title: See far with TPNET: a Tile Processor and a CNN Symbiosis

abstract: Throughout the evolution of the neural networks more specialized cells were added to the set of basic building blocks. These cells aim to improve training convergence, increase the overall performance, and reduce the number of required labels, all while preserving the expressive power of the universal network. Inspired by the partitioning of the human visual perception system between the eyes and the cerebral cortex, we present TPNET, which offloads universal and application-specific CNN from the bulk processing of the high resolution pixel data and performs the translation-variant image correction while delegating all non-linear decision making to the network. In this work, we explore application of TPNET to 3D perception with a narrow-baseline (0.0001-0.0025) quad stereo camera and prove that a trained network provides a disparity prediction from the 2D phase correlation output by the Tile Processor (TP) that is twice as accurate as the prediction from a carefully hand-crafted algorithm. The TP in turn reduces the dimensions of the input features of the network and provides instrument-invariant and translation-invariant data, making real-time high resolution stereo 3D perception feasible and easing the requirement to have a complete end-to-end network.


Practical Malware Analysis: The Hands-On Guide to Dissecting Malicious Software


Biobanking from the patient perspective


title: Belief Propagation by Message Passing in Junction Trees: Computing Each Message Faster Using GPU Parallelization

abstract: Compiling Bayesian networks (BNs) to junction trees and performing belief propagation over them is among the most prominent approaches to computing posteriors in BNs. However, belief propagation over junction tree is known to be computationally intensive in the general case. Its complexity may increase dramatically with the connectivity and state space cardinality of Bayesian network nodes. In this paper, we address this computational challenge using GPU parallelization. We develop data structures and algorithms that extend existing junction tree techniques, and specifically develop a novel approach to computing each belief propagation message in parallel. We implement our approach on an NVIDIA GPU and test it using BNs from several applications. Experimentally, we study how junction tree parameters affect parallelization opportunities and hence the performance of our algorithm. We achieve speedups ranging from 0.68 to 9.18 for the BNs studied.


title: Learning the Latent “Look”: Unsupervised Discovery of a Style-Coherent Embedding from Fashion Images

abstract: What defines a visual style? Fashion styles emerge organically from how people assemble outfits of clothing, making them difficult to pin down with a computational model. Low-level visual similarity can be too specific to detect stylistically similar images, while manually crafted style categories can be too abstract to capture subtle style differences. We propose an unsupervised approach to learn a style-coherent representation. Our method leverages probabilistic polylingual topic models based on visual attributes to discover a set of latent style factors. Given a collection of unlabeled fashion images, our approach mines for the latent styles, then summarizes outfits by how they mix those styles. Our approach can organize galleries of outfits by style without requiring any style labels. Experiments on over 100K images demonstrate its promise for retrieving, mixing, and summarizing fashion images by their style.


title: Protecting respondents’ identities in microdata release

abstract: Today's globally networked society places great demands on the dissemination and sharing of information. While in the past released information was mostly in tabular and statistical form, many situations call for the release of specific data (microdata). In order to protect the anonymity of the entities (called respondents) to which information refers, data holders often remove or encrypt explicit identifiers such as names, addresses, and phone numbers. Deidentifying data, however, provides no guarantee of anonymity. Released information often contains other data, such as race, birth date, sex, and ZIP code, that can be linked to publicly available information to reidentify respondents and inferring information that was not intended for disclosure. In this paper we address the problem of releasing microdata while safeguarding the anonymity of respondents to which the data refer. The approach is based on the definition of k-anonymity. A table provides k-anonymity if attempts to link explicitly identifying information to its content map the information to at least k entities. We illustrate how k-anonymity can be provided without compromising the integrity (or truthfulness) of the information released by using generalization and suppression techniques. We introduce the concept of minimal generalization that captures the property of the release process not distorting the data more than needed to achieve k-anonymity, and present an algorithm for the computation of such a generalization. We also discuss possible preference policies to choose among different minimal generalizations.


title: Using Suffix Arrays to Compute Term Frequency and Document Frequency for All Substrings in a Corpus

abstract: Bigrams and trigrams are commonly used in statistical natural language processing; this paper will describe techniques for working with much longer n-grams. Suffix arrays (Manber and Myers 1990) were first introduced to compute the frequency and location of a substring (n-gram) in a sequence (corpus) of length N. To compute frequencies over all N(N + 1)/2 substrings in a corpus, the substrings are grouped into a manageable number of equivalence classes. In this way, a prohibitive computation over substrings is reduced to a manageable computation over classes. This paper presents both the algorithms and the code that were used to compute term frequency (tf) and document frequency (dr)for all n-grams in two large corpora, an English corpus of 50 million words of Wall Street Journal and a Japanese corpus of 216 million characters of Mainichi Shimbun.The second half of the paper uses these frequencies to find "interesting" substrings. Lexicographers have been interested in n-grams with high mutual information (MI) where the joint term frequency is higher than what would be expected by chance, assuming that the parts of the n-gram combine independently. Residual inverse document frequency (RIDF) compares document frequency to another model of chance where terms with a particular term frequency are distributed randomly throughout the collection. MI tends to pick out phrases with noncompositional semantics (which often violate the independence assumption) whereas RIDF tends to highlight technical terminology, names, and good keywords for information retrieval (which tend to exhibit nonrandom distributions over documents). The combination of both MI and RIDF is better than either by itself in a Japanese word extraction task.


title: A low-power accelerator for the SPHINX 3 speech recognition system

abstract: Accurate real-time speech recognition is not currently possible in the mobile embedded space where the need for natural voice interfaces is clearly important. The continuous nature of speech recognition coupled with an inherently large working set creates significant cache interference with other processes. Hence real-time recognition is problematic even on high-performance general-purpose platforms. This paper provides a detailed analysis of CMU's latest speech recognizer (Sphinx 3.2), identifies three distinct processing phases, and quantifies the architectural requirements for each phase. Several optimizations are then described which expose parallelism and drastically reduce the bandwidth and power requirements for real-time recognition. A special-purpose accelerator for the dominant Gaussiann probability phase is developed for a 0.25μ CMOS process which is then analyzed and compared with Sphinx's measured energy and performance on a 0.13μ 2.4 GHz Pentium 4 system. The results show an improvement in power consumption by a factor of 29 at equivalent processing throughput. However after normalizing for process, the special-purpose approach has twice the throughput, and consumes 104 times less energy than the general-purpose processor. The energy-delay product is a better comparison metric due to the inherent design trade-offs between energy consumption and performance. The energy-delay product of the special-purpose approach is 196 times better than the Pentium 4. These results provide strong evidence that real-time large vocabulary speech recognition can be done within a power budget commensurate with embedded processing using today's technology.


title: Mutual Hypothesis Verification for 6D Pose Estimation of Natural Objects

abstract: Estimating the 6D pose of natural objects, such as vegetables and fruit, is a challenging problem due to the high variability of their shape. The shape variation limits the accuracy of previous pose estimation approaches because they assume that the training model and the object in the target scene have the exact same shape. To overcome this issue, we propose a novel framework that consists of a local and a global hypothesis generation pipeline with a mutual verification step. The new local descriptor is proposed to find critical parts of the natural object while the global estimator calculates object pose directly. To determine the best pose estimation result, a novel hypothesis verification step, Mutual Hypothesis Verification, is proposed. It interactively uses information from the local and the global pipelines. New hypotheses are generated by setting the initial pose using the global estimation and guiding an iterative closest point refinement using local shape correspondences. The confidence of a pose candidate is calculated by comparing with estimation results from both pipelines. We evaluate our framework with real fruit randomly piled in a box. The potential for estimating the pose of any natural object is proved by the experimental results that outperform global feature based approaches.


title: Customer engagement in an online social platform: A conceptual model and scale development

abstract: The rise of new media channels in the last few years has changed the ways customers communicate and exchange information. Managing customer relationship through social media engagement has become an important component of successful electronic marketing strategies. Though the concept of customer engagement has received considerable attention in the business community, there is a paucity of theory-guided empirical research in customer engagement in an online social platform. More importantly, there is a lack of consensus regarding its definition, forms, dimensionality, and operationalization. This study addresses these limitations in part by theoretically conceptualizing and empirically validating a scale to measure customer engagement in an online social platform. In this research-in-progress paper, we present our conceptual model of customer engagement and propose a rigorous approach in scale development and validation. The authors believe that this research will contribute to the development of online community literature and add to the repository of rigorous research instruments for researchers to use.


title: Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations

abstract: The authors address the role of marketing in hypermedia computer-mediated environments (CMEs). Their approach considers hypermedia CMEs to be large-scale (i.e., national or global) networked enviro...


title: Improving Intrusion Detection System based on Snort rules for network probe attack detection

abstract: Data and network system security is the most important roles. An organization should find the methods to protect their data and network system to reduce the risk from attacks. Snort Intrusion Detection System (Snort-IDS) is a security tool of network security. It has been widely used for protecting the network of the organizations. The Snort-IDS utilize the rules to matching data packets traffic. If some packet matches the rules, Snort-IDS will generate the alert messages. However, Snort-IDS contain many rules and it also generates a lot of false alerts. In this paper, we present the procedure to improve the Snort-IDS rules for the network probe attack detection. In order to test the performance evaluation, we utilized the data set from the MIT-DAPRA 1999, which includes the normal and abnormal traffics. Firstly, we analyzed and explored the existing the Snort-IDS rules to improve the proposed Snort-IDS rules. Secondly, we applied the WireShark software to analyze data packets form of attack in data set. Finally, the Snort-IDS was improved, and it can detect the network probe attack. This paper, we had classified the attacks into several groups based on the nature of network probe attack. In addition, we also compared the efficacy of detection attacks between Snort-IDS rules to be updated with the Detection Scoring Truth. As the experimental results, the proposed Snort-IDS efficiently detected the network probe attacks compared to the Detection Scoring Truth. It can achieve higher accuracy. However, there were some detecting alert that occur over the attack in Detection Scoring Truth, because some attack occur in several time but the Detection Scoring Truth indentify as one time.


title: Operational Risk Management Based on Bayesian MCMC

abstract: The aim of this paper is to introduce a new framework for operational risk management, based on Bayesian Markov chain Monte Carlo (MCMC). Under the LDA approach, non-conjugate distribution is used to fit the frequency and severity. One of the problems relative to the non-conjugate distribution is difficult to estimate the parameter. Then the Bayesian MCMC approach is brought forward. The Bayesian is implemented to obtain the posterior of non-conjugate distribution, the MCMC algorithm is employed to estimate the posterior parameters. The Bayesian MCMC framework is strongly recommended in the operational risk management as it incorporate internal and external loss data observations in combination with expert opinion. A numerical example is constructed to illustrate the performance of the framework advocated by this paper.


title: Arbitrarily shaped formations of mobile robots: artificial potential fields and coordinate transformation

abstract: In this paper we describe a novel decentralized control strategy to realize formations of mobile robots. We first describe how to design artificial potential fields to obtain a formation with the shape of a regular polygon. We provide a formal proof of the asymptotic stability of the system, based on the definition of a proper Lyapunov function. We also prove that our control strategy is not affected by the problem of local minima. Then, we exploit a bijective coordinate transformation to deform the polygonal formation, thus obtaining a completely arbitrarily shaped formation. Simulations and experimental tests are provided to validate the control strategy.


title: Aiding the Audit: Using the IT Audit as a Springboard for Continuous Controls Monitoring

abstract: Demand for continuous monitoring of internal controls by large firms has increased in recent years as enterprise resource planning (ERP) systems have become more robust and monitoring platforms have matured. The question remains of how to effectively implement the continuous controls monitoring (CCM) model. We followed the IT audit of a large, recently acquired software company and worked with the internal audit team to create an set of automated rules based on the parent company's existing audit program. By the end of the systems audit, rules for approximately 63% of the controls tests were successfully automated. Additionally, due to the homogeneity of the parent company's information systems, the sets of rules and reports created can be easily scaled up for use with other subsidiaries without the need to repeat the entire creation process. This paper details the benefits and limitations of using the IT audit as a model for CCM implementation.


Oriya Language Text Mining Using C5.0 Algorithm


title: Language-based security on Android

abstract: In this paper, we initiate a formal study of security on Android: Google's new open-source platform for mobile devices. Specifically, we present a core typed language to describe Android applications, and to reason about their data-flow security properties. Our operational semantics and type system provide some necessary foundations to help both users and developers of Android applications deal with their security concerns.


title: Sorption of inorganic nanoparticles in woven cellulose fabrics

abstract: Abstract Titanium dioxide was deposited from aqueous suspension onto cellulosic surfaces. Titania was sourced from Degussa (P25™, 70:30 anatase:rutile). Dry uptake of particles was shown to be rapid and dominant with one-third of the deposition occurring in less than 30 s and over one-half in the first minute. Isotherms were recorded to compare the rate of titanium deposition on dry and pre-wetted cotton. In the dry case uptake reached a maximum in 30 min whereas in the pre-wetted case the uptake was seen to continue beyond 180 min. A broad trend of higher deposition occurring at lower pH was seen, corresponding to the region where surface charges were opposite and thus attractive. Dry pickup was less significant at high pH. The response to varying ionic strength was complex and was attributed to the combined effect of charge screening, particle aggregation and consequent particle entrapment or occlusion. Titania deposition into the interstices of woven cotton sheets resulted in the formation of inorganic, nanoparticulate skeletons which could be isolated by controlled combustion of the cellulose and thus cotton was suggested to have potential for the templated synthesis of high surface area semiconductor materials.


title: Automated rule selection for aspect extraction in opinion mining

abstract: Aspect extraction aims to extract fine-grained opinion targets from opinion texts. Recent work has shown that the syntactical approach, which employs rules about grammar dependency relations between opinion words and aspects, performs quite well. This approach is highly desirable in practice because it is unsupervised and domain independent. However, the rules need to be carefully selected and tuned manually so as not to produce too many errors. Although it is easy to evaluate the accuracy of each rule automatically, it is not easy to select a set of rules that produces the best overall result due to the overlapping coverage of the rules. In this paper, we propose a novel method to select an effective set of rules. To our knowledge, this is the first work that selects rules automatically. Our experiment results show that the proposed method can select a subset of a given rule set to achieve significantly better results than the full rule set and the existing state-of-the-art CRF-based supervised method.


title: Design of multiband fractal antenna for IRNSS and GAGAN applications

abstract: In this paper, a simple self similar multi fractal cantor antenna is designed for IRNSS and GAGAN applications in India. A new navigational system which is developed and controlled by Indian government is called Indian Regional Navigational satellite system (IRNSS). IRNSS simply replaces the role of Global Positioning System in India by which, it provides integrity, availability, continuity and selectivity requirements. A new augmentation system for India, erect over the GPS system is expected to provide continuous navigation support over the Asia-Pacific regions is called GPS Aided Geo Augmented Navigation system (GAGAN) which is under the total control of Airport Authority of India (AAI). This paper describes a fractal cantor antenna on FR-4 substrate with the dielectric constant of 4.8 and the substrate height of about 1.6mm. These specifications should satisfied at the frequencies of L5 (1175 MHz), L1 (1575.42MHz) and S (2492.08MHz). The simulations are carried out through ADS software.


title: Review and comparison of face detection algorithms

abstract: With the tremendous increase in video and image database there is a great need of automatic understanding and examination of data by the intelligent systems as manually it is becoming out of reach. Narrowing it down to one specific domain, one of the most specific objects that can be traced in the images are people i.e. faces. Face detection is becoming a challenge by its increasing use in number of applications. It is the first step for face recognition, face analysis and detection of other features of face. In this paper, various face detection algorithms are discussed and analyzed like Viola-Jones, SMQT features & SNOW Classifier, Neural Network-Based Face Detection and Support Vector Machine-Based face detection. All these face detection methods are compared based on the precision and recall value calculated using a DetEval Software which deals with precised values of the bounding boxes around the faces to give accurate results.


title: Adaptation to Information Technology: A Holistic Nomological Network from Implementation to Job Outcomes

abstract: Information technology (IT) implementation is a major organizational change event that substantially disrupts an employee’s work environment. We develop a model of technology adaptation behaviors that employees perform to cope with a new IT that causes such disruptions. Our model posits technology adaptation behaviors as a key linking mechanism between IT implementation and employee job outcomes, thus offering a holistic nomological network of technology adaptation behaviors. Two field studies conducted over a period of six months, with four waves of data collection each, in two organizations ( N = 211 and N = 181) implementing two different ITs, supported the model. We found that employees performed four different technology adaptation behaviors—exploration-to-innovate, exploitation, exploration-to-revert, and avoidance—based on whether they appraised an IT as an opportunity or a threat and whether they had perceptions of control over an IT. Employees’ experiential engagements (i.e., user participation and training effectiveness) and psychological engagements (i.e., user involvement and management support) during the implementation jointly determined their appraisal of an IT. Finally, we found that technology adaptation behaviors influenced changes in two key job outcomes, job performance and job satisfaction. This paper was accepted by Sandra Slaughter, information systems .


Twitter Named Entity Extraction and Linking Using Differential Evolution.


title: Towards better understanding of academic search

abstract: Academics have relied heavily on search engines to identify and locate research manuscripts that are related to their research areas. Many of the early information retrieval sys- tems and technologies were developed while catering for li- brarians to help them sift through books and proceedings, followed by recent online academic search engines such as Google Scholar and Microsoft Academic Search. In spite of their popularity among academics and importance to academia, the usage, query behaviors, and retrieval models for aca- demic search engines have not been well studied. To this end, we study the distribution of queries that are received by an academic search engine. Furthermore, we delve deeper into academic search queries and classify them into navigational and informational queries. This work in- troduces a definition for navigational queries in academic search engines under which a query is considered naviga- tional if the user is searching for a specific paper or docu- ment. We describe multiple facets of navigational academic queries, and introduce a machine learning approach with a set of features to identify such queries.


Adaptive Edge Detection for Real-Time Video Processing using FPGAs I.


title: Stroop effect in Spanish–English bilinguals

abstract: The aim of this study was to analyze the performance of Spanish‐English bilinguals on the Golden Stroop Test. The effects of bilingualism, participants’ age, age of acquisition of the second language, and proficiency in each language were analyzed. Participants consisted of 71 Spanish‐English bilinguals, 40 English monolinguals, and 11 Spanish monolinguals from South Florida. Proficiency in Spanish and English was established using a self-report questionnaire and the English and Spanish versions of the Boston Naming Test. In bilinguals, the Golden Stroop Test was administered in English and in Spanish. Overall, performance was slower in bilinguals than in monolinguals. No significant differences were observed in color reading but bilinguals performed worse in the naming color condition. Even though bilinguals were 5% to 10% slower in the color‐word condition, one-way ANOVAs revealed no statistically significant differences between groups. Within the bilingual group, the Stroop Test scores were similar in both English and Spanish. Age of acquisition of the second language did not predict the Stroop Test performance. Repeated measures ANOVA demonstrated a significant interaction between Language Proficiency 3 Language (in which the test was administered) in some of the ST conditions. In balanced bilinguals, the language used in the ST did not matter, but in unbalanced subjects, the best-spoken language showed better results. In addition, our results support the presence of both between- and within-language interference in Spanish‐English bilinguals. Different conceptualization models of the structure of bilingual memory are disclosed. (JINS, 2002, 8, 819‐827.)


title: Using learning analytics to assess students' behavior in open-ended programming tasks

abstract: There is great interest in assessing student learning in unscripted, open-ended environments, but students' work can evolve in ways that are too subtle or too complex to be detected by the human eye. In this paper, I describe an automated technique to assess, analyze and visualize students learning computer programming. I logged hundreds of snapshots of students' code during a programming assignment, and I employ different quantitative techniques to extract students' behaviors and categorize them in terms of programming experience. First I review the literature on educational data mining, learning analytics, computer vision applied to assessment, and emotion detection, discuss the relevance of the work, and describe one case study with a group undergraduate engineering students


title: Visualization & assessment of ADS-B security for green ATM

abstract: Energy efficiency, noise and emissions footprint reduction are major “green” performance targets of next-generation air transportation systems. Automatic Dependent Surveillance Broadcast (ADS-B) is an advanced “cyber-physical” concept that can help meet such targets by enabling highly accurate air traffic surveillance. However, beneficial, confident application of ADS-B requires a foundational understanding of vulnerabilities and mitigation requirements. This paper focuses on the assessment and mitigation of threats from unauthorized access and unanticipated disruption of ADS-B communications. We present a solution to protect message integrity in ADS-B IN based airborne surveillance, discuss the accuracy and robustness of the solution in the presence of spoofed or compromised nodes and intentional jamming. To study effects of “intelligent jamming” strategies on ADS-B, visualize and analyze system-wide impact, we propose a security simulation tool concept and design.


title: YGGDRASIL - A Statistical Package for Learning Split Models

abstract: There are two main objectives of this paper. The first is to present a statistical framework for models with context specific independence structures, i.e. conditional independencies holding only for specific values of the conditioning variables. This framework is constituted by the class of split models. Split models are an extension of graphical models for contingency tables and allow for a more sophisticated modelling than graphical models. The treatment of split models include estimation, representation and a Markov property for reading off those independencies holding in a specific context. The second objective is to present a software package named YGGDRASIL which is designed for statistical inference in split models, i.e. for learning such models on the basis of data.


: Using Live Streaming to Evade Internet Censorship


title: Why to decouple the uplink and downlink in cellular networks and how to do it

abstract: Ever since the inception of mobile telephony, the downlink and uplink of cellular networks have been coupled, that is, mobile terminals have been constrained to associate with the same base station in both the downlink and uplink directions. New trends in network densification and mobile data usage increase the drawbacks of this constraint, and suggest that it should be revisited. In this article we identify and explain five key arguments in favor of downlink/uplink decoupling based on a blend of theoretical, experimental, and architectural insights. We then overview the changes needed in current LTE-A mobile systems to enable this decoupling, and then look ahead to fifth generation cellular standards. We demonstrate that decoupling can lead to significant gains in network throughput, outage, and power consumption at a much lower cost compared to other solutions that provide comparable or lower gains.


title: Systems and Techniques 1 Articles

abstract: This article reviews research in the development of plan generation systems. Our goal is to familiarize the reader with some of the important problems that have arisen in the design of planning systems and to discuss some of the many solutions that have been developed in the over 30 years of research in this area. In this article, we broadly cover the major ideas in the field of AI planning and show the direction in which some current research is going. We define some of the terms commonly used in the planning literature, describe some of the basic issues coming from the design of planning systems, and survey results in the area. Because such tasks are virtually never ending, and thus, any finite document must be incomplete, we provide references to connect each idea to the appropriate literature and allow readers access to the work most relevant to their own research or applications.


title: Semantic Learning Machine: A Feedforward Neural Network Construction Algorithm Inspired by Geometric Semantic Genetic Programming

abstract: Geometric Semantic Genetic Programming (GSGP) is a recently proposed form of Genetic Programming in which the fitness landscape seen by its variation operators is unimodal with a linear slope by construction and, consequently, easy to search. This is valid across all supervised learning problems. In this paper we propose a feedforward Neural Network construction algorithm derived from GSGP. This algorithm shares the same fitness landscape as GSGP, which allows an efficient search to be performed on the space of feedforward Neural Networks, without the need to use backpropagation. Experiments are conducted on real-life multidimensional symbolic regression datasets and results show that the proposed algorithm is able to surpass GSGP, with statistical significance, in terms of learning the training data. In terms of generalization, results are similar to GSGP.


title: Stereoscopic Dark Flash for Low-light Photography

abstract: In this work, we present a camera configuration for acquiring "stereoscopic dark flash" images: a simultaneous stereo pair in which one camera is a conventional RGB sensor, but the other camera is sensitive to near-infrared and near-ultraviolet instead of R and B. When paired with a "dark" flash (i.e., one having near-infrared and near-ultraviolet light, but no visible light) this camera allows us to capture the two images in a flash/no-flash image pair at the same time, all while not disturbing any human subjects or onlookers with a dazzling visible flash. We present a hardware prototype of this camera that approximates an idealized camera, and we present an imaging procedure that let us acquire dark flash stereo pairs that closely resemble those we would get from that idealized camera. We then present a technique for fusing these stereo pairs, first by performing registration and warping, and then by using recent advances in hyperspectral image fusion and deep learning to produce a final image. Because our camera configuration and our data acquisition process allow us to capture true low-noise long exposure RGB images alongside our dark flash stereo pairs, our learned model can be trained end-to-end to produce a fused image that retains the color and tone of a real RGB image while having the low-noise properties of a flash image.


title: Vision-based human tracking and activity recognition

abstract: A series of mono, bi and tricarbocyclic compounds, most of which have olefinic unsaturation in the ring, which may or may not have substituents thereon. While the bi and tricyclic rings may be unsubstituted, these compounds which have olefinic unsaturation, particularly multiple olefinic unsaturation, are polymerizable and copolymerizable in known polymerization systems. They are particularly good crosslinking agents. These compounds are further useful in the sense that they can be cleaved oxidatively, to corresponding carboxylic acids, aldehydes and/or alcohols which have known utility in the plasticizer and detergent arts. The compounds which do not have olefinic unsaturation can also be oxidatively cleaved to produce oxygenated, e.g., acid, alcohol or aldehyde, compounds having known utility.


title: Information-Based Objective Functions for Active Data Selection

abstract: Learning can be made more efficient if we can actively select particularly salient data points. Within a Bayesian learning framework, objective functions are discussed that measure the expected informativeness of candidate measurements. Three alternative specifications of what we want to gain information about lead to three different criteria for data selection. All these criteria depend on the assumption that the hypothesis space is correct, which may prove to be their main weakness.


title: Welding parameter optimization based on Gaussian process regression Bayesian optimization algorithm

abstract: In welding processes, welding parameters have a significant impact on weld quality and mechanical properties of welded joints. For example, if the welding current is not tuned properly, the welding arc becomes unstable which will cause an unacceptable weld. Therefore welding parameters must be optimized in order to achieve best weld quality. However current methods have many limitations in exploring optimal welding parameters. In this paper, Gaussian Process Regression is applied to model the relationship between the welding performance indices and welding parameters. Bayesian Optimization Algorithm is adopted to balance the modeling and optimization processes and optimize welding parameters. Experiments were performed for the Gas tungsten arc welding (GTAW) process and the results demonstrate the effectiveness of the proposed algorithm. Compared to the existing methods, the proposed method greatly improves the welding parameter optimization process; moreover it can be applied with fewer experiments compared with existing methods which will reduce the testing cost and effort.


title: Cloudlet Mesh for Securing Mobile Clouds from Intrusions and Network Attacks

abstract: This paper presents a new cloudlet mesh architecture for security enforcement to establish trusted mobile cloud computing. The cloudlet mesh is WiFi-or mobile-connected to the Internet. This security framework establishes a cyber trust shield to fight against intrusions to distance clouds, prevent spam/virus/worm attacks on mobile cloud resources, and stop unauthorized access of shared datasets in offloading the cloud. We have specified a sequence of authentication, authorization, and encryption protocols for securing communications among mobile devices, cloudlet servers, and distance clouds. Some analytical and experimental results prove the effectiveness of this new security infrastructure to safeguard mobile cloud services.


title: F-quantum spaces and their dynamics

abstract: Abstract F-quantum space has been suggested by Riecan as an alternative model for quantum mechanics. In this paper, we study the dynamics of F-quantum spaces.


title: Efficient large-scale multi-view stereo for ultra high-resolution image sets

abstract: We present a new approach for large-scale multi-view stereo matching, which is designed to operate on ultra high-resolution image sets and efficiently compute dense 3D point clouds. We show that, using a robust descriptor for matching purposes and high-resolution images, we can skip the computationally expensive steps that other algorithms require. As a result, our method has low memory requirements and low computational complexity while producing 3D point clouds containing virtually no outliers. This makes it exceedingly suitable for large-scale reconstruction. The core of our algorithm is the dense matching of image pairs using DAISY descriptors, implemented so as to eliminate redundancies and optimize memory access. We use a variety of challenging data sets to validate and compare our results against other algorithms.


title: Real-time deep virtual machine introspection and its applications

abstract: Virtual Machine Introspection (VMI) provides the ability to monitor virtual machines (VM) in an agentless fashion by gathering VM execution states from the hypervisor and analyzing those states to extract information about a running operating system (OS) without installing an agent inside the VM. VMI's main challenge lies in the difficulty in converting low-level byte string values into high-level semantic states of the monitored VM's OS. In this work, we tackle this challenge by developing a real-time kernel data structure monitoring (RTKDSM) system that leverages the rich OS analysis capabilities of Volatility, an open source computer forensics framework, to significantly simplify and automate analysis of VM execution states. The RTKDSM system is designed as an extensible software framework that is meant to be extended to perform application-specific VM state analysis. In addition, the RTKDSM system is able to perform real-time monitoring of any changes made to the extracted OS states of guest VMs. This real-time monitoring capability is especially important for VMI-based security applications. To minimize the performance overhead associated with real-time kernel data structure monitoring, the RTKDSM system has incorporated several optimizations whose effectiveness is reported in this paper.


title: IMMUNOHISTOCHEMICAL DEMONSTRATION OF SEROTONIN IN NERVES SUPPLYING HUMAN CEREBRAL AND MESENTERIC BLOOD-VESSELS: Some Speculations about their Involvement in Vascular Disorders

abstract: Serotonin (5-hydroxytryptamine, 5-HT) can produce vasoconstriction or vasodilatation and has been implicated in the pathogenesis of many vascular disorders. Nerves containing 5-HT were demonstrated by immunofluorescence histochemistry in fetal cerebral and mesenteric blood-vessels.


Deterrence in criminology and social policy.


title: A Review on Different Approaches for Speech Recognition System

abstract: This paper presents the basic idea of speech recognition, proposed types of speech recognition, issues in speech recognition, different useful approaches for feature extraction of the speech signal with its advantage and disadvantage and various pattern matching approaches for recognizing the speech of the different speaker. Now day’s research in speech recognition system is motivated for ASR system with a large vocabulary that supports speaker independent operations and continuous speech in different language.


title: PUSHOVER ANALYSIS OF REINFORCED CONCRETE FRAME STRUCTURES: A CASE STUDY

abstract: Recent earthquakes in which many concrete structures have been severely damaged or collapsed, have indicated the need for evaluating the seismic adequacy of existing buildings. About 60% of the land area of our country is susceptible to damaging levels of seismic hazard. We can’t avoid future earthquakes, but preparedness and safe building construction practices can certainly reduce the extent of damage and loss. In order to strengthen and resist the buildings for future earthquakes, some procedures have to be adopted. One of the procedures is the nonlinear static pushover analysis which is becoming a popular tool for seismic performance evaluation of existing and new structures. By conducting this push over analysis, we can know the weak zones in the structure and then we will decide whether the particular part is to be retrofitted or rehabilitated according to the requirement. In the present study the push over analysis is performed on multistoried frame structures by using most common software SAP2000 (version 14). To achieve this objective, two framed buildings with 5 and 12 stories respectively were analyzed. The results obtained from this study show that properly designed frames will perform well under seismic loads.


title: Ultrasound Absorption in Trifluoroacetic Acid and its Equimolar Mixture with Acetic Acid

abstract: Abstract Ultrasound absorption has been measured in pure trifluoroacetic acid and its approximately equimolar mixture with acetic acid (51.5 mole% of acetic acid) at 20, 30, 40, and 50 °C and atmospheric pressure over the frequency range 3 - 57 MHz. The absorption curve of trifluoroacetic acid could be flitted by a single relaxation step (relaxation frequency 3.5 - 11 MHz, low frequency absorption about one tenth of that of acetic acid), whereas the equimolar mixture followed a two step relaxation curve (lower relaxation frequency 1.5 - 3 MHz, absorption correspondingly higher than in pure trifluoroacetic acid, higher relaxation frequency 12.5 - 39 MHz, absorption about 3% of the low frequency absorption).


title: Probabilistic Generation of Weather Forecast Texts

abstract: This paper reports experiments in which pC RU — a generation framework that combines probabilistic generation methodology with a comprehensive model of the generation space — is used to semi-automatically create several versions of a weather forecast text generator. The generators are evaluated in terms of output quality, development time and computational efficiency against (i) human forecasters, (ii) a traditional handcrafted pipelined NLG system, and (iii) a HALOGEN-style statistical generator. The most striking result is that despite acquiring all decision-making abilities automatically, the best pC RU generators receive higher scores from human judges than forecasts written by experts.


title: Pragmatic model of patient satisfaction in general practice: progress towards a theory.

abstract: A major problem in the measurement of patient satisfaction is the lack of an adequate theory to explain the meaning of satisfaction, and hence how it should be measured and how the findings are interpreted. Because of the lack of a fully developed theory, when developing patient satisfaction questionnaires for use in general practice, a simple model was used. This model was pragmatic in that it linked together empirical evidence about patient satisfaction without recourse to more general social or psychological theory of behaviour, other than to define satisfaction as an attitude. Several studies with the questionnaires confirm in general the components of the model. However, the importance of personal care had not been sufficiently emphasised, and therefore the model has been revised. It can now serve as a basis for future research into patient satisfaction, in particular as a stimulus for investigating the links between components of the model and underlying psychological or other behavioural theories.


title: Document Structure Analysis and Text Normalization for Chinese Putonghua and Cantonese Text-to-Speech Synthesis

abstract: This paper describes our recent effort on document structure analysis (DSA) and text normalization (NORM) for Chinese Putonghua and Cantonese text-to-speech synthesis. A unified framework has been proposed, where DSA and NORM procedures are language-independent for the two-dialects of Chinese. For document structure analysis, regular expressions have been utilized to detect and identify the non-standard-words (NSWs) and punctuations related to document structure; a new document segmentation approach is then proposed by considering the information provided by NSWs and punctuations. For text normalization, a method which considers the contextual information is put forward to handle the ambiguity of the NSWs, symbols and punctuations.


title: A Vegetable Category Recognition System Using Deep Neural Network

abstract: In recent years, Ambient Intelligence (AmI) has attracted increased attention within the advanced technology industry in an effort to modernize and develop a more intelligent and reliable information system. Technologies to detect a specific object in images are expected to further expand to wide range of applications, such as car detection functions for intelligent transport system and other systems. Computer vision and pattern recognition are emerging fast and will continue to grow together with local feature detection methods. In this paper, we used the Deep Neural Network (DNN) for object category recognition by extracting and learning the object. We applied deep learning to vegetable object recognition, and explored the Convolutional Neural Network (CNN). From the evaluation results, we found that for vegetable recognition learning process with CNN, 3 million iterations were suitable. The results of learning rate was 99.14% and recognition rate was 97.58%, respectively.


title: Real-Time Strategy Game Competitions

abstract: In recent years, real-time strategy (RTS) games have gained attention in the AI research community for their multitude of challenging and relevant real-time decision problems that have to be solved in order to win against human experts or to effectively collaborate with other players in team-games. In this article we motivate research in this area, give an overview of past RTS game AI competitions, and discuss future directions.


title: Compact Representation of Dynamic Driving Environments for ADAS by Parametric Free Space and Dynamic Object Maps

abstract: We present a novel parametric representation of general dynamic driving environments. It is particularly suitable for near-future Advanced Driver Assistance Systems due to its compactness, inherent consistency between static and dynamic entities, suppression of irrelevant details, as well as its sensor-independent, real-time capable generation. By building upon a common occupancy grid map-based environment representation, cells belonging to dynamic objects are simultaneously extracted, classified, and tracked in an object-based manner via an Interacting-MultipleModel-Unscented-Kalman-Probabilistic-Data-Association (IMM-UK-PDA) filter and cleared from the grid. The remaining static environment grid is subsequently processed by methods from the image analysis domain, followed by an Information Filter-based contour tracking of relevant free space boundaries to create a spatiotemporally smooth compact Parametric Free Space (PFS) map. The PFS map represents the static part of the traffic scene with modest bandwidth requirements. The system runs in real time on an experimental vehicle equipped with an automotive radar and a stereo camera and is evaluated within real traffic environments.


title: Linetic: Technical, Usability and Aesthetic Implications of a Ferrofluid-based Organic User Interface

abstract: We present an OUI that combines Hall Effect sensing and actuation through electromagnetically-manipulated ferrofluid. The movement of magnets worn on the fingertips, over a surface embedded with a Hall Effect sensor array and electromagnets, gives the user the ability to interact with ferrofluid. This system provides a three-dimensional, physically animated response, as well as three-dimensional, spatial-sensing inputs. The vibration of the magnets worn on the fingertips, produced by the repulsing polarity of the electromagnets, provides the user with haptic feedback. Linetic is a multimodal interface with a visual, audio and haptic experience. In this manuscript we explain the overall system from a technical, usability and aesthetic viewpoint by outlining significant experiments conducted that contribute to the development of the system. Furthermore we discuss the philosophical and aesthetical implications of the Linetic system, as well as characterize Linetic’s disposition to Analogness or Digitalness.


title: Optimizing Artificial Neural Networks using Cat Swarm Optimization Algorithm

abstract: An Artificial Neural Network (ANN) is an abstract representation of the biological nervous system which has the ability to solve many complex problems. The interesting attributes it exhibits makes an ANN capable of ―learning‖. ANN learning is achieved by training the neural network using a training algorithm. Aside from choosing a training algorithm to train ANNs, the ANN structure can also be optimized by applying certain pruning techniques to reduce network complexity. The Cat Swarm Optimization (CSO) algorithm, a swarm intelligence-based optimization algorithm mimics the behavior of cats, is used as the training algorithm and the Optimal Brain Damage (OBD) method as the pruning algorithm. This study suggests an approach to ANN training through the simultaneous optimization of the connection weights and ANN structure. Experiments performed on benchmark datasets taken from the UCI machine learning repository show that the proposed CSONN- OBD is an effective tool for training neural networks.


title: Unified Framework for Automated Iris Segmentation Using Distantly Acquired Face Images

abstract: Remote human identification using iris biometrics has high civilian and surveillance applications and its success requires the development of robust segmentation algorithm to automatically extract the iris region. This paper presents a new iris segmentation framework which can robustly segment the iris images acquired using near infrared or visible illumination. The proposed approach exploits multiple higher order local pixel dependencies to robustly classify the eye region pixels into iris or noniris regions. Face and eye detection modules have been incorporated in the unified framework to automatically provide the localized eye region from facial image for iris segmentation. We develop robust postprocessing operations algorithm to effectively mitigate the noisy pixels caused by the misclassification. Experimental results presented in this paper suggest significant improvement in the average segmentation errors over the previously proposed approaches, i.e., 47.5%, 34.1%, and 32.6% on UBIRIS.v2, FRGC, and CASIA.v4 at-a-distance databases, respectively. The usefulness of the proposed approach is also ascertained from recognition experiments on three different publicly available databases.


title: Extracting Interpretable Models from Matrix Factorization Models

abstract: Matrix factorization models have been successfully used in many real-world tasks, such as knowledge base completion and recommendation systems. However, explaining the causes that elicit a particular prediction by a manual inspection of its latent representations is a difficult task. In this paper we try to overcome this problem by exploring descriptive model classes in their ability to faithfully approximate the behavior of a pre-trained matrix factorization model. Crucially, our choice of descriptive model will allow us to provide an interpretable structured proof for each prediction of the original model. We compare the descriptive models in these two scopes: Fidelity and interpretability. We find that Bayesian network trees, a class of models that has not been considered for this purpose before, capture the matrix factorization model faithfully while providing multistep explanations of predictions.


title: Discovering Students' Navigation Paths in Moodle

abstract: In this paper, we apply clustering and process mining techniques to discover students’ navigation paths or trails in Moodle. We use data from 84 undergraduate Psychology students who followed an online course. Firstly, we group students using on Moodle’s usage data and the students’ final grades obtained in the course. Then, we apply process mining with each cluster/group of students separately in order to obtain more specific and accurate trails than using all logs together.


title: Low-Shot Learning with Imprinted Weights

abstract: Human vision is able to immediately recognize novel visual categories after seeing just one or a few training examples. We describe how to add a similar capability to ConvNet classifiers by directly setting the final layer weights from novel training examples during low-shot learning. We call this process weight imprinting as it directly sets weights for a new category based on an appropriately scaled copy of the embedding layer activations for that training example. The imprinting process provides a valuable complement to training with stochastic gradient descent, as it provides immediate good classification performance and an initialization for any further fine-tuning in the future. We show how this imprinting process is related to proxy-based embeddings. However, it differs in that only a single imprinted weight vector is learned for each novel category, rather than relying on a nearest-neighbor distance to training instances as typically used with embedding methods. Our experiments show that using averaging of imprinted weights provides better generalization than using nearest-neighbor instance embeddings.


Competiveness and the EMU: At the roots of the Euro-zone crisis


title: Improving the Architecture of an Autoencoder for Dimension Reduction

abstract: Dimension reduction is used by scientists to deal with huge amount of high-dimensional data because of the "curse of dimensionality". There exist many methods of dimension reduction, such as principal components analysis (PCA), Locally Linear Embedding (LLE), Stochastic Neighbor Embedding (SNE), etc. Auto encoder is also applied for dimension reduction recently. It uses deep learning to train the network and has been applied in image reconstruction successfully. However, one important problem in auto encoder application is how to find the best architecture of the network. In this paper, we propose an improved architecture of the auto encoder for dimension reduction. The experimental results show the effectiveness of the proposed method.


title: Multilingual representations for low resource speech recognition and keyword search

abstract: This paper examines the impact of multilingual (ML) acoustic representations on Automatic Speech Recognition (ASR) and keyword search (KWS) for low resource languages in the context of the OpenKWS15 evaluation of the IARPA Babel program. The task is to develop Swahili ASR and KWS systems within two weeks using as little as 3 hours of transcribed data. Multilingual acoustic representations proved to be crucial for building these systems under strict time constraints. The paper discusses several key insights on how these representations are derived and used. First, we present a data sampling strategy that can speed up the training of multilingual representations without appreciable loss in ASR performance. Second, we show that fusion of diverse multilingual representations developed at different LORELEI sites yields substantial ASR and KWS gains. Speaker adaptation and data augmentation of these representations improves both ASR and KWS performance (up to 8.7% relative). Third, incorporating un-transcribed data through semi-supervised learning, improves WER and KWS performance. Finally, we show that these multilingual representations significantly improve ASR and KWS performance (relative 9% for WER and 5% for MTWV) even when forty hours of transcribed audio in the target language is available. Multilingual representations significantly contributed to the LORELEI KWS systems winning the OpenKWS15 evaluation.


title: Classroom Management: A Critical Part of Educational Psychology, With Implications for Teacher Education

abstract: Research on classroom management is reviewed, with an emphasis on lines of inquiry originating in educational psychology with implications for teacher education. Preventive, group based approaches to management provide a basis for teachers to plan and organize classroom activities and behaviors. Studies of teacher expertise and affect provide additional perspective on teacher development and on factors that influence management. Cooperative learning activities and inclusion of children with special needs illustrate particular contexts that affect management. Utilization of classroom management content in educational psychology components of teacher preparation is discussed.


Grounded Semantic Role Labeling


title: Vessel Bend-Based Cup Segmentation in Retinal Images

abstract: In this paper, we present a method for cup boundary detection from monocular colour fundus image to help quantify cup changes. The method is based on anatomical evidence such as vessel bends at cup boundary, considered relevant by glaucoma experts. Vessels are modeled and detected in a curvature space to better handle inter-image variations. Bends in a vessel are robustly detected using a region of support concept, which automatically selects the right scale for analysis. A reliable subset called r-bends is derived using a multi-stage strategy and a local splinetting is used to obtain the desired cup boundary. The method has been successfully tested on 133 images comprising 32 normal and 101 glaucomatous images against three glaucoma experts. The proposed method shows high sensitivity in cup to disk ratio-based glaucoma detection and local assessment of the detected cup boundary shows good consensus with the expert markings.


title: Factors influencing pain management in children

abstract: Aim: To identify factors that influence nurses' pain management in children.Method: A qualitative design was used. Twenty-one nurses working in one paediatric department were interviewed using semi ...


title: Artificial general intelligence through recursive data compression and grounded reasoning: a position paper

abstract: This paper presents a tentative outline for the construction of an artificial, generally intelligent system (AGI). It is argued that building a general data compression algorithm solving all problems up to a complexity threshold should be the main thrust of research. A measure for partial progress in AGI is suggested. Although the details are far from being clear, some general properties for a general compression algorithm are fleshed out. Its inductive bias should be flexible and adapt to the input data while constantly searching for a simple, orthogonal and complete set of hypotheses explaining the data. It should recursively reduce the size of its representations thereby compressing the data increasingly at every iteration. ::: Abstract Based on that fundamental ability, a grounded reasoning system is proposed. It is argued how grounding and flexible feature bases made of hypotheses allow for resourceful thinking. While the simulation of representation contents on the mental stage accounts for much of the power of propositional logic, compression leads to simple sets of hypotheses that allow the detection and verification of universally quantified statements. ::: Abstract Together, it is highlighted how general compression and grounded reasoning could account for the birth and growth of first concepts about the world and the commonsense reasoning about them.


title: A theory of plenoptic multiplexing

abstract: Multiplexing is a common technique for encoding high-dimensional image data into a single, two-dimensional image. Examples of spatial multiplexing include Bayer patterns to capture color channels, and integral images to encode light fields. In the Fourier domain, optical heterodyning has been used to acquire light fields. In this paper, we develop a general theory of multiplexing the dimensions of the plenoptic function onto an image sensor. Our theory enables a principled comparison of plenoptic multiplexing schemes, including noise analysis, as well as the development of a generic reconstruction algorithm. The framework also aides in the identification and optimization of novel multiplexed imaging applications.


title: BreathingFrame: An Inflatable Frame for Remote Breath Signal Sharing

abstract: We present BreathingFrame, a breath signal sharing device that supports pairwise remote communication through delivering the physical inflating movement of the other party's breathing on the surface of a digital photo frame. The results of our observational study with eight couples showed that participants could associate the other party on the frame, feel sentimental connectedness and the breathing movement from the other party, and stimulate their curiosity in a positive way. We also found that it was effective for conveying affection and making users to do natural abdominal breathing through intentional breath signal delivery. Allowing the users to share breathing at a distance by applying unconscious but partially controllable breath signals on the digital picture frame which is used in everyday lives, BreathingFrame can deliver new forms of telepresence and support emotional communication.


title: Research on Improvement of Dynamic Load Balancing in MongoDB

abstract: As a representative of NO-SQL database, MongoDBis widely preferred for its automatic load-balancing to some extent, which including distributing read load to secondary node to reducethe load of primary one and auto-sharding to reduce the load onspecific node through automatically split data and migrate some ofthem to other nodes. However, on one hand, this process isstorage-load-- Cbased, which can't meet the demand due to the factsthat some particular data are accessed much more frequently thanothers and the 'heat' is not constant as time going on, thus the loadon a node keeps changing even if with unchanged data. On the otherhand, data migration will bring out too much cost to affectperformance of system. In this paper, we will focus on the mechanismof automatic load balancing of MongoDB and proposean heat-based dynamic load balancing mechanism with much lesscost.


title: Ontology-Based Personalised and Context-Aware Recommendations of News Items

abstract: News@hand is a news recommender system that makes use of semantic technologies to provide several on-line news recommendation services. News contents and user preferences are described in terms of concepts appearing in a set of domain ontologies. Based on the similarities between item descriptions and user profiles, and the se-mantic relations between concepts, content-based and collaborative recommendation models are supported by the system. In this paper, we evaluate a model that personalises the order in which news articles are shown to the user according to his long-term interest profile, and other model that reorders the news items lists taking into account the current semantic context of interest of the user. The combination of those models is investigated showing significant improvements on the experimental tasks performed.


title: SUN database: Large-scale scene recognition from abbey to zoo

abstract: Scene categorization is a fundamental problem in computer vision. However, scene understanding research has been constrained by the limited scope of currently-used databases which do not capture the full variety of scene categories. Whereas standard databases for object categorization contain hundreds of different classes of objects, the largest available dataset of scene categories contains only 15 classes. In this paper we propose the extensive Scene UNderstanding (SUN) database that contains 899 categories and 130,519 images. We use 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition and establish new bounds of performance. We measure human scene classification performance on the SUN database and compare this with computational methods. Additionally, we study a finer-grained scene representation to detect scenes embedded inside of larger scenes.


title: Task constraints in visual working memory

abstract: This paper examines the nature of visual representations that direct ongoing performance in sensorimotor tasks. Performance of such natural tasks requires relating visual information from different gaze positions. To explore this we used the technique of making task relevant display changes during saccadic eye movements. Subjects copied a pattern of colored blocks on a computer monitor, using the mouse to drag the blocks across the screen. Eye position was monitored using a dual-purkinje eye tracker, and the color of blocks in the pattern was changed at different points in task performance. When the target of the saccade changed color during the saccade, the duration of fixations on the model pattern increased, depending on the point in the task that the change was made. Thus different fixations on the same visual stimulus served a different purpose. The results also indicated that the visual information that is retained across successive fixations depends on moment by moment task demands. This is consistent with previous suggestions that visual representations are limited and task dependent. Changes in blocks in addition to the saccade target led to greater increases in fixation duration. This indicated that some global aspect of the pattern was retained across different fixations. Fixation durations revealed effects of the display changes that were not revealed in perceptual report. This can be understood by distinguishing between processes that operate at different levels of description and different time scales. Our conscious experience of the world may reflect events over a longer time scale than those underlying the substructure of the perceptuo-motor machinery.


title: The Three-Dimension Path Planning of UAV Based on Improved Artificial Potential Field in Dynamic Environment

abstract: In order to solve problem of path planning for Unmanned Aerial Vehicle (UAV) in dynamic environment, a three-dimensional (3D) path planning algorithm of UAV which Based on improved artificial potential field is proposed in this paper. First, the problem of target unreachable in path planning is solved through the improvement of artificial potential field function. Then the improved algorithm can avoid UAV going into local pole and shock area by setting potential field guide point. Finally a path optimization method is proposed to optimize the generate path. Simulation results show that the path planning algorithm can ensure UAV to real-time path replanning in dynamic environment and generate a smooth path, it proves the feasibility and effectiveness of the improved algorithm.


title: Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation

abstract: Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party for fact checking. If this party identifies the story as misinformation, it is marked as disputed. However, given the uncertain number of exposures, the high cost of fact checking, and the trade-off between flags and exposures, the above mentioned procedure requires careful reasoning and smart algorithms which, to the best of our knowledge, do not exist to date. In this paper, we first introduce a flexible representation of the above procedure using the framework of marked temporal point processes. Then, we develop a scalable online algorithm, CURB, to select which stories to send for fact checking and when to do so to efficiently reduce the spread of misinformation with provable guarantees. In doing so, we need to solve a novel stochastic optimal control problem for stochastic differential equations with jumps, which is of independent interest. Experiments on two real-world datasets gathered from Twitter and Weibo show that our algorithm may be able to effectively reduce the spread of fake news and misinformation.


title: Modeling multidimensional databases

abstract: The authors propose a data model and a few algebraic operations that provide semantic foundation to multidimensional databases. The distinguishing feature of the proposed model is the symmetric treatment not only of all dimensions but also measures. The model provides support for multiple hierarchies along each dimension and support for ad hoc aggregates. The proposed operators are composable, reorderable, and closed in application. These operators are also minimal in the sense that none can be expressed in terms of others nor can any one be dropped without sacrificing functionality. They make possible the declarative specification and optimization of multidimensional database queries that are currently specified operationally. The operators have been designed to be translated to SQL and can be implemented either on top of a relational database system or within a special purpose multidimensional database engine. In effect, they provide an algebraic application programming interface (API) that allows the separation of the front end from the back end. Finally, the proposed model provides a framework in which to study multidimensional databases and opens several new research problems.


title: mHealthDroid: A Novel Framework for Agile Development of Mobile Health Applications

abstract: Mobile health is an emerging field which is attracting much attention. Nevertheless, tools for the development of mobile health applications are lacking. This work presents mHealthDroid, an open source Android implementation of a mHealth Framework designed to facilitate the rapid and easy development of biomedical apps. The framework is devised to leverage the potential of mobile devices like smartphones or tablets, wearable sensors and portable biomedical devices. The framework provides functionalities for resource and communication abstraction, biomedical data acquisition, health knowledge extraction, persistent data storage, adaptive visualization, system management and value-added services such as intelligent alerts, recommendations and guidelines.


title: Visualizing High-Dimensional Data: Advances in the Past Decade

abstract: Massive simulations and arrays of sensing devices, in combination with increasing computing resources, have generated large, complex, high-dimensional datasets used to study phenomena across numerous fields of study. Visualization plays an important role in exploring such datasets. We provide a comprehensive survey of advances in high-dimensional data visualization that focuses on the past decade. We aim at providing guidance for data practitioners to navigate through a modular view of the recent advances, inspiring the creation of new visualizations along the enriched visualization pipeline, and identifying future opportunities for visualization research.


title: Task assignment optimization in knowledge-intensive crowdsourcing

abstract: We present SmartCrowd, a framework for optimizing task assignment in knowledge-intensive crowdsourcing (KI-C). SmartCrowd distinguishes itself by formulating, for the first time, the problem of worker-to-task assignment in KI-C as an optimization problem, by proposing efficient adaptive algorithms to solve it and by accounting for human factors, such as worker expertise, wage requirements, and availability inside the optimization process. We present rigorous theoretical analyses of the task assignment optimization problem and propose optimal and approximation algorithms with guarantees, which rely on index pre-computation and adaptive maintenance. We perform extensive performance and quality experiments using real and synthetic data to demonstrate that the SmartCrowd approach is necessary to achieve efficient task assignments of high-quality under guaranteed cost budget.


title: Adaptable Game Experience Based on Player's Performance and EEG

abstract: For high skilled players, an easy game might become boring and for low skilled players, a difficult game might become frustrating. The purpose of this research was to create new and better ways to offer players with different skills, an appropriate experience. We focused on adapting the difficulty levels of a simple 2D platform game, designing and building levels automatically. The proposed method consists of Dynamic Difficulty Adjustment and Rhythm-Group Theory (a procedural content generation method), combined with levels of attention obtained from EEG data. Experiments were designed in the way that players had to clear five different levels that were created automatically using the player's performance and EEG data obtained from a biosensor while playing. Results showed that the method successfully adapts the level difficulty according to the player's status. In addition, the designed method calculates difficulty using values calculated in real time to decide how the level should be created. We consider that this new method can be implemented not only in platformers but also in other genres, also, it could be used by game developers as a tool of playtesting when designing new levels for their games.


title: Combining PSO and k-means to enhance data clustering

abstract: In this paper we propose a clustering method based on combination of the particle swarm optimization (PSO) and the k-mean algorithm. PSO algorithm was showed to successfully converge during the initial stages of a global search, but around global optimum, the search process will become very slow. On the contrary, k-means algorithm can achieve faster convergence to optimum solution. At the same time, the convergent accuracy for k-means can be higher than PSO. So in this paper, a hybrid algorithm combining particle swarm optimization (PSO) algorithm with k-means algorithm is proposed we refer to it as PSO-KM algorithm. The algorithm aims to group a given set of data into a user specified number of clusters. We evaluate the performance of the proposed algorithm using five datasets. The algorithm performance is compared to K-means and PSO clustering.


title: A 250 mV 8 kb 40 nm Ultra-Low Power 9T Supply Feedback SRAM (SF-SRAM)

abstract: Low voltage operation of digital circuits continues to be an attractive option for aggressive power reduction. As standard SRAM bitcells are limited to operation in the strong-inversion regimes due to process variations and local mismatch, the development of specially designed SRAMs for low voltage operation has become popular in recent years. In this paper, we present a novel 9T bitcell, implementing a Supply Feedback concept to internally weaken the pull-up current during write cycles and thus enable low-voltage write operations. As opposed to the majority of existing solutions, this is achieved without the need for additional peripheral circuits and techniques. The proposed bitcell is fully functional under global and local variations at voltages from 250 mV to 1.1 V. In addition, the proposed cell presents a low-leakage state reducing power up to 60%, as compared to an identically supplied 8T bitcell. An 8 kbit SF-SRAM array was implemented and fabricated in a low-power 40 nm process, showing full functionality and ultra-low power.


Acoustic Modeling for Google Home


title: A Hybrid PCA-CART-MARS-Based Prognostic Approach of the Remaining Useful Life for Aircraft Engines

abstract: Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks) also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines.


title: Stability Analysis and Design of Wideband Howland Current Pumps Using Monolithic Current-Feedback Amplifiers

abstract: This paper focuses on the stability analysis of wideband improved Howland currents pumps using monolithic current-feedback operational amplifiers (CFOAs). Based on the application of the Nyquist criterion the possibilities of CFOAs for stable operation in the current pumps at output current i L larger than 1 mA and upper cut-off frequency f- 3dB higher than 100 kHz are investigated. Formulas for closed loop gain T=β+ A F − of the system with positive and negative feedback at active and complex load are presented. The complex coefficients of the amplifier circuit A F and the positive feedback s+ are determined by using equivalent circuits based on the high frequency CFOA model, taking into account the input impedances, one-pole transfer function, output impedance and parasitic board capacities. Mathematical formulas for the poles and zeros based on the obtained coefficients are derived. Moreover, using these formulas a design procedure of improved Howland currents pumps are proposed. Verification of the defined hypotheses and the proposed formulas is performed by testing of sample electronic circuits using discrete passive components and commercially available CFOA THS3091 with maximum output current ± 250 mA and unity-gain bandwidth ≈ 200 MHz.


title: The use of fuzzy logic in predicting house selling price

abstract: In this paper, a new grading model has been developed for prediction of the selling price of house-building. Fuzzy logic systems, considering the city plans, the nearness to cultural, medical, training and educational buildings, the public transportations systems, the other environmental factors and the increased technological upgrading deals with information about construction, have been employed in order to construct the model and achieve the aim. Such factors are used as the inputs. Besides, a questionnaire application including these factors has been applied to determine the values of fuzzy training and testing sets. In this way, the constructed model has been applied to the prediction of selling prices of houses located in different regions of Eskisehir city in Turkey. The predicted values and real selling prices determined by selling market have been compared with each other. Consequently, real selling price of house-building has shown variety with respect to the regional aspects and salesmen. The suggested fuzzy logic model can be capable and usable for similar applications.


title: Pharmacological properties of cannabidiol in the treatment of psychiatric disorders: a critical overview

abstract: Cannabidiol (CBD) represents a new promising drug due to a wide spectrum of pharmacological actions. In order to relate CBD clinical efficacy to its pharmacological mechanisms of action, we performed a bibliographic search on PUBMED about all clinical studies investigating the use of CBD as a treatment of psychiatric symptoms. Findings to date suggest that (a) CBD may exert antipsychotic effects in schizophrenia mainly through facilitation of endocannabinoid signalling and cannabinoid receptor type 1 antagonism; (b) CBD administration may exhibit acute anxiolytic effects in patients with generalised social anxiety disorder through modification of cerebral blood flow in specific brain sites and serotonin 1A receptor agonism; (c) CBD may reduce withdrawal symptoms and cannabis/tobacco dependence through modulation of endocannabinoid, serotoninergic and glutamatergic systems; (d) the preclinical pro-cognitive effects of CBD still lack significant results in psychiatric disorders. In conclusion, current evidences suggest that CBD has the ability to reduce psychotic, anxiety and withdrawal symptoms by means of several hypothesised pharmacological properties. However, further studies should include larger randomised controlled samples and investigate the impact of CBD on biological measures in order to correlate CBD's clinical effects to potential modifications of neurotransmitters signalling and structural and functional cerebral changes.


title: A Theory on Urban Resilience to Floods—A Basis for Alternative Planning Practices

abstract: River cities require a management approach based on resilience to floods rather than on resistance. Resisting floods by means of levees, dams, and channelization neglects inherent uncertainties arising from human-nature couplings and fails to address the extreme events that are expected to increase with climate change, and is thereby not a reliable approach to long-term flood safety. By applying resilience theory to address system persistence through changes, I develop a theory on "urban resilience to floods" as an alternative framework for urban flood hazard management. Urban resilience to floods is defined as a city's capacity to tolerate flooding and to reorganize should physical damage and socioeconomic disruption occur, so as to prevent deaths and injuries and maintain current socioeconomic identity. It derives from living with periodic floods as learning opportunities to prepare the city for extreme ones. The theory of urban resilience to floods challenges the conventional wisdom that cities cannot live without flood control, which in effect erodes resilience. To operationalize the theory for planning practice, a surrogate measure—the percent floodable area—is developed for assessing urban resilience to floods. To enable natural floodplain functions to build urban resilience to floods, flood adaptation is advocated in order to replace flood control for mitigating flood hazards.


title: A Hybrid Approach for Detecting Automated Spammers in Twitter

abstract: Twitter is one of the most popular microblogging services, which is generally used to share news and updates through short messages restricted to 280 characters. However, its open nature and large user base are frequently exploited by automated spammers, content polluters, and other ill-intended users to commit various cybercrimes, such as cyberbullying, trolling, rumor dissemination, and stalking. Accordingly, a number of approaches have been proposed by researchers to address these problems. However, most of these approaches are based on user characterization and completely disregarding mutual interactions. In this paper, we present a hybrid approach for detecting automated spammers by amalgamating community-based features with other feature categories, namely metadata- , content- , and interaction-based features. The novelty of the proposed approach lies in the characterization of users based on their interactions with their followers given that a user can evade features that are related to his/her own activities, but evading those based on the followers is difficult. Nineteen different features, including six newly defined features and two redefined features, are identified for learning three classifiers, namely, random forest , decision tree , and Bayesian network , on a real dataset that comprises benign users and spammers. The discrimination power of different feature categories is also analyzed, and interaction- and community-based features are determined to be the most effective for spam detection, whereas metadata-based features are proven to be the least effective.


title: Genetic Algorithms, Noise, and the Sizing of Populations

abstract: This paper considers the effect of stochasticity on the quality of convergence of genetic algorithms (GAs). In many problems, the variance of building-block fitness or so-called collateral noise is the major source of variance, and a population-sizing equation is derived to ensure that average signal-to-collateral-noise ratios are favorable to the discrimination of the best building blocks required to solve a problem of bounded deception. The sizing relation is modified to permit the inclusion of other sources of stochasticity, such as the noise of selection, the noise of genetic operators, and the explicit noise or nondeterminism of the objective function. In a test suite of five functions, the sizing relation proves to be a conservative predictor of average correct convergence, as long as all major sources of noise are considered in the sizing calculation. These results suggest how the sizing equation may be viewed as a coarse delineation of a boundary between what a physicist might call two distinct phases of GA behavior. At low population sizes the GA makes many errors of decision, and the quality of convergence is largely left to the vagaries of chance or the serial fixup of flawed results through mutation or other serial injection of diversity. At large population sizes, GAs can reliably discriminate between good and bad building blocks, and parallel processing and recombination of building blocks lead to quick solution of even difficult deceptive problems. Additionally, the paper outlines a number of extensions to this work, including the development of more refined models of the relation between generational average error and ultimate convergence quality, the development of online methods for sizing populations via the estimation of populations via the estimation of population-sizing parameters, and the investigation of populationsizing in the context of niching and other schemes designed for use in problems with high cardinality solution sets. The paper also discusses how these results may one day lead to rigorous proofs of convergence for recombinative G As operating on problems of bounded description.


title: c.light: A Tool for Exploring Light Properties in Early Design Stage

abstract: Although a light becomes an important design element, there are little techniques available to explore shapes and light effects in early design stages. We present c.light, a design tool that consists of a set of modules and a mobile application for visualizing the light in a physical world. It allows designers to easily fabricate both tangible and intangible properties of a light without a technical barrier. We analyzed how c.light contributes to the ideation process of light design through a workshop. The results showed that c.light largely expands designers' capability to manipulate intangible properties of light and, by doing so, it facilitates collaborative and inverted ideation process in early design stages. It is expected that the results of this study could enhance our understanding of how designers manipulate light in a physical world in early design stages and could be a good stepping stone for future tool development.


title: Intelligent appliances controller using Raspberry Pi

abstract: Using the inexpensive Raspberry Pi to automate the tasks at home such as switching appliances on & off over Wi-Fi (Wireless Fidelity) or LAN(Local Area Network) using a personal computer or a mobile or a tablet through the browser. This can also be done by using the dedicated Android application. The conventional switch boards will be added with a touch screen or replaced with a touch screen to match the taste of the user's home decor. PIR (Passive Infrared Sensor) sensor will be used to detect human detection and automate the on and off functionality.


title: EEG Classification of Different Imaginary Movements within the Same Limb

abstract: The task of discriminating the motor imagery of different movements within the same limb using electroencephalography (EEG) signals is challenging because these imaginary movements have close spatial representations on the motor cortex area. There is, however, a pressing need to succeed in this task. The reason is that the ability to classify different same-limb imaginary movements could increase the number of control dimensions of a brain-computer interface (BCI). In this paper, we propose a 3-class BCI system that discriminates EEG signals corresponding to rest, imaginary grasp movements, and imaginary elbow movements. Besides, the differences between simple motor imagery and goal-oriented motor imagery in terms of their topographical distributions and classification accuracies are also being investigated. To the best of our knowledge, both problems have not been explored in the literature. Based on the EEG data recorded from 12 able-bodied individuals, we have demonstrated that same-limb motor imagery classification is possible. For the binary classification of imaginary grasp and elbow (goal-oriented) movements, the average accuracy achieved is 66.9%. For the 3-class problem of discriminating rest against imaginary grasp and elbow movements, the average classification accuracy achieved is 60.7%, which is greater than the random classification accuracy of 33.3%. Our results also show that goal-oriented imaginary elbow movements lead to a better classification performance compared to simple imaginary elbow movements. This proposed BCI system could potentially be used in controlling a robotic rehabilitation system, which can assist stroke patients in performing task-specific exercises.


title: Methodological Approach and Technological Framework to Break the Current Limitations of MOOC Model

abstract: A methodological approach and technological framework are proposed to improve learning outcomes in Massive Open Online Courses (MOOCs), taking into account the distinguishing features of this kind of massive courses over traditional online courses. The proposed methodology integrates the learning strategies of xMOOCs and cMOOCs with adaptivity and knowledge management capabilities. In order to test the learning results of the methodology and the need of supporting technological framework for it, a MOOC was made based on the methodological proposal and using a MOOC platform called MiriadaX. The quantitative results have improved considerably the MOOC completion rate (compared to the average of the rest of MOOC MiriadaX) and the qualitative results show a great satisfaction with the learning outcomes of the learners. However, the technological environment did not allow us develop all the methodological capabilities and it was one of the main concerns of the MOOC attendances. Therefore, from the analysis of collected data and considering the limitations of current MOOC technology platforms, a technological framework has been designed. It may incorporate the proposed methodology in an efficient and effective way. Based on this proposed technological framework, a MOOC platform has been developed and delivered, used by three Spanish Universities to offer MOOCs. This new platform and the supported technological framework have been tested with a first pilot with promising results.


title: An analysis of sentiments on facebook during the 2016 U.S. presidential election

abstract: Social networking sites (SNS), such as Facebook and Twitter, are important spaces for political engagement. SNS have become common elements in political participation, campaigns, and elections. However, little is known about the dynamics between candidate posts and commentator sentiment in response to those posts on SNS. This study enriches computational political science by studying the 2016 U.S. elections and how candidates and commentators engage on Facebook. This paper also examines how online activity might be connected to offline activity and vice versa. We extracted 9,700 Facebook posts by five presidential candidates (Hillary Clinton, Donald Trump, Bernie Sanders, Ted Cruz, and John Kasich) from their official Facebook pages and 12,050,595 comments on those posts. We employed topic modeling, sentiment analysis, and trends detection using wavelet transforms to discover topics, trends, and reactions. Our findings suggest that Republican candidates are more likely to share information on controversial events that have taken place during the election cycle, while Democratic candidates focus on social policy issues. As expected, commentators on Republican candidate pages express negative sentiments toward current public policies as they seldom support decisions made by the Obama administration, while commentators on democratic candidate pages are more likely to express support for continuation or advancement of existing policies. However, the significance (strong/weak) and nature (positive/negative) of sentiments varied between candidates within political parties based on perceived credibility of the candidate's degree of credibility on a given issue. Additionally, we explored correlation between online trends of comments/sentiment and offline events. When analyzing the trend patterns, we found that changes in online trends are driven by three factors: 1) popular post, 2) offline debates, and 3) candidates dropping out of the race.


title: A systematic mapping study of web application testing

abstract: Abstract Context The Web has had a significant impact on all aspects of our society. As our society relies more and more on the Web, the dependability of web applications has become increasingly important. To make these applications more dependable, for the past decade researchers have proposed various techniques for testing web-based software applications. Our literature search for related studies retrieved 147 papers in the area of web application testing, which have appeared between 2000 and 2011. Objective As this research area matures and the number of related papers increases, it is important to systematically identify, analyze, and classify the publications and provide an overview of the trends in this specialized field. Method We review and structure the body of knowledge related to web application testing through a systematic mapping (SM) study. As part of this study, we pose two sets of research questions, define selection and exclusion criteria, and systematically develop and refine a classification schema. In addition, we conduct a bibliometrics analysis of the papers included in our study. Results Our study includes a set of 79 papers (from the 147 retrieved papers) published in the area of web application testing between 2000 and 2011. We present the results of our systematic mapping study. Our mapping data is available through a publicly-accessible repository. We derive the observed trends, for instance, in terms of types of papers, sources of information to derive test cases, and types of evaluations used in papers. We also report the demographics and bibliometrics trends in this domain, including top-cited papers, active countries and researchers, and top venues in this research area. Conclusion We discuss the emerging trends in web application testing, and discuss the implications for researchers and practitioners in this area. The results of our systematic mapping can help researchers to obtain an overview of existing web application testing approaches and indentify areas in the field that require more attention from the research community.


title: 210-NP: measuring the mechanical engineering design process

abstract: The grades assigned at quarterly intervals to 10 projects in a project-based mechatronics systems design class named ME210 were plotted alongside the number of distinct noun phrases (NPs) in the project reports. It was found that the grades were strongly associated (gamma>0.7) with the number of distinct NPs, while they were weakly associated with other variables, like readability and the number of words. These initial results open up a new set of ways for assessing design work and, as a consequence, improving the performance of students doing design tasks.


title: Social and Personality Psychology Compass 2/5 (2008): 2016–2033, 10.1111/j.1751-9004.2008.00143.x Applying the Cognitive-Affective Processing Systems Approach to Conceptualizing Rejection Sensitivity

abstract: The Cognitive-Affective Processing Systems or CAPS theory (Mischel & Shoda, 1995) was proposed to account for the processes that explain why and how people's behavior varies stably across situations. Research on Rejection Sensitivity is reviewed as a programmatic attempt to illustrate how personality dispositions can be studied within the CAPS framework. This research reveals an if ... then ... (e.g., if situation X, he does A, but if situation Y, he does B) pattern of rejection sensitivity such that high rejection sensitive people's goal to prevent rejection can lead to accommodating behavior; yet, the failure to achieve this goal can lead to aggression, reactivity, and lack of self-concept clarity. These situation–behavior relations or personality signatures reflect a stable activation network of distinctive personality processing dynamics. These dynamics link fears and expectations of rejection, perceptions/attributions of rejection, and affective/behavioral overreactions to perceived rejection. Self-regulatory and attentional mechanisms may interact with these dynamics as buffers against high rejection sensitivity, illustrating how multiple processes within a CAPS network play out in behavior.