms and digital signature scheme applied using new customized hash function for CRNs, the proposed model simulated and tested to provide both primary and secondary users data authentication, data integrity and non-repudiation in CRNs, achieving a defend strategy from the well-known attacks that could be launched by malicious, selfish users, threats and vulnerabilities in CRNs.


title: Traffic classification through simple statistical fingerprinting

abstract: The classification of IP ows according to the application that generated them is at the basis of any modern network management platform. However, classical techniques such as the ones based on the analysis of transport layer or application layer information are rapidly becoming ineffective. In this paper we present a ow classification mechanism based on three simple properties of the captured IP packets: their size, inter-arrival time and arrival order. Even though these quantities have already been used in the past to define classification techniques, our contribution is based on new structures called protocol fingerprints, which express such quantities in a compact and efficient way, and on a simple classification algorithm based on normalized thresholds. Although at a very early stage of development, the proposed technique is showing promising preliminary results from the classification of a reduced set of protocols.


title: Effect of superstrate height on gain of MSA fed Fabry Perot Cavity antenna

abstract: This article highlights the effect of superstrate height on gain of MSA fed Fabry Perot Cavity (FPC) antenna. The FPC antenna structure consists of a microstrip antenna, which feeds an array of square parasitic metal patches fabricated on a FR4 superstrate. These patches are good reflector of microwave frequency and therefore are used to enhance the reflection coefficient and the gain. Field amplitude and phase depend on dimensions and spacing between parasitic patches. Antenna gain can be further increased by increasing the superstrate height. However, differential gain improvement decreases with superstrate height. High gain is achieved by placing superstrate layer at about integral multiple of half wavelength above the ground plane. Structures are designed, fabricated and tested. Measured VSWR is less than 2 over 5.725–5.875 GHz, ISM band. The analysed structures can be packaged inside an application platform.


title: Resource Allocation for Downlink NOMA Systems: Key Techniques and Open Issues

abstract: This article presents advances in resource allocation (RA) for downlink non-orthogonal multiple access (NOMA) systems, focusing on user pairing (UP) and power allocation (PA) algorithms. The former pairs the users to obtain the high capacity gain by exploiting the channel gain difference between the users, while the later allocates power to users in each cluster to balance system throughput and user fairness. Additionally, the article introduces the concept of cluster fairness and proposes the divideand- next largest difference-based UP algorithm to distribute the capacity gain among the NOMA clusters in a controlled manner. Furthermore, performance comparison between multiple-input multiple-output NOMA (MIMO-NOMA) and MIMO-OMA is conducted when users have pre-defined quality of service. Simulation results are presented, which validate the advantages of NOMA over OMA. Finally, the article provides avenues for further research on RA for downlink NOMA.


title: Substituted Judgment: The Limitations of Autonomy in Surrogate Decision Making

abstract: Substituted judgment is often invoked as a guide for decision making when a patient lacks decision making capacity and has no advance directive. Using substituted judgment, doctors and family members try to make the decision that the patient would have made if he or she were able to make decisions. However, empirical evidence suggests that the moral basis for substituted judgment is unsound. In spite of this, many physicians and bioethicists continue to rely on the notion of substituted judgment. Given compelling evidence that the use of substituted judgment has insurmountable flaws, other approaches should be considered. One approach provides limits on decision making using a best interest standard based on community norms. A second approach uses narrative techniques and focuses on each patient's dignity and individuality rather than his or her autonomy.


title: On Enhancing Lane Estimation Using Contextual Cues

abstract: Vision-based lane detection is a critical component of modern automotive active safety systems. Although a number of robust and accurate lane estimation (LE) algorithms have been proposed, computationally efficient systems that can be realized on embedded platforms have been less explored and addressed. This paper presents a framework that incorporates contextual cues for LE to further enhance the performance in terms of both computational efficiency and accuracy. The proposed context-aware LE framework considers the state of the ego vehicle, its surroundings, and the system-level requirements to adapt and scale the LE process resulting in substantial computational savings. This is accomplished by synergistically fusing data from multiple sensors along with the visual data to define the context around the ego vehicle. The context is then incorporated as an input to the LE process to scale it depending on the contextual requirements. A detailed evaluation of the proposed framework on real-world driving conditions shows that the dynamic and static configuration of the lane detection process results in computation savings as high as 90%, without compromising on the accuracy of LE.


title: Digital LDO with Time-Interleaved Comparators for Fast Response and Low Ripple

abstract: On-chip voltage regulation using distributed Digital Low Drop Out (LDO) voltage regulators has been identified as a promising technique for efficient power-management for emerging multi-core processors. Digital LDOs (DLDO) can offer low voltage operation, faster transient response, and higher current efficiency. Response time as well as output voltage ripple can be reduced by increasing the speed of the dynamic comparators. However, the comparator offset steeply increases for high clock frequencies, thereby leading to enhanced variations in output voltage. In this work we explore the design of digital LDOs with multiple dynamic comparators that can overcome this bottleneck. In the proposed topology, we apply time-interleaved comparators with the same voltage threshold and uniform current step in order to accomplish the aforementioned features. Simulation based analysis shows that the DLDO with time-interleaved comparators can achieve better overall performance in terms of current efficiency, ripple and settling time. For a load step of 50mA, a DLDO with 8 time-interleaved comparators could achieve an output ripple of less than 5mV, while achieving a settling time of less than 0.5us. Load current dependant dynamic adjustment of clock frequency is proposed to maintain high current efficiency of ~97%.


title: A 2.3 pJ/bit frequency-stable impulse OOK transmitter powered directly by an RF energy harvesting circuit with −19.5 dBm sensitivity

abstract: The proposed 2.5-GHz-band impulse transmitter technology realizes frequency-stable impulse generation against PVT variation and superior energy-per-bit operation, and it can be powered directly from a - 19.5-dBm-sensitivity RF energy harvesting circuit without any regulators that are generally essential to power RF circuits. The transmitter occupies 0.38 mm ::: 2 ::: in a 65nm CMOS technology. The maximum frequency difference among measured output return-loss peak of 9 chips with 3 different process corners under 0.5 V supply is about 50 MHz without any frequency calibration. Our prototype achieves 1 Mb/s signal transmission under 2.3 μW power consumption from 0.5 V supply thanks to pulse-level duty cycling operation of maximally digital architecture.


title: Depression and Internet Addiction in Adolescents

abstract: Background: The aim of the study was to evaluate the relationship between depression and Internet addiction among adolescents. Sampling and Method: A total of 452


title: Smart-Evac: Big Data-Based Decision Making for Emergency Evacuation

abstract: Smart-Evac manages emergency evacuation systems after a natural disaster. Unlike existing disaster risk reduction (DRR) systems, Smart-Evac takes cloud computing and big data characteristics into consideration during decision making. The authors consider human anxiety to be a major contributor to network congestion immediately following a disaster. They envision tracing clusters of trapped peoples where network usage is comparatively high. The Smart-Evac system ranks these clusters based on volume, velocity, and variety (the 3Vs of big data). Based on this ranking, the system provides immediate evacuation service to the top-listed clusters to mitigate the exponential rise of network congestion. The system used the analytical hierarchy process (AHP) to achieve the proper ranking of the clusters to support decision makers efficiently. In addition, Smart-Evac provides immediate cloud-based basic healthcare facilities and ambulatory medical services to victims after successful evacuation. Immediate priority-based healthcare in turn reduces the haphazard rush to nearby hospitals.


title: PicSOM—Self-organizing image retrieval with MPEG-7 content descriptions

abstract: Development of content-based image retrieval (CBIR) techniques has suffered from the lack of standardized ways for describing visual image content. Luckily, the MPEG-7 international standard is now emerging as both a general framework for content description and a collection of specific agreed-upon content descriptors. We have developed a neural, self-organizing technique for CBIR. Our system is named PicSOM and it is based on pictorial examples and relevance feedback (RF). The name stems from "picture" and the self-organizing map (SOM). The PicSOM system is implemented by using tree structured SOMs. In this paper, we apply the visual content descriptors provided by MPEG-7 in the PicSOM system and compare our own image indexing technique with a reference system based on vector quantization (VQ). The results of our experiments show that the MPEG-7-defined content descriptors can be used as such in the PicSOM system even though Euclidean distance calculation, inherently used in the PicSOM system, is not optimal for all of them. Also, the results indicate that the PicSOM technique is a bit slower than the reference system in starting to find relevant images. However, when the strong RF mechanism of PicSOM begins to function, its retrieval precision exceeds that of the reference system.


title: A Random Graph Model for Power Law Graphs

abstract: We propose a random graph model which is a special case of sparserandom graphs with given degree sequences which satisfy a power law. This model involves only a small number of paramo eters, called logsize and log-log growth rate. These parameters capture some universal characteristics of massive graphs. From these parameters, various properties of the graph can be derived. For example, for certai n ranges of the parameters, we wi II compute the expected distribution of the sizes of the connected components which almost surely occur with high probability. We illustrate the consistency of our model with the behavior of some massive graphs derived from data in telecommunications. We also discuss the threshold function, the giant component, and the evolution of random graphs in this model.


title: Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

abstract: Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. Efforts to obtain embeddings for larger chunks of text, such as sentences, have however not been so successful. Several attempts at learning unsupervised representations of sentences have not reached satisfactory enough performance to be widely adopted. In this paper, we show how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference datasets can consistently outperform unsupervised methods like SkipThought vectors on a wide range of transfer tasks. Much like how computer vision uses ImageNet to obtain features, which can then be transferred to other tasks, our work tends to indicate the suitability of natural language inference for transfer learning to other NLP tasks. Our encoder is publicly available.


title: Towards reducing taxicab cruising time using spatio-temporal profitability maps

abstract: Taxicab service plays a vital role in public transportation by offering passengers quick personalized destination service in a semiprivate and secure manner. Taxicabs cruise the road network looking for a fare at designated taxi stands or alongside the streets. However, this service is often inefficient due to a low ratio of live miles (miles with a fare) to cruising miles (miles without a fare). The unpredictable nature of passengers and destinations make efficient systematic routing a challenge. With higher fuel costs and decreasing budgets, pressure mounts on taxicab drivers who directly derive their income from fares and spend anywhere from 35-60 percent of their time cruising the road network for these fares. Therefore, the goal of this paper is to reduce the number of cruising miles while increasing the number of live miles, thus increasing profitability, without systematic routing. This paper presents a simple yet practical method for reducing cruising miles by suggesting profitable locations to taxicab drivers. The concept uses the same principle that a taxicab driver uses: follow your experience. In our approach, historical data serves as experience and a derived Spatio-Temporal Profitability (STP) map guides cruising taxicabs. We claim that the STP map is useful in guiding for better profitability and validate this by showing a positive correlation between the cruising profitability score based on the STP map and the actual profitability of the taxicab drivers. Experiments using a large Shanghai taxi GPS data set demonstrate the effectiveness of the proposed method.


title: Low rank matrix recovery from rank one measurements

abstract: We study the recovery of Hermitian low rank matrices $X \in \mathbb{C}^{n \times n}$ from undersampled measurements via nuclear norm minimization. We consider the particular scenario where the measurements are Frobenius inner products with random rank-one matrices of the form $a_j a_j^*$ for some measurement vectors $a_1,...,a_m$, i.e., the measurements are given by $y_j = \mathrm{tr}(X a_j a_j^*)$. The case where the matrix $X=x x^*$ to be recovered is of rank one reduces to the problem of phaseless estimation (from measurements, $y_j = |\langle x,a_j\rangle|^2$ via the PhaseLift approach, which has been introduced recently. We derive bounds for the number $m$ of measurements that guarantee successful uniform recovery of Hermitian rank $r$ matrices, either for the vectors $a_j$, $j=1,...,m$, being chosen independently at random according to a standard Gaussian distribution, or $a_j$ being sampled independently from an (approximate) complex projective $t$-design with $t=4$. In the Gaussian case, we require $m \geq C r n$ measurements, while in the case of $4$-designs we need $m \geq Cr n \log(n)$. Our results are uniform in the sense that one random choice of the measurement vectors $a_j$ guarantees recovery of all rank $r$-matrices simultaneously with high probability. Moreover, we prove robustness of recovery under perturbation of the measurements by noise. The result for approximate $4$-designs generalizes and improves a recent bound on phase retrieval due to Gross, Kueng and Krahmer. In addition, it has applications in quantum state tomography. Our proofs employ the so-called bowling scheme which is based on recent ideas by Mendelson and Koltchinskii.


title: BRITTLE CULM1, Which Encodes a COBRA-Like Protein, Affects the Mechanical Properties of Rice Plants

abstract: Plant mechanical strength is an important agronomic trait. To understand the molecular mechanism that controls the plant mechanical strength of crops, we characterized the classic rice mutant brittle culm1 (bc1) and isolated BC1 using a map-based cloning approach. BC1, which encodes a COBRA-like protein, is expressed mainly in developing sclerenchyma cells and in vascular bundles of rice. In these types of cells, mutations in BC1 cause not only a reduction in cell wall thickness and cellulose content but also an increase in lignin level, suggesting that BC1, a gene that controls the mechanical strength of monocots, plays an important role in the biosynthesis of the cell walls of mechanical tissues.


TrajAnalytics: A Web-Based Visual Analytics Software of Urban Trajectory


title: Fuzzy Restricted Boltzmann Machine for the Enhancement of Deep Learning

abstract: In recent years, deep learning caves out a research wave in machine learning. With outstanding performance, more and more applications of deep learning in pattern recognition, image recognition, speech recognition, and video processing have been developed. Restricted Boltzmann machine (RBM) plays an important role in current deep learning techniques, as most of existing deep networks are based on or related to it. For regular RBM, the relationships between visible units and hidden units are restricted to be constants. This restriction will certainly downgrade the representation capability of the RBM. To avoid this flaw and enhance deep learning capability, the fuzzy restricted Boltzmann machine (FRBM) and its learning algorithm are proposed in this paper, in which the parameters governing the model are replaced by fuzzy numbers. This way, the original RBM becomes a special case in the FRBM, when there is no fuzziness in the FRBM model. In the process of learning FRBM, the fuzzy free energy function is defuzzified before the probability is defined. The experimental results based on bar-and-stripe benchmark inpainting and MNIST handwritten digits classification problems show that the representation capability of FRBM model is significantly better than the traditional RBM. Additionally, the FRBM also reveals better robustness property compared with RBM when the training data are contaminated by noises.


title: Plotting basic control charts: tutorial notes for healthcare practitioners

abstract: There is considerable interest in the use of statistical process control (SPC) in healthcare. Although SPC is part of an overall philosophy of continual improvement, the implementation of SPC usually requires the production of control charts. However, as SPC is relatively new to healthcare practitioners and is not routinely featured in medical statistics texts/courses, there is a need to explain the issues involved in the selection and construction of control charts in practice. Following a brief overview of SPC in healthcare and preliminary issues, we use a tutorial-based approach to illustrate the selection and construction of four commonly used control charts (xmr-chart, p-chart, u-chart, c-chart) using examples from healthcare. For each control chart, the raw data, the relevant formulae and their use and interpretation of the final SPC chart are provided together with a notes section highlighting important issues for the SPC practitioner. Some more advanced topics are also mentioned with suggestions for further reading.


title: Attending to the present: Mindfulness meditation reveals distinct modes of self-reference

abstract: It has long been theorised that there are two temporally distinct forms of self-reference: extended self-reference linking experiences across time, and momentary self-reference centred on the present. To characterise these two aspects of awareness, we used functional magnetic resonance imaging (fMRI) to examine monitoring of enduring traits (’narrative’ focus, NF) or momentary experience (’experiential’ focus, EF) in both novice participants and those having attended an 8 week course in mindfulness meditation, a program that trains individuals to develop focused attention on the present. In novices, EF yielded focal reductions in self-referential cortical midline regions (medial prefrontal cortex, mPFC) associated with NF. In trained participants, EF resulted in more marked and pervasive reductions in the mPFC, and increased engagement of a right lateralised network, comprising the lateral PFC and viscerosomatic areas such as the insula, secondary somatosensory cortex and inferior parietal lobule. Functional connectivity analyses further demonstrated a strong coupling between the right insula and the mPFC in novices that was uncoupled in the mindfulness group. These results suggest a fundamental neural dissociation between two distinct forms of self-awareness that are habitually integrated but can be dissociated through attentional training: the self across time and in the present moment.


title: Accelerating MapReduce on a coupled CPU-GPU architecture

abstract: The work presented here is driven by two observations. First, heterogeneous architectures that integrate a CPU and a GPU on the same chip are emerging, and hold much promise for supporting power-efficient and scalable high performance computing. Second, MapReduce has emerged as a suitable framework for simplified parallel application development for many classes of applications, including data mining and machine learning applications that benefit from accelerators. This paper focuses on the challenge of scaling a MapReduce application using the CPU and GPU together in an integrated architecture. We develop different methods for dividing the work, which are the map-dividing scheme, where map tasks are divided between both devices, and the pipelining scheme, which pipelines the map and the reduce stages on different devices. We develop dynamic work distribution schemes for both the approaches. To achieve high load balance while keeping scheduling costs low, we use a runtime tuning method to adjust task block sizes for the map-dividing scheme. Our implementation of MapReduce is based on a continuous reduction method, which avoids the memory overheads of storing key-value pairs. We have evaluated the different design decisions using 5 popular MapReduce applications. For 4 of the applications, our system achieves 1.21 to 2.1 speedup over the better of the CPU-only and GPU-only versions. The speedups over a single CPU core execution range from 3.25 to 28.68. The runtime tuning method we have developed achieves very low load imbalance, while keeping scheduling overheads low. Though our current work is specific to MapReduce, many underlying ideas are also applicable towards intra-node acceleration of other applications on integrated CPU-GPU nodes.


title: Design optimisation and trade-offs in multi-kW DC-DC converters

abstract: This work investigates design optimisation and design trade-offs for multi-kW DC-DC Interleaved Boost Converters (IBC). A general optimisation procedure for weight minimisation is presented, and the trade-offs between the key design variables (e.g. switching frequency, topology) and performance metrics (e.g. power density, efficiency) are explored. It is shown that the optimal selection of components, switching frequency, and topology are heavily dependent on operating specifications such as voltage ratio, output voltage, and output power. With the device and component technologies considered, the single-phase boost converter is shown to be superior to the interleaved topologies in terms of power density for lower power, lower voltage specifications, whilst for higher-power specifications, interleaved designs are preferable. Comparison between an optimised design and an existing prototype for a 220 V–600 V, 40 kW specification, further illustrates the potential weight reduction that is afforded through design optimisation, with the optimised design predicting a reduction in component weight of around 33%.


title: An open platform telepresence robot with natural human interface

abstract: This paper explains our low-cost approach to building an open platform telepresence robot, which uses an iRobot Create, ROS (Robot Operating System) and mobile devices. Besides using existing ROS packages we developed an Android application based on rosjava_core and android_core to control and view a live video stream from the remote robot. The robot could be used to have a video conference with people at a remote location. It is also equipped with capabilities such as autonomous navigation, hand-gesture recognition, and speech recognition through cloud computing services provided by Google. Experimental results verify the effectiveness of the developed telepresence robot.


Why Time Slows Down during an Accident


title: Application and effectiveness of the Beneslider: a device to move molars distally.

abstract: Aim: Distal movement of maxillary molars is a reasonable but often challenging treatment alternative for patients with a dental Class II occlusion and an increased overjet or anterior crowding. One problem is that most of the conventional noncompliance devices that distally move maxillary molars lead to some anchorage loss. As such, a new appliance was designed that is connected to two coupled mini-implants with exchangeable abutments. The aim of this study was to evaluate the effectiveness of this system for distal movement and the extent of its adverse effects. Methods: Maxillary molar distal movement was performed in 18 patients (10 females, eight males) in 6 to 10 months. The appliance (Beneslider) combined elements of the distal jet and Keles slider with two abutment mini-implants (spider screws or Benefit mini-implants). Pre- and posttreatment casts were scanned with cone beam computed tomography. To assess the amount of molar distal movement, molar rotation and transverse expansion the 3D scans were digitally superimposed. Lateral cephalograms were used to measure molar tipping. Results: The mean distal movement of the first molars amounted to 4.6 ± 1.5 mm, the mean mesial rotation to 3.4 ± 2.0 degrees, the transverse expansion in the first molar region to 1.9 ± 1.0 mm, and the distal tipping to 1.9 ± 1.3 degrees. Conclusion: Two coupled mini-implants with exchangeable abutments and a heavy wire were an effective way to bodily move maxillary molars distally. World J Orthod 2010;11:331‐340.


title: Wireless power transfer system for biomedical application: A review

abstract: Wireless power transfer system is an emerging technology that is useful to recharge the battery wirelessly for various portable and biomedical implant devices, battery free sensors, passive RF identification, near-field communications, and many others in near field region. WPT is a fundamental enabling technology which eliminates wired power connections. It is a very broad research area that has recently become applicable to implantable medical devices. Biomedical implanted devices are becoming popular in health and medical applications in a wide range of areas, such as, cardiac pacemakers, retinal prosthesis, cochlear implants, defibrillator, smart orthopedic implants, artificial hearts etc. The traditional approach of supplying power to these devices is implantable batteries, bio-fuel cell and percutaneous links. However, any battery has limited energy storage and life span similarly bio-fuel cell has low output power and percutaneous links are susceptible to infection and reliability problems. Currently, most commercial implanted devices utilize high volume, non-rechargeable batteries. These batteries inevitably need to be replaced at the end of their life span by costly surgery. In addition, bulky size of the batteries due to high energy requirement becomes an obstacle in design of compact implantable devices. The aim of this paper is to review WPT technology in biomedical Application and challenges in WPT system design. The most important advantage of wireless power transfer system is longer life span as compared to non rechargeable batteries and capability to deliver power without costly invasive surgery. In addition invasive surgery involves serious health hazards which are totally eliminated by Wireless Power Transfer System.


title: On the Continuity of High Efficiency Modes in Linear RF Power Amplifiers

abstract: A novel formulation for the voltage waveforms in high efficiency linear power amplifiers is described. This formulation demonstrates that a constant optimum efficiency and output power can be obtained over a continuum of solutions by utilizing appropriate harmonic reactive impedance terminations. A specific example is confirmed experimentally. This new formulation has some important implications for the possibility of realizing broadband >10% high efficiency linear RF power amplifiers.


title: A Framework for Understanding Customer Relationship Management Systems Benefits

abstract: This paper presents a structured framework for understanding the benefits of CRM systems. The framework is comprised of a set of benefits grouped into categories with empirical indicators and example metrics. The framework provides researchers with a systematic approach for exploring CRM system benefits and provides a sound base for further empirical research. It also provides practitioners with a means of defining objectives for CRM projects during specification of the business case and for conducting post-implementation reviews.


title: Parallel Discourse Annotations on a Corpus of Short Texts

abstract: We present the first corpus of texts annotated with two alternative approaches to discourse structure, Rhetorical Structure Theory (Mann and Thompson, 1988) and Segmented Discourse Representation Theory (Asher and Lascarides, 2003). 112 short argumentative texts have been analyzed according to these two theories. Furthermore, in previous work, the same texts have already been annotated for their argumentation structure, according to the scheme of Peldszus and Stede (2013). This corpus therefore enables studies of correlations between the two accounts of discourse structure, and between discourse and argumentation. We converted the three annotation formats to a common dependency tree format that enables to compare the structures, and we describe some initial findings.


title: Algorithms for feature selection: An evaluation

abstract: A large number of algorithms have been proposed for doing feature subset selection. The goal of this paper is to evaluate the quality of feature subsets generated by the various algorithms, and also compare their computational requirements. Our results show that the sequential forward floating selection (SFFS) algorithm, proposed by Pudil et al. (1994), dominates the other algorithms tested. This paper also illustrates the dangers of using feature selection in small sample size situations. It gives the results of applying feature selection to land use classification of SAR satellite images using four different texture models. Pooling features derived from different texture models, followed by a feature selection results in a substantial improvement in the classification accuracy. Application of feature selection to classification of handprinted characters illustrates the value of feature selection in reducing the number of features needed for classifier design.


title: A 65nm CMOS Ramp Generator Design and its Application Towards a BIST Implementation of the Reduced-Code Static Linearity Test Technique for Pipeline ADCs

abstract: This work presents an efficient on-chip ramp generator targeting to facilitate the deployment of Built-In Self-Test (BIST) techniques for ADC static linearity characterization. The proposed ramp generator is based on a fully-differential switched-capacitor integrator that is conveniently modified to produce a very small integration gain, such that the ramp step size is a small fraction of the LSB of the target ADC. The proposed ramp generator is employed in a servo-loop configuration to implement a BIST version of the reduced-code linearity test technique for pipeline ADCs, which drastically reduces the volume of test data and, thereby, the test time, as compared to the standard test based on a histogram. The demonstration of the pipeline ADC BIST is carried out based on a mixture of transistor-level and behavioral-level simulations that employ actual production test data.


title: Location privacy preservation in collaborative spectrum sensing

abstract: Collaborative spectrum sensing has been regarded as a promising approach to enable secondary users to detect primary users by exploiting spatial diversity. In this paper, we consider a converse question: could space diversity be exploited by a malicious entity, e.g., an external attacker or an untrusted Fusion Center (FC), to achieve involuntary geolocation of a secondary user by linking his location-dependent sensing report to his physical position. We answer this question by identifying a new security threat in collaborative sensing from testbed implementation, and it is shown that the attackers could geo-locate a secondary user from its sensing report with a successful rate of above 90% even in the presence of data aggregation. We then introduce a novel location privacy definition to quantify the location privacy leaking in collaborative sensing. We propose a Privacy Preserving collaborative Spectrum Sensing (PPSS) scheme, which includes two primitive protocols: Privacy Preserving Sensing Report Aggregation protocol (PPSRA) and Distributed Dummy Report Injection Protocol (DDRI). Specifically, PPSRA scheme utilizes applied cryptographic techniques to allow the FC to obtain the aggregated result from various secondary users without learning each individual's values while DDRI algorithm can provide differential location privacy for secondary users by introducing a novel sensing data randomization technique. We implement and evaluate the PPSS scheme in a real-world testbed. The evaluation results show that PPSS can significantly improve the secondary user's location privacy with a reasonable security overhead in collaborative sensing.


title: Eclat Algorithm for FIM on CPU-GPU co-operative & parallel environment

abstract: Extracting the frequent itemsets from a transactional database is a fundamental task in data mining field because of its broad applications in mining association rules, time series, correlations etc. The Apriori or Eclat approaches are the commonly used generate-and-check approach to obtain frequent itemsets from a database with a given threshold value. Implementations take advantage of the GPU's massively multi-threaded SIMD (Single Instruction, Multiple Data) architecture which will employ a bitmap data structure to represent vertical transaction list ,to exploit the GPU's SIMD parallelism , and to perform the support counting operation. The implementation runs entirely on the GPU and eliminates intermediate data transfer between the GPU memory and the CPU memory, which can reduce computation time and improve overall performance .OpenCL is a platform independent Open Computing Language for GPU computation. Thus, the aim of our approach is to develop efficient parallel new advanced Eclat strategy of Frequent Itemset Mining that utilize new-generation graphics processing units (GPUs) to speed-up the process.


title: Topic discovery and future trend forecasting for texts

abstract: Finding topics from a collection of documents, such as research publications, patents, and technical reports, is helpful for summarizing large scale text collections and the world wide web. It can also help forecast topic trends in the future. This can be beneficial for many applications, such as modeling the evolution of the direction of research and forecasting future trends of the IT industry. In this paper, we propose using association analysis and ensemble forecasting to automatically discover topics from a set of text documents and forecast their evolving trend in a near future. In order to discover meaningful topics, we collect publications from a particular research area, data mining and machine learning, as our data domain. An association analysis process is applied to the collected data to first identify a set of topics, followed by a temporal correlation analysis to help discover correlations between topics, and identify a network of topics and communities. After that, an ensemble forecasting approach is proposed to predict the popularity of research topics in the future. Our experiments and validations on data with 9 years of publication records validate the effectiveness of the proposed design.


title: Towards using blockchain technology for IoT data access protection

abstract: In the past few years, the number of wireless devices connected to the Internet has increased to a number that could reach billions in the next few years. While cloud computing is being seen as the solution to process this data, security challenges could not be addressed solely with this technology. Security problems will continue to increase with such a model, especially for private and sensitive data such as data personal and medical data collected with more and more sophisticated connected devices (forming the IoT). Thus the need for a fully decentralized peer to peer and secure technology to overcome these problems. The blockchain Technology is a promising approach giving the properties it brings to the field. This paper illustrates an architecture based on blockchain technology, and a protocol for data access, using smart contracts and a publisher-subscriber mechanism.


title: Communicating bad news: a model for emergency mental health helpers.

abstract: This article addresses the concerns of the messenger/helper who must convey tragic news to individuals and families. It offers a model to be used as a guide to ease the stress on both the deliverer and receiver of bad news. The model uses the mnemonic, PEWTER (Prepare, Evaluate, Warn, Tell, Emotional Response, Regroup), to represent the six components of the communication process.


title: A State Space Framework for Automatic Forecasting Using Exponential Smoothing Methods

abstract: We provide a new approach to automatic business forecasting based on an extended range of exponential smoothing methods. Each method in our taxonomy of exponential smoothing methods can be shown to be equivalent to the forecasts obtained from a state space model. This allows (1) the easy calculation of the likelihood, the AIC and other model selection criteria; (2) the computation of prediction intervals for each method; and (3) random simulation from the underlying state space model. We demonstrate the methods by applying them to the data from the M-competition and the M3-competition.