Query: Feature Selection as a One-Player Game

Relevant passages:
Classification and Regression Trees <=SEP=> 

Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning <=SEP=> Algorithms for feature selection fall into two broad categories: wrappers that use the learning algorithm itself to evaluate the usefulness of features and filters that evaluate features according to heuristics based on general characteristics of the data. For application to large databases, filters have proven to be more practical than wrappers because they are much faster. However, most existing filter algorithms only work with discrete classification problems. This paper describes a fast, correlation-based filter algorithm that can be applied to continuous and discrete problems. The algorithm often outperforms the well-known ReliefF attribute estimator when used as a preprocessing step for naive Bayes, instance-based learning, decision trees, locally weighted regression, and model trees. It performs more feature selection than ReliefF does—reducing the data dimensionality by fifty percent in most cases. Also, decision and model trees built from the preprocessed data are often significantly smaller.

Regression Shrinkage and Selection via the Lasso <=SEP=> We propose a new method for estimation in linear models. The 'lasso' minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients hat are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree-based models are briefly described.

Algorithms for Infinitely Many-Armed Bandits <=SEP=> We consider multi-armed bandit problems where the number of arms is larger than the possible number of experiments. We make a stochastic assumption on the mean-reward of a new selected arm which characterizes its probability of being a near-optimal arm. Our assumption is weaker than in previous works. We describe algorithms based on upper-confidence-bounds applied to a restricted set of randomly selected arms and provide upper-bounds on the resulting expected regret. We also derive a lower-bound which matches (up to a logarithmic factor) the upper-bound in some cases.

Wrappers for Feature Subset Selection <=SEP=> In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set, a feature subset selection method should consider how the algorithm and the training set interact. We explore the relation between optimal feature subset selection and relevance. Our wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain. We study the strengths and weaknesses of the wrapper approach and show a series of improved designs. We compare the wrapper approach to induction without feature subset selection and to Relief, a filter approach to feature subset selection. Significant improvement in accuracy is achieved for some datasets for the two families of induction algorithms used: decision trees and Naive-Bayes. @ 1997 Elsevier Science B.V.

Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach <=SEP=> An accurate State-of-Charge (SoC) estimation plays a significant role in battery systems used in electric vehicles due to the arduous operation environments and the requirement of ensuring safe and reliable operations of batteries. Among the conventional methods to estimate SoC, the Coulomb counting method is widely used, but its accuracy is limited due to the accumulated error. Another commonly used method is model-based online iterative estimation with the Kalman filters, which improves the estimation accuracy in some extent. To improve the performance of Kalman filters in SoC estimation, the adaptive extended Kalman filter (AEKF), which employs the covariance matching approach, is applied in this paper. First, we built an implementation flowchart of the AEKF for a general system. Second, we built an online open-circuit voltage (OCV) estimation approach with the AEKF algorithm so that we can then get the SoC estimate by looking up the OCV-SoC table. Third, we proposed a robust online model-based SoC estimation approach with the AEKF algorithm. Finally, an evaluation on the SoC estimation approaches is performed by the experiment approach from the aspects of SoC estimation accuracy and robustness. The results indicate that the proposed online SoC estimation with the AEKF algorithm performs optimally, and for different error initial values, the maximum SoC estimation error is less than 2% with close-loop state estimation characteristics.

Movie recommendations based in explicit and implicit features extracted from the Filmtipset dataset <=SEP=> In this paper, we describe the experiments conducted by the Information Retrieval Group at the Universidad Autónoma de Madrid (Spain) in order to better recommend movies for the 2010 CAMRa Challenge edition. Experiments were carried out on the dataset corresponding to social Filmtipset track. To obtain the movies recommendations we have used different algorithms based on Random Walks, which are well documented in the literature of collaborative recommendation. We have also included a new proposal in one of the algorithms in order to get better results. The results obtained have been computed by means of the trec_eval standard NIST evaluation procedure.

Classification and Prediction of Future Weather by using Back Propagation Algorithm-An Approach <=SEP=> Weather forecasting is the process of recording the parameters of weather like wind direction, wind speed, humidity, rainfall, temperature etc. Back Propagation Algorithm can be applied on these parameters in order to predict the future weather. In “Classification and Prediction of Future Weather by using Back Propagation Algorithm” technique, one parameter say temperature varies by some unit and the variation on other parameters say humidity, temperature and rainfall will be predicted with respect to temperature. In this paper, different models which were used in the past for weather forecasting are discussed. This paper is focusing on three parts, first is different models which were used in weather forecasting, second part is introducing a new wireless kit use for weather forecasting and third part includes the Back Propagation Algorithm can be applied on different parameters of weather forecast. Keywords--Back Propagation Algorithm, Artificial Neural Network, Wireless kit, Classification and Prediction

Review of Video and Image Defogging Algorithms and Related Studies on Image Restoration and Enhancement <=SEP=> Video and images acquired by a visual system are seriously degraded under hazy and foggy weather, which will affect the detection, tracking, and recognition of targets. Thus, restoring the true scene from such a foggy video or image is of significance. The main goal of this paper was to summarize current video and image defogging algorithms. We first presented a review of the detection and classification method of a foggy image. Then, we summarized existing image defogging algorithms, including image restoration algorithms, image contrast enhancement algorithms, and fusion-based defogging algorithms. We also presented current video defogging algorithms. We summarized objective image quality assessment methods that have been widely used for the comparison of different defogging algorithms, followed by an experimental comparison of various classical image defogging algorithms. Finally, we presented the problems of video and image defogging which need to be further studied. The code of all algorithms will be available at <;uri xlink:href="http://www.yongxu.org/lunwen.html" xlink:type="simple">http://www.yongxu.org/lunwen.html<;/uri>.

An Efficient Oblivious Database for the Public Cloud <=SEP=> Hardware enclaves such as Intel SGX are a promising technology to increase the security of databases outsourced to the cloud. These enclaves provide an execution environment isolated from the hypervisor/OS, and encryption of data in memory. However, for applications that use large amounts of memory—including most realistic databases—enclaves do not protect against access pattern leaks, where an attacker observes which locations in memory are accessed. The näıve way to address this issue, using Oblivious RAM (ORAM) primitives, adds prohibitive overhead. In this paper, we propose ObliDB, a database that co-designs both its data structures (e.g., oblivious B+ trees) and physical operators to accelerate oblivious relational queries, giving up to 329× speedup over näıve ORAM. On analytics workloads, ObliDB ranges from competitive to 19× faster than previous oblivious systems designed only for analytics, such as Opaque, and comes within 2.6× of Spark SQL. Moreover, ObliDB also supports point queries, insertions, and deletions with latencies of 3–10ms, which is 7–22× faster than previously published oblivious data structures, and makes ObliDB suitable for transactional workloads too. To our knowledge, ObliDB is the first general-purpose oblivious database to approach practical performance. PVLDB Reference Format: Saba Eskandarian and Matei Zaharia. An Efficient Oblivious Database for the Public Cloud. PVLDB, 11 (4): xxxx-yyyy, 2017. DOI: https://doi.org/TBD

Probabilistic Graphical Models - Principles and Techniques <=SEP=> 

Colors and emotions: preferences and combinations. <=SEP=> Within three age groups (7-year-old children, 11-year-old children, and adults), preferences for colors and emotions were established by means of two distinct paired-comparison tasks. In a subsequent task, participants were asked to link colors to emotions by selecting an appropriate color. It was hypothesized that the number of times that each color was tied to a specific emotion would be predictable from the separate preferences for colors and emotions. Within age groups, participants had consistent preferences for colors and emotions, but preferences differed from one age group to another. Especially in the youngest group, the pattern of combinations between colors and emotions appeared to be meaningfully related to the preference order for colors and emotions.

Spark: A Big Data Processing Platform Based on Memory Computing <=SEP=> Spark is a memory-based computing framework which has a better ability of computing and fault tolerance, supports batch, interactive, iterative and flow calculations. In this paper, we analyze the Spark's primary framework, core technologies, and point out the advantages and disadvantages of the Spark. In the end, we make a discussion for the future trends of the Spark technologies.

Random projection-based multiplicative data perturbation for privacy preserving distributed data mining <=SEP=> This paper explores the possibility of using multiplicative random projection matrices for privacy preserving distributed data mining. It specifically considers the problem of computing statistical aggregates like the inner product matrix, correlation coefficient matrix, and Euclidean distance matrix from distributed privacy sensitive data possibly owned by multiple parties. This class of problems is directly related to many other data-mining problems such as clustering, principal component analysis, and classification. This paper makes primary contributions on two different grounds. First, it explores independent component analysis as a possible tool for breaching privacy in deterministic multiplicative perturbation-based models such as random orthogonal transformation and random rotation. Then, it proposes an approximate random projection-based technique to improve the level of privacy protection while still preserving certain statistical characteristics of the data. The paper presents extensive theoretical analysis and experimental results. Experiments demonstrate that the proposed technique is effective and can be successfully used for different types of privacy-preserving data mining applications.

Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review <=SEP=> Stress is a major problem of our society, as it is the cause of many health problems and huge economic losses in companies. Continuous high mental workloads and non-stop technological development, which leads to constant change and need for adaptation, makes the problem increasingly serious for office workers. To prevent stress from becoming chronic and provoking irreversible damages, it is necessary to detect it in its early stages. Unfortunately, an automatic, continuous and unobtrusive early stress detection method does not exist yet. The multimodal nature of stress and the research conducted in this area suggest that the developed method will depend on several modalities. Thus, this work reviews and brings together the recent works carried out in the automatic stress detection looking over the measurements executed along the three main modalities, namely, psychological, physiological and behavioural modalities, along with contextual measurements, in order to give hints about the most appropriate techniques to be used and thereby, to facilitate the development of such a holistic system.

An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination <=SEP=> This paper reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles, and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations.

Music Cognition and the Cognitive Sciences <=SEP=> Why should music be of interest to cognitive scientists, and what role does it play in human cognition? We review three factors that make music an important topic for cognitive scientific research. First, music is a universal human trait fulfilling crucial roles in everyday life. Second, music has an important part to play in ontogenetic development and human evolution. Third, appreciating and producing music simultaneously engage many complex perceptual, cognitive, and emotional processes, rendering music an ideal object for studying the mind. We propose an integrated status for music cognition in the Cognitive Sciences and conclude by reviewing challenges and big questions in the field and the way in which these reflect recent developments.

Competency based management: a review of systems and approaches <=SEP=> Purpose – Aims to review the key concepts of competency management (CM) and to propose method for developing competency method. Design/methodology/approach – Examines the CM features of 22 CM systems and 18 learning management systems. Findings – Finds that the areas of open standard (XML, web services, RDF), semantic technologies (ontologies and the semantic web) and portals with self-service technologies are going to play a significant part in the evolution of CM systems. Originality/value – Emphasizes the beneficial attributes of CM for private and public organizations.

Nonoutsourceable Scratch-Off Puzzles to Discourage Bitcoin Mining Coalitions <=SEP=> An implicit goal of Bitcoin's reward structure is to diffuse network influence over a diverse, decentralized population of individual participants. Indeed, Bitcoin's security claims rely on no single entity wielding a sufficiently large portion of the network's overall computational power. Unfortunately, rather than participating independently, most Bitcoin miners join coalitions called mining pools in which a central pool administrator largely directs the pool's activity, leading to a consolidation of power. Recently, the largest mining pool has accounted for more than half of network's total mining capacity. Relatedly, "hosted mining" service providers offer their clients the benefit of economies-of-scale, tempting them away from independent participation. We argue that the prevalence of mining coalitions is due to a limitation of the Bitcoin proof-of-work puzzle -- specifically, that it affords an effective mechanism for enforcing cooperation in a coalition. We present several definitions and constructions for "nonoutsourceable" puzzles that thwart such enforcement mechanisms, thereby deterring coalitions. We also provide an implementation and benchmark results for our schemes to show they are practical.

LTE-V for Sidelink 5G V2X Vehicular Communications: A New 5G Technology for Short-Range Vehicle-to-Everything Communications <=SEP=> This article provides an overview of the long-term evolution-vehicle (LTE-V) standard supporting sidelink or vehicle-to-vehicle (V2V) communications using LTE's direct interface named PC5 in LTE. We review the physical layer changes introduced under Release 14 for LTE-V, its communication modes 3 and 4, and the LTE-V evolutions under discussion in Release 15 to support fifth-generation (5G) vehicle-to-everything (V2X) communications and autonomous vehicles' applications. Modes 3 and 4 support direct V2V communications but differ on how they allocate the radio resources. Resources are allocated by the cellular network under mode 3. Mode 4 does not require cellular coverage, and vehicles autonomously select their radio resources using a distributed scheduling scheme supported by congestion control mechanisms. Mode 4 is considered the baseline mode and represents an alternative to 802.11p or dedicated shortrange communications (DSRC). In this context, this article also presents a detailed analysis of the performance of LTE-V sidelink mode 4, and proposes a modification to its distributed scheduling.

MatrixWave: Visual Comparison of Event Sequence Data <=SEP=> Event sequence data analysis is common in many domains, including web and software development, transportation, and medical care. Few have investigated visualization techniques for comparative analysis of multiple event sequence datasets. Grounded in the real-world characteristics of web clickstream data, we explore visualization techniques for comparison of two clickstream datasets collected on different days or from users with different demographics. Through iterative design with web analysts, we designed MatrixWave, a matrix-based representation that allows analysts to get an overview of differences in traffic patterns and interactively explore paths through the website. We use color to encode differences and size to offer context over traffic volume. User feedback on MatrixWave is positive. Our study participants made fewer errors with MatrixWave and preferred it over the more familiar Sankey diagram.

Wireless Power Transfer for Vehicular Applications: Overview and Challenges <=SEP=> More than a century-old gasoline internal combustion engine is a major contributor to greenhouse gases. Electric vehicles (EVs) have the potential to achieve eco-friendly transportation. However, the major limitation in achieving this vision is the battery technology. It suffers from drawbacks such as high cost, rare material, low energy density, and large weight. The problems related to battery technology can be addressed by dynamically charging the EV while on the move. In-motion charging can reduce the battery storage requirement, which could significantly extend the driving range of an EV. This paper reviews recent advances in stationary and dynamic wireless charging of EVs. A comprehensive review of charging pad, power electronics configurations, compensation networks, controls, and standards is presented.

Using NLP techniques for file fragment classification <=SEP=> The classification of file fragments is an important problem in digital forensics. The literature does not include comprehensive work on applying machine learning techniques to this problem. In this work, we explore the use of techniques from natural language processing to classify file fragments. We take a supervised learning approach, based on the use of support vector machines combined with the bag-of-words model, where text documents are represented as unordered bags of words. This technique has been repeatedly shown to be effective and robust in classifying text documents (e.g., in distinguishing positive movie reviews from negative ones). In our approach, we represent file fragments as “bags of bytes” with feature vectors consisting of unigram and bigram counts, as well as other statistical measurements (including entropy and others). We made use of the publicly available Garfinkel data corpus to generate file fragments for training and testing. We ran a series of experiments, and found that this approach is effective in this domain as well. a 2012 O. Zhulyn, S. Fitzgerald & G. Mathews. Published by Elsevier Ltd. All rights reserved.

Perceptual Generative Adversarial Networks for Small Object Detection <=SEP=> Detecting small objects is notoriously challenging due to their low resolution and noisy representation. Existing object detection pipelines usually detect small objects through learning representations of all the objects at multiple scales. However, the performance gain of such ad hoc architectures is usually limited to pay off the computational cost. In this work, we address the small object detection problem by developing a single architecture that internally lifts representations of small objects to super-resolved ones, achieving similar characteristics as large objects and thus more discriminative for detection. For this purpose, we propose a new Perceptual Generative Adversarial Network (Perceptual GAN) model that improves small object detection through narrowing representation difference of small objects from the large ones. Specifically, its generator learns to transfer perceived poor representations of the small objects to super-resolved ones that are similar enough to real large objects to fool a competing discriminator. Meanwhile its discriminator competes with the generator to identify the generated representation and imposes an additional perceptual requirement - generated representations of small objects must be beneficial for detection purpose - on the generator. Extensive evaluations on the challenging Tsinghua-Tencent 100K [45] and the Caltech [9] benchmark well demonstrate the superiority of Perceptual GAN in detecting small objects, including traffic signs and pedestrians, over well-established state-of-the-arts.

Techniques for Machine Learning based Spatial Data Analysis: Research Directions <=SEP=> Today, machine learning techniques play a significant role in data analysis, predictive modeling and visualization. The main aim of the machine learning algorithms is that they first learn from the empirical data and can be utilized in cases for which the modeled phenomenon is hidden or not yet described. Several significant techniques like Artificial Neural Network, Support Vector Regression, k-Nearest Neighbour, Bayesian Classifiers and Decision Trees are developed in past years to acheive this task. The first and most complicated problem concerns is the amount of available data. This paper reviews state-of-the-art in the domain of spatial data analysis by employing machine learning approaches. First various methods have been summarized, which exist in the literature. Further, the current research scenarios which are going on in this area are described. Based on the research done in past years, identification of the problem in the existing system is also presented in this paper and have given future research directions.

DRCD: a Chinese Machine Reading Comprehension Dataset <=SEP=> 重視硫酸的種類以及它們在醫學上的價值...... 在 1746年,John Roebuck則運用這個原則,開 創鉛室法,以更低成本有效地大量生產硫 酸。...... ... Others, such as Ibn Sīnā, are more concerned about the type of sulfuric acid and their medical value. In 1746, John Roebuck applied this method to create lead chamber process to efficiently produce large quantities of sulfuric acid at a lower cost. Question: 重視硫酸的種類以及它們在醫學上的 價值地為哪位醫師? Who value the type of sulfuric acid and their medical value? Answer: 伊本·西那/ Ibn Sīnā Question: 鉛室法於西元幾年開創? / When did lead chamber process being invented? Answer: 1746年 / 1746

Parental delay or refusal of vaccine doses, childhood vaccination coverage at 24 months of age, and the Health Belief Model. <=SEP=> OBJECTIVE
We evaluated the association between parents' beliefs about vaccines, their decision to delay or refuse vaccines for their children, and vaccination coverage of children at aged 24 months.


METHODS
We used data from 11,206 parents of children aged 24-35 months at the time of the 2009 National Immunization Survey interview and determined their vaccination status at aged 24 months. Data included parents' reports of delay and/or refusal of vaccine doses, psychosocial factors suggested by the Health Belief Model, and provider-reported up-to-date vaccination status.


RESULTS
In 2009, approximately 60.2% of parents with children aged 24-35 months neither delayed nor refused vaccines, 25.8% only delayed, 8.2% only refused, and 5.8% both delayed and refused vaccines. Compared with parents who neither delayed nor refused vaccines, parents who delayed and refused vaccines were significantly less likely to believe that vaccines are necessary to protect the health of children (70.1% vs. 96.2%), that their child might get a disease if they aren't vaccinated (71.0% vs. 90.0%), and that vaccines are safe (50.4% vs. 84.9%). Children of parents who delayed and refused also had significantly lower vaccination coverage for nine of the 10 recommended childhood vaccines including diphtheria-tetanus-acellular pertussis (65.3% vs. 85.2%), polio (76.9% vs. 93.8%), and measles-mumps-rubella (68.4% vs. 92.5%). After adjusting for sociodemographic differences, we found that parents who were less likely to agree that vaccines are necessary to protect the health of children, to believe that their child might get a disease if they aren't vaccinated, or to believe that vaccines are safe had significantly lower coverage for all 10 childhood vaccines.


CONCLUSIONS
Parents who delayed and refused vaccine doses were more likely to have vaccine safety concerns and perceive fewer benefits associated with vaccines. Guidelines published by the American Academy of Pediatrics may assist providers in responding to parents who may delay or refuse vaccines.

An Improved Bank Credit Scoring Model: A Naïve Bayesian Approach <=SEP=> Credit scoring is a decision tool used by organizations to grant or reject credit requests from their customers. Series of artificial intelligent and traditional approaches have been used to building credit scoring model and credit risk evaluation. Despite being ranked amongst the top 10 algorithm in Data mining, Naïve Bayesian algorithm has not been extensively used in building credit score cards. Using demographic and material indicators as input variables, this paper investigate the ability of Bayesian classifier towards building credit scoring model in banking sector.

The combinatorial assignment problem: approximate competitive equilibrium from equal incomes <=SEP=> Impossibility theorems suggest that the only efficient and strategyproof mechanisms for the problem of combinatorial assignment - e.g., assigning schedules of courses to students - are dictatorships. Dictatorships are mostly rejected as unfair: for any two agents, one chooses all their objects before the other chooses any. Any solution will involve compromise amongst efficiency, incentive and fairness considerations.
 This paper proposes a solution to the combinatorial assignment problem. It is developed in four steps. First, I propose two new criteria of outcome fairness, the maximin share guarantee and envy bounded by a single good, which weaken well-known criteria to accommodate indivisibilities; the criteria formalize why dictatorships are unfair. Second, I prove existence of an approximation to Competitive Equilibrium from Equal Incomes in which (i) incomes are unequal but arbitrarily close together; (ii) the market clears with error, which approaches zero in the limit and is small for realistic problems. Third, I show that this Approximate CEEI satisfies the fairness criteria. Last, I define a mechanism based on Approximate CEEI that is strategyproof for the zero-measure agents economists traditionally regard as price takers. The proposed mechanism is calibrated on real data and is compared to alternatives from theory and practice: all other known mechanisms are either manipulable by zero-measure agents or unfair ex-post, and most are both manipulable and unfair.

Political Issue Extraction Model: A Novel Hierarchical Topic Model That Uses Tweets By Political And Non-Political Authors <=SEP=> People often use social media to discuss opinions, including political ones. We refer to relevant topics in these discussions as political issues, and the alternate stands towards these topics as political positions. We present a Political Issue Extraction (PIE) model that is capable of discovering political issues and positions from an unlabeled dataset of tweets. A strength of this model is that it uses twitter timelines of political and non-political authors, and affiliation information of only political authors. The model estimates word-specific distributions (that denote political issues and positions) and hierarchical author/group-specific distributions (that show how these issues divide people). Our experiments using a dataset of 2.4 million tweets from the US show that this model effectively captures the desired properties (with respect to words and groups) of political discussions. We also evaluate the two components of the model by experimenting with: (a) Use to alternate strategies to classify words, and (b) Value addition due to incorporation of group membership information. Estimated distributions are then used to predict political affiliation with 68% accuracy.