Conditional Dynamic Mutual Information-Based Feature Selection

With emergence of new techniques, data in many fields are getting larger and larger, especially in dimensionality aspect. The high dimensionality of data may pose great challenges to traditional learning algorithms. In fact, many of features in large volume of data are redundant and noisy. Their presence not only degrades the performance of learning algorithms, but also confuses end-users in the post-analysis process. Thus, it is necessary to eliminate irrelevant features from data before being fed into learning algorithms. Currently, many endeavors have been attempted in this field and many outstanding feature selection methods have been developed. Among different evaluation criteria, mutual information has also been widely used in feature selection because of its good capability of quantifying uncertainty of features in classification tasks. However, the mutual information estimated on the whole dataset can not exactly represent the correlation between features. To cope with this issue, in this paper we firstly re-estimate mutual information on identified instances dynamically, and then introduce a new feature selection method based on 1194 H. Liu, Y. Mo, J. Zhao conditional mutual information. Performance evaluations on sixteen UCI datasets show that our proposed method achieves comparable performance to other wellestablished feature selection algorithms in most cases.


Learning Sparse Feature Representations for Music Annotation and Retrieval

We present a data-processing pipeline based on sparse feature learning and describe its applications to music annotation and retrieval. Content-based music annotation and retrieval systems process audio starting with features. While commonly used features, such as MFCC, are handcrafted to extract characteristics of the audio in a succinct way, there is increasing interest in learning features automatically from data using unsupervised algorithms. We describe a systemic approach applying feature-learning algorithms to music data, in particular, focusing on a highdimensional sparse-feature representation. Our experiments show that, using only a linear classifier, the newly learned features produce results on the CAL500 dataset comparable to state-of-the-art music annotation and retrieval systems.


Cost-Aware WWW Proxy Caching Algorithms

Difference between Web Caching & Conventional Paging Problems: Web caching is variable-size caching (web documents vary dramatically in size depending on the information they carry (text, image, video, etc.). Web pages take different amounts of time to download, even if they are of the same size (download latency). Access streams seen by the proxy cache are the union of web access streams from tens to thousands of users (instead of coming from a few programmed sources as in the case of virtual memory paging)


Deep into the Brain: Artificial Intelligence in Stroke Imaging

Artificial intelligence (AI), a computer system aiming to mimic human intelligence, is gaining increasing interest and is being incorporated into many fields, including medicine. Stroke medicine is one such area of application of AI, for improving the accuracy of diagnosis and the quality of patient care. For stroke management, adequate analysis of stroke imaging is crucial. Recently, AI techniques have been applied to decipher the data from stroke imaging and have demonstrated some promising results. In the very near future, such AI techniques may play a pivotal role in determining the therapeutic methods and predicting the prognosis for stroke patients in an individualized manner. In this review, we offer a glimpse at the use of AI in stroke imaging, specifically focusing on its technical principles, clinical application, and future perspectives.


Most approaches to statistical stylometry have concentrated on lexical features, such as relative word frequencies or type-token ratios. Syntactic features have been largely ignored. This work attempts to fill that void by introducing a technique for authorship attribution based on dependency grammar. Syntactic features are extracted from texts using a common dependency parser, and those features are used to train a classifier to identify texts by author. While the method described does not outperform existing methods on most tasks, it does demonstrate that purely syntactic features carry information which could be useful for stylometric analysis. Index words: stylometry, authorship attribution, dependency grammar, machine learning Syntactic Stylometry: Using Sentence Structure for Authorship Attribution


Cooperative flood detection using GSMD via SMS

This paper proposes architecture for an early warning floods system to alert public against flood disasters. An effective early warning system must be developed with linkages between four elements, which are accurate data collection to undertake risk assessments, development of hazard monitoring services, communication on risk related information and existence of community response capabilities. This project focuses on monitoring water level remotely using wireless sensor network. The project also utilizes Global System for Mobile communication (GSM) and short message service (SMS) to relay data from sensors to computers or directly alert the respective victim's through their mobile phone. It is hope that the proposed architecture can be further develop into a functioning system, which would be beneficial to the community and act as a precautionary action to save lives in the case of flood disaster.


Energy-Efficient Hierarchical Routing for Wireless Sensor Networks: A Swarm Intelligence Approach

Energy efficient routing in wireless sensor networks (WSNs) require non-conventional paradigm for design and development of power aware protocols. Swarm intelligence (SI) based metaheuristic can be applied for optimal routing of data, in an energy constraint WSNs environment. In this paper, we present BeeSwarm, a SI based energyefficient hierarchical routing protocol for WSNs. Our protocol consists of three phases: (1) Set-up phase-BeeCluster, (2) Route discovery phase-BeeSearch and (3) Data transmission phase-BeeCarrier. Integration of three phases for clustering, data routing and transmission, is the key aspect of our proposed protocol, which ultimately contributes to its robustness. Evaluation of simulation results show that BeeSwarm perform better in terms of packet delivery, energy consumption and throughput with increased network life compared to other SI based hierarchical routing protocols.


Children's engagement with educational iPad apps: Insights from a Spanish classroom

This study investigates the effects of a story-making app called Our Story and a selection of other educational apps on the learning engagement of forty-one Spanish 4–5-year-olds. Children were observed interacting in small groups with the story-making app and this was compared to their engagement with a selection of construction and drawing apps. Children’s engagement was analysed in two ways: it was categorised using Bangert-Drowns and Pyke’s taxonomy for individual hands-on engagement with educational software, and using the concept of exploratory talk as developed by Mercer et al. to analyse peer engagement. For both approaches, quantitative and qualitative indices of children’s engagement were considered. The overall findings suggested that in terms of the BangertDrowns and Pyke taxonomy, the quality of children’s individual engagement was higher with the OS app in contrast to their engagement with other app software. The frequency of children’s use of exploratory talk was similar with the OS and colouring and drawing apps, and a detailed qualitative analysis of the interaction transcripts revealed several instances of the OS and drawing apps supporting joint problem-solving and collaborative engagement. We suggest that critical indices of an app’s educational value are the extent to which the app supports opportunities for open-ended content and children’s independent use of increasingly difficult features. 2013 Elsevier Ltd. All rights reserved.


Integrating the Quality Attribute Workshop ( QAW ) and the Attribute-Driven Design ( ADD ) Method

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Action-Reaction: Forecasting the Dynamics of Human Interaction

Forecasting human activities from visual evidence is an emerging area of research which aims to allow computational systems to make predictions about unseen human actions. We explore the task of activity forecasting in the context of dual-agent interactions to understand how the actions of one person can be used to predict the actions of another. We model dual-agent interactions as an optimal control problem, where the actions of the initiating agent induce a cost topology over the space of reactive poses – a space in which the reactive agent plans an optimal pose trajectory. The technique developed in this work employs a kernel-based reinforcement learning approximation of the soft maximum value function to deal with the high-dimensional nature of human motion and applies a mean-shift procedure over a continuous cost function to infer a smooth reaction sequence. Experimental results show that our proposed method is able to properly model human interactions in a high dimensional space of human poses. When compared to several baseline models, results show that our method is able to generate highly plausible simulations of human interaction.


Modeling Human Education Data: From Equation-Based Modeling to Agent-Based Modeling

Agent-based simulation is increasingly used to analyze the performance of complex systems. In this paper we describe results of our work on one specific agent-based model, showing how it can be validated against the equation-based model from which it was derived, and demonstrating the extent to which it can be used to derive additional results over and above those that the equation-based model can provide.


LITMUS: Landslide detection by integrating multiple sources

Disasters often lead to other kinds of disasters, forming multi-hazards such as landslides, which may be caused by earthquakes, rainfalls, water erosion, among other reasons. Effective detection and management of multihazards cannot rely only on one information source. In this paper, we evaluate a landslide detection system LITMUS, which combines multiple physical sensors and social media to handle the inherent varied origins and composition of multi-hazards. LITMUS integrates near real-time data from USGS seismic network, NASA TRMM rainfall network, Twitter, YouTube, and Instagram. The landslide detection process consists of several stages of social media filtering and integration with physical sensor data, with a final ranking of relevance by integrated signal strength. Applying LITMUS to data collected in October 2013, we analyzed and filtered 34.5k tweets, 2.5k video descriptions and 1.6k image captions containing landslide keywords followed by integration with physical sources based on a Bayesian model strategy. It resulted in detection of all 11 landslides reported by USGS and 31 more landslides unreported by USGS. An illustrative example is provided to demonstrate how LITMUS’ functionality can be used to determine landslides related to the recent Typhoon Haiyan.


An ontology-based model to determine the automation level of an automated vehicle for co-driving

Full autonomy of ground vehicles is a major goal of the ITS (Intelligent Transportation Systems) community. However, reaching such highest autonomy level in all situations (weather, traffic, ...) may seem difficult in practice, despite recent results regarding driverless cars (e.g., Google Cars). In addition, an automated vehicle should also self-assess its own perception abilities, and not only perceive its environment. In this paper, we propose an intermediate approach towards full automation, by defining a spectrum of automation layers, from fully manual (the car is driven by a driver) to fully automated (the car is driven by a computer), based on an ontological model for representing knowledge. We also propose a second ontology for situation assessment (what does the automated car perceive?), including the sensors/actuators state, environmental conditions and driver's state. Finally, we also define inference rules to link the situation assessment ontology to the automation level one. Both ontological models have been built and first results are presented.


Optimization for allocating BEV recharging stations in urban areas by using hierarchical clustering

The Battery Electric Vehicle (BEV) prototypes are evolving into a reality in a foreseeable future. Before BEV can embark on a mass adoption by road commuters, a new infrastructure for battery charging will have to be ready in place. By 2015, access to BEV charging will be available at nearly one million charge points in the United States, as claimed by CleanMPG. Early BEV adopters will primarily charge their vehicles at home, in their private garages. However, for many Asia-Pacific regions where people live in concrete ghettos, public charging will play a more central role due to reduced access to convenient home charging. This research focuses on planning BEV charging locations to be installed in urbanized areas where they are usually characterized by dense traffic concentrations, restricted street spaces and other complex factors such as the distribution of power grids. We proposed a two-steps model that first quantified the road information into data points, and subsequently they are converged into 'demand clusters' over an urbanized area by hierarchical clustering analysis. Optimization techniques then applied on the demand clusters with the aim of meeting the supplies and demands, while at the same time certain constraints and cost factors are considered. The model is believed to be an important decision-support tool for city planning and BEV charging stations allocation.


Relevance and lexical pragmatics *

The goal of lexical pragmatics is to explain how linguistically specified (‘literal’) word meanings are modified in use. While lexical-pragmatic processes such as narrowing, broadening and metaphorical extension are generally studied in isolation from each other, relevance theorists (Carston 2002, Wilson & Sperber 2002) have been arguing for a unified approach. I will continue this work by underlining some of the problems with more standard treatments, and show how a variety of lexicalpragmatic processes may be analysed as special cases of a general pragmatic adjustment process which applies spontaneously, automatically and unconsciously to fine-tune the interpretation of virtually every word.


Underwater sensor networks: applications, advances and challenges.

This paper examines the main approaches and challenges in the design and implementation of underwater wireless sensor networks. We summarize key applications and the main phenomena related to acoustic propagation, and discuss how they affect the design and operation of communication systems and networking protocols at various layers. We also provide an overview of communications hardware, testbeds and simulation tools available to the research community.


An improvement on the Euler number computing algorithm used in MATLAB

Computation of the Euler number of a binary image is often necessary in image matching, image database retrieval, image analysis, pattern recognition, and computer vision. This paper proposes an improvement on the Euler number computing algorithm used in the famous image processing tool MATLAB. By use of the information obtained during processing the previous pixel, the number of times of checking the neighbor pixels for processing a pixel decrease from 4 to 2. Our method is very simple in principle, and easily implemented. The experimental results demonstrated that our method outperforms significantly conventional Euler number computing algorithms.


DIP: the Database of Interacting Proteins

The Database of Interacting Proteins (DIP; http://dip.doe-mbi.ucla.edu) is a database that documents experimentally determined protein-protein interactions. This database is intended to provide the scientific community with a comprehensive and integrated tool for browsing and efficiently extracting information about protein interactions and interaction networks in biological processes. Beyond cataloging details of protein-protein interactions, the DIP is useful for understanding protein function and protein-protein relationships, studying the properties of networks of interacting proteins, benchmarking predictions of protein-protein interactions, and studying the evolution of protein-protein interactions.


Effects of wine, alcohol and polyphenols on cardiovascular disease risk factors: evidences from human studies.

AIMS
The aim of this review was to focus on the knowledge of the cardiovascular benefits of moderate alcohol consumption, as well as to analyze the effects of the different types of alcoholic beverages.

METHODS
Systematic revision of human clinical studies and meta-analyses related to moderate alcohol consumption and cardiovascular disease (CVD) from 2000 to 2012.

RESULTS
Heavy or binge alcohol consumption unquestionably leads to increased morbidity and mortality. Nevertheless, moderate alcohol consumption, especially alcoholic beverages rich in polyphenols, such as wine and beer, seems to confer cardiovascular protective effects in patients with documented CVD and even in healthy subjects.

CONCLUSIONS
In conclusion, wine and beer (but especially red wine) seem to confer greater cardiovascular protection than spirits because of their polyphenolic content. However, caution should be taken when making recommendations related to alcohol consumption.


Powering MEMS portable devices — a review of non-regenerative and regenerative power supply systems with special emphasis on piezoelectric energy harvesting systems

Power consumption is forecast by the International Technology Roadmap of Semiconductors (ITRS) to pose long-term technical challenges for the semiconductor industry. The purpose of this paper is threefold: (1) to provide an overview of strategies for powering MEMS via non-regenerative and regenerative power supplies; (2) to review the fundamentals of piezoelectric energy harvesting, along with recent advancements, and (3) to discuss future trends and applications for piezoelectric energy harvesting technology. The paper concludes with a discussion of research needs that are critical for the enhancement of piezoelectric energy harvesting devices. (Some figures in this article are in colour only in the electronic version)


Cyber-Physical Systems in the SmartGrid

Radical changes are expected to occur in the next years in the electricity domain and the grid itself which has been almost unchanged the last 100 years. Value is created when interactions exist and this is the main thrust of the emerging SmartGrid which will heavily rely on IT technologies at several layers for monitoring and control. The basic building blocks are the existing efforts in the domain of the Internet of Things and Internet of Services, that come together with cooperation as the key enabler. The SmartGrid is a complex ecosystem of heterogeneous (cooperating) entities that interact in order to provide the envisioned functionality. Advanced business services will take advantage of the near real-time information flows among all participants. In order to realize the SmartGrid promise we will have to heavily depend on Cyber-Physical Systems (CPS) that will be able to monitor, share and manage information and actions on the business as well as the real world. CPS is seen as an integral part of the SmartGrid, hence several open issues will need to be effectively addressed.


The New Frontier of Smart Grids

The power grid is a massive interconnected network used to deliver electricity from suppliers to consumers and has been a vital energy supply. To minimize the impact of climate change while at the same time maintaining social prosperity, smart energy must be embraced to ensure a balanced economical growth and environmental sustainability. There fore, in the last few years, the new concept of a smart grid (SG) became a critical enabler in the contempo rary world and has attracted increas ing attention of policy makers and engineers. This article introduces the main concepts and technological challenges of SGs and presents the authors' views on some required challenges and opportunities pre sented to the IEEE Industrial Electronics Society (IES) in this new and exciting frontier.


An information-centric energy infrastructure: The Berkeley view

We describe an approach for how to design an essentially more scalable, flexible and resilient electric power infrastructure – one that encourages efficient use, integrates local generation, and manages demand through omnipresent awareness of energy availability and use over time. We are inspired by how the Internet has revolutionized communications infrastructure, by pushing intelligence to the edges while hiding the diversity of underlying technologies through well-defined interfaces. Any end device is a traffic source or sink and intelligent endpoints adapt their traffic to what the infrastructure can


Power and energy management for server systems

This survey shows that heterogeneous server clusters can be made more efficient by conserving power and energy while exploiting information from the service level, such as request priorities established by service-level agreements.


A Multilevel Inverter for Photovoltaic Systems With Fuzzy Logic Control

Converters for photovoltaic (PV) systems usually consist of two stages: a dc/dc booster and a pulsewidth modulated (PWM) inverter. This cascade of converters presents efficiency issues, interactions between its stages, and problems with the maximum power point tracking. Therefore, only part of the produced electrical energy is utilized. In this paper, the authors propose a single-phase H-bridge multilevel converter for PV systems governed by a new integrated fuzzy logic controller (FLC)/modulator. The novelties of the proposed system are the use of a fully FLC (not requiring any optimal PWM switching-angle generator and proportional-integral controller) and the use of an H-bridge power-sharing algorithm. Most of the required signal processing is performed by a mixed-mode field-programmable gate array, resulting in a fully integrated System-on-Chip controller. The general architecture of the system and its main performance in a large spectrum of practical situations are presented and discussed. The proposed system offers improved performance over two-level inverters, particularly at low-medium power.


Robot Programming by Demonstration with Crowdsourced Action Fixes

Programming by Demonstration (PbD) can allow endusers to teach robots new actions simply by demonstrating them. However, learning generalizable actions requires a large number of demonstrations that is unreasonable to expect from end-users. In this paper, we explore the idea of using crowdsourcing to collect action demonstrations from the crowd. We propose a PbD framework in which the end-user provides an initial seed demonstration, and then the robot searches for scenarios in which the action will not work and requests the crowd to fix the action for these scenarios. We use instance-based learning with a simple yet powerful action representation that allows an intuitive visualization of the action. Crowd workers directly interact with these visualizations to fix them. We demonstrate the utility of our approach with a user study involving local crowd workers (N=31) and analyze the collected data and the impact of alternative design parameters so as to inform a real-world deployment of our system.


Efficient online structured output learning for keypoint-based object tracking

Efficient keypoint-based object detection methods are used in many real-time computer vision applications. These approaches often model an object as a collection of keypoints and associated descriptors, and detection then involves first constructing a set of correspondences between object and image keypoints via descriptor matching, and subsequently using these correspondences as input to a robust geometric estimation algorithm such as RANSAC to find the transformation of the object in the image. In such approaches, the object model is generally constructed offline, and does not adapt to a given environment at runtime. Furthermore, the feature matching and transformation estimation stages are treated entirely separately. In this paper, we introduce a new approach to address these problems by combining the overall pipeline of correspondence generation and transformation estimation into a single structured output learning framework. Following the recent trend of using efficient binary descriptors for feature matching, we also introduce an approach to approximate the learned object model as a collection of binary basis functions which can be evaluated very efficiently at runtime. Experiments on challenging video sequences show that our algorithm significantly improves over state-of-the-art descriptor matching techniques using a range of descriptors, as well as recent online learning based approaches.


Basics of dermal filler rheology.

BACKGROUND
Hyaluronic acid injectable fillers are the most widely used dermal fillers to treat facial volume deficits, providing long-term facial aesthetic enhancement outcomes for the signs of aging and/or facial contouring.

OBJECTIVES
The purpose of this article was to explain how rheology, the study of the flow of matter, can be used to help physicians differentiate between dermal fillers targeted to certain areas of the face.

METHODS
This article describes how rheological properties affect performance when filler is used in various parts of the face and exposed to mechanical stress (shear deformation and compression/stretching forces) associated with daily facial animation and other commonly occurring external forces.

RESULTS
Improving facial volume deficits with filler is linked mainly to gel viscoelasticity and cohesivity. These 2 properties set the level of resistance to lateral and vertical deformations of the filler and influence filler tissue integration through control of gel spreading.

CONCLUSION
Selection of dermal filler with the right rheological properties is a key factor in achieving a natural-looking long-lasting desired aesthetic outcome.


Smart agent based prepaid wireless energy meter

Prepaid meter (PM) is getting very popular especially in developing countries. There are many advantages to use prepaid meter as opposed to postpaid meter both to the utility provider and to the consumer. Brunei Darussalam has adopted PM but it is not intelligent and not wireless enabled. Reading meters and topping up balance are still done manually. Utility provider does not have information on the usage statistics and has only limited functionalities in the grid control. So accordingly a novel software agent based wireless prepaid energy meter was developed using Java Agent Development Environment (JADE-LEAP) allowing agent from utility provider to query wireless energy meter for energy values of every household. These statistics can be used for statistical computation of the power consumed and for policy and future planning.


Online Keyword Spotting with a Character-Level Recurrent Neural Network

In this paper, we propose a context-aware keyword spotting m odel employing a character-level recurrent neural network (RNN) for spoken term detection in co tinuous speech. The RNN is end-toend trained with connectionist temporal classification (CT C) to generate the probabilities of character and word-boundary labels. There is no need for the phonetic t ranscription, senone modeling, or system dictionary in training and testing. Also, keywords can easi ly be added and modified by editing the text based keyword list without retraining the RNN. Moreover, th e unidirectional RNN processes an infinitely long input audio streams without pre-segmentation and keyw ords are detected with low-latency before the utterance is finished. Experimental results show that th e proposed keyword spotter significantly outperforms the deep neural network (DNN) and hidden Markov m del (HMM) based keyword-filler model even with less computations.


RACOG and wRACOG: Two Probabilistic Oversampling Techniques

As machine learning techniques mature and are used to tackle complex scientific problems, challenges arise such as the imbalanced class distribution problem, where one of the target class labels is under-represented in comparison with other classes. Existing oversampling approaches for addressing this problem typically do not consider the probability distribution of the minority class while synthetically generating new samples. As a result, the minority class is not represented well which leads to high misclassification error. We introduce two probabilistic oversampling approaches, namely RACOG and wRACOG, to synthetically generating and strategically selecting new minority class samples. The proposed approaches use the joint probability distribution of data attributes and Gibbs sampling to generate new minority class samples. While RACOG selects samples produced by the Gibbs sampler based on a predefined lag, wRACOG selects those samples that have the highest probability of being misclassified by the existing learning model. We validate our approach using nine UCI data sets that were carefully modified to exhibit class imbalance and one new application domain data set with inherent extreme class imbalance. In addition, we compare the classification performance of the proposed methods with three other existing resampling techniques.


Enhanced SAR ADC energy efficiency from the early reset merged capacitor switching algorithm

The early reset merged capacitor switching algorithm (EMCS) is proposed as an energy reducing switching technique for a binary weighted, capacitive successive approximation (SAR) analog to digital converter (ADC). The method uses the merged capacitor switching (MCS) architecture and optimizes the use of the VCM level during the SAR conversion. This algorithm can reduce switching power by over 12% with no additional DAC driver activity when compared to the MCS scheme. The MCS and EMCS approaches are analyzed mathematically and the EMCS energy consumption is shown to be lower than or equal to that of the MCS technique for every digital code. Static linearity improvements for this structure are also shown with the integral non-linearity (INL) reducing by a factor of two due to the utilization of the MCS three level DAC. The EMCS implementation methodology is also described.


Data programming with DDLite: putting humans in a different part of the loop

Populating large-scale structured databases from unstructured sources is a critical and challenging task in data analytics. As automated feature engineering methods grow increasingly prevalent, constructing sufficiently large labeled training sets has become the primary hurdle in building machine learning information extraction systems. In light of this, we have taken a new approach called data programming [7]. Rather than hand-labeling data, in the data programming paradigm, users generate large amounts of noisy training labels by programmatically encoding domain heuristics as simple rules. Using this approach over more traditional distant supervision methods and fully supervised approaches using labeled data, we have been able to construct knowledge base systems more rapidly and with higher quality. Since the ability to quickly prototype, evaluate, and debug these rules is a key component of this paradigm, we introduce DDLite, an interactive development framework for data programming. This paper reports feedback collected from DDLite users across a diverse set of entity extraction tasks. We share observations from several DDLite hackathons in which 10 biomedical researchers prototyped information extraction pipelines for chemicals, diseases, and anatomical named entities. Initial results were promising, with the disease tagging team obtaining an F1 score within 10 points of the state-of-the-art in only a single day-long hackathon's work. Our key insights concern the challenges of writing diverse rule sets for generating labels, and exploring training data. These findings motivate several areas of active data programming research.


Governance of an Enterprise Social Intranet Implementation: The Statkraft Case

Recent studies demonstrate that the implementation of enterprise social systems (ESSs) will transfer organizations into new paradigm of social business which results in enormous economic returns and competitive advantage. Social business creates a completely new way of working and organizing characterised by social collaboration, intrinsic knowledge sharing, voluntarily mass participation, just name a few. Thus, implementation of ESSs should tackle the uniqueness of the new way of working and organizing. However, there is a shortage of knowledge about implementation of these large enterprise systems. The purpose of this paper is to study governance model of ESSs implementation. A case study is conducted to investigate the implementation of the social intranet called the ‘Stream’ at Statkraft, which is a world-leading energy company in Norway. The governance model of ‘Stream’ emphasizes the close cooperation and accountability between corporate communication, human resources and IT, which implies paradigm shift in governance of implementing ESSs. Benefits and challenges in the implementation are also identified. Based on the knowledge and insights gained in the study, recommendations are proposed to assist the company in improving governance of ESSs implementation. The study contributes knowledge/know-how on governance of ESSs implementation.


Do Better ImageNet Models Transfer Better?

Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship between architecture and transfer. An implicit hypothesis in modern computer vision research is that models that perform better on ImageNet necessarily perform better on other vision tasks. However, this hypothesis has never been systematically tested. Here, we compare the performance of 16 classification networks on 12 image classification datasets. We find that, when networks are used as fixed feature extractors or fine-tuned, there is a strong correlation between ImageNet accuracy and transfer accuracy (r = 0.99 and 0.96, respectively). In the former setting, we find that this relationship is very sensitive to the way in which networks are trained on ImageNet; many common forms of regularization slightly improve ImageNet accuracy but yield penultimate layer features that are much worse for transfer learning. Additionally, we find that, on two small fine-grained image classification datasets, pretraining on ImageNet provides minimal benefits, indicating the learned features from ImageNet do not transfer well to fine-grained tasks. Together, our results show that ImageNet architectures generalize well across datasets, but ImageNet features are less general than previously suggested.


A randomized, efficient, and distributed protocol for the detection of node replication attacks in wireless sensor networks

Wireless sensor networks are often deployed in hostile environments, where anadversary can physically capture some of the nodes. Once a node is captured, the attackercan re-program it and replicate the node in a large number of clones, thus easily taking over the network. The detection of node replication attacks in a wireless sensor network is therefore a fundamental problem. A few distributed solutions have recently been proposed. However, these solutions are not satisfactory. First, they are energy and memory demanding: A serious drawback for any protocol that is to be used in resource constrained environment such as a sensor network. Further, they are vulnerable to specific adversary models introduced in this paper.
 The contributions of this work are threefold. First, we analyze the desirable properties of a distributed mechanism for the detection of node replication attacks. Second, we show that the known solutions for this problem do not completely meet our requirements. Third, we propose a new Randomized, Efficient, and Distributed (RED) protocol for the detection of node replication attacks and we show that it is completely satisfactory with respect to the requirements. Extensive simulations also show that our protocol is highly efficient in communication, memory, and computation, that it sets out an improved attack detection probability compared to the best solutions in the literature, and that it is resistant to the new kind of attacks we introduce in this paper, while other solutions are not.


TechWare: Financial Data and Analytic Resources [Best of the Web]

In this issue, “Best of the Web” focuses on data resources and analytic tools for quantitative analysis of financial markets. An abundance of financial data is reshaping trading, investment research, and risk management. The broad availability of this information creates opportunities to introduce analysis techniques that are new to the financial industry. The financial industry is currently dominated by a handful of workhorse models, such as the capital asset pricing model and the Black-Scholes options pricing model.


Web Mining Techniques in E-Commerce Applications

Today web is the best medium of communication in modern business. Many companies are redefining their business strategies to improve the business output. Business over internet provides the opportunity to customers and partners where their products and specific business can be found. Nowadays online business breaks the barrier of time and space as compared to the physical office. Big companies around the world are realizing that e-commerce is not just buying and selling over Internet, rather it improves the efficiency to compete with other giants in the market. For this purpose data mining sometimes called as knowledge discovery is used. Web mining is data mining technique that is applied to the WWW. There are vast quantities of information available over the Internet.


You Are What You Post: What the Content of Instagram Pictures Tells About Users' Personality

Instagram is a popular social networking application that allows users to express themselves through the uploaded content and the different filters they can apply. In this study we look at the relationship between the content of the uploaded Instagram pictures and the personality traits of users. To collect data, we conducted an online survey where we asked participants to fill in a personality questionnaire, and grant us access to their Instagram account through the Instagram API. We gathered 54,962 pictures of 193 Instagram users. Through the Google Vision API, we analyzed the pictures on their content and clustered the returned labels with the k-means clustering approach. With a total of 17 clusters, we analyzed the relationship with users’ personality traits. Our findings suggest a relationship between personality traits and picture content. This allow for new ways to extract personality traits from social media trails, and new ways to facilitate personalized systems. Author


The stressed hippocampus, synaptic plasticity and lost memories

Stress is a biologically significant factor that, by altering brain cell properties, can disturb cognitive processes such as learning and memory, and consequently limit the quality of human life. Extensive rodent and human research has shown that the hippocampus is not only crucially involved in memory formation, but is also highly sensitive to stress. So, the study of stress-induced cognitive and neurobiological sequelae in animal models might provide valuable insight into the mnemonic mechanisms that are vulnerable to stress. Here, we provide an overview of the neurobiology of stress–memory interactions, and present a neural–endocrine model to explain how stress modifies hippocampal functioning.


Physics 101: Learning Physical Object Properties from Unlabeled Videos

We study the problem of learning physical properties of objects from unlabeled videos. Humans can learn basic physical laws when they are very young, which suggests that such tasks may be important goals for computational vision systems. We consider various scenarios: objects sliding down an inclined surface and colliding; objects attached to a spring; objects falling onto various surfaces, etc. Many physical properties like mass, density, and coefficient of restitution influence the outcome of these scenarios, and our goal is to recover them automatically. We have collected 17,408 video clips containing 101 objects of various materials and appearances (shapes, colors, and sizes). Together, they form a dataset, named Physics 101, for studying object-centered physical properties. We propose an unsupervised representation learning model, which explicitly encodes basic physical laws into the structure and use them, with automatically discovered observations from videos, as supervision. Experiments demonstrate that our model can learn physical properties of objects from video. We also illustrate how its generative nature enables solving other tasks such as outcome prediction.


A tutorial on training recurrent neural networks , covering BPPT , RTRL , EKF and the " echo state network " approach - Semantic Scholar

This tutorial is a worked-out version of a 5-hour course originally held at AIS in September/October 2002. It has two distinct components. First, it contains a mathematically-oriented crash course on traditional training methods for recurrent neural networks, covering back-propagation through time (BPTT), real-time recurrent learning (RTRL), and extended Kalman filtering approaches (EKF). This material is covered in Sections 2 – 5. The remaining sections 1 and 6 – 9 are much more gentle, more detailed, and illustrated with simple examples. They are intended to be useful as a stand-alone tutorial for the echo state network (ESN) approach to recurrent neural network training.


Advanced Features for Enterprise-Wide Role-Based Access Control

The administration of users and access rights in large enterprises is a complex and challenging task. Roles are a powerful concept for simplifying access control, but their implementation is normally restricted to single systems and applications. In this article we define Enterprise Roles capable of spanning all IT systems in an organisation. We show how the Enterprise Role-Based Access Control (ERBAC) model exploits the RBAC model outlined in the NIST standard draft[5] and describe its extensions. We have implemented ERBAC as a basic concept of SAM Jupiter, a commercial security administration tool. Based on practical experience with the deployment of Enterprise Roles during SAM implementation projects in large organisations, we have enhanced the ERBAC model by including different ways of parametrising the roles. We show that using parameters can significantly reduce the number of roles needed in an enterprise and simplify the role structure, thereby reducing the administration effort considerably. The enhanced ERBAC features are illustrated by reallife examples.


Video-based emotion recognition in the wild using deep transfer learning and score fusion

Multimodal recognition of affective states is a difficult problem, unless the recording conditions are carefully controlled. For recognition “in the wild”, large variances in face pose and illumination, cluttered backgrounds, occlusions, audio and video noise, as well as issues with subtle cues of expression are some of the issues to target. In this paper, we describe a multimodal approach for video-based emotion recognition in the wild. We propose using summarizing functionals of complementary visual descriptors for video modeling. These features include deep convolutional neural network (CNN) based features obtained via transfer learning, for which we illustrate the importance of flexible registration and fine-tuning. Our approach combines audio and visual features with least squares regression based classifiers and weighted score level fusion. We report state-of-the-art results on the EmotiW Challenge for “in the wild” facial expression recognition. Our approach scales to other problems, and ranked top in the ChaLearn-LAP First Impressions Challenge 2016 from video clips collected in the wild.


Learning deep representations by mutual information estimation and maximization

This work investigates unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality in the input into the objective can significantly improve a representation’s suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and compares favorably with fully-supervised learning on several classification tasks in with some standard architectures. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation learning objectives for specific end-goals.


Clinical practice guidelines for the management of pain, agitation, and delirium in adult patients in the intensive care unit.

OBJECTIVE
To revise the "Clinical Practice Guidelines for the Sustained Use of Sedatives and Analgesics in the Critically Ill Adult" published in Critical Care Medicine in 2002.

METHODS
The American College of Critical Care Medicine assembled a 20-person, multidisciplinary, multi-institutional task force with expertise in guideline development, pain, agitation and sedation, delirium management, and associated outcomes in adult critically ill patients. The task force, divided into four subcommittees, collaborated over 6 yr in person, via teleconferences, and via electronic communication. Subcommittees were responsible for developing relevant clinical questions, using the Grading of Recommendations Assessment, Development and Evaluation method (http://www.gradeworkinggroup.org) to review, evaluate, and summarize the literature, and to develop clinical statements (descriptive) and recommendations (actionable). With the help of a professional librarian and Refworks database software, they developed a Web-based electronic database of over 19,000 references extracted from eight clinical search engines, related to pain and analgesia, agitation and sedation, delirium, and related clinical outcomes in adult ICU patients. The group also used psychometric analyses to evaluate and compare pain, agitation/sedation, and delirium assessment tools. All task force members were allowed to review the literature supporting each statement and recommendation and provided feedback to the subcommittees. Group consensus was achieved for all statements and recommendations using the nominal group technique and the modified Delphi method, with anonymous voting by all task force members using E-Survey (http://www.esurvey.com). All voting was completed in December 2010. Relevant studies published after this date and prior to publication of these guidelines were referenced in the text. The quality of evidence for each statement and recommendation was ranked as high (A), moderate (B), or low/very low (C). The strength of recommendations was ranked as strong (1) or weak (2), and either in favor of (+) or against (-) an intervention. A strong recommendation (either for or against) indicated that the intervention's desirable effects either clearly outweighed its undesirable effects (risks, burdens, and costs) or it did not. For all strong recommendations, the phrase "We recommend …" is used throughout. A weak recommendation, either for or against an intervention, indicated that the trade-off between desirable and undesirable effects was less clear. For all weak recommendations, the phrase "We suggest …" is used throughout. In the absence of sufficient evidence, or when group consensus could not be achieved, no recommendation (0) was made. Consensus based on expert opinion was not used as a substitute for a lack of evidence. A consistent method for addressing potential conflict of interest was followed if task force members were coauthors of related research. The development of this guideline was independent of any industry funding.

CONCLUSION
These guidelines provide a roadmap for developing integrated, evidence-based, and patient-centered protocols for preventing and treating pain, agitation, and delirium in critically ill patients.


Emotional Facial Expression Classification for Multimodal User Interfaces

We present a simple and computationally feasible method to perform automatic emotional classification of facial expressions. We propose the use of 10 characteristic points (that are part of the MPEG4 feature points) to extract relevant emotional information (basically five distances, presence of wrinkles and mouth shape). The method defines and detects the six basic emotions (plus the neutral one) in terms of this information and has been fine-tuned with a data-base of 399 images. For the moment, the method is applied to static images. Application to sequences is being now developed. The extraction of such information about the user is of great interest for the development of new multimodal user interfaces.


A framework for evaluating multimodal music mood classification

This research proposes a framework of music mood classification that utilizes multiple and complementary information sources, namely, music audio, lyric text and social tags associated to music pieces. This article presents the framework and a thorough evaluation on each of its components. Experiment results on a large dataset of 18 mood categories show that combining lyrics and audio significantly outperformed systems using audio-only features. Automatic feature selection techniques were further proved to have reduced feature space. In addition, the examination of learning curves shows that the hybrid systems using lyrics and audio needed fewer training samples and shorter audio clips to achieve the same or better classification accuracies than systems using lyrics or audio singularly. Last but not least, performance comparisons reveal relative importance of audio and lyric features across mood categories. Introduction Music is an essential information type in people’s everyday life. Nowadays, there have been a large number of music collections, repositories, and websites that strive to provide convenient access to music for various users, from musicians to the general public. These repositories and users often use different types of metadata to describe music such as genre, artist, country of source and music mood 1 . (Vignoli, 2004; Hu & Downie, 2007). Many of these music repositories have been relying on manual supply of music metadata, but the increasing amount of music data calls for tools that can automatically classify music pieces. Music mood classification has thus been attracting researchers’ attention in the last decade, but many existing classification systems are solely based on information extracted from the audio recordings of music and have achieved suboptimal performances or reached a “glass ceiling” of performance (Lu, Liu, & Zhang, 2006; Trohidis, Tsoumakas, Kalliris, & Vlahavas, 2008; Hu, Downie, Laurier, Bay, & Ehmann, 2008, Yang & Chen, 2012, Barthet, Fazekas, & Sandler, 2013). At roughly the same time, studies have reported that lyrics and social tags associated with music have important values in Music Information Retrieval (MIR) research. For example, Cunningham, Downie, and Bainbridge (2005) reported lyrics as the most mentioned feature by respondents in answering why they hated a song. Geleijnse, Schedl, and Knees (2007) proposed an effective method of measuring artists similarity using social tags associated with the artists. As lyrics often bear with semantics of human language, they have been exploited in music classification as well (e.g., He et al, 2008; Hu, Chen, & Yang, 2009; Van Zaanen & Kanters, 2010; Dakshina & Sridhar, 2014). Furthermore, based on the hypothesis that lyrics and music audio 2 are different enough and thus may complement each other, researchers have started to combine lyrics and audio for improved classification performances (Laurier, Grivolla, & Herrera, 2008; Yang et al., 2008; Björn, Johannes, & Gerhard, 2010; Brilis et al., 2012). Such approach of combining multiple information sources in solving classification problems is commonly called multimodal classification (Kim, et al., 2010; Yang & Chen, 2012; Barthet et al., 2013). Multimodal classification approaches in general are reported to have improved classification performances over those based on a single source. However, there are many options and decisions involved in a multimodal classification approach, and to date, there has not been any general guidance on how to make these decisions in order to achieve more effective classifications. This study proposes a framework of multimodal music mood classification where research questions on each specific stage or component of the classification process could be answered. This is one of the first studies presenting a comprehensive experiment on a multimodal dataset of 5,296 unique songs, which exemplifies every stage of the framework and evidences how the performances of music mood classification can be improved by a multimodal approach. Specifically, novelty and contributions of this study can be summarized as follows: 1. Conceptualize a framework for the entire process of automatic music mood classification using multiple information sources. The framework is flexible in that each component can be easily extended by adding new methods, algorithms and tools. Under the framework, this study systematically answers questions often involved in multimodal classification: feature extraction, feature selection, ensemble methods, etc. 2. Following a previous study evaluating a wide range of lyric features and their combinations (Hu & Downie, 2010), this study further explores feature selection and effect of dimension reduction of feature spaces. Thus, it pushes forward the state-of-the-art on sentiment analysis for music lyrics; 3. Examine the reduction of training data brought by the multimodal approach. This aspect of improvement has rarely been addressed by previous studies on multimodal music classification. Both the effect on the number of training examples and that on the length of audio clips are evaluated in this study. 4. Compare relative advantages of lyrics and audio across different mood categories. To date, there is little evidence on which information source works better for which mood category(ies). Gaining insight on this question can contribute to deeper understanding of sources and components of music mood. 5. Build a large ground truth dataset for the task of multimodal music mood classification. The dataset contains 5,296 unique songs in 18 mood categories. This is one of the largest experimental datasets in music mood classification with both audio and lyrics available (Kim, et al., 2010; Yang & Chen, 2012; Barthet et al., 2013). Results from a large dataset with realistic and representative mood categories are more generalizable and of higher practical values. The rest of the paper is organized as follows. Related work is reviewed and research questions are stated. After that, a framework for multimodal music mood classification is proposed. We then report an experiment with ternary information and conclude by discussing the findings and pointing out future work on enriching the proposed framework.


Malaysian E-government : Issues and Challenges in Public Administration

E-government has become part and parcel of every government’s agenda. Many governments have embraced its significant impacts and influences on governmental operations. As the technology mantra has become more ubiquitous, so government have decided to inaugurate e-government policy in its agencies and departments in order to enhance the quality of services, better transparency and greater accountability. As for Malaysia, the government is inspired by the wave of the e-government, as its establishment can improve the quality of public service delivery, and also its internal operations. This qualitative study will explore the status implementation of e-government initiatives as a case study, and will also provide a comparative evaluation of these findings, using the South Korean government as a benchmark study, given its outstanding performance in e-government. The findings of this study will highlight potential areas for improvement in relation to the public administration perspective and from this comparative approach too, Malaysia can learn some lessons from South Korea’s practices to ensure the success of e-government projects.


Affect in Human-Robot Interaction

More and more, robots are expected to interact with humans in a social, easily understandable manner, which presupposes effective use of robot affect. This chapter provides a brief overview of research advances into this important aspect of human-robot interaction. Keywords: human-robot interaction, affective robotics, robot behavior I. Introduction
  and
  Motivation Humans possess an amazing capability of attributing life and affect to inanimate objects (Reeves and Nass 96, Melson et al 09). Robots take this to the next level, even beyond that of virtual characters due to their embodiment and situatedness. They offer the opportunity for people to bond with them by maintaining a physical presence in their world, in some ways comparable to other beings, such as fellow humans and pets. This raises a broad range of questions in terms of the role of affect in human-robot interaction (HRI), which will be discussed in this article: • What is the role of affect for a robot and in what ways can it add value and risk to humanrobot relationships? Can robots be companions, friends, even intimates to people? • Is it necessary for a robot to actually experience emotion in order to convey its internal state to a person? Is emotion important in enhancing HRI and if so when and where? • What approaches, theories, representations, and experimental methods inform affective HRI research? Report Documentation Page Form Approved OMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE 2014 2. REPORT TYPE 3. DATES COVERED 00-00-2014 to 00-00-2014 4. TITLE AND SUBTITLE Affect in Human-Robot Interaction 5a. CONTRACT NUMBER


Collective annotation of Wikipedia entities in web text

To take the first step beyond keyword-based search toward entity-based search, suitable token spans ("spots") on documents must be identified as references to real-world entities from an entity catalog. Several systems have been proposed to link spots on Web pages to entities in Wikipedia. They are largely based on local compatibility between the text around the spot and textual metadata associated with the entity. Two recent systems exploit inter-label dependencies, but in limited ways. We propose a general collective disambiguation approach. Our premise is that coherent documents refer to entities from one or a few related topics or domains. We give formulations for the trade-off between local spot-to-entity compatibility and measures of global coherence between entities. Optimizing the overall entity assignment is NP-hard. We investigate practical solutions based on local hill-climbing, rounding integer linear programs, and pre-clustering entities followed by local optimization within clusters. In experiments involving over a hundred manually-annotated Web pages and tens of thousands of spots, our approaches significantly outperform recently-proposed algorithms.


Knowledge Base Population: Successful Approaches and Challenges

In this paper we give an overview of the Knowledge Base Population (KBP) track at the 2010 Text Analysis Conference. The main goal of KBP is to promote research in discovering facts about entities and augmenting a knowledge base (KB) with these facts. This is done through two tasks, Entity Linking – linking names in context to entities in the KB – and Slot Filling – adding information about an entity to the KB. A large source collection of newswire and web documents is provided from which systems are to discover information. Attributes (“slots”) derived from Wikipedia infoboxes are used to create the reference KB. In this paper we provide an overview of the techniques which can serve as a basis for a good KBP system, lay out the remaining challenges by comparison with traditional Information Extraction (IE) and Question Answering (QA) tasks, and provide some suggestions to address these challenges.


Mixed Initiative in Dialogue: An Investigation into Discourse Segmentation

Conversation between two people is usually of Mixed-Initiative, with Control over the conversation being transferred from one person to another. We apply a set of rules for the transfer of control to 4 sets of dialogues consisting of a total of 1862 turns. The application of the control rules lets us derive domain-independent discourse structures. The derived structures indicate that initiative plays a role in the structuring of discourse. In order to explore the relationship of control and initiative to discourse processes like centering, we analyze the distribution of four different classes of anaphora for two data sets. This distribution indicates that some control segments are hierarchically related to others. The analysis suggests that discourse participants often mutually agree to a change of topic. We also compared initiative in Task Oriented and Advice Giving dialogues and found that both allocation of control and the manner in which control is transferred is radically different for the two dialogue types. These differences can be explained in terms of collaborative planning principles.


Large-Scale Named Entity Disambiguation Based on Wikipedia Data

This paper presents a large-scale system for the recognition and semantic disambiguation of named entities based on information extracted from a large encyclopedic collection and Web search results. It describes in detail the disambiguation paradigm employed and the information extraction process from Wikipedia. Through a process of maximizing the agreement between the contextual information extracted from Wikipedia and the context of a document, as well as the agreement among the category tags associated with the candidate entities, the implemented system shows high disambiguation accuracy on both news stories and Wikipedia articles.


DBpedia: A Nucleus for a Web of Open Data

DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extraction of the DBpedia datasets, and how the resulting information is published on the Web for humanand machineconsumption. We describe some emerging applications from the DBpedia community and show how website authors can facilitate DBpedia content within their sites. Finally, we present the current status of interlinking DBpedia with other open datasets on the Web and outline how DBpedia could serve as a nucleus for an emerging Web of open data.


On error management: lessons from aviation.

Copies of the full protocol and details of training programmes are available from Association of Litigation and Risk Management (ALARM), Royal Society of Medicine, 1 Wimpole Street, London W1. Contributors: CV and ST-A carried out the research on which the original protocol was based. All authors participated equally in the development of the protocol, in which successive versions were tested in clinical practice and refined in the light of experience. The writing of the original protocol and present paper was primarily carried out by CV, ST-A, EJC, and DH, but all authors contributed to the final version. CV and DH are the guarantors. Competing interests: CV received funding from Healthcare Risk Resources International to support the work of ST-A during the development of the protocol.


Self-assembly of neural networks viewed as swarm intelligence

While self-assembly is a fairly active area of research in swarm intelligence, relatively little attention has been paid to the issues surrounding the construction of network structures. In this paper we extend methods developed previously for controlling collective movements of agent teams to serve as the basis for self-assembly or “growth” of networks, using neural networks as a concrete application to evaluate our approach. Our central innovation is having network connections arise as persistent “trails” left behind moving agents, trails that are reminiscent of pheromone deposits made by agents in ant colony optimization models. The resulting network connections are thus essentially a record of agent movements. We demonstrate our model’s effectiveness by using it to produce two large networks that support subsequent learning of topographic and feature maps. Improvements produced by the incorporation of collective movements are also examined through computational experiments. These results indicate that methods for directing collective movements can be adopted to facilitate network self-assembly.


Client Profiling for an Anti-Money Laundering System

We present a data mining approach for profiling bank clients in order to support the process of detection of antimoney laundering operations. We first present the overall system architecture, and then focus on the relevant component for this paper. We detail the experiments performed on real world data from a financial institution, which allowed us to group clients in clusters and then generate a set of classification rules. We discuss the relevance of the founded client profiles and of the generated classification rules. According to the defined overall agent-based architecture, these rules will be incorporated in the knowledge base of the intelligent agents responsible for the signaling of suspicious transactions.


Strength Training for Endurance Athletes: Theory to Practice

THE PURPOSE OF THIS REVIEW IS TWOFOLD: TO ELUCIDATE THE UTILITY OF RESISTANCE TRAINING FOR ENDURANCE ATHLETES, AND PROVIDE THE PRACTITIONER WITH EVIDENCED-BASED PERIODIZATION STRATEGIES FOR CONCURRENT STRENGTH AND ENDURANCE TRAINING IN ATHLETIC POPULATIONS. BOTH LOW-INTENSITY EXERCISE ENDURANCE (LIEE) AND HIGH-INTENSITY EXERCISE ENDURANCE (HIEE) HAVE BEEN SHOWN TO IMPROVE AS A RESULT OF MAXIMAL, HIGH FORCE, LOW VELOCITY (HFLV) AND EXPLOSIVE, LOW-FORCE, HIGH-VELOCITY STRENGTH TRAINING. HFLV STRENGTH TRAINING IS RECOMMENDED INITIALLY TO DEVELOP A NEUROMUSCULAR BASE FOR ENDURANCE ATHLETES WITH LIMITED STRENGTH TRAINING EXPERIENCE. A SEQUENCED APPROACH TOSTRENGTHTRAINING INVOLVING PHASES OF STRENGTHENDURANCE, BASIC STRENGTH, STRENGTH, AND POWER WILL PROVIDE FURTHER ENHANCEMENTS IN LIEE AND HIEE FOR HIGHLEVEL ENDURANCE ATHLETES.


Dual Switches DC/DC Converter With Three-Winding-Coupled Inductor and Charge Pump

In order to obtain a high step-up voltage gain, high-efficiency converter, this paper proposed a dual switches dc/dc converter with three-winding-coupled inductor and charge pump. The proposed converter composed of dual switches structure, three-winding-coupled inductor, and charge pump. This combination facilitates realization of high step-up voltage gain with a low voltage/current stress on the power switches. Meanwhile, the voltage across the diodes is low and the diode reverse-recovery problem is alleviated by the leakage inductance of the three-winding-coupled inductor. Taking all these into consideration, the efficiency can be high. This paper illustrated the operation principle of the proposed converter; discussed the effect of leakage inductance on voltage gain; the conditions of zero current shutting off of the diodes are illustrated; the voltage and current stress of the power devices are shown; a comparison between the performance of the proposed converter and previous high step-up converters was conducted. Finally, a prototype rated at 500 W has been established, and the experimental results verify the correctness of the analysis.


Learning from positive and unlabeled examples

In many machine learning settings, labeled examples are difficult to collect while unlabeled data are abundant. Also, for some binary classification problems, positive examples which are elements of the target concept are available. Can these additional data be used to improve accuracy of supervised learning algorithms? We investigate in this paper the design of learning algorithms from positive and unlabeled data only. Many machine learning and data mining algorithms, such as decision tree induction algorithms and naive Bayes algorithms, use examples only to evaluate statistical queries (SQ-like algorithms). Kearns designed the statistical query learning model in order to describe these algorithms. Here, we design an algorithm scheme which transforms any SQ-like algorithm into an algorithm based on positive statistical queries (estimate for probabilities over the set of positive instances) and instance statistical queries (estimate for probabilities over the instance space). We prove that any class learnable in the statistical query learning model is learnable from positive statistical queries and instance statistical queries only if a lower bound on the weight of any target concept f can be estimated in polynomial time. Then, we design a decision tree induction algorithm POSC4.5, based on C4.5, that uses only positive and unlabeled examples and we give experimental results for this algorithm. In the case of imbalanced classes in the sense that one of the two classes (say the positive class) is heavily underrepresented compared to the other class, the learning problem remains open. This problem is challenging because it is encountered in many real-world applications. © 2005 Elsevier B.V. All rights reserved.


Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins.

OBJECTIVE
Data on use and misuse of six classes of illicit substances by male twin pairs were used to examine whether genetic and shared environmental risk factors for substance use disorders are substance-specific or -nonspecific in their effect.

METHOD
Lifetime history of use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates was assessed at personal interview in both members of 1,196 male-male twin pairs ascertained by the Virginia Twin Registry. Multivariate twin modeling of substance-nonspecific (common) and substance-specific genetic, shared environmental, and unique environmental risk factors was performed by using the program Mx.

RESULTS
High levels of comorbidity involving the different substance categories were observed for both use and abuse/dependence. One common genetic factor was found to have a strong influence on risk for illicit use and abuse/dependence for all six substance classes. A modest influence of substance-specific genetic factors was seen for use but not for abuse/dependence. Shared environmental factors were more important for use than for abuse/dependence and were mediated entirely through a single common factor.

CONCLUSIONS
In an adult population-based sample of male twins, both the genetic and the shared environmental effects on risk for the use and misuse of six classes of illicit substances were largely or entirely nonspecific in their effect. Environmental experiences unique to the person largely determine whether predisposed individuals will use or misuse one class of psychoactive substances rather than another.


Question Answering on SQuAD

In this project, we exploit several deep learning architectures in Question Answering field, based on the newly released Stanford Question Answering dataset (SQuAD)[7]. We introduce a multi-stage process that encodes context paragraphs at different levels of granularity, uses co-attention mechanism to fuse representations of questions and context paragraphs, and finally decodes the co-attention vectors to get the answers. Our best model gets 62.23% F1 score and 48.72% EM score on the test set.


Genetic Evidence That Carbohydrate-Stimulated Insulin Secretion Leads to Obesity.

BACKGROUND
A fundamental precept of the carbohydrate-insulin model of obesity is that insulin secretion drives weight gain. However, fasting hyperinsulinemia can also be driven by obesity-induced insulin resistance. We used genetic variation to isolate and estimate the potentially causal effect of insulin secretion on body weight.

METHODS
Genetic instruments of variation of insulin secretion [assessed as insulin concentration 30 min after oral glucose (insulin-30)] were used to estimate the causal relationship between increased insulin secretion and body mass index (BMI), using bidirectional Mendelian randomization analysis of genome-wide association studies. Data sources included summary results from the largest published metaanalyses of predominantly European ancestry for insulin secretion (n = 26037) and BMI (n = 322154), as well as individual-level data from the UK Biobank (n = 138541). Data from the Cardiology and Metabolic Patient Cohort study at Massachusetts General Hospital (n = 1675) were used to validate genetic associations with insulin secretion and to test the observational association of insulin secretion and BMI.

RESULTS
Higher genetically determined insulin-30 was strongly associated with higher BMI (β = 0.098, P = 2.2 × 10-21), consistent with a causal role in obesity. Similar positive associations were noted in sensitivity analyses using other genetic variants as instrumental variables. By contrast, higher genetically determined BMI was not associated with insulin-30.

CONCLUSIONS
Mendelian randomization analyses provide evidence for a causal relationship of glucose-stimulated insulin secretion on body weight, consistent with the carbohydrate-insulin model of obesity.


Slip-aware Model Predictive optimal control for Path following

Traditional control and planning algorithms for wheeled mobile robots (WMR) either totally ignore or make simplifying assumptions about the effects of wheel slip on the motion. While this approach works reasonably well in practice on benign terrain, it fails very quickly when the WMR is deployed in terrain that induces significant wheel slip. We contribute a novel control framework that predictively corrects for the wheel slip to effectively minimize path following errors. Our framework, the Receding Horizon Model Predictive Path Follower (RHMPPF), specifically addresses the problem of path following in challenging environments where the wheel slip substantially affects the vehicle mobility. We formulate the solution to the problem as an optimal controller that utilizes a slip-aware model predictive component to effectively correct the controls generated by a strictly geometric pure-pursuit path follower. We present extensive experimental validation of our approach using a simulated 6-wheel skid-steered robot in a high-fidelity data-driven simulator, and on a real 4-wheel skid-steered robot. Our results show substantial improvement in the path following performance in both simulation and real world experiments.


Datum: Managing Data Purchasing and Data Placement in a Geo-Distributed Data Market

This paper studies two design tasks faced by a geo-distributed cloud data market: which data to purchase data purchasing and where to place/replicate the data for delivery data placement. We show that the joint problem of data purchasing and data placement within a cloud data market can be viewed as a facility location problem and is thus NP-hard. However, we give a provably optimal algorithm for the case of a data market made up of a single data center and then generalize the structure from the single data center setting in order to develop a near-optimal, polynomial-time algorithm for a geo-distributed data market. The resulting design, $\mathsf {Datum}$ , decomposes the joint purchasing and placement problem into two subproblems, one for data purchasing and one for data placement, using a transformation of the underlying bandwidth costs. We show, via a case study, that $\mathsf {Datum}$ is near optimal within 1.6% in practical settings.


Reducing Length of Stay Using a Robotic-assisted Approach for Retromuscular Ventral Hernia Repair: A Comparative Analysis From the Americas Hernia Society Quality Collaborative.

OBJECTIVE
The aim of this study was to compare length of stay (LOS) after robotic-assisted and open retromuscular ventral hernia repair (RVHR).

BACKGROUND
RVHR has traditionally been performed by open techniques. Robotic-assisted surgery enables surgeons to perform minimally invasive RVHR, but with unknown benefit. Using real-world evidence, this study compared LOS after open (o-RVHR) and robotic-assisted (r-RVHR) approach.

METHODS
Multi-institutional data from patients undergoing elective RVHR in the Americas Hernia Society Quality Collaborative between 2013 and 2016 were analyzed. Propensity score matching was used to compare median LOS between o-RVHR and r-RVHR groups. This work was supported by an unrestricted grant from Intuitive Surgical, and all clinical authors have declared direct or indirect relationships with Intuitive Surgical.

RESULTS
In all, 333 patients met inclusion criteria for a 2:1 match performed on 111 r-RVHR patients using propensity scores, with 222 o-RVHR patients having similar characteristics as the robotic-assisted group. Median LOS [interquartile range (IQR)] was significantly decreased for r-RVHR patients [2 days (IQR 2)] compared with o-RVHR patients [3 days (IQR 3), P < 0.001]. No differences in 30-day readmissions or surgical site infections were observed. Higher surgical site occurrences were noted with r-RVHR, consisting mostly of seromas not requiring intervention.

CONCLUSIONS
Using real-world evidence, a robotic-assisted approach to RVHR offers the clinical benefit of reduced postoperative LOS. Ongoing monitoring of this technique should be employed through continuous quality improvement to determine the long-term effect on hernia recurrence, complications, patient satisfaction, and overall cost.


Possibilistic interest discovery from uncertain information in social networks

User generated content on the microblogging social network Twitter continues to grow with significant amount of information. The semantic analysis offers the opportunity to discover and model latent interests’ in the users’ publications. This article focuses on the problem of uncertainty in the users’ publications that has not been previously treated. It proposes a new approach for users’ interest discovery from uncertain information that augments traditional methods using possibilistic logic. The possibility theory provides a solid theoretical base for the treatment of incomplete and imprecise information and inferring the reliable expressions from a knowledge base. More precisely, this approach used the product-based possibilistic network to model knowledge base and discovering possibilistic interests. DBpedia ontology is integrated into the interests’ discovery process for selecting the significant topics. The empirical analysis and the comparison with the most known methods proves the significance of this approach.


Mechanisms of emotional arousal and lasting declarative memory

Neuroscience is witnessing growing interest in understanding brain mechanisms of memory formation for emotionally arousing events, a development closely related to renewed interest in the concept of memory consolidation. Extensive research in animals implicates stress hormones and the amygdaloid complex as key, interacting modulators of memory consolidation for emotional events. Considerable evidence suggests that the amygdala is not a site of long-term explicit or declarative memory storage, but serves to influence memory-storage processes in other brain regions, such as the hippocampus, striatum and neocortex. Human-subject studies confirm the prediction of animal work that the amygdala is involved with the formation of enhanced declarative memory for emotionally arousing events.


Human Activity Recognition Using Recurrent Neural Networks

Human activity recognition using smart home sensors is one of the bases of ubiquitous computing in smart environments and a topic undergoing intense research in the field of ambient assisted living. The increasingly large amount of data sets calls for machine learning methods. In this paper, we introduce a deep learning model that learns to classify human activities without using any prior knowledge. For this purpose, a Long Short Term Memory (LSTM) Recurrent Neural Network was applied to three real world smart home datasets. The results of these experiments show that the proposed approach outperforms the existing ones in terms of accuracy and performance.


An Introductory Study on Time Series Modeling and Forecasting

ACKNOWLEDGEMENT The timely and successful completion of the book could hardly be possible without the helps and supports from a lot of individuals. I will take this opportunity to thank all of them who helped me either directly or indirectly during this important work. First of all I wish to express my sincere gratitude and due respect to my supervisor Dr. R.K. Agrawal, Associate Professor SC & SS, JNU. I am immensely grateful to him for his valuable guidance, continuous encouragements and positive supports which helped me a lot during the period of my work. I would like to appreciate him for always showing keen interest in my queries and providing important suggestions. I also express whole hearted thanks to my friends and classmates for their care and moral supports. The moments, I enjoyed with them during my M.Tech course will always remain as a happy memory throughout my life. I owe a lot to my mother for her constant love and support. She always encouraged me to have positive and independent thinking, which really matter in my life. I would like to thank her very much and share this moment of happiness with her. Last but not the least I am also thankful to entire faculty and staff of SC & SS for their unselfish help, I got whenever needed during the course of my work.ABSTRACT Time series modeling and forecasting has fundamental importance to various practical domains. Thus a lot of active research works is going on in this subject during several years. Many important models have been proposed in literature for improving the accuracy and effeciency of time series modeling and forecasting. The aim of this book is to present a concise description of some popular time series forecasting models used in practice, with their salient features. In this book, we have described three important classes of time series models, viz. the stochastic, neural networks and SVM based models, together with their inherent forecasting strengths and weaknesses. We have also discussed about the basic issues related to time series modeling, such as stationarity, parsimony, overfitting, etc. Our discussion about different time series models is supported by giving the experimental forecast results, performed on six real time series datasets. While fitting a model to a dataset, special care is taken to select the most parsimonious one. To evaluate forecast accuracy as well as to compare among different models …


On the development of name search techniques for Arabic

The need for effective identity matching systems has led to extensive research in the area of name search. For the most part, such work has been limited to English and other Latin-based languages. Consequently, algorithms such as Soundex and n-gram matching are of limited utility for languages such as Arabic, which has a vastly different morphology that relies heavily on phonetic information. The dearth of work in this field is partly due to the lack of standardized test data. Consequently, we built a collection of 7,939 Arabic names, along with 50 training queries and 111 test queries. We use this collection to evaluate a variety of algorithms, including a derivative of Soundex tailored to Arabic (ASOUNDEX), measuring effectiveness using standard information retrieval measures. Our results show an improvement of 70% over existing approaches. Introduction Identity matching systems frequently employ name search algorithms to effectively locate relevant information about a given person. Such systems are used for applications as diverse as tax fraud detection and immigration control. Using names to retrieve information makes such systems susceptible to problems arising from typographical errors. That is, exact match search approaches will not find instances of misspelled names or those names that have more than one accepted spelling. An example of the severity of the problem is noted in an NCR (1998) report that estimates that the state of Texas saved $43 million over 18 months in the field of tax compliance using an improved name search approach. Thus, the importance of such name-based search applications has resulted in improved name matching algorithms for English that make use of phonetic information, but these language-dependent techniques have not been extended to Arabic.


Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach

Tax fraud is the intentional act of lying on a tax return form with intent to lower one's tax liability. Under-reporting is one of the most common types of tax fraud, it consists in filling a tax return form with a lesser tax base. As a result of this act, fiscal revenues are reduced, undermining public investment.
 Detecting tax fraud is one of the main priorities of local tax authorities which are required to develop cost-efficient strategies to tackle this problem. Most of the recent works in tax fraud detection are based on supervised machine learning techniques that make use of labeled or audit-assisted data. Regrettably, auditing tax declarations is a slow and costly process, therefore access to labeled historical information is extremely limited. For this reason, the applicability of supervised machine learning techniques for tax fraud detection is severely hindered.
 Such limitations motivate the contribution of this work. We present a novel approach for the detection of potential fraudulent tax payers using only unsupervised learning techniques and allowing the future use of supervised learning techniques. We demonstrate the ability of our model to identify under-reporting taxpayers on real tax payment declarations, reducing the number of potential fraudulent tax payers to audit. The obtained results demonstrate that our model doesn't miss on marking declarations as suspicious and labels previously undetected tax declarations as suspicious, increasing the operational efficiency in the tax supervision process without needing historic labeled data.


Discriminative Label Consistent Domain Adaptation

Domain adaptation (DA) is transfer learning which aims to learn an effective predictor on target data from source data despite data distribution mismatch between source and target. We present in this paper a novel unsupervised DA method for cross-domain visual recognition which simultaneously optimizes the three terms of a theoretically established error bound. Specifically, the proposed DA method iteratively searches a latent shared feature subspace where not only the divergence of data distributions between the source domain and the target domain is decreased as most state-of-the-art DA methods do, but also the inter-class distances are increased to facilitate discriminative learning. Moreover, the proposed DA method sparsely regresses class labels from the features achieved in the shared subspace while minimizing the prediction errors on the source data and ensuring label consistency between source and target. Data outliers are also accounted for to further avoid negative knowledge transfer. Comprehensive experiments and in-depth analysis verify the effectiveness of the proposed DA method which consistently outperforms the state-of-the-art DA methods on standard DA benchmarks, i.e., 12 cross-domain image classification tasks.


Feature Selection by Joint Graph Sparse Coding

This paper takes manifold learning and regression simultaneously into account to perform unsupervised spectral feature selection. We first extract the bases of the data, and then represent the data sparsely using the extracted bases by proposing a novel joint graph sparse coding model, JGSC for short. We design a new algorithm TOSC to compute the resulting objective function of JGSC, and then theoretically prove that the proposed objective function converges to its global optimum via the proposed TOSC algorithm. We repeat the extraction and the TOSC calculation until the value of the objective function of JGSC satisfies pre-defined conditions. Eventually the derived new representation of the data may only have a few non-zero rows, and we delete the zero rows (a.k.a. zero-valued features) to conduct feature selection on the new representation of the data. Our empirical studies demonstrate that the proposed method outperforms several state-of-the-art algorithms on real datasets in term of the kNN classification performance.


Discovering Available Drinks Through Natural , Robot-Led , Human-Robot Interaction Between a Waiter and a Bartender

This research focuses on natural, robot-led, human-robot interaction that enables a robot to discover what drinks a barman can prepare through continuous application of speech recognition, understanding and generation. Speech was recognised using Google Cloud’s speech to text API, understood by matching either the object or main verb of a sentence against a list of key words and, finally, generated using templates with variable parts. The difficulty lies in the large quantity of key words, as they are based on the properties of the ordered drinks. The results show that having the aforementioned interaction works well to some extent, i.e. the naturalness of the interaction was ranked 5.5 on average. Furthermore, the obtained precision when identifying the unavailable drinks was 0.625 and the obtained recall was 1, resulting in an F1 measure of 0.769.


Visually Guided Spatial Relation Extraction from Text

Extraction of spatial relations from sentences with complex/nesting relationships is very challenging as often needs resolving inherent semantic ambiguities. We seek help from visual modality to fill the information gap in the text modality and resolve spatial semantic ambiguities. We use various recent vision and language datasets and techniques to train inter-modality alignment models, visual relationship classifiers and propose a novel global inference model to integrate these components into our structured output prediction model for spatial role and relation extraction. Our global inference model enables us to utilize the visual and geometric relationships between objects and improves the state-of-art results of spatial information extraction from text.


Does Microfinance Really Help the Poor ? New Evidence from Flagship Programs in Bangladesh

The microfinance movement has built on innovations in financial intermediation that reduce the costs and risks of lending to poor households. Replications of the movement’s flagship, the Grameen Bank of Bangladesh, have now spread around the world. While programs aim to bring social and economic benefits to clients, few attempts have been made to quantify benefits rigorously. This paper draws on a new cross-sectional survey of nearly 1800 households, some of which are served by the Grameen Bank and two similar programs, and some of which have no access to programs. Households that are eligible to borrow and have access to the programs do not have notably higher consumption levels than control households, and, for the most part, their children are no more likely to be in school. Men also tend to work harder, and women less. More favorably, relative to controls, households eligible for programs have substantially (and significantly) lower variation in consumption and labor supply across seasons. The most important potential impacts are thus associated with the reduction of vulnerability, not of poverty per se. The consumption-smoothing appears to be driven largely by income-smoothing, not by borrowing and lending. The evaluation holds lessons for studies of other programs in low-income countries. While it is common to use fixed effects estimators to control for unobservable variables correlated with the placement of programs, using fixed effects estimators can exacerbate biases when, as here, programs target their programs to specific populations within larger communities.


Elastic Bands: Connecting Path Planning and Control

Elastic bands are proposed as the basis for a new framework to close the gap between global path planning and real-time sensor-based robot control. An elastic band is a deformable collision-free path. The initial shape of the elastic is the free path generated by a planner. Subjected to artificial forces, the elastic band deforms in real time to a short and smooth path that maintains clearance from the obstacles. The elastic continues to deform as changes in the environment are detected by sensors, enabling the robot to accommodate uncertainties and react to unexpected and moving obstacles. While providing a tight connection between the robot and its environment, the elastic band preserves the global nature of the planned path. This paper outlines the framework and discusses an efficient implementation based on bubbles.


RRT-Connect: An Efficient Approach to Single-Query Path Planning

A simple and efficient randomized algorithm is presented for solving single-query path planning problems in high-dimensional configuration spaces. The method works by incrementally building two Rapidly-exploring Random Trees (RRTs) rooted at the start and the goal configurations. The trees each explore space around them and also advance towards each other through the use of a simple greedy heuristic. Although originally designed to plan motions for a human arm (modeled as a 7-DOF kinematic chain) for the automatic graphic animation of collision-free grasping and manipulation tasks, the algorithm has been successfully applied to a variety of path planning problems. Computed examples include generating collision-free motions for rigid objects in 2D and 3D, and collision-free manipulation motions for a 6-DOF PUMA arm in a 3D workspace. Some basic theoretical analysis is also presented.


Rapidly-Exploring Random Trees: A New Tool for Path Planning


A Random Sampling Scheme for Path Planning

Several randomized path planners have been proposed during the last few years. Their attractiveness stems from their applicability to virtually any type of robots, and their empirically observed success. In this paper we attempt to present a unifying view of these planners and to theoretically explain their success. First, we introduce a general planning scheme that consists of randomly sampling the robot’s configuration space. We then describe two previously developed planners as instances of planners based on this scheme, but applying very different sampling strategies. These planners are probabilistically complete: if a path exists, they will find one with high probability, if we let them run long enough. Next, for one of the planners, we analyze the relation between the probability of failure and the running time. Under assumptions characterizing the “goodness” of the robot’s free space, we show that the running time only grows as the absolute value of the logarithm of the probability of failure that we are willing to tolerate. We also show that it increases at a reasonable rate as the space goodness degrades. In the last section we suggest directions for future research.


V-Clip: Fast and Robust Polyhedral Collision Detection

This article presents the Voronoi-clip, or V-Clip, collision detection alogrithm for polyhedral objects specified by a boundary representation. V-Clip tracks the closest pair of features between convex polyhedra, using an approach reminiscent of the Lin-Canny closest features algorithm. V-Clip is an improvement over the latter in several respects. Coding complexity is reduced, and robustness is significantly improved; the implementation has no numerical tolerances and does not exhibit cycling problems. The algorithm also handles penetrating polyhedra, and can therefore be used to detect collisions between nonvconvex polyhedra described as hierarchies of convex pieces. The article presents the theoretical principles of V-Clip, and gives a pseudocode description of the algorithm. It also documents various test that compare V-Clip, Lin-Canny, and the Enhanced GJK algorithm, a simplex-based algorithm that is widely used for the same application. The results show V-Clip to be a strong contender in this field, comparing favorably with the other algorithms in most of the tests, in term of both performance and robustness.