title: Personalized Web Search

abstract: Personalization is important for search engines to improve user experience. Most of the existing work do pure feature engineering and extract a lot of session-style features and then train a ranking model. Here we proposed a novel way to model both long term and short term user behavior using Multi-armed bandit algorithm. Our algorithm can generalize session information across users well, and as an Explore-Exploit style algorithm, it can generalize to new urls and new users well. Experiments show that our algorithm can improve performance over the default ranking and outperforms several popular Multi-armed bandit algorithms.


title: Clinical diagnostics for homocysteine: a rogue amino acid?

abstract: Homocysteine has gathered considerable interest in recent years after being implicated in the pathogenesis of various clinical conditions. It has been an elusive target with many conflicting studies casting some confusion over its significance, and much more needs to be done to ascertain its pathophysiological role--especially with respect to regimes aimed at lowering its plasma concentration. Central to this is the development of robust analytical strategies for its determination. This review summarizes the clinical relevance of homocysteine as a diagnostic marker of disease and explores and critically assesses the detection methods that have been developed.


title: Passive active L- and S-band (PALS) microwave sensor for ocean salinity and soil moisture measurements

abstract: A passive/active WS-band (PALS) microwave aircraft instrument to measure ocean salinity and soil moisture has been built and tested. Because the L-band brightness temperatures associated with salinity changes are expected to be small, it was necessary to build a very sensitive and stable system. This new instrument has dual-frequency, dual polarization radiometer and radar sensors. The antenna is a high beam efficiency conical horn. The PALS instrument was installed on the NCAR C-130 aircraft and soil moisture measurements were made in support of the Southern Great Plains 1999 experiment in Oklahoma from July 8-14, 1999. Data taken before and after a rainstorm showed significant changes in the brightness temperatures, polarization ratios and radar backscatter, as a function of soil moisture. Salinity measurement missions were flown on July 17-19, 1999, southeast of Norfolk, VA, over the Gulf Stream. The measurements indicated a clear and repeatable salinity signal during these three days, which was in good agreement with the Cape Hatteras ship salinity data. Data were also taken in the open ocean and a small decrease of 0.2 K was measured in the brightness temperature, which corresponded to the salinity increase of 0.4 psu measured by the M/V Oleander vessel.


title: CodeCity for (and by) JavaScript

abstract: JavaScript is one of the most popular programming languages on the web. Despite the language popularity and the increasing size of JavaScript systems, there is a limited number of visualization tools that can be used by developers to comprehend, maintain, and evolve JavaScript software. In this paper, we introduce JSCity, an implementation in JavaScript of the well-known Code City software visualization metaphor. JSCity relies on JavaScript features and libraries to show "software cities" in standard web browsers, without requiring complex installation procedures. We also report our experience on producing visualizations for 40 popular JavaScript systems using JScity.


title: Child protection and free speech: mapping the territory

abstract: Advocates of online child protection and freedom of expression share a deep-seated belief in the importance of protecting basic human rights. Yet these beliefs are often clouded by perceived (and real) opposition in the actual practice of law, policy, and regulation. This has restricted the policy options available for dealing with threats to both child safety and free speech online, and has often resulted in these interests being portrayed as diametrically opposed. Advocates on both sides of this debate met in Oxford in October 2009 to explore their different perspectives on these fundamental rights and to identify possible areas of agreement. By defining a new framework to discuss online child protection that rejects the current moral panics that have dominated the debate, and focuses instead on accurately defining risks in line with the evolving capacity of the child, participants were able to find some common ground. The most fruitful avenues came from calls for precision and transparency in policy responses that touch on these issues. Participants discussed how, by working together, both sides could advance their agendas and defend the rights of children while preventing child protection from being used as a strategic pretext for broader goals of censorship and repression.


title: Motivation and English Achievement: An Exploratory and Confirmatory Factor Analysis of a New Measure for Chinese Students of English Learning

abstract: In the field of second language acquisition (SLA) there have been various attempts to define second language acquisition motivation and to discover the relationship between motivation and English achievement. It has been argued that learning a second language differs from learning other school subjects because of its social nature (Dornyei, 2003). Many theories of language learning motivation tend to be social-psychological, among which the most influential is Gardner's socio-educational model, including attitudes, motivations and anxiety variables (Gardner, 1988; Gardner, Masgoret, Tennant, & Mihic, 2004; Masgoret & Gardner, 2003). Integrative orientation and instrumental orientation are two important factors in his theory. An integrative orientation occurs when learners hold favorable attitudes to the language/culture, and wish to identify with the culture of speakers of that language. An instrumental orientation refers to the utility value of learning a second/foreign language, such as passing examinations, financial rewards or future career. Burke (2004) found that aspects of integrative motivation are related to greater motivational effort as well as better language competence in learning a second language. An instrumental orientation was likely related to outcomes such as job-seeking and social status. In recent years Gardner's socio-educational model has met with some criticism. Much of the criticism seems to question the importance of social aspects of second language learning motivation, and its relevance in different contexts (Mori & Gobel, 2006). Furthermore, Csize and Dornyei (2005) found that the term integrativeness was not so much related to any actual integration into a second language community as to a more basic identification process within the individual's self-concept. It is necessary to seek potential new conceptualizations and interpretations. In the 1990s guided by self-determination theory (SDT) some researchers tried to explore the motivation of SLA (Noels, 2003, 2005; Noels, Pelletier, Clement, & Vallerand, 2001; Shaikholeslami, 2006; Tae-II, 2008). Moreover, psychology researchers have become increasingly aware of the importance of contextual and cultural variables affecting motivational processes, especially the difference between Asian students and American or European students (Yi-Guang, McKeachie, & Yung, 2003). In China, English is regarded as a foreign language (EFL) and Chinese culture is somewhat different from American culture on account of its booming economy, rapidly growing international trade and expanding presence in the world science and technology market, so it is very important to investigate the English learning motivation of Chinese students. Furthermore, most researchers focus on western culture and there is no appropriate scale to assess the motivation of English learning of Chinese students. Therefore, one important purpose of the study is to construct a new measure of English learning motivation. Intrinsic motivation originates from within the individual and results in enjoyment of the process of increasing one's competency in regard to particular academic tasks. Extrinsic motivation is motivation induced by rewards or punishment dependent upon success or failure in the task (Deci & Ryan 2000; Walker, Greene, & Mansell, 2006). Contrasted with intrinsically motivated students, who work for the feeling of satisfaction or accomplishment, extrinsically motivated students will perform mainly for the attainment of a desired external reward or to avoid external punishment. Are extrinsic and intrinsic motives necessarily incompatible? In the past, researchers found that extrinsic motivation sometimes has a destructive impact on intrinsic motivation by perceived constraints on autonomy (Lepper, Greene, & Nisbett, 1973). Hennessey and Amabile (1998) also found that a promised reward for non-specific performance lessened intrinsic motivation by reducing perceived self-determination or increasing attention to the reward. …


BioTextRetriever : yet another Information Retrieval system


Overview of record linkage and current research directions


Suburban Sprawl: Exposing Hidden Costs, Identifying Innovations


title: Expected classification error of the Fisher linear classifier with pseudoinverse covariance matrix

abstract: The pseudo-Fisher linear classifier is considered as the ''diagonal'' Fisher linear classifier applied to the principal components corresponding to non-zero eigenvalues of the sample covariance matrix. An asymptotic formula for the expected (generalization) error of the Fisher classifier with the pseudo-inversion is derived which explains the peaking behaviour: with an increasing number of learning observations from one up to the number of features, the generalization error first decreases, and then starts to increase.


title: iReduct: differential privacy with reduced relative errors

abstract: Prior work in differential privacy has produced techniques for answering aggregate queries over sensitive data in a privacy-preserving way. These techniques achieve privacy by adding noise to the query answers. Their objective is typically to minimize absolute errors while satisfying differential privacy. Thus, query answers are injected with noise whose scale is independent of whether the answers are large or small. The noisy results for queries whose true answers are small therefore tend to be dominated by noise, which leads to inferior data utility. This paper introduces iReduct, a differentially private algorithm for computing answers with reduced relative error. The basic idea of iReduct is to inject different amounts of noise to different query results, so that smaller (larger) values are more likely to be injected with less (more) noise. The algorithm is based on a novel resampling technique that employs correlated noise to improve data utility. Performance is evaluated on an instantiation of iReduct that generates marginals, i.e., projections of multi-dimensional histograms onto subsets of their attributes. Experiments on real data demonstrate the effectiveness of our solution.


title: Ultrasound assisted extraction of food and natural products. Mechanisms, techniques, combinations, protocols and applications. A review.

abstract: This review presents a complete picture of current knowledge on ultrasound-assisted extraction (UAE) in food ingredients and products, nutraceutics, cosmetic, pharmaceutical and bioenergy applications. It provides the necessary theoretical background and some details about extraction by ultrasound, the techniques and their combinations, the mechanisms (fragmentation, erosion, capillarity, detexturation, and sonoporation), applications from laboratory to industry, security, and environmental impacts. In addition, the ultrasound extraction procedures and the important parameters influencing its performance are also included, together with the advantages and the drawbacks of each UAE techniques. Ultrasound-assisted extraction is a research topic, which affects several fields of modern plant-based chemistry. All the reported applications have shown that ultrasound-assisted extraction is a green and economically viable alternative to conventional techniques for food and natural products. The main benefits are decrease of extraction and processing time, the amount of energy and solvents used, unit operations, and CO2 emissions.


title: Algorithms for graph partitioning on the planted partition model

abstract: The NP-hard graph bisection problem is to partition the nodes of an undirected graph into two equal-sized groups so as to minimize the number of edges that cross the partition. The more general graph l-partition problem is to partition the nodes of an undirected graph into l equal-sized groups so as to minimize the total number of edges that cross between groups. We present a simple, linear-time algorithm for the graph I-partition problem and analyze it on a random planted l-partition model. In this model, the n nodes of a graph are partitioned into l groups, each of size n/l; two nodes in the same group are connected by an edge with some probability p, and two nodes in different groups are connected by an edge with some probability r n -1/2+ ∈ for some constant ∈, then the algorithm finds the optimal partition with probability 1 - exp(-n Θ( ∈ ) ).


title: Graph-based mining of multiple object usage patterns

abstract: The interplay of multiple objects in object-oriented programming often follows specific protocols, for example certain orders of method calls and/or control structure constraints among them that are parts of the intended object usages. Unfortunately, the information is not always documented. That creates long learning curve, and importantly, leads to subtle problems due to the misuse of objects. In this paper, we propose GrouMiner, a novel graph-based approach for mining the usage patterns of one or multiple objects. GrouMiner approach includes a graph-based representation for multiple object usages, a pattern mining algorithm, and an anomaly detection technique that are efficient, accurate, and resilient to software changes. Our experiments on several real-world programs show that our prototype is able to find useful usage patterns with multiple objects and control structures, and to translate them into user-friendly code skeletons to assist developers in programming. It could also detect the usage anomalies that caused yet undiscovered defects and code smells in those programs.


title: Bakar Kiasan: flexible contract checking for critical systems using symbolic execution

abstract: SPARK, a subset of Ada for engineering safety and security-critical systems, is designed for verification and includes a software contract language for specifying functional properties of procedures. Even though SPARK and its static analysis components are beneficial and easy to use, its contract language is almost never used due to the burdens the associated tool support imposes on developers. In this paper, we present: (a) SymExe techniques for checking software contracts in embedded critical systems, and (b) Bakar Kiasan, a tool that implements these techniques in an integrated development environment for SPARK. We describe a methodology for using Bakar Kiasan that provides significant increases in automation, usability, and functionality over existing Spark tools, and we present results from experiments on its application to industrial examples.


title: ISAR - radar imaging of targets with complicated motion

abstract: ISAR imaging is described for general motion of a radar target. ISAR imaging may be seen as a 3D to 2D projection, and the importance of the ISAR image projection plane is stated. For general motion, ISAR images are often smeared when using FFT processing. Time frequency methods are used to analyze such images, and to form sharp images. A given smeared image is shown to be the result of changes both in scale and in the projection plane orientation.


title: DeepMutation: Mutation Testing of Deep Learning Systems

abstract: Deep learning (DL) defines a new data-driven programming paradigm where the internal system logic is largely shaped by the training data. The standard way of evaluating DL models is to examine their performance on a test dataset. The quality of the test dataset is of great importance to gain confidence of the trained models. Using an inadequate test dataset, DL models that have achieved high test accuracy may still lack generality and robustness. In traditional software testing, mutation testing is a well-established technique for quality evaluation of test suites, which analyzes to what extent a test suite detects the injected faults. However, due to the fundamental difference between traditional software and deep learning-based software, traditional mutation testing techniques cannot be directly applied to DL systems. In this paper, we propose a mutation testing framework specialized for DL systems to measure the quality of test data. To do this, by sharing the same spirit of mutation testing in traditional software, we first define a set of source-level mutation operators to inject faults to the source of DL (i.e., training data and training programs). Then we design a set of model-level mutation operators that directly inject faults into DL models without a training process. Eventually, the quality of test data could be evaluated from the analysis on to what extent the injected faults could be detected. The usefulness of the proposed mutation testing techniques is demonstrated on two public datasets, namely MNIST and CIFAR-10, with three DL models.


title: Task Cube: A three-dimensional conceptual space of user tasks in visualization design and evaluation

abstract: User tasks play a pivotal role in visualization design and evaluation. However, the term ‘task’ is used ambiguously within the visualization community. In this article, we critically analyze the re...


title: From "smart objects" to "social objects": The next evolutionary step of the internet of things

abstract: Social networking concepts have been applied to several communication network settings, which span from delay-tolerant to peer-to-peer networks. More recently, one can observe a flourish of proposals aimed at giving social-like capabilities to the objects in the Internet of Things. Such proposals address the design of conceptual (and software) platforms, which can be exploited to easily develop and implement complex applications that require direct interactions among objects. The major goal is to build techniques that allow the network to enhance the level of trust between objects that are "friends" with each other. Furthermore, a social paradigm could definitely guarantee network navigability even if the number of nodes becomes orders of magnitude higher than in the traditional Internet. Objectives of this article are to analyze the major opportunities arising from the integration of social networking concepts into the Internet of Things, present the major ongoing research activities, and point out the most critical technical challenges.


title: Clustering and collision detection for clustered shape matching

abstract: In this paper, we address clustering and collision detection in the clustered shape matching simulation framework for deformable bodies. Our clustering algorithm is "fuzzy," meaning that it gives particles weighted membership in clusters. These weights are a significant extension to the basic clustered shape matching framework as they are used to divide particle mass among the clusters. We explore several weighting schemes and demonstrate that the choice of weighting scheme gives artists additional control over material behavior. Furthermore, by design our clustering algorithm yields spherical clusters, which not only results in sparse weight vectors, but also exceptionally efficient collision geometry. We further enhance this simple collision proxy by intersecting with half-spaces to allow for even better, yet still simple and computationally efficient, collision proxies. The resulting approach is fast, versatile, and simple to implement.


title: Automatic Functional Harmonic Analysis

abstract: Music scholars have been studying tonal harmony intensively for centuries, yielding numerous theories and models. Unfortunately, a large number of these theories are formulated in a rather informal fashion and lack mathematical precision. In this article we present HarmTrace, a functional model of Western tonal harmony that builds on well-known theories of tonal harmony. In contrast to other approaches that remain purely theoretical, we present an implemented system that is evaluated empirically. Given a sequence of symbolic chord labels, HarmTrace automatically derives the harmonic relations between chords. For this, we use advanced functional programming techniques that are uniquely available in the Haskell programming language. We show that our system is fast, easy to modify and maintain, robust against noisy data, and that its harmonic analyses comply with Western tonal harmony theory.


title: Detecting Malicious Flux Service Networks through Passive Analysis of Recursive DNS Traces

abstract: In this paper we propose a novel, passive approach for detecting and tracking malicious flux service networks. Our detection system is based on passive analysis of recursive DNS (RDNS) traffic traces collected from multiple large networks. Contrary to previous work, our approach is not limited to the analysis of suspicious domain names extracted from spam emails or precompiled domain blacklists. Instead, our approach is able to detect malicious flux service networks in-the-wild, i.e., as they are accessed by users who fall victims of malicious content advertised through blog spam, instant messaging spam, social website spam, etc., beside email spam. We experiment with the RDNS traffic passively collected at two large ISP networks. Overall, our sensors monitored more than 2.5 billion DNS queries per day from millions of distinct source IPs for a period of 45 days. Our experimental results show that the proposed approach is able to accurately detect malicious flux service networks. Furthermore, we show how our passive detection and tracking of malicious flux service networks may benefit spam filtering applications.


title: Burnout as a predictor of all-cause mortality among industrial employees: A 10-year prospective register-linkage study

abstract: Abstract Objective Burnout, a psychological consequence of prolonged work stress, has been shown to coexist with physical and mental disorders. The aim of this study was to investigate whether burnout is related to all-cause mortality among employees. Methods In 1996, of 15,466 Finnish forest industry employees, 9705 participated in the ‘Still Working’ study and 8371 were subsequently identified from the National Population Register. Those who had been treated in a hospital for the most common causes of death prior to the assessment of burnout were excluded on the basis of the Hospital Discharge Register, resulting in a final study population of 7396 people. Burnout was measured using the Maslach Burnout Inventory–General Survey. Dates of death from 1996 to 2006 were extracted from the National Mortality Register. Mortality was predicted with Cox hazard regression models, controlling for baseline sociodemographic factors and register-based health status according to entitled medical reimbursement and prescribed medication for mental health problems, cardiac risk factors, and pain problems. Results During the 10-year 10-month follow-up, a total of 199 employees had died. The risk of mortality per one-unit increase in burnout was 35% higher (95% CI 1.07–1.71) for total score and 26% higher (0.99–1.60) for exhaustion, 29% higher for cynicism (1.03–1.62), and 22% higher for diminished professional efficacy (0.96–1.55) in participants who had been under 45 at baseline. After adjustments, only the associations regarding burnout and exhaustion were statistically significant. Burnout was not related to mortality among the older employees. Conclusion Burnout, especially work-related exhaustion, may be a risk for overall survival.


title: Hysteretic PWM control method for all types of DC-to-DC converters

abstract: A new control method of a hysteretic PWM controller for all types of DC-to-DC converters are proposed. The triangular voltage obtained from a simple RC network connected between hysteretic comparator output and converter output is superimposed to the output voltage and fed to the hysteretic comparator as a feedback signal. Since the hysteretic PWM controller essentially has derivative characteristics and has no error amplifier, the presented method provides no steady-state error voltage on the output and excellent dynamic performances for the load current transient by choosing proper values of time constants in the RC network. Performances of the proposed controller are experimentally verified for the buck, buck-boost and boost converters.


A Study Using n-gram Features for Text Categorization


title: Attentional biases for emotional faces in young children of mothers with chronic or recurrent depression.

abstract: Attentional biases for negative stimuli have been observed in school-age and adolescent children of depressed mothers and may reflect a vulnerability to depression. The direction of these biases and whether they can be identified in early childhood remains unclear. The current study examined attentional biases in 5–7-year-old children of depressed and non-depressed mothers. Following a mood induction, children participated in a dot-probe task assessing biases for sad and happy faces. There was a significant interaction of group and sex: daughters of depressed mothers attended selectively to sad faces, while children of controls and sons of depressed mothers did not exhibit biases. No effects were found for happy stimuli. These findings suggest that attentional biases are discernible in early childhood and may be vulnerability markers for depression. The results also raise the possibility that sex differences in cognitive biases are evident before the emergence of sex differences in the prevalence of depression.


title: Cascaded Microresonator-Based Matrix Switch for Silicon On-Chip Optical Interconnection

abstract: This paper reviews developments in cascaded microresonator-based matrix switches for silicon photonic interconnection networks in many-core computing applications. Specifically, we emphasize our recently proposed 5 times 5 matrix switch comprising two-dimensionally cascaded microring resonator-based electrooptic switches coupled to a waveguide cross-grid on a silicon chip. The cross-grid adopts low-loss low-crosstalk multimode-interference-based waveguide crossings. Such a microresonator-based matrix switch offers nonblocking interconnections among multiple inputs and multiple outputs, with the key merits of i) a tens to hundreds of micrometers-scale footprint, ii) gigabit/second-scale data transmission, iii) nanosecond-speed circuit-switching, iv) 100-muW-scale DC power consumption per link, and v) large-scale integration for networks-on-chips applications. We analyze in detail the microring resonator-based cross-grid switch design for high-data-rate signal transmission in the context of our proposed 5 times 5 matrix switch. We also study the feasibility of large-scale integration of the matrix switch. We report proof-of-concept experiments of a single cross-grid switch element and a 2 times 2 matrix switch, propose design guidelines, and discuss future engineering challenges.


title: Hairpin-Contained i-Motif Based Fluorescent Ratiometric Probe for High-Resolution and Sensitive Response of Small pH Variations

abstract: Intracellular pH (pHi) is an important parameter associated with cellular behaviors and pathological conditions. Sensing pHi and monitoring its changes are essential but challenging due to the lack of high-sensitive probes. Herein, a ratiometric fluorescent probe with ultra pH-sensitivity is developed based on hairpin-contained i-motif strand (I-strand, labeled with Rhodamine Green and BHQ2 at two termini) and complementary strand (C-strand, labeled with Rhodamine Red at its 5'-end). At neutral pH, both I-strand and C-strand hybridize into a rigid duplex (I-C), which holds the Rhodamine Red and the BHQ2 in close proximity. As a result, the fluorescence emission (F597 nm) of the Rhodamine Red is strongly suppressed, while the Rhodamine Green (F542 nm) is in a "signal on" state. However, the slightly acidic pH enforced the I-strand to form an intramolecular i-motif and initiated the dehybridization of I-C duplex, leading to Rhodamine Red in a "signal on" state and a decreased fluorescence of Rhodamine Green. The ratio (F542 nm/F597 nm) can be used as a signal for pH sensing. Due to the rational internal hairpin design of I-C duplex probe, almost 70-fold change in the ratio was observed in the physiological pH range (6.50-7.40). This probe possesses efficient stability, fast response, and reversible pH measurement capabilities. Furthermore, intracellular application of the ratiometric probe was demonstrated on the example of SMMC-7721 cells. With different recognition elements in engineering of i-motif based platforms, the design might hold great potential to become a versatile strategy for intracellular pH sensing.


title: Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions

abstract: We present a novel graph-based summarization framework (Opinosis) that generates concise abstractive summaries of highly redundant opinions. Evaluation results on summarizing user reviews show that Opinosis summaries have better agreement with human summaries compared to the baseline extractive method. The summaries are readable, reasonably well-formed and are informative enough to convey the major opinions.


title: Group-aware deep feature learning for facial age estimation

abstract: In this paper, we propose a group-aware deep feature learning (GA-DFL) approach for facial age estimation. Unlike most existing methods which utilize hand-crafted descriptors for face representation, our GA-DFL method learns a discriminative feature descriptor per image directly from raw pixels for face representation under the deep convolutional neural networks framework. Motivated by the fact that age labels are chronologically correlated and the facial aging datasets are usually lack of labeled data for each person in a long range of ages, we split ordinal ages into a set of discrete groups and learn deep feature transformations across age groups to project each face pair into the new feature space, where the intra-group variances of positive face pairs from the training set are minimized and the inter-group variances of negative face pairs are maximized, simultaneously. Moreover, we employ an overlapped coupled learning method to exploit the smoothness for adjacent age groups. To further enhance the discriminative capacity of face representation, we design a multi-path CNN approach to integrate the complementary information from multi-scale perspectives. Experimental results show that our approach achieves very competitive performance compared with most state-of-the-arts on three public face aging datasets that were captured under both controlled and uncontrolled environments. HighlightsWe propose a group-aware deep feature learning (GA-DFL) method under the deep convolutional neural networks framework. With the learned nonlinear filters, the chronological age information can be well exploited.We propose an overlapped coupled learning method to achieve the smoothness for the neighboring age groups. With this learning strategy, the age difference information on the age-group specific overlaps can be well measured.We employ a multi-path deep CNN architecture to integrate multi-scale facial information into the learned face presentation to further improve the estimation performance.Compared with most state-of-the-arts, experimental results show that our proposed methods have obtain significant performance on three released face aging datasets.


title: Biomimetic finger extension mechanism for soft wearable hand rehabilitation devices

abstract: For the rehabilitation and assistance of the hand functions, wearable devices have been developed, and the interest in tendon driven mechanisms have especially increased since it allows light weight and compact design. The tendon driven hand rehabilitation devices provides grasping force via exo-tendons routed on the dorsal and palmar sides of the hand pulled by remotely located actuators. However, most of the devices were not able to provide natural joint extension sequence of the finger and showed hyperextension of finger joints because the tendons for extension were fixed at the fingertip, concentrating the torque at the distal interphalangeal joint. In this study, a ring-type biomimetic finger extension mechanism was developed, which mimics the origin, structure, and orientation of the extensor tendon. The biomimetic mechanism was evaluated by comparing the motion with voluntary finger extension and the motion made by other conventional tendon driven finger extension mechanisms. The biomimetic extension mechanism provided the same joint extension sequence with voluntary finger extension, and the fully extended posture was most close to the voluntary finger extension among the tendon-driven mechanisms used in the experiments. The joint angle differences between the proposed tendon mechanism and the voluntary finger extension was −1.2 °±3.4 °, −2.9°±2.0°, and −3.1°±8.0° for distal phalangeal, proximal phalangeal, and metacarpo-phalangeal joint, respectively.


title: Report on NTCIR-12 : The Twelfth Round of NII Testbeds and Community for Information Access Research

abstract: This is a report on the NTCIR-12 conference held in June 2016, in Tokyo, Japan. NTCIR-12 is the twelfth sesquiannual research project for evaluating information access technologies that organizes a diverse set of tasks related to information retrieval, question answering, and natural language processing. The NTCIR-12 conference is a venue in which task organizers and task participants presented their effort on their participating tasks, and attracted 236 participants from 21 countries/regions in this round. This report introduces the highlights of the conference, describes the scope and task designs of nine tasks organized at NTCIR-12, and provides a brief introduction to NTCIR-13, which started from June 2016 and will be closed in December 2017.


title: Artificial immune recognition systems in medical diagnosis

abstract: Medical diagnosis is an exciting are of research and many researchers have been working on the application of Artificial Intelligence techniques to develop disease recognition systems. They are analysing currently available information and also biochemical data collecting from clinical laboratories and experts for identifying pathological status of the patient. During the process of diagnosis, the clinical data so obtained from several sources must be inferred and classified into a particular pathology. Computer aided diagnosis tools designed based on biologically inspired methods such as artificial neural/immune networks can be employed to improve the regular diagnostic process and to avoid misdiagnosis. In this paper pre-processing and classification techniques are used to train the system. Artificial immune recognition method is used for pre-processing and KNN classifier is used for classification. The system is tested with some sample data and obtained the results. The system is validated with annotated data.


title: Sales forecasting for computer wholesalers: A comparison of multivariate adaptive regression splines and artificial neural networks

abstract: Artificial neural networks (ANNs) have been found to be useful for sales/demand forecasting. However, one of the main shortcomings of ANNs is their inability to identify important forecasting variables. This study uses multivariate adaptive regression splines (MARS), a nonlinear and non-parametric regression methodology, to construct sales forecasting models for computer wholesalers. Through the outstanding variable screening ability of MARS, important sales forecasting variables for computer wholesalers can be obtained to enable them to make better sales management decisions. Two sets of real sales data collected from Taiwanese computer wholesalers are used to evaluate the performance of MARS. The experimental results show that the MARS model outperforms backpropagation neural networks, a support vector machine, a cerebellar model articulation controller neural network, an extreme learning machine, an ARIMA model, a multivariate linear regression model, and four two-stage forecasting schemes across various performance criteria. Moreover, the MARS forecasting results provide useful information about the relationships between the forecasting variables selected and sales amounts through the basis functions, important predictor variables, and the MARS prediction function obtained, and hence they have important implications for the implementation of appropriate sales decisions or strategies.


Mental spaces , language modalities , and conceptual integration


title: On searching and indexing sequences of temporal intervals

abstract: In several application domains, including sign language, sensor networks, and medicine, events are not necessarily instantaneous but they may have a time duration. Such events build sequences of temporal intervals, which may convey useful domain knowledge; thus, searching and indexing these sequences is crucial. We formulate the problem of comparing sequences of labeled temporal intervals and present a distance measure that can be computed in polynomial time. We prove that the distance measure is metric and satisfies the triangle inequality. For speeding up search in large databases of sequences of temporal intervals, we propose an approximate indexing method that is based on embeddings. The proposed indexing framework is shown to be contractive and can guarantee no false dismissal. The distance measure is tested and benchmarked through rigorous experimentation on real data taken from several application domains, including: American Sign Language annotated video recordings, robot sensor data, and Hepatitis patient data. In addition, the indexing scheme is tested on a large synthetic dataset. Our experiments show that speedups of over an order of magnitude can be achieved while maintaining high levels of accuracy. As a result of our work, it becomes possible to implement recommender systems, search engines and assistive applications for the fields that employ sequences of temporal intervals.


title: Cloud Computing: Solution to ICT in Higher Education in Nigeria

abstract: Information and communication technologies (ICT) have become commonplace entities in all aspects of life. Across the past twenty years the use of ICT has fundamentally changed the practices and procedures of nearly all forms of endeavour within business and governance. Within education, ICT has begun to have a presence but the value of ICT is not affordable. Unfortunately, there are some limitations confronting institutions in Nigeria from infusing ICT. The basic principle of cloud computing entails the reduction of in-house data centres and the delegation of a portion or all of the Information Technology infrastructure capability to a third party. This holds the promise of driving down cost while fostering innovation and promoting agility. Institutions of higher learning, such as Universities and Colleges, are the core of innovation through their advanced research and development. Subsequently, Higher Institutions may benefit greatly by harnessing the power of cloud computing, including cost cutting as well as all the above types of cloud services. This paper explores the application of cloud computing in higher education in Nigeria, issues with ICT in Nigeria and touches upon some aspired benefits as well as expected limitations of cloud computing.


Biostatistics 104: correlational analysis.


title: Vaginal microbiome of reproductive-age women

abstract: The means by which vaginal microbiomes help prevent urogenital diseases in women and maintain health are poorly understood. To gain insight into this, the vaginal bacterial communities of 396 asymptomatic North American women who represented four ethnic groups (white, black, Hispanic, and Asian) were sampled and the species composition characterized by pyrosequencing of barcoded 16S rRNA genes. The communities clustered into five groups: four were dominated by Lactobacillus iners, L. crispatus, L. gasseri, or L. jensenii, whereas the fifth had lower proportions of lactic acid bacteria and higher proportions of strictly anaerobic organisms, indicating that a potential key ecological function, the production of lactic acid, seems to be conserved in all communities. The proportions of each community group varied among the four ethnic groups, and these differences were statistically significant [χ(2)(10) = 36.8, P < 0.0001]. Moreover, the vaginal pH of women in different ethnic groups also differed and was higher in Hispanic (pH 5.0 ± 0.59) and black (pH 4.7 ± 1.04) women as compared with Asian (pH 4.4 ± 0.59) and white (pH 4.2 ± 0.3) women. Phylotypes with correlated relative abundances were found in all communities, and these patterns were associated with either high or low Nugent scores, which are used as a factor for the diagnosis of bacterial vaginosis. The inherent differences within and between women in different ethnic groups strongly argues for a more refined definition of the kinds of bacterial communities normally found in healthy women and the need to appreciate differences between individuals so they can be taken into account in risk assessment and disease diagnosis.


A Hybrid Probabilistic Model for Camera Relocalization


title: Edge-Aware Context Encoder for Image Inpainting

abstract: We present Edge-aware Context Encoder (E-CE): an image inpainting model which takes scene structure and context into account. Unlike previous CE which predicts the missing regions using context from entire image, E-CE learns to recover the texture according to edge structures, attempting to avoid context blending across boundaries. In our approach, edges are extracted from the masked image, and completed by a full-convolutional network. The completed edge map together with the original masked image are then input into the modified CE network to predict the missing region. The experiments demonstrate that E-CE can generate images with better shapes and structures than CE.


title: NDroid: Toward Tracking Information Flows Across Multiple Android Contexts

abstract: For performance and compatibility reasons, developers tend to use native code in their applications (or simply apps). This makes a bidirectional data flow through multiple contexts, i.e., the Java context and the native context, in Android apps. Unfortunately, this interaction brings serious challenges to existing dynamic analysis systems, which fail to capture the data flow across different contexts. In this paper, we first performed a large-scale study on apps using native code and reported some observations. Then, we identified several scenarios where data flow cannot be tracked by existing systems, leading to uncaught information leakage. Based on these insights, we designed and implemented NDroid , an efficient dynamic taint analysis system that could track the data flow between both Java context and native context. The evaluation of real apps demonstrated the effectiveness of NDroid in identifying information leakage with reasonable performance overhead.


title: Composition-Preserving Deep Photo Aesthetics Assessment

abstract: Photo aesthetics assessment is challenging. Deep convolutional neural network (ConvNet) methods have recently shown promising results for aesthetics assessment. The performance of these deep ConvNet methods, however, is often compromised by the constraint that the neural network only takes the fixed-size input. To accommodate this requirement, input images need to be transformed via cropping, scaling, or padding, which often damages image composition, reduces image resolution, or causes image distortion, thus compromising the aesthetics of the original images. In this paper, we present a composition-preserving deep Con-vNet method that directly learns aesthetics features from the original input images without any image transformations. Specifically, our method adds an adaptive spatial pooling layer upon the regular convolution and pooling layers to directly handle input images with original sizes and aspect ratios. To allow for multi-scale feature extraction, we develop the Multi-Net Adaptive Spatial Pooling ConvNet architecture which consists of multiple sub-networks with different adaptive spatial pooling sizes and leverage a scene–based aggregation layer to effectively combine the predictions from multiple sub-networks. Our experiments on the large-scale aesthetics assessment benchmark (AVA [29]) demonstrate that our method can significantly improve the state-of-the-art results in photo aesthetics assessment.


title: A Passive Circulator with High Isolation using a Directional Coupler for RFID

abstract: In radio-frequency identification (RFID) system, a directional coupler has been used to isolate RX from TX due to its simplicity and low cost compared with a circulator. Nevertheless, a conventional microstrip directional coupler has inherent drawback of poor isolation due to unequal phase velocity between even and odd mode. In this paper, a newly proposed circulator using a microstrip directional coupler is proposed to achieve good isolation between TX and RX. The proposed circulator is used for UHF band RFID reader with a single antenna, of which the operating frequency band is 860 MHz ? 960 MHz. The measurement of the fabricated circulator using a modified directional coupler exhibits excellent isolation of 64 dB and directivity of 49 dB in its frequency band. The validity of the proposed circulator is demonstrated by comparing the TX leakage at the RX port of RFID reader system with that of a conventional directional coupler.


title: Analysis and Test Results of a Brushless Doubly Fed Induction Machine With Rotary Transformer

abstract: This paper analyzes a 90-kW brushless doubly fed three-phase induction machine in which a wound rotor circuit is connected to a rotary transformer. It presents the advantages of substituting brushes and slip rings by a rotary transformer. In addition, it shows the rotary transformer design and presents the doubly fed induction machine operation. The steady-state model considers electrical circuit techniques to provide information about current, power factor, and efficiency on load. Equivalent circuit parameters are obtained through laboratory tests under a prototype. Comparisons between simulation and measurement results attest the good performance of the adopted model.


title: Improved supply chain management based on hybrid demand forecasts §

abstract: Demand forecasts play a crucial role for supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Several forecasting techniques have been developed, each one with its particular advantages and disadvantages compared to other approaches. This motivates the development of hybrid systems combining different techniques and their respective strengths. In this paper, we present a hybrid intelligent system combining Autoregressive Integrated Moving Average (ARIMA) models and neural networks for demand forecasting. We show improvements in forecasting accuracy and propose a replenishment system for a Chilean supermarket, which leads simultaneously to fewer sales failures and lower inventory levels than the previous solution.


title: Pre-processing of Mammogram Images

abstract: Digital mammogram has become the most effective technique for early breast cancer detection modality. Digital mammogram takes an electronic image of the breast and stores it directly in a computer. High quality mammogram images are high resolution and large size images. Processing these images require high computational capabilities. The transmission of these images over the net is sometimes critical especially if the diagnosis of remote radiologists is required. The aim of this study is to develop an automated system for assisting the analysis of digital mammograms. Computer image processing techniques will be applied to enhance images and this is followed by segmentation of the region of interest (ROI). Subsequently, the textural features will be extracted from the ROI. The texture features will be used to classify the ROIs as either masses or non-masses.


Thermodvnamics Thermodynamics of Wax Precipitation in Petroleum Mixtures


Quantifying the components of the mass and energy balance of a black soldier fly ( BSF ) food waste composting system Tiny but Hungry


title: Bayesian Network Classifiers

abstract: Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper we evaluate approaches for inducing classifiers from data, based on the theory of learning Bayesian networks. These networks are factored representations of probability distributions that generalize the naive Bayesian classifier and explicitly represent statements about independence. Among these approaches we single out a method we call Tree Augmented Naive Bayes (TAN), which outperforms naive Bayes, yet at the same time maintains the computational simplicity (no search involved) and robustness that characterize naive Bayes. We experimentally tested these approaches, using problems from the University of California at Irvine repository, and compared them to C4.5, naive Bayes, and wrapper methods for feature selection.


title: Kernel Dimensionality Reduction on Sleep Stage Classification using ECG Signal

abstract: The purpose of this study is to apply Kernel Dimensionality Reduction (KDR) to classify sleep stage from electrocardiogram (ECG) signal. KDR is supervised dimensionality reduction method that retains statistical relationship between input variables and target class. KDR was chosen to reduce dimensionality of features extracted from ECG signal because this method doesn’t need special assumptions regarding the conditional distribution, the marginal distribution, or both. In this study we extract 9 time and frequency domain heart rate variability (HRV) features from ECG signal of Polysomnographic Database from Physionet. To evaluate KDR performance, we perform sleep stage classification using kNN, Random Forest and SVM method, and then compare the classification performance before and after dimensionality reduction using KDR. Experimental result suggested KDR implementation on sleep stage classification using SVM could reduce dimensionality of feature vector into 2 w ithout affecting the classification performance. KDR performance on Random Forest and k Nearest Neighbour classification only show slight advantage compared to without implementing KDR.


title: Social Computing: From Social Informatics to Social Intelligence

abstract: Social computing represents a new computing paradigm and an interdisciplinary research and application field. Undoubtedly, it strongly influences system and software developments in the years to come. We expect that social computing's scope continues to expand and its applications multiply. From both theoretical and technological perspectives, social computing technologies moves beyond social information processing towards emphasizing social intelligence. As we've discussed, the move from social informatics to social intelligence is achieved by modeling and analyzing social behavior, by capturing human social dynamics, and by creating artificial social agents and generating and managing actionable social knowledge


title: Behavioral/Systems/Cognitive Differential Encoding of Losses and Gains in the

abstract: Studies on human monetary prediction and decision making emphasize the role of the striatum in encoding prediction errors for financial reward. However, less is known about how the brain encodes financial loss. Using Pavlovian conditioning of visual cues to outcomes that simultaneously incorporate the chance of financial reward and loss, we show that striatal activation reflects positively signed prediction errors for both. Furthermore, we show functional segregation within the striatum, with more anterior regions showing relative selectivity for rewards and more posterior regions for losses. These findings mirror the anteroposterior valence-specific gradient reported in rodents and endorse the role of the striatum in aversive motivational learning about financial losses, illustrating functional and anatomical consistencies with primary aversive outcomes such as pain.


title: [Interferon-beta 1a in relapsing-remitting multiple sclerosis. First report of a Mexican population].

abstract: OBJECTIVE ::: We present the results of a prospective and a longitudinal study of IFN-beta 1a (Rebif) in the treatment of Mexican patients with relapsing-remittin multiple sclerosis (MS). ::: ::: ::: PATIENTS AND METHODS ::: Twenty five patients were included, all of them fulfilled the diagnostic criteria proposed by Poser, had a clinical evolution of at least 2 years, had 2 to 3 acute attacks of MS in the 12 months preceding study entry and an EDSS < 5.5. Six patients were excluded from the trial, one because of anaphylactic shock, 2 progressed to the chronic form and 3 decided not to continue in the study. These patients were considered as treatment failures and represented 24% of the participants entering the trial. The remaining 19 patients (mean age 29.8, 74% females) completed 2-year follow-up. The mean time of disease duration was 48.8 months. Six million units of IFN-beta 1a were applied thrice weekly, in 8 patients (42%) dosage reduction to 6 millions units twice a week was necessary because of leukopenia or elevation in liver enzymes. ::: ::: ::: RESULTS ::: In the year before treatment with IFN-beta 1a patients had 2.7 +/- 0.93 acute attacks, after treatment the mean number of acute attacks per year was reduced to 1.94 +/- 2.39 (28.9% reduction). When analyzed by gender there was a 62.9% reduction in acute episodes per year in women and a 27.7% reduction in men (p < 0.05). The most frequent side effects were fatigue, headache, local skin reactions, depressed mood, myalgia, weight variations and somnolence. A few patients developed leukopenia, anemia or elevated liver enzymes. In 31.25% of the patients there was an increase in the number of lesions on MRI, in 31.25% there was a reduction in the number of lesions and in 37.5% no change was noted. ::: ::: ::: CONCLUSION ::: The best-tolerated dose of IFN-beta 1a was 12 million units/week in women and 18 million units/week in men.


title: Circular Polarization Reconfigurable Wideband E-Shaped Patch Antenna for Wireless Applications

abstract: A polarization reconfigurable E-shaped patch antenna with wideband performance is proposed in this communication. The antenna is capable of switching its polarization from right hand circular polarization (RHCP) to left hand circular polarization (LHCP) and vice versa. Its structure is simple and consists of a single-layer single-feed E-shaped patch and two RF switches placed at appropriate locations in the slots. The design targets the WLAN IEEE 802.11 b/g frequency band (2.4-2.5 GHz) being used in various wireless communication systems. Full wave simulation is used for the antenna analysis, and a prototype of the antenna with an integrated DC biasing circuit has been fabricated and tested. Good agreement is obtained between simulated and measured results. The antenna exhibits a 7% effective bandwidth from 2.4 GHz to 2.57 GHz with a 8.7 dBic maximum gain. The antenna radiation symmetry is maintained upon switching between the two circular polarization modes.


title: Daylighting Metrics: Is there a relation between Useful Daylight Illuminance and Daylight Glare Probabilty?

abstract: The establishment of climate-based daylight modeling within research and practice has led to a fundamental reassessment of both the basis and purpose of daylight metrics. Whilst there is no consensus yet on the precise nature of the metric(s) that should replace the daylight factor, it is generally agreed that these should be founded on climate-based daylight modeling (CBDM). In this paper we examine the relation between the predicted annual occurrence of glare and one of the candidate CBDM metrics that has been proposed, called useful daylight illuminance (UDI). The purpose is to determine if one or more of the UDI metrics (predicted for the horizontal workplane) could serve as a proxy for the probability of daylight glare (i.e. a measure of vertical illuminance received at the eye). For glare we use the simplified daylight glare probability model. The setting is a residential building which we use as a ‘virtual laboratory’ in two design configurations, each evaluated under all 32 combinations of 8 European climates and 4 building orientations.


title: Uncovering Large Groups of Active Malicious Accounts in Online Social Networks

abstract: The success of online social networks has attracted a constant interest in attacking and exploiting them. Attackers usually control malicious accounts, including both fake and compromised real user accounts, to launch attack campaigns such as social spam, malware distribution, and online rating distortion. To defend against these attacks, we design and implement a malicious account detection system called SynchroTrap. We observe that malicious accounts usually perform loosely synchronized actions in a variety of social network context. Our system clusters user accounts according to the similarity of their actions and uncovers large groups of malicious accounts that act similarly at around the same time for a sustained period of time. We implement SynchroTrap as an incremental processing system on Hadoop and Giraph so that it can process the massive user activity data in a large online social network efficiently. We have deployed our system in five applications at Facebook and Instagram. SynchroTrap was able to unveil more than two million malicious accounts and 1156 large attack campaigns within one month.


title: The application visualization system: a computational environment for scientific visualization

abstract: A software system for developing interactive scientific visualization applications quickly, with a minimum of programming effort, is described. This application visualization system (AVS) is an application framework targeted at scientists and engineers. The goal of the system is to make applications that combine interactive graphics and high computational requirements easier to develop for both programmers and nonprogrammers. AVS is designed around the concept of software building blocks, or modules, which can be interconnected to form visualization applications. AVS allows flow networks of existing modules to be constructed using a direct-manipulation user interface, and it automatically generates a simple user interface to each module. >


Performance in Sharing Economy: Evidence from Room-Sharing Service


Axially Symmetric Cavitational Flow


title: Simulation techniques for EMC compliant design of automotive IC chips and modules

abstract: Automotive electronic units need to comply fully with electromagnetic compatibility (EMC) regulations. This paper proposes a comprehensive simulation methodology and techniques for analyzing EMC performance of integrated circuits (ICs) in chip-package-system (CPS) board integration. Extended chip power models (ECPMs) model automotive ICs for noise emission (EMI) and also for noise susceptibility (EMS), and provide a suite of EMC models that facilitate joint design of EMC compliant systems by semiconductor chip vendors and electronic module integrators. Automotive local interconnect network (LIN)-bus transceivers in a 0.13 μm Bipolar/CMOS/DMOS (BiCD) chip were analyzed under the direct power injection (DPI) method for EMS testing standardized in IEC62132-4. The ECPM derived for the complete LIN integrated circuit by a high-capacity mesh solver provides a passive network that models not only power delivery networks, but also isolation structures in the circuit's silicon substrate. These isolation structures include junction diodes and trench insulators surrounding BiCD devices coupled to the substrate. The chip/package/system simulation including the ECPM of the whole LIN circuit predicts the coupling of noise sinusoids induced by high-power EMS tests and explains the root cause of the susceptibility of LIN circuits, as verified by on-chip measurements in test chips.


title: A Systematic Approach to Cryptocurrency Fees

abstract: This paper is devoted to the study of transaction fees in massively replicated open blockchain systems. In such systems, like Bitcoin, a snapshot of current state required for the validation of transactions is being held in the memory, which eventually becomes a scarce resource. Uncontrolled state growth can lead to security issues. We propose a modification of a transaction fee scheme based on how much additional space will be needed for the objects created as a result of transaction processing and for how long will they live in the state. We also work out the way to combine fees charged for different resources spent (bandwidth, random-access state memory, processor cycles) in a composite fee and demonstrate consistency of the approach by analyzing the statistics from Ethereum network. We show a possible implementation for state-related fee in a form of regular payments to miners.


title: A Genetic Algorithm for the Multidimensional Knapsack Problem

abstract: In this paper we present a heuristic based upon genetic algorithms for the multidimensional knapsack problem. A heuristic operator which utilises problem-specific knowledge is incorporated into the standard genetic algorithm approach. Computational results show that the genetic algorithm heuristic is capable of obtaining high-quality solutions for problems of various characteristics, whilst requiring only a modest amount of computational effort. Computational results also show that the genetic algorithm heuristic gives superior quality solutions to a number of other heuristics.


title: Empathy circuits

abstract: The social neuroscientific investigation of empathy has revealed that the same neural networks engaged during first-hand experience of affect subserve empathic responses. Recent meta-analyses focusing on empathy for pain for example reliably identified a network comprising anterior insula and anterior midcingulate cortex. Moreover, recent studies suggest that the generation of empathy is flexibly supported by networks involved in action simulation and mentalizing depending on the information available in the environment. Further, empathic responses are modulated by many factors including the context they occur in. Recent work shows how this modulation can be afforded by the engagement of antagonistic motivational systems or by cognitive control circuits, and these modulatory systems can also be employed in efforts to regulate one's empathic responses.


title: Cohort comparisons in resources and functioning among centenarians: Findings from the Georgia Centenarian Study

abstract: The purpose of this study was to examine cohort comparisons in levels of resources (e.g., mental health, physical functioning, economic and social resources, and cognitive functioning) for 211 community-dwelling centenarians (whose Mini-Mental Status Examination score was 23 or higher) of phases I and III of the Georgia Centenarian Study. The earlier cohort was defined as those born between 1881 and 1895 (part of phase I) and the later cohort included persons born between 1901 and 1907 (part of phase III). Five specific domains were compared: mental health; mental status; physical functioning; social resources; and economic resources. Results showed that there were significant cohort comparisons in five domains: mental health; mental status; physical functioning; social networks; and economic resources. Findings suggest that the later centenarian cohort was more satisfied with life, felt less depressed, showed less positive or negative emotion, had higher scores on perceived economic status, and higher le...


title: Multilevel Inverter For Grid-Connected PV System Employing Digital PI Controller

abstract: This paper presents a single-phase five-level photovoltaic (PV) inverter topology for grid-connected PV systems with a novel pulsewidth-modulated (PWM) control scheme. Two reference signals identical to each other with an offset equivalent to the amplitude of the triangular carrier signal were used to generate PWM signals for the switches. A digital proportional-integral current control algorithm is implemented in DSP TMS320F2812 to keep the current injected into the grid sinusoidal and to have high dynamic performance with rapidly changing atmospheric conditions. The inverter offers much less total harmonic distortion and can operate at near-unity power factor. The proposed system is verified through simulation and is implemented in a prototype, and the experimental results are compared with that with the conventional single-phase three-level grid-connected PWM inverter.


title: Knowledge Matters: Importance of Prior Information for Optimization

abstract: We explore the effect of introducing prior information into the intermediate level of neural networks for a learning task on which all the state-of-the-art machine learning algorithms tested failed to learn. We motivate our work from the hypothesis that humans learn such intermediate concepts from other individuals via a form of supervision or guidance using a curriculum. The experiments we have conducted provide positive evidence in favor of this hypothesis. In our experiments, a two-tiered MLP architecture is trained on a dataset with 64x64 binary inputs images, each image with three sprites. The final task is to decide whether all the sprites are the same or one of them is different. Sprites are pentomino tetris shapes and they are placed in an image with different locations using scaling and rotation transformations. The first part of the two-tiered MLP is pre-trained with intermediate-level targets being the presence of sprites at each location, while the second part takes the output of the first part as input and predicts the final task's target binary event. The two-tiered MLP architecture, with a few tens of thousand examples, was able to learn the task perfectly, whereas all other algorithms (include unsupervised pre-training, but also traditional algorithms like SVMs, decision trees and boosting) all perform no better than chance. We hypothesize that the optimization difficulty involved when the intermediate pre-training is not performed is due to the {\em composition} of two highly non-linear tasks. Our findings are also consistent with hypotheses on cultural learning inspired by the observations of optimization problems with deep learning, presumably because of effective local minima.


title: Automated API Property Inference Techniques

abstract: Frameworks and libraries offer reusable and customizable functionality through Application Programming Interfaces (APIs). Correctly using large and sophisticated APIs can represent a challenge due to hidden assumptions and requirements. Numerous approaches have been developed to infer properties of APIs, intended to guide their use by developers. With each approach come new definitions of API properties, new techniques for inferring these properties, and new ways to assess their correctness and usefulness. This paper provides a comprehensive survey of over a decade of research on automated property inference for APIs. Our survey provides a synthesis of this complex technical field along different dimensions of analysis: properties inferred, mining techniques, and empirical results. In particular, we derive a classification and organization of over 60 techniques into five different categories based on the type of API property inferred: unordered usage patterns, sequential usage patterns, behavioral specifications, migration mappings, and general information.


title: Toward an IoT-enabled adaptive interactive bus transportation system

abstract: Due to the rapid increasing usage of public transportation in everyday life, saving passenger time plays a vital role in passenger comfort as well as in his productivity which in turn affect the notational economy. Moreover, especially in the developing countries, bus transportation is the most commonly used way by people to travel to their jobs, universities, etc. On the other hand, bus transportation is the most transportation way that is affected by traffic congestion, as well it is affected by route violation. Therefore, there is an increasing demand for a system that monitors bus transportations, detects traffic jam, detects route violation and update the passenger about any unexpected condition that may occur. Moreover, since the presence of IoT technology as well as the increasing usage of smart phones, it seems evident that design and implementation of smart and interactive bus transportation system applicable to support smart cities environment. The aim of this paper is to present an adaptive system that uses IoT tracking unit, Geoserver reverse geocoding, QGIS, and web application server to monitor and manage bus transportation as well interact with the passenger through a mobile-enabled app.


title: The Adoption of Mobile Payment Services for "Fintech"

abstract: This paper examined the acceptance of payment-type Fintech services of users by utilizing the Elaboration Likelihood Model by Petty and Cacioppo [1] and by applying variables associated with the Technology Acceptance Model. In addition, it analyzed the causal relationship between concern for information privacy and self-efficacy by adopting them as moderating variables. Results suggested that usefulness, ease of use and credibility had an effect on intention to use, and self-efficacy was found to have an moderating effect on independent and dependent variables. Further, concern for information privacy was found to be a factor obstructing the path to intention to use. The implications of this study are that in the promotion of payment-type Fintech services, convenience and usefulness are the most critical and influential variables in terms of usage, while government deregulation and stronger security are called for from an institutional aspect.


Transferring sentiment knowledge between words and tweets


title: Girona 500 AUV: From Survey to Intervention

abstract: This paper outlines the specifications and basic design approach taken on the development of the Girona 500, an autonomous underwater vehicle whose most remarkable characteristic is its capacity to reconfigure for different tasks. The capabilities of this new vehicle range from different forms of seafloor survey to inspection and intervention tasks.


title: Gaming Social Capital : Exploring Civic Value in Multiplayer Video Games

abstract: Recent research suggests that social interactions in video games may lead to the development of community bonding and prosocial attitudes. Building on this line of research, a national survey of U.S. adults finds that gamers who develop ties with a community of fellow gamers possess gaming social capital, a new gaming-related community construct that is shown to be a positive antecedent in predicting both face-to-face social capital and civic participation.


title: Comparing endurance- and resistance-exercise training in people with multiple sclerosis: a randomized pilot study

abstract: Objective: The purpose of this study was to compare adaptations in functional and quality of life measures following endurance- and resistance-exercise training in people with multiple sclerosis.Design: Cross-over design with an eight-week washout period.Setting: Community health centre.Subjects: Sixteen individuals with multiple sclerosis.Intervention: Subjects completed both an eight-week endurance- and an eight-week resistance-exercise training programme in a randomized order. The exercise training comprised individualized progressive programmes that were completed twice weekly in a supervised group setting.Main measures: Grip strength, functional reach, four step square, timed up and go and six-minute walk tests, Multiple Sclerosis Impact and Modified Fatigue Impact Scales, Becks Depression Inventory and the Health Status Questionnaire Short Form-36.Results: Sixteen of 21 (76%) subjects completed the study. Subjects attended 13.2 ± 1.6 endurance- and 15.8 ± 1.9 resistance-exercise training sessions. N...


title: Learning from Ontology Streams with Semantic Concept Drift

abstract: Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept drift i.e., unexpected changes in data distribution, causing most of models to be less accurate as time passes. To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic embeddings. The experiments show accurate prediction with data from Dublin and Beijing.


title: Quality Assessment of Software as a Service on Cloud Using Fuzzy Logic

abstract: Cloud computing is a business model which provides on demand services on the pay-per-use premise. Software as a Service (SaaS) is one of the delivery models for cloud computing, where software ownership by the SaaS provider is isolated from its use by the SaaS customer. The notion of quality is central to any service provision. Also, it is important to evaluate the quality of SaaS in order to be able to improve it. Traditional software engineering quality models are not effusively suitable for this purpose due to difference in the nature of software and service. In the past, a few approaches to service quality estimation have been proposed. Some of these approaches extend quality characteristics from existing quality models and even devise SaaS quality metrics, while others discuss quality around Service Level Agreement (SLA) and Quality of Service (QoS) parameters. In this paper, some representative quality factors have been identified by analyzing literature and a model based on fuzzy logic has been proposed to assess SaaS quality. Such a model of quality criteria may provide a ground for more comprehensive quality model which may assist a SaaS customer to choose a higher quality service from available services on cloud; the quality model may also serve as a guideline to SaaS provider to improve the quality of service provided.


title: Modeling and vector control of a planar magnetic levitator

abstract: The authors designed and implemented a magnetically levitated stage with large planar motion capability. This planar magnetic levitator employs four novel permanent-magnet linear motors. Each motor generates vertical force for suspension against gravity, as well as a horizontal force for drive. These linear levitation motors can be used as building blocks in the general class of multi-degree-of-freedom motion stages. In this paper, they discuss the electromechanical modeling and real-time vector control of such a permanent-magnet levitator. They describe the dynamics in a dq frame introduced to decouple the forces acting on the magnetically levitated moving part, namely, the platen. A transformation similar to the Blondel-Park transformation is derived for commutation of the stator phase currents. They provide test results on step responses of the magnetically levitated stage. They show 5 nm RMS positioning noise in x and y, which demonstrates the applicability of such stages in the next-generation photolithography in semiconductor manufacturing.


title: Incorporating version histories in Information Retrieval based bug localization

abstract: Fast and accurate localization of software defects continues to be a difficult problem since defects can emanate from a large variety of sources and can often be intricate in nature. In this paper, we show how version histories of a software project can be used to estimate a prior probability distribution for defect proneness associated with the files in a given version of the project. Subsequently, these priors are used in an IR (Information Retrieval) framework to determine the posterior probability of a file being the cause of a bug. We first present two models to estimate the priors, one from the defect histories and the other from the modification histories, with both types of histories as stored in the versioning tools. Referring to these as the base models, we then extend them by incorporating a temporal decay into the estimation of the priors. We show that by just including the base models, the mean average precision (MAP) for bug localization improves by as much as 30%. And when we also factor in the time decay in the estimates of the priors, the improvements in MAP can be as large as 80%.


title: Face frontalization for Alignment and Recognition

abstract: Recently, it was shown that excellent results can be achieved in both face landmark localization and pose-invariant face recognition. These breakthroughs are attributed to the efforts of the community to manually annotate facial images in many different poses and to collect 3D faces data. In this paper, we propose a novel method for joint face landmark localization and frontal face reconstruction (pose correction) using a small set of frontal images only. By observing that the frontal facial image is the one with the minimum rank from all different poses we formulate an appropriate model which is able to jointly recover the facial landmarks as well as the frontalized version of the face. To this end, a suitable optimization problem, involving the minimization of the nuclear norm and the matrix $\ell_1$ norm, is solved. The proposed method is assessed in frontal face reconstruction (pose correction), face landmark localization, and pose-invariant face recognition and verification by conducting experiments on $6$ facial images databases. The experimental results demonstrate the effectiveness of the proposed method.


title: A Dual-Loop Delay-Locked Loop Using Multiple Voltage-Controlled Delay Lines

abstract: This paper describes a dual-loop delay-locked loop (DLL) which overcomes the problem of a limited delay range by using multiple voltage-controlled delay lines (VCDLs). A reference loop generates quadrature clocks, which are then delayed with controllable amounts by four VCDLs and multiplexed to generate the output clock in a main loop. This architecture enables the DLL to emulate the infinite-length VCDL with multiple finite-length VCDLs. The DLL incorporates a replica biasing circuit for low-jitter characteristics and a duty cycle corrector immune to prevalent process mismatches. A test chip has been fabricated using a 0.25-/spl mu/m CMOS process. At 400 MHz, the peak-to-peak jitter with a quiet 2.5-V supply is 54 ps, and the supply-noise sensitivity is 0.32 ps/mV.


Comparative Study of Various Decision Tree Classification Algorithm Using WEKA


title: Coplanar antenna miniaturization using high permittivity perovskite substrates

abstract: A miniature coplanar antenna on a perovskite substrate is analyzed and designed using short circuit technique. The overall dimensions are minimized to 0.09 λ × 0.09 λ. The antenna geometry, the design concept, as well as the simulated and the measured results are discussed in this paper.


title: Via-hole-less planar microstrip-to-waveguide transition in millimeter-wave band

abstract: A via-hole-less planar microstrip-to-waveguide transition is proposed. A proximity coupling type transition requires via holes to prevent a leakage of parallel plate mode in the substrate. We propose to replace the via holes to the choke structure. In order to remove the via holes of the proximity coupling type transition, a choke of the parallel plate waveguide was applied. The dimensions of the waveguide short metal with a parallel plate waveguide which is located on the waveguide end is examined to achieve the choke circuit. The length from the edge of the waveguide short metal to the edge of the waveguide aperture is set to a quarter wavelength of the parallel plate waveguide which is constructed with the closely-spaced waveguide short metal and the surrounding ground of the patch element. All the dimensions were optimized numerically. Proposed transition has an insertion loss of 0.68 dB from 76 to 77 GHz, and a bandwidth of 6.4 % (4.9 GHz) for the reflection coefficient below −15 dB.


title: Practical considerations for real-time implementation of speech-based gender detection

abstract: This paper describes a detailed analysis and implementation of a robust gender detector for audio stream applications. The implementation, based on melcepstral features and a Gaussian mixture model classifier, is designed to maximize gender classification performance in continuous speech. The described detector outperforms other reported systems based on statistically significant numbers of gender verifications (2136 unique speakers) obtained from the FISHER speech corpus. The system yields high accuracies for long and short utterances while a confidence figure of merit score for the decision ensures reliability in continuous audio streams.


title: Software Development Cost Estimation Using Analogy: A Review

abstract: Software project managers require reliable methods for estimating software project costs, and it is especially important at the early stage of software cycle. Analogy for software cost estimation has been considered as a suitable alternative to regression-based estimation method, and empirical studies have shown that it can be used successfully in many circumstances. It is important for project managers to understand the strengths and weaknesses of each useful software cost estimation method, more importantly when to use these methods and at which stage of the software development. This paper provides a comprehensive overview of the background and the underlying theory of analogy for software cost estimation, published in major software engineering journals and conferences over the past 15 years. Investigation on the dataset quality evaluation and its relevance to the target problem for analogy are further discussed, the result allows researchers and project managers to familiarize the underlying nature of the analogy-based approach.


title: Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications

abstract: Abstract Easy-to-use collaborative robotics solutions, where human workers and robots share their skills, are entering the market, thus becoming the new frontier in industrial robotics. They allow to combine the advantages of robots, which enjoy high levels of accuracy, speed and repeatability, with the flexibility and cognitive skills of human workers. However, to achieve an efficient human–robot collaboration, several challenges need to be tackled. First, a safe interaction must be guaranteed to prevent harming humans having a direct contact with the moving robot. Additionally, to take full advantage of human skills, it is important that intuitive user interfaces are properly designed, so that human operators can easily program and interact with the robot. In this survey paper, an extensive review on human–robot collaboration in industrial environment is provided, with specific focus on issues related to physical and cognitive interaction. The commercially available solutions are also presented and the main industrial applications where collaborative robotic is advantageous are discussed, highlighting how collaborative solutions are intended to improve the efficiency of the system and which the open issue are.


title: Strategic alignment: a practitioner's perspective

abstract: Purpose – Organizations are becoming increasingly aware of the importance of aligning information systems with organizational processes, goals and strategies. One way of representing and analysing strategic alignment is through the creation of a causal‐loop diagram, a subject which this paper seeks to examine.Design/methodology/approach – The exploratory research presented here involved six senior IS/IT managers during three two‐hour focus group sessions, which led to the development of such a diagram. The focus group sessions were recorded, transcribed and analysed using content analysis.Findings – The diagram presents a systemic view of IS/business alignment within organizations, as seen through the lens of these practitioners. The research suggests that, although practitioners understand that a high level of connection between IS and business planning processes may be dependent on the level of integration between the IS group and other sections of the organization, they are still unable to develop the ...


title: Semantic octree: Unifying recognition, reconstruction and representation via an octree constrained higher order MRF

abstract: On the one hand, mainly within the computer vision community, multi-resolution image labelling problems with pixel, super-pixel and object levels, have made great progress towards the modelling of holistic scene understanding. On the other hand, mainly within the robotics and graphics communities, multi-resolution 3D representations of the world have matured to be efficient and accurate. In this paper we bring together the two hands and move towards the new direction of unified recognition, reconstruction and representation. We tackle the problem by embedding an octree into a hierarchical robust PN Markov Random Field. This allows us to jointly infer the multi-resolution 3D volume along with the object-class labels, all within the constraints of an octree data-structure. The octree representation is chosen as this data-structure is efficient for further processing such as dynamic updates, data compression, and surface reconstruction. We perform experiments in inferring our semantic octree on the The kitti Vision Benchmark Suite in order to demonstrate its efficacy.


title: Benefactive/Malefactive Event and Writer Attitude Annotation

abstract: This paper presents an annotation scheme for events that negatively or positively affect entities (benefactive/malefactive events) and for the attitude of the writer toward their agents and objects. Work on opinion and sentiment tends to focus on explicit expressions of opinions. However, many attitudes are conveyed implicitly, and benefactive/malefactive events are important for inferring implicit attitudes. We describe an annotation scheme and give the results of an inter-annotator agreement study. The annotated corpus is available online.


title: On the rate of channel polarization

abstract: It is shown that for any binary-input discrete memoryless channel $W$ with symmetric capacity $I(W)$ and any rate $R <I(W)$, the probability of block decoding error for polar coding under successive cancellation decoding satisfies $P_e \le 2^{-N^\beta}$ for any $\beta<\frac12$ when the block-length $N$ is large enough.


title: SOx uptake and release properties of TiO2/Al2O3 and BaO/TiO2/Al2O3 mixed oxide systems as NOx storage materials

abstract: Abstract Titania was used as a promoter to obtain novel materials in the form of TiO2/Al2O3 (Ti/Al) and BaO/TiO2/Al2O3 (Ba/Ti/Al, containing 8 wt% or 20 wt% BaO) that are relevant to NOx storage reduction (NSR) catalysis. Two different protocols (P1, P2) were utilized in the synthesis. Ti/Al(P1) manifests itself as crystallites of TiO2 on γ-Al2O3, while Ti/Al(P2) reveals an amorphous AlxTiyOz mixed oxide. The structures of the synthesized materials were investigated via TEM, EDX, BET analysis and XPS while the catalytic functionality/performance of these support materials upon SOx and subsequent NOx adsorption were investigated with TPD and in situ FTIR spectroscopy. Ti/Al(P1, P2) revealed a high affinity towards SOx. Overall thermal stabilities of the adsorbed SOx species and the total SOx uptake of the Ba-free samples increase in the following order: TiO2(anatase) ≪ γ-Al2O3


title: Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes

abstract: MotivationMultivariate quantitative traits arise naturally in recent neuroimaging genetics studies, in which both structural and functional variability of the human brain is measured non-invasively through techniques such as magnetic resonance imaging (MRI). There is growing interest in detecting genetic variants associated with such multivariate traits, especially in genome-wide studies. Random forests (RFs) classifiers, which are ensembles of decision trees, are amongst the best performing machine learning algorithms and have been successfully employed for the prioritisation of genetic variants in case-control studies. RFs can also be applied to produce gene rankings in association studies with multivariate quantitative traits, and to estimate genetic similarities measures that are predictive of the trait. However, in studies involving hundreds of thousands of SNPs and high-dimensional traits, a very large ensemble of trees must be inferred from the data in order to obtain reliable rankings, which makes the application of these algorithms computationally prohibitive.ResultsWe have developed a parallel version of the RF algorithm for regression and genetic similarity learning tasks in large-scale population genetic association studies involving multivariate traits, called PaRFR (Parallel Random Forest Regression). Our implementation takes advantage of the MapReduce programming model and is deployed on Hadoop, an open-source software framework that supports data-intensive distributed applications. Notable speed-ups are obtained by introducing a distance-based criterion for node splitting in the tree estimation process. PaRFR has been applied to a genome-wide association study on Alzheimer's disease (AD) in which the quantitative trait consists of a high-dimensional neuroimaging phenotype describing longitudinal changes in the human brain structure. PaRFR provides a ranking of SNPs associated to this trait, and produces pair-wise measures of genetic proximity that can be directly compared to pair-wise measures of phenotypic proximity. Several known AD-related variants have been identified, including APOE4 and TOMM40. We also present experimental evidence supporting the hypothesis of a linear relationship between the number of top-ranked mutated states, or frequent mutation patterns, and an indicator of disease severity.AvailabilityThe Java codes are freely available at http://www2.imperial.ac.uk/~gmontana.


title: Low cost self-oscillating ZVS-CV driver for power LEDs

abstract: This paper presents a self-oscillating zero-voltage-switching clamped-voltage (ZVS-CV) power electronics driver for LED strings. A simplified mathematical model of LED has been obtained and used to characterize the series resonant converter for the power LED driver. For a large voltage at the input, this converter does not need a current sensor to stabilize the average current through the LED string. By using the proposed converter the effects of the LED manufacturing tolerances and temperature drifts almost has no influence on the LED string average current, and this is analyzed with the application of the LED electrical model. The simplification of the control design, and of the converter, reduces the component number and the cost. The prototype utilizes a low cost series saturable transformer to drive the bipolar transistors (BJTpsilas). The saturable transformer is a component of the self-oscillating drives and determines the switching frequency of the resonant converter. Analytical results are verified by laboratory experiment.


title: A scalable, parallel algorithm for maximal clique enumeration

abstract: The problem of maximal clique enumeration (MCE) is to enumerate all of the maximal cliques in a graph. Once enumerated, maximal cliques are widely used to solve problems in areas such as 3-D protein structure alignment, genome mapping, gene expression analysis, and detection of social hierarchies. Even the most efficient serial MCE algorithms require large amounts of time to enumerate the maximal cliques in networks arising from these problems that contain hundreds, thousands, or larger numbers of vertices. The previous attempts to provide practical solutions to the MCE problem through parallel implementation have had limited success, largely due to a number of challenges inherent to the nature of the MCE combinatorial search space. On the one hand, MCE algorithms often create a backtracking search tree that has a highly irregular and hard-or-impossible to predict structure; therefore, almost any static decomposition of the search tree by parallel processors results in highly unbalanced processor execution times. On the other hand, the data-intensive nature of the MCE problem often makes naive dynamic load distribution strategies that require extensive data movement prohibitively expensive. As a result, good scaling of the overall execution time of parallel MCE algorithms has been reported for only up to a couple hundred processors. In this paper, we propose a parallel, scalable, and memory-efficient MCE algorithm for distributed and/or shared memory high performance computing architectures, whose runtime scales linearly for thousands of processors on real-world application graphs with hundreds and thousands of nodes. Its scalability and efficiency are attributed to the proposed: (a) representation of the search tree decomposition to enable parallelization; (b) parallel depth-first backtracking search to both constrain the search space and minimize memory requirement; (c) least stringent synchronization to minimize data movement; and (d) on-demand work stealing intelligently coupled with work stack splitting to minimize computing elements' idle time. To the best of our knowledge, the proposed parallel MCE algorithm is the first to achieve a linear scaling runtime using up to 2048 processors on Cray XT machines for a number of real-world biological networks.


title: A Literature Survey and Comprehensive Study of Intrusion Detection

abstract: With the rapid expansion of computer usage and computer network the security of the computer system has became very important. Every day new kind of attacks are being faced by industries. As the threat becomes a serious matter year by year, intrusion detection technologies are indispensable for network and computer security. A variety of intrusion detection approaches be present to resolve this severe issue but the main problem is performance. It is important to increase the detection rates and reduce false alarm rates in the area of intrusion detection. In order to detect the intrusion, various approaches have been developed and proposed over the last decade. In this paper, a detailed survey of intrusion detection based various techniques has been presented. Here, the techniques are classified as follows: i) papers related to Neural network ii) papers related to Support vector machine iii) papers related to K-means classifier iv) papers related to hybrid technique and v) paper related to other detection techniques. For comprehensive analysis, detection rate, time and false alarm rate from various research papers have been taken.


title: Dynamic Android Malware Classification Using Graph-Based Representations

abstract: Malware classification for the Android ecosystem can be performed using a range of techniques. One major technique that has been gaining ground recently is dynamic analysis based on system call invocations recorded during the executions of Android applications. Dynamic analysis has traditionally been based on converting system calls into flat feature vectors and feeding the vectors into machine learning algorithms for classification. In this paper, we implement three traditional feature-vector-based representations for Android system calls. For each feature vector representation, we also propose a novel graph-based representation. We then use graph kernels to compute pair-wise similarities and feed these similarity measures into a Support Vector Machine (SVM) for classification. To speed up the graph kernel computation, we compress the graphs using the Compressed Row Storage format, and then we apply OpenMP to parallelize the computation. Experiments show that the graph-based representations are able to improve the classification accuracy over the corresponding feature-vector-based representations from the same input. Finally we show that different representations can be combined together to further improve classification accuracy.


title: Framework of integrated big data: A review

abstract: Currently, how to deeply distill potential attributes of big data has become a great challenge for structured, semi-structured and unstructured data (SSU data) with a unified model. Structured data refers to any data that resides in a fixed field within a record or file including data contained in relational databases and spreadsheets. Unstructured data refers to data from text, pictures, audio, video, and other sources that do not fit into a relational database. Semi-structured data is information that doesn't reside in a relational database but that does have some organizational properties that make it easier to analyze, such as XML, and HTML documents. In this paper, we present a literature survey and a framework, namely integrated big data (IBD), which aims at exploring the approaches for constructing a universal IBD model, including representation, storage and management, computation, and visual analysis. Firstly, we present a systematic framework to decompose big data analytics into four modules. Next, we present a detailed survey of numerous approaches for these four modules. The main contributions of this paper are summarized in two dimensions. First, we propose a novel integrated big data framework for unified big data representation, storage, computation, and visual analysis. Second, we present the possible future methods in realizing the framework by reviewing methods. Through this paper, we would like to point out a promising research direction in unified investigation and application of big data.


Evaluation on Feature Importance for Favorite Song Detection