title: ML confidential: Machine learning on encrypted data. Cryptology ePrint Archive, Report 2012/323

abstract: We demonstrate that, by using a recently proposed leveled homomorphic encryption scheme, it is possible to delegate the execution of a machine learning algorithm to a computing service while retaining confidentiality of the training and test data. Since the computational complexity of the homomorphic encryption scheme depends primarily on the number of levels of multiplications to be carried out on the encrypted data, we define a new class of machine learning algorithms in which the algorithm's predictions, viewed as functions of the input data, can be expressed as polynomials of bounded degree. We propose confidential algorithms for binary classification based on polynomial approximations to least-squares solutions obtained by a small number of gradient descent steps. We present experimental validation of the confidential machine learning pipeline and discuss the trade-offs regarding computational complexity, prediction accuracy and cryptographic security.


Predicting Airfare Prices


title: Designing Automatic Meter Reading System Using Open Source Hardware and Software

abstract: The importance of quality of the measured values is very dependent on the device that measures these values: the size of the sample, the time of measurement, periods of measurement, the mobility and the robustness of the device, etc. Contemporary devices intended for the measurement of physical quantities that are on the market vary in price, as well as and the quality of the measured values. The rule "the more expensive the better" is not necessarily always a rule that is valid because it all depends on the characteristics and capabilities of the device, and the customer’s needs. In this paper, a device based on "Open Source" components of hardware and software will be presented. Device was used to measure voltages and currents on low voltage networks, on which a virtually unlimited number of sensors can be added, and the device is assembled of components available on electronic components Internet.


title: Security and Privacy Challenges and Potential Solutions for DLT based IoT Systems

abstract: The use of distributed ledger technologies introduces new security and privacy challengesThese challenges are dependent on properties of the ledgers, such as transaction latency and throughput. Some use cases may be outright impossible to implement securely, or in a privacy-retaining manner. Consequently, it is important that these concerns are taken into account when distributed ledger technologies are evaluated and selected as building blocks for higher-levelsystems. In this paper, we illustrate these concerns through use case examples. We discuss theimplications these concerns on the use of distributed ledgers within higher-level systems, such as in SOFIE, a DLT-based approach to securely and openly federate IoT systems.


title: Dynamic updating of hippocampal object representations reflects new conceptual knowledge

abstract: Concepts organize the relationship among individual stimuli or events by highlighting shared features. Often, new goals require updating conceptual knowledge to reflect relationships based on different goal-relevant features. Here, our aim is to determine how hippocampal (HPC) object representations are organized and updated to reflect changing conceptual knowledge. Participants learned two classification tasks in which successful learning required attention to different stimulus features, thus providing a means to index how representations of individual stimuli are reorganized according to changing task goals. We used a computational learning model to capture how people attended to goal-relevant features and organized object representations based on those features during learning. Using representational similarity analyses of functional magnetic resonance imaging data, we demonstrate that neural representations in left anterior HPC correspond with model predictions of concept organization. Moreover, we show that during early learning, when concept updating is most consequential, HPC is functionally coupled with prefrontal regions. Based on these findings, we propose that when task goals change, object representations in HPC can be organized in new ways, resulting in updated concepts that highlight the features most critical to the new goal.


title: A survey on leveraging deep neural networks for object tracking

abstract: Object tracking is the task of estimating over time the state of a single or multiple objects based on noisy measurements received from one or several sensors. The field of object tracking spans over several application domains ranging from military radar systems and sensor fusion approaches, to today's computer vision tracking methods employed in consumer electronics and surveillance systems. It also plays a substantial role in autonomous driving. In recent years, the use of deep neural networks has spiked in various fields, due to their impressive performance in detection and classification tasks. This aspect also makes these methods applicable to object tracking. Therefore, the aim of this survey is to give the reader a brief yet comprehensive start into the widespread field of object tracking with a focus on the latest deep-learning based extensions and approaches. At first, traditional non-deep tracking systems are briefly reviewed and a generic model of the individual components of such systems is introduced. Based on this structure the representative deep-based tracking applications in the literature are classified and presented.


title: Sub-maximal and maximal Yo–Yo intermittent endurance test level 2: heart rate response, reproducibility and application to elite soccer

abstract: The aims of this study were to (1) determine the reproducibility of sub-maximal and maximal versions of the Yo–Yo intermittent endurance test level 2 (Yo–Yo IE2 test), (2) assess the relationship between the Yo–Yo IE2 test and match performance and (3) quantify the sensitivity of the Yo–Yo IE2 test to detect test–retest changes and discriminate between performance for different playing standards and positions in elite soccer. Elite (n = 148) and sub-elite male (n = 14) soccer players carried out the Yo–Yo IE2 test on several occasions over consecutive seasons. Test–retest coefficient of variation (CV) in Yo–Yo IE2 test performance and heart rate after 6 min were 3.9% (n = 37) and 1.4% (n = 32), respectively. Elite male senior and youth U19 players Yo–Yo IE2 performances were better (P < 0.01) than elite youth U16s and sub-elite players (2,603 ± 451 and 2,534 ± 549 vs. 1,855 ± 535 vs. 1,749 ± 382 m). The intra- and inter-season CV for Yo–Yo IE2 test performance were 4.2 and 5.6%, respectively. A correlation was observed (P < 0.05) between Yo–Yo IE2 test performance and the total (r = 0.74) and high-intensity (r = 0.58) running distance covered in a match. A correlation was also evident (P < 0.01) between Yo–Yo IE2 test heart rate after 6 min expressed in percentage of maximal heart rate and the peak values for high-intensity running performed by midfielders in 5-min (r = −0.71), 15-min (r = −0.75) and 45-min periods (r = −0.77). The present data demonstrate that the Yo–Yo IE2 test is reproducible and can be used to determine the capacity of elite soccer players to perform intense intermittent exercise. Furthermore, the Yo–Yo IE2 test was shown to be a sensitive tool that not only relates to match performance but can also differentiate between intermittent exercise performance of players in various standards, stages of the season and playing positions.


title: MODELTEST: testing the model of DNA substitution.

abstract: Summary: The program MODELTEST uses log likelihood scores to establish the model of DNA evolution that best fits the data. Availability: The MODELTEST package, including the source code and some documentation is available at http://bioag.byu.edu/zoology/crandall―lab/modeltest.html. Contact: dp47@email.byu.edu.


title: Version Management of XML Documents

abstract: The problem of ensuring efficient storage and fast retrieval for multi-version structured documents is important because of the recent popularity of XML documents and semistructured information on the web.Traditional document version control systems, e.g. RCS, which model documents as a sequence of lines of text and use the shortest edit script to represent version differences, can be inefficient and they do not preserve the logical structure of the original document. Therefore, we propose a new approach where the structure of the documents is preserved intact, and their sub-objects are timestamped hierarchically for efficient reconstruction of current and past versions. Our technique, called the Usefulness Based Copy Control (UBCC), is geared towards efficient version reconstruction while using small storage overhead. Our analysis and experiments illustrate the effectiveness of the overall approach to version control for structured documents. Moreover UBCC can easily support multiple concurrent versions as well as partial document retrieval.


title: Benford's Law and First Letter of Word

abstract: Abstract A universal First-Letter Law (FLL) is derived and described. It predicts the percentages of first letters for words in novels. The FLL is akin to Benford’s law (BL) of first digits, which predicts the percentages of first digits in a data collection of numbers. Both are universal in the sense that FLL only depends on the numbers of letters in the alphabet, whereas BL only depends on the number of digits in the base of the number system. The existence of these types of universal laws appears counter-intuitive. Nonetheless both describe data very well. Relations to some earlier works are given. FLL predicts that an English author on the average starts about 16 out of 100 words with the English letter ‘t’. This is corroborated by data, yet an author can freely write anything. Fuller implications and the applicability of FLL remain for the future.


title: LsPS: A Job Size-Based Scheduler for Efficient Task Assignments in Hadoop

abstract: The MapReduce paradigm and its open source implementation Hadoop are emerging as an important standard for large-scale data-intensive processing in both industry and academia. A MapReduce cluster is typically shared among multiple users with different types of workloads. When a flock of jobs are concurrently submitted to a MapReduce cluster, they compete for the shared resources and the overall system performance in terms of job response times, might be seriously degraded. Therefore, one challenging issue is the ability of efficient scheduling in such a shared MapReduce environment. However, we find that conventional scheduling algorithms supported by Hadoop cannot always guarantee good average response times under different workloads. To address this issue, we propose a new Hadoop scheduler, which leverages the knowledge of workload patterns to reduce average job response times by dynamically tuning the resource shares among users and the scheduling algorithms for each user. Both simulation and real experimental results from Amazon EC2 cluster show that our scheduler reduces the average MapReduce job response time under a variety of system workloads compared to the existing FIFO and Fair schedulers.


title: Mobile music touch: mobile tactile stimulation for passive learning

abstract: Mobile Music Touch (MMT) helps teach users to play piano melodies while they perform other tasks. MMT is a lightweight, wireless haptic music instruction system consisting of fingerless gloves and a mobile Bluetooth enabled computing device, such as a mobile phone. Passages to be learned are loaded into the mobile phone and are played repeatedly while the user performs other tasks. As each note of the music plays, vibrators on each finger in the gloves activate, indicating which finger is used to play each note. We present two studies on the efficacy of MMT. The first measures 16 subjects' ability to play a passage after using MMT for 30 minutes while performing a reading comprehension test. The MMT system was significantly more effective than a control condition where the passage was played repeatedly but the subjects' fingers were not vibrated. The second study compares the amount of time required for 10 subjects to replay short, randomly generated passages using passive training versus active training. Participants with no piano experience could repeat the passages after passive training while subjects with piano experience often could not.


title: Preventing Collusion Attack in Android

abstract: Globally, the number of Smartphone users has risen above a billion, and most of users use them to do their day-to- day activities. Therefore, the security of smartphones turns to a great concern. Recently, Android as the most popular smartphone platform has been targeted by the attackers. Many severe attacks to Android are caused by malicious applications which acquire excessive privileges at install time. Moreover some applications are able to collude together in order to increase their privileges by sharing their permissions. This paper proposes a mechanism for preventing this kind of collusion attack on Android by detecting the applications which are able to share their acquired permissions. By applying the proposed mechanism on a set of 290 applications downloaded from the Android official market, Google Play, the number of detected applications which potentially are able to conduct malicious activities increased by 12.90% in compare to the existing detection mechanism. Results showed that there were 4 applications among the detected applications which were able to collude together in order to acquire excessive privileges and were totally ignored by the existing method.


title: The Sixth Sense-Emotional Contagion; Review of Biophysical Mechanisms Influencing Information Transfer in Groups

abstract: Rioting historically has been known to cause changes in individual cognition, with heightened emotionality, increased excitement and reduced reflective thought. A cross-disciplinary literature review hypothesises emotional states generate corresponding metabolic bio magnetic fields that radiate in patterns reflecting these emotional states. The oscillatory phase of these waves is advanced as being more significant than the power of signal, permitting stochastic effects to magnify signals by appropriating ambient noise. Possible transmission and detection structures in the body are discussed, induced paramagnetic sensitivity in crowd participants in a phase transition process is hypothesised, the effect may be proportional to crowd numbers. It is suspected that positive effects seen in Transcranial Magnetic Therapy used to treat depression may be operating on this mechanism, acting on natural receptors in the limbic system which also capture light and are implicated in mood transitions. A number of paramagnetic neurotransmitters may be implicated.


title: Fully Deniable Message Authentication Protocols Preserving Confidentiality

abstract: Although the objective of secure communication can be achieved by using cryptographic tools, the undeniability that results from cryptographic properties may create a potential threat to the sender of the message. Unfortunately, most existing deniable protocols only provide 1-out-of-2 deniability. When both parties (the sender and the receiver) are allowed to deny generating the message, a dispute might occur between these two parties. The 1-out-of-2 deniable protocol can result in an unfair resolution of the dispute. Therefore, we propose a new model of deniability, called 1-out-of-∞ deniability, that can provide full deniability. The 1-out-of-∞ deniability protocol allows the originator of the message to deny that he or she generated the message, since there are an infinite number of possible message generators; at the same time, all transmitted messages can be protected and authenticated between the sender and the intended receiver. Our design can be implemented by using any public-key cryptography technique. We also analyze the correctness of the proposed protocols based on logical rules, and two practical examples are given to illustrate our design.


title: PsychSim: Modeling Theory of Mind with Decision-Theoretic Agents

abstract: Agent-based modeling of human social behavior is an increasingly important research area. A key factor in human social interaction is our beliefs about others, a theory of mind. Whether we believe a message depends not only on its content but also on our model of the communicator. How we act depends not only on the immediate effect but also on how we believe others will react. In this paper, we discuss PsychSim, an implemented multiagent-based simulation tool for modeling interactions and influence. While typical approaches to such modeling have used first-order logic, Psych-Sim agents have their own decision-theoretic model of the world, including beliefs about its environment and recursive models of other agents. Using these quantitative models of uncertainty and preferences, we have translated existing psychological theories into a decision-theoretic semantics that allow the agents to reason about degrees of believability in a novel way. We discuss PsychSim's underlying architecture and describe its application to a school violence scenario for illustration.


title: Using Belbin’s leadership role to improve team effectiveness: An empirical investigation”, The

abstract: Abstract This paper presents a controlled experiment conducted with senior software engineering students that demonstrates the utility of forming teams based on R. Meredith Belbin's set of team roles. The overall research effort focuses on the general utility of Belbin's roles in improving the effectiveness of teams, which can be viewed in two ways: performance and team viability. Performance effectiveness, which is the focus of this paper, clearly addresses a team's productivity. To address this problem, the first phase of the total research project consists of a controlled experiment that demonstrates that teams containing leadership roles perform better than teams that do not have this role filled. In a laboratory setting, a number of teams were formed that contained a single leader; others were formed that had no leader or multiple leaders. The results of this experiment are positive; they demonstrate that indeed Belbin's roles provide useful information to form teams. The specific conclusion of this controlled experiment is that a single leader on a team improves a team's performance over teams having multiple leaders or no leader. In other words, as one would expect, the mean time-to-completion for the leaderless teams was significantly greater than the teams with leaders. This means that Belbin's roles can be utilized in formation of new teams as well as in evaluation of extant teams, making certain that a team has a single leader. Both of these aspects, formation and evaluation, are extremely useful to managers of software programmers.


title: Quantifying the Creativity Support of Digital Tools through the Creativity Support Index

abstract: Creativity support tools help people engage creatively with the world, but measuring how well a tool supports creativity is challenging since creativity is ill-defined. To this end, we developed the Creativity Support Index (CSI), which is a psychometric survey designed for evaluating the ability of a creativity support tool to assist a user engaged in creative work. The CSI measures six dimensions of creativity support: Exploration, Expressiveness, Immersion, Enjoyment, Results Worth Effort, and Collaboration. The CSI allows researchers to understand not just how well a tool supports creative work overall, but what aspects of creativity support may need attention. In this article, we present the CSI, along with scenarios for how it can be deployed in a variety of HCI research settings and how the CSI scores can help target design improvements. We also present the iterative, rigorous development and validation process used to create the CSI.


title: DeepMob: Learning Deep Knowledge of Human Emergency Behavior and Mobility from Big and Heterogeneous Data

abstract: The frequency and intensity of natural disasters has increased significantly in recent decades, and this trend is expected to continue. Hence, understanding and predicting human evacuation behavior and mobility will play a vital role in planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, existing models are shallow models, and it is difficult to apply them for understanding the “deep knowledge” of human mobility. Therefore, in this study, we collect big and heterogeneous data (e.g., GPS records of 1.6 million users over 3 years, data on earthquakes that have occurred in Japan over 4 years, news report data, and transportation network data), and we build an intelligent system, namely, DeepMob, for understanding and predicting human evacuation behavior and mobility following different types of natural disasters. The key component of DeepMob is based on a deep learning architecture that aims to understand the basic laws that govern human behavior and mobility following natural disasters, from big and heterogeneous data. Furthermore, based on the deep learning model, DeepMob can accurately predict or simulate a person’s future evacuation behaviors or evacuation routes under different disaster conditions. Experimental results and validations demonstrate the efficiency and superior performance of our system, and suggest that human mobility following disasters may be predicted and simulated more easily than previously thought.


title: RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems

abstract: Open-domain human-computer conversation has been attracting increasing attention over the past few years. However, there does not exist a standard automatic evaluation metric for open-domain dialog systems; researchers usually resort to human annotation for model evaluation, which is time- and labor-intensive. In this paper, we propose RUBER, a Referenced metric and Unreferenced metric Blended Evaluation Routine, which evaluates a reply by taking into consideration both a groundtruth reply and a query (previous user-issued utterance). Our metric is learnable, but its training does not require labels of human satisfaction. Hence, RUBER is flexible and extensible to different datasets and languages. Experiments on both retrieval and generative dialog systems show that RUBER has a high correlation with human annotation.


title: Social and monetary reward learning engage overlapping neural substrates

abstract: Learning to make choices that yield rewarding outcomes requires the computation of three distinct signals: stimulus values that are used to guide choices at the time of decision making, experienced utility signals that are used to evaluate the outcomes of those decisions and prediction errors that are used to update the values assigned to stimuli during reward learning. Here we investigated whether monetary and social rewards involve overlapping neural substrates during these computations. Subjects engaged in two probabilistic reward learning tasks that were identical except that rewards were either social (pictures of smiling or angry people) or monetary (gaining or losing money). We found substantial overlap between the two types of rewards for all components of the learning process: a common area of ventromedial prefrontal cortex (vmPFC) correlated with stimulus value at the time of choice and another common area of vmPFC correlated with reward magnitude and common areas in the striatum correlated with prediction errors. Taken together, the findings support the hypothesis that shared anatomical substrates are involved in the computation of both monetary and social rewards.


title: Asymptotic BER Analysis for MIMO-BICM with Zero-Forcing Detectors Assuming Imperfect CSI

abstract: In this paper, we derive the asymptotic bit error rate (BER) for multiple-input multiple-output bit-interleaved coded modulation (MIMO-BICM) with linear zero-forcing (ZF) receivers for a temporally correlated flat Rayleigh fading channel. Pilot symbol assisted modulation (PSAM) in combination with linear minimum mean-squared error (LMMSE) channel estimation is considered. We also demonstrate that the deterioration due to imperfect channel state information (CSI) can be represented by a signal-to-noise ratio (SNR) degradation.


title: A self-reconfigurable platform for general purpose image processing systems on low-cost spartan-6 FPGAs

abstract: There is still no partial reconfiguration tool support on low-cost Field Programmable Gate Arrays (FPGAs) such as old-fashioned Spartan-3 and state-of-the-art Spartan-6 FPGA families by Xilinx. This forces the designers and engineers, who are using the partial reconfiguration capability of FPGAs, to use expensive families such as Virtex-4, Virtex-5 and Virtex-6 which are officially supported by partial reconfiguration (PR) software. Moreover, Xilinx still does not offer a portable, dedicated self-reconfiguration engine for all of the FPGAs. Self-reconfiguration is achieved with general-purpose processors such as MicroBlaze and PowerPC which are too overqualified for this purpose. In this study, we propose a new self-reconfiguration mechanism for Spartan-6 FPGAs. This mechanism can be used to implement large and complex designs on small FPGAs as chip area can be dramatically reduced by exploiting the dynamic partial reconfiguration feature for on-demand functionality loading and maximal utilization of the hardware. This approach is highly attractive for designing low-cost compute-intensive applications such as high performance image processing systems. For Spartan-6 FPGAs, we have developed hard-macros and exploited the self-reconfiguration engine, compressed Parallel Configuration Access Port (cPCAP) [1], that we designed for Spartan-3. The modified cPCAP core with block RAM controller, bitstream decompressor unit and Internal Configuration Access Port (ICAP) Finite State Machine (FSM) occupies only about 82 of 6,822 slices (1.2% of whole device) on a Spartan-XC6SLX45 FPGA and it achieves the maximum theoretical reconfiguration speed of 200MB/s (ICAP, 16-bit at 100MHz) proposed by Xilinx. We have also implemented a Reconfigurable Processing Element (RPE) whose arithmetic unit can be reconfigured on-the-fly. Multiple RPEs can be utilized to design a General Purpose Image Processing System (GPIPS) that can implement a number of different algorithms during runtime. As an illustrative example, we programmed the GPIPS on Spartan-6 for switching between two applications on-demand such as two-dimensional filtering and block-matching.


title: Anton, a special-purpose machine for molecular dynamics simulation

abstract: The ability to perform long, accurate molecular dynamics (MD) simulations involving proteins and other biological macro-molecules could in principle provide answers to some of the most important currently outstanding questions in the fields of biology, chemistry and medicine. A wide range of biologically interesting phenomena, however, occur over time scales on the order of a millisecond--about three orders of magnitude beyond the duration of the longest current MD simulations. In this paper, we describe a massively parallel machine called Anton, which should be capable of executing millisecond-scale classical MD simulations of such biomolecular systems. The machine, which is scheduled for completion by the end of 2008, is based on 512 identical MD-specific ASICs that interact in a tightly coupled manner using a specialized high-speed communication network. Anton has been designed to use both novel parallel algorithms and special-purpose logic to dramatically accelerate those calculations that dominate the time required for a typical MD simulation. The remainder of the simulation algorithm is executed by a programmable portion of each chip that achieves a substantial degree of parallelism while preserving the flexibility necessary to accommodate anticipated advances in physical models and simulation methods.


title: A Survey of Text Similarity Approaches

abstract: ABSTRACT Measuring the similarity between words, sentences, paragraphs and documents is an important component in various tasks such as information retrieval, document clustering, word-sense disambiguation, automatic essay scoring, short answer grading, machine translation and text summarization. This survey discusses the existing works on text similarity through partitioning them into three approaches; String-based, Corpus-based and Knowledge-based similarities. Furthermore, samples of combination between these similarities are presented. General Terms Text Mining, Natural Language Processing. Keywords BasedText Similarity, Semantic Similarity, String-Based Similarity, Corpus-Based Similarity, Knowledge-Based Similarity. NeedlemanWunsch 1. INTRODUCTION Text similarity measures play an increasingly important role in text related research and applications in tasks Nsuch as information retrieval, text classification, document clustering, topic detection, topic tracking, questions generation, question answering, essay scoring, short answer scoring, machine translation, text summarization and others. Finding similarity between words is a fundamental part of text similarity which is then used as a primary stage for sentence, paragraph and document similarities. Words can be similar in two ways lexically and semantically. Words are similar lexically if they have a similar character sequence. Words are similar semantically if they have the same thing, are opposite of each other, used in the same way, used in the same context and one is a type of another. DistanceLexical similarity is introduced in this survey though different String-Based algorithms, Semantic similarity is introduced through Corpus-Based and Knowledge-Based algorithms. String-Based measures operate on string sequences and character composition. A string metric is a metric that measures similarity or dissimilarity (distance) between two text strings for approximate string matching or comparison. Corpus-Based similarity is a semantic similarity measure that determines the similarity between words according to information gained from large corpora. Knowledge-Based similarity is a semantic similarity measure that determines the degree of similarity between words using information derived from semantic networks. The most popular for each type will be presented briefly. This paper is organized as follows: Section two presents String-Based algorithms by partitioning them into two types character-based and term-based measures. Sections three and four introduce Corpus-Based and knowledge-Based algorithms respectively. Samples of combinations between similarity algorithms are introduced in section five and finally section six presents conclusion of the survey.


title: Small-footprint high-performance deep neural network-based speech recognition using split-VQ

abstract: Due to a large number of parameters in deep neural networks (DNNs), it is challenging to design a small-footprint DNN-based speech recognition system while maintaining a high recognition performance. Even with a singular value matrix decomposition (SVD) method and scalar quantization, the DNN model is still too large to be deployed on many mobile devices. Common practices like reducing the number of hidden nodes often result in significant accuracy loss. In this work, we propose to split each row vector of weight matrices into sub-vectors, and quantize them into a set of codewords using a split vector quantization (split-VQ) algorithm. The codebook can be fine-tuned using back-propagation when an aggressive quantization is performed. Experimental results demonstrate that the proposed method can further reduce the model size by 75% to 80% and save 10% to 50% computation on top of an already very compact SVD-DNN without a noticeable performance degradation. This results in a 3.2 MB-footprint DNN giving similar recognition performance as what a 59.1 MB standard DNN can achieve.


title: A Kalman Filter Based Background Updating Algorithm Robust to Sharp Illumination Changes

abstract: A novel algorithm, based on Kalman filtering is presented for updating the background image within video sequences. Unlike existing implementations of the Kalman filter for this task, our algorithm is able to deal with both gradual and sudden global illumination changes. The basic idea is to measure global illumination change and to use it as an external control of the filter. This allows the system to better fit the assumptions about the process to be modeled. Moreover, we propose methods to estimate measurement noise variance and to deal with the problem of saturated pixels, to improve the accuracy and robustness of the algorithm. The algorithm has been successfully tested in a traffic surveillance task by comparing it to a background updating algorithm, based on Kalman filtering, taken from literature.


title: Disclosure, Liquidity, and the Cost of Capital

abstract: This paper shows that revealing public information to reduce information asymmetry can reduce a firm's cost of capital by attracting increased demand from large investors due to increased liquidity of its securities. Large firms will disclose more information since they benefit most. Disclosure also reduces the risk-bearing capacity available through market makers. If initial information asymmetry is large, reducing it will increase the current price of the security. However, the maximum current price occurs with some asymmetry of information: further reduction of information asymmetry accentuates the undesirable effects of exit from market making. Copyright 1991 by American Finance Association.


title: Solving the symbol grounding problem: a critical review of fifteen years of research

abstract: This article reviews eight proposed strategies for solving the symbol grounding problem (SGP), which was given its classic formulation in Harnad (1990). After a concise introduction, the paper provides an analysis of the requirement that must be satisfied by any hypothesis seeking to solve the SGP, the zero semantical commitment condition. It is then used to assess the eight strategies, which are organized into three main approaches: representationalism, semi-representationalism and non-representationalism. The conclusion is that all the strategies are semantically committed and hence that none of them provides a valid solution to the SGP, which remains an open problem.


title: Social media: back to the roots and back to the future

abstract: Purpose – The purpose of this paper is to provide a viewpoint on the historical roots and future evolution of social media.Design/methodology/approach – This paper provides a summary of the authors' previous research and experience in the area of social media.Findings – This paper contains practical insights on the consumer use and business potential of social media applications.Originality/value – This viewpoint provides insights to anyone who is interested in researching consumer use of social media or using social media in a managerial context. It will be particularly helpful to business leaders who are looking for answers in the fast‐moving area of social media applications.


title: Rolling the Dice: Multidimensional Visual Exploration using Scatterplot Matrix Navigation

abstract: Scatterplots remain one of the most popular and widely-used visual representations for multidimensional data due to their simplicity, familiarity and visual clarity, even if they lack some of the flexibility and visual expressiveness of newer multidimensional visualization techniques. This paper presents new interactive methods to explore multidimensional data using scatterplots. This exploration is performed using a matrix of scatterplots that gives an overview of the possible configurations, thumbnails of the scatterplots, and support for interactive navigation in the multidimensional space. Transitions between scatterplots are performed as animated rotations in 3D space, somewhat akin to rolling dice. Users can iteratively build queries using bounding volumes in the dataset, sculpting the query from different viewpoints to become more and more refined. Furthermore, the dimensions in the navigation space can be reordered, manually or automatically, to highlight salient correlations and differences among them. An example scenario presents the interaction techniques supporting smooth and effortless visual exploration of multidimensional datasets.


title: A divide-and-conquer framework for large-scale subspace clustering

abstract: Given data that lies in a union of low-dimensional subspaces, the problem of subspace clustering aims to learn — in an unsupervised manner — the membership of the data to their respective subspaces. State-of-the-art subspace clustering methods typically adopt a two-step procedure. In the first step, an affinity measure among data points is constructed, usually by exploiting some form of data self-representation. In the second step, spectral clustering is applied to the affinity measure to find the membership of the data to their respective subspaces. While such methods are broadly applicable to mid-size datasets with 10,000 data points in 10,000 variables, they cannot be directly applied to large-scale datasets. This paper proposes a divide-and-conquer framework for large-scale subspace clustering. The data is first divided into chunks and subspace clustering is applied to each chunk. After removing potential outliers from each cluster, a new cross-representation measure for the similarity between subspaces is used to merge clusters from different chunks that correspond to the same subspace. A self-representation method is then used to assign outliers to clusters. We evaluate the proposed strategy on synthetic large-scale dataset with 1,000,000 data points, as well as on the MNIST database, which contains 70,000 images of handwritten digits. The numerical results highlight the scalability of our approach.


title: Determining Semantic Textual Similarity using Natural Deduction Proofs

abstract: Determining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow information, capturing accurate semantics is difficult. By contrast, logical semantic representations capture deeper levels of sentence semantics, but their symbolic nature does not offer graded notions of textual similarity. We propose a method for determining semantic textual similarity by combining shallow features with features extracted from natural deduction proofs of bidirectional entailment relations between sentence pairs. For the natural deduction proofs, we use ccg2lambda, a higher-order automatic inference system, which converts Combinatory Categorial Grammar (CCG) derivation trees into semantic representations and conducts natural deduction proofs. Experiments show that our system was able to outperform other logic-based systems and that features derived from the proofs are effective for learning textual similarity.


title: Evaluation of Features Detectors and Descriptors based on 3D Objects

abstract: We explore the performance of a number of popular feature detectors and descriptors in matching 3D object features across viewpoints and lighting conditions. To this end we design a method, based on intersecting epipolar constraints, for providing ground truth correspondence automatically. These correspondences are based purely on geometric information, and do not rely on the choice of a specific feature appearance descriptor. We test detector-descriptor combinations on a database of 100 objects viewed from 144 calibrated viewpoints under three different lighting conditions. We find that the combination of Hessian-affine feature finder and SIFT features is most robust to viewpoint change. Harris-affine combined with SIFT and Hessian-affine combined with shape context descriptors were best respectively for lighting change and change in camera focal length. We also find that no detector-descriptor combination performs well with viewpoint changes of more than 25---30?.


title: Are wound-rotor synchronous motors suitable for use in high efficiency torque-dense automotive drives?

abstract: This paper discusses the possibility of using wound-rotor synchronous motors in automotive drive applications. Currently the brushless rare-earth magnet machine is the main choice of drive motor because it has high efficiency and it is very torque-dense, although some vehicles using induction motors. Issues with rare-earth material supply means manufacturers are looking at alternatives. This paper reviews the requirements for a wound-rotor synchronous machine using a current automotive drive application and carries out some load simulations using 2D finite element analysis. Direct comparison is made to a brushless permanent-magnet machine and first-pass designs are presented. It is found that there are possibilities for this machine in this application; possibilities that current practice largely ignores.


title: Uptake and dissipation of neonicotinoid residues in nectar and foliage of systemically treated woody landscape plants

abstract: Systemic neonicotinoid insecticides used in urban arboriculture could pose a risk to bees and other pollinators foraging on treated plants. We measured uptake and dissipation of soil-applied imidacloprid and dinotefuran in nectar and leaves of 2 woody plant species, a broadleaf evergreen tree (Ilex × attenuata) and a deciduous shrub (Clethra alnifolia), to assess concentrations to which pollinators and pests might be exposed in landscape settings. Three application timings, autumn (postbloom), spring (prebloom), and summer (early postbloom), were evaluated to see if taking advantage of differences in the neonicotinoids’ systemic mobility and persistence might enable pest control while minimizing transference into nectar. Nectar and tissue samples were collected from in-ground plants and analyzed for residues by high-performance liquid chromatography–tandem mass spectrometry (HPLC–MS/MS) in 2 successive years. Concentrations found in nectar following autumn or spring applications ranged from 166 to 515 ng/g for imidacloprid and from 70 to 1235 ng/gg for dinotefuran, depending on plant and timing. These residues exceed concentrations shown to adversely affect individual- and colony-level traits of bees. Summer application mitigated concentrations of imidacloprid (8–31 ng/g), but not dinotefuran (235–1191 ng/g), in nectar. Our data suggest that dinotefuran may be more persistent than is generally believed. Implications for integrated pest and pollinator management in urban landscapes are discussed. Environ Toxicol Chem 2017;9999:1–11. © 2017 SETAC


title: A survey of perception and computation of human beauty

abstract: Perception of (facial or bodily) beauty has long been debated amongst philosophers, artists, psychologists and anthropologists. Ancient philosophers claimed that there is a timeless, aesthetic ideal concept of beauty based on proportions, symmetry, harmony, and geometry, that goes well beyond the observer. Modern philosophers, on the other hand, have commonly suggested that beauty is in the eye of the beholder, and that beauty canons depend on culture. Despite the continuous interest and extensive research in cognitive, evolutionary and social sciences, modeling and analysis of human beauty and aesthetic canons remains open. Therefore, this paper aims to put the beauty trait under the spotlight by investigating various aspects involved in its perception and computation.


title: Efficient Optical flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone

abstract: Micro Aerial Vehicles (FOV) are very suitable for flying in indoor environments, but autonomous navigation is challenging due to their strict hardware limitations. This paper presents a highly efficient computer vision algorithm called Edge-FS for the determination of velocity and depth. It runs at 20 Hz on a 4 g stereo camera with an embedded STM32F4 microprocessor (168 MHz, 192 kB) and uses edge distributions to calculate optical flow and stereo disparity. The stereo-based distance estimates are used to scale the optical flow in order to retrieve the drone's velocity. The velocity and depth measurements are used for fully autonomous flight of a 40 g pocket drone only relying on on-board sensors. This method allows the MAV to control its velocity and avoid obstacles.


title: Applying Asynchronous Deep Classification Networks and Gaming Reinforcement Learning-Based Motion Planners to Mobile Robots

abstract: In this paper, we propose a new methodology to embed deep learning-based algorithms in both visual recognition and motion planning for general mobile robotic platforms. A framework for an asynchronous deep classification network is introduced to integrate heavy deep classification networks into a mobile robot with no loss of system bandwidth. Moreover, a gaming reinforcement learning-based motion planner, a novel and convenient embodiment of reinforcement learning, is introduced for simple implementation and high applicability. The proposed approaches are implemented and evaluated on a developed robot, TT2-bot. The evaluation was based on a mission devised for a qualitative evaluation of the general purposes and performances of a mobile robotic platform. The robot was required to recognize targets with a deep classifier and plan the path effectively using a deep motion planner. As a result, the robot verified that the proposed approaches successfully integrate deep learning technologies on the stand-alone mobile robot. The embedded neural networks for recognition and path planning were critical components for the robot.


title: Fuzzy Identity Based Encryption Scheme with Some Assigned Attributes

abstract: In this paper the concept of Fuzzy IBE schemes with some fixed attributes (SAA-FIBE) is proposed and one construction of it is presented. SAA-FIBE scheme can be viewed as a variant of SW scheme described in \cite{SahaiW05} which demanding no fixed positive or negative attributes. In our scheme, a user with identity $ \omega $ can decrypt the message that is encrypted with a set of attributes, $ \omega ' $, if and only if $ | \omega ' \cap \omega | \geq d $ and $ \omega $ must have or must have not some attributes described in encryption policy. The scheme are both error-tolerant and secure against collusion attacks in the SPID-FIBE attack model.


title: Advances in Dielectric Elastomers for Actuators and Artificial Muscles

abstract: A number of materials have been explored for their use as artificial muscles. Among these, dielectric elastomers (DEs) appear to provide the best combination of properties for true muscle-like actuation. DEs behave as compliant capacitors, expanding in area and shrinking in thickness when a voltage is applied. Materials combining very high energy densities, strains, and efficiencies have been known for some time. To date, however, the widespread adoption of DEs has been hindered by premature breakdown and the requirement for high voltages and bulky support frames. Recent advances seem poised to remove these restrictions and allow for the production of highly reliable, high-performance transducers for artificial muscle applications.


title: Location-aware brokering for consumers in multi-cloud computing environments

abstract: The variety and complexity in cloud marketplaces is growing, making it difficult for cloud consumers to choose cloud services from multiple providers in an economic and suitable way by taking into account multiple objectives and constraints. In this paper, we present an extension of CloudSim implementing cloud management functionality to enable the assessment of consumer-oriented brokering schemes. The underlying discrete-event simulation framework allows evaluating their performance in more realistic operating conditions in a repeatable manner. We integrate brokering mechanisms to support a multi-criteria location-aware selection of virtual machines in multi-cloud environments by implementing a greedy heuristic and two large neighborhood search metaheuristics. Based on microbenchmarks of real cloud offerings and a diverse set of scenarios and workloads, we conduct simulation experiments to assess the performance of our approaches. The results show that approximately 10 12% of the total costs can be saved by using a large neighborhood search approach compared to the greedy heuristic. Finally, we analyze and discuss the trade-off between costs and latency as well as the impact of region constraints, showing, e.g., that latency improvements often come at a high price and a greater regional flexibility can lead to latency improvements while solely optimizing costs. Using real data of cloud marketplaces, we show that the proposed CloudSim extension can support decision makers as a tool for assessing cloud portfolios and market dynamics.


Strategies for improving the quality of work life.


title: Developing an extended technology acceptance model: Doctor's acceptance of electronic medical records in Jordan

abstract: The purpose of this study is to construct doctors’ acceptance model of Electronic Medical Records (EMR) in private hospitals.The model extends the Technology Acceptance Model (TAM) with two factors of Individual Capabilities; Self-Efficacy (SE) and Perceived Behavioral Control (PBC).The initial findings proposes additional factors over the original factors in TAM making Perceived Usefulness (PU), Perceived Ease Of Use (PEOU), Behavioral Intention to use (BI), SE, and PBC working in incorporation.A cross-sectional survey was used in which data were gathered by a personal administered questionnaire as the instrument for data collection.Doctors of public hospitals were involved in this study which proves that all factors are reliable.


title: Supramolecular Interactions Involved in the Solid State Structure of N,N'-[bis(pyridin-2-yl)formylidene]ethane-1,2-diamine

abstract: The structure of the symmetrical Schiff base, N,N'-(bis(pyridin-2-yl)formylidene)ethane-1,2- diamine (bpfd) has been characterized by single crystal X-ray diffraction. The non-covalent supramolecular chemistry involved in the crystal structure of this ligand has been carefully investigated. The structure adopted different motifs of nitrogen-hydrogen interactions that led to the formation of centrosymmetric dimers. In addition, edge-edge and face-face nitrogen-nitrogen interactions were ob- served and reported. The Schiff base (bpfd) ligand crystallizes in a monoclinic space group C12/c1 with a


title: DBSCAN on Resilient Distributed Datasets

abstract: DBSCAN is a well-known density-based data clustering algorithm that is widely used due to its ability to find arbitrarily shaped clusters in noisy data. However, DBSCAN is hard to scale which limits its utility when working with large data sets. Resilient Distributed Datasets (RDDs), on the other hand, are a fast data-processing abstraction created explicitly for in-memory computation of large data sets. This paper presents a new algorithm based on DBSCAN using the Resilient Distributed Datasets approach: RDD-DBSCAN. RDD-DBSCAN overcomes the scalability limitations of the traditional DBSCAN algorithm by operating in a fully distributed fashion. The paper also evaluates an implementation of RDD-DBSCAN using Apache Spark, the official RDD implementation.


title: Initial observations on economics, pricing, and penetration of the internet of things market

abstract: One expectation about the future Internet is the participation of billions of sensor nodes, integrating the physical with the digital world. This Internet of Things can offer new and enhanced services and applications based on knowledge about the environment and the entities within. Millions of micro-providers could come into existence, forming a highly fragmented market place with new business opportunities to offer commercial services. In the related field of Internet and Telecommunication services, the design of markets and pricing schemes has been a vital research area in itself. We discuss how these findings can be transferred to the Internet of Things. Both the appropriate market structure and corresponding pricing schemes need to be well understood to enable a commercial success of sensor-based services. We show some steps that an evolutionary establishment of this market might have to take.


title: Strangers on Your Phone: Why People Use Anonymous Communication Applications

abstract: Anonymity online is important to people at times in their lives. Anonymous communication applications such as Whisper and YikYak enable people to communicate with strangers anonymously through their smartphones. We report results from semi-structured interviews with 18 users of these apps. The goal of our study was to identify why and how people use anonymous apps, their perceptions of their audience and interactions on the apps, and how these apps compare with other online social communities. We present a typology of the content people share, and their motivations for participation in anonymous apps. People share various types of content that range from deep confessions and secrets to lighthearted jokes and momentary feelings. An important driver for participation and posting is to get social validation from others, even though they are anonymous strangers. We also find that participants believe these anonymous apps allow more honesty, openness, and diversity of opinion than they can find elsewhere. Our results provide implications for how anonymity in mobile apps can encourage expressiveness and interaction among users.


title: Automatic labeling of molecular biomarkers of whole slide immunohistochemistry images using fully convolutional networks

abstract: This paper addresses the problem of quantifying biomarkers in multi-stained tissues, based on color and spatial information. A deep learning based method that can automatically localize and quantify the cells expressing biomarker(s) in a whole slide image is proposed. The deep learning network is a fully convolutional network (FCN) whose input is the true RGB color image of a tissue and output is a map of the different biomarkers. The FCN relies on a convolutional neural network (CNN) that classifies each cell separately according to the biomarker it expresses. In this study, images of immunohistochemistry (IHC) stained slides were collected and used. More than 4,500 RGB images of cells were manually labeled based on the expressing biomarkers. The labeled cell images were used to train the CNN (obtaining an accuracy of 92% in a test set). The trained CNN is then extended to an FCN that generates a map of all biomarkers in the whole slide image acquired by the scanner (instead of classifying every cell image). To evaluate our method, we manually labeled all nuclei expressing different biomarkers in two whole slide images and used theses as the ground truth. Our proposed method for immunohistochemical analysis compares well with the manual labeling by humans (average F-score of 0.96).


title: Scale-aware multi-level guidance for interactive instance segmentation

abstract: In interactive instance segmentation, users give feedback to iteratively refine segmentation masks. The user-provided clicks are transformed into guidance maps which provide the network with necessary cues on the whereabouts of the object of interest. Guidance maps used in current systems are purely distance-based and are either too localized or non-informative. We propose a novel transformation of user clicks to generate scale-aware guidance maps that leverage the hierarchical structural information present in an image. Using our guidance maps, even the most basic FCNs are able to outperform existing approaches that require state-of-the-art segmentation networks pre-trained on large scale segmentation datasets. We demonstrate the effectiveness of our proposed transformation strategy through comprehensive experimentation in which we significantly raise state-of-the-art on four standard interactive segmentation benchmarks.


title: Time Series Featurization via Topological Data Analysis: an Application to Cryptocurrency Trend Forecasting

abstract: We propose a novel methodology for feature extraction from time series data based on topological data analysis. The proposed procedure applies a dimensionality reduction technique via principal component analysis to the point cloud of the Takens' embedding from the observed time series and then evaluates the persistence landscape and silhouettes based on the corresponding Rips complex. We define a new notion of Rips distance function that is especially suited for persistence homologies built on Rips complexes and prove stability theorems for it. We use these results to demonstrate in turn some stability properties of the topological features extracted using our procedure with respect to additive noise and sampling. We further apply our method to the problem of trend forecasting for cryptocurrency prices, where we manage to achieve significantly lower error rates than more standard, non TDA-based methodologies in complex pattern classification tasks. We expect our method to provide a new insight on feature engineering for granular, noisy time series data.


title: Complete the Look: Scene-Based Complementary Product Recommendation

abstract: Modeling fashion compatibility is challenging due to its complexity and subjectivity. Existing work focuses on predicting compatibility between product images (e.g. an image containing a t-shirt and an image containing a pair of jeans). However, these approaches ignore real-world ‘scene’ images (e.g. selfies); such images are hard to deal with due to their complexity, clutter, variations in lighting and pose (etc.) but on the other hand could potentially provide key context (e.g. the user’s body type, or the season) for making more accurate recommendations. In this work, we propose a new task called ‘Complete the Look’, which seeks to recommend visually compatible products based on scene images. We design an approach to extract training data for this task, and propose a novel way to learn the scene-product compatibility from fashion or interior design images. Our approach measures compatibility both globally and locally via CNNs and attention mechanisms. Extensive experiments show that our method achieves significant performance gains over alternative systems. Human evaluation and qualitative analysis are also conducted to further understand model behavior. We hope this work could lead to useful applications which link large corpora of real-world scenes with shoppable products.


title: Background Subtraction Based on Color and Depth Using Active Sensors

abstract: Depth information has been used in computer vision for a wide variety of tasks. Since active range sensors are currently available at low cost, high-quality depth maps can be used as relevant input for many applications. Background subtraction and video segmentation algorithms can be improved by fusing depth and color inputs, which are complementary and allow one to solve many classic color segmentation issues. In this paper, we describe one fusion method to combine color and depth based on an advanced color-based algorithm. This technique has been evaluated by means of a complete dataset recorded with Microsoft Kinect, which enables comparison with the original method. The proposed method outperforms the others in almost every test, showing more robustness to illumination changes, shadows, reflections and camouflage.


Balanced and Unbalanced Voltage Sag Mitigation Using DSTATCOM with Linear and Nonlinear Loads


title: Dead reckoning on smartphones to reduce GPS usage

abstract: GPS localization drains battery aggressively if it is done continuously on a smartphone. This high battery drainage could be reduced by lowering the sampling rate of GPS. However, to fill the reception gaps introduced by low sampling rate, other methods need to be explored. The sensors available in the smartphones allow incremental positioning based on dead reckoning. Even though dead reckoning is a widely researched topic in navigation and walking based localization, it still lacks research on smartphone low-quality sensor based localization for vehicular motion. Therefore this paper describes a dead reckoning system design and implementation for vehicular motion on Android smartphones. It adapts and presents currently used methods for each system step (i.e. filtering, gravity removal, heading estimation and velocity/displacement integration). It also presents new research finding on accelerometer filtering process (i.e. the relationship between acceleration/deceleration to main frequency components of the motion).


title: Health Promotion by Social Cognitive Means

abstract: This article examines health promotion and disease prevention from the perspective of social cognitive theory. This theory posits a multifaceted causal structure in which self-efficacy beliefs operate together with goals, outcome expectations, and perceived environmental impediments and facilitators in the regulation of human motivation, behavior, and well-being. Belief in one’s efficacy to exercise control is a common pathway through which psychosocial influences affect health functioning. This core belief affects each of the basic processes of personal change—whether people even consider changing their health habits, whether they mobilize the motivation and perseverance needed to succeed should they do so, their ability to recover from setbacks and relapses, and how well they maintain the habit changes they have achieved. Human health is a social matter, not just an individual one. A comprehensive approach to health promotion also requires changing the practices of social systems that have widespread effects on human health.


title: One-shot learning for RGB-D hand-held object recognition

abstract: With the advance of computer technology and smart device, many applications, such as face recognition and object recognition, have been developed to facilitate human-computer interaction (HCI) efficiently. In this respect, the hand-held object recognition plays an important role in HCI. It can be used not only to help computer understand useros intentions but also to meet useros requirements. In recent years the appearance of convolutional neural networks (CNNs) greatly enhances the performance of object recognition and this technology has been applied to hand-held object recognition in some works. However, these supervised learning models need large number of labelled data and many iterations to train their large number of parameters. This is a huge challenge for HCI, because HCI need to deal with in-time and itos difficult to collect enough labeled data. Especially when a new category need to be learnt, it will spend a lot of time to update the model. In this work, we adopt the one-shot learning method to solve this problem. This method does not need to update the model when a new category need to be learnt. Moreover, depth image is robust to light and color variation. We fuse depth image information to harness the complementary relationship between the two modalities to improve the performance of hand-held object recognition. Experimental results on our handheld object dataset demonstrate that our method for hand-held object recognition achieves an improvement of performance.


title: Parameter identification and automatic control loop tuning for PMAC servo motor drives

abstract: High performance servo motor drives generally consisted of current, velocity, and position control loops. Tuning these controllers to achieve satisfactory and consistent dynamic responses is important. In this paper, a parameter identification and auto-tuning scheme for PMAC servo motor drives is presented. Motor electrical parameters such as resistance and inductances are identified first for current control loop tuning. Then, torque constant and mechanical parameters are identified for velocity and position loop tuning. The experimental results verified that the proposed scheme can estimate parameters with good accuracy. Furthermore, depending on the setting of the tuning process, the auto-tune process can be completed within 1.9 seconds. The control system tuned by the proposed scheme can achieve good dynamic performance.


title: Speed following control for differential steering of 4WID electric vehicle

abstract: This paper proposed a control strategy of differential steering specialized for four wheel independent driving (4WID) EVs. Different from traditional single torque-based control method, this strategy is designed to use the advantage of fast speed control of in-wheel motor. Four wheels are regulated to desired differential speed based on the reference vehicle velocity. Combined with Ackermann-Jeantand steering model, kinematic model of the distributed wheels is introduced to achieve electrical differential by speed following control. This method can reduce tyre wearing and increase manual stability. By the simulation and experimental study of differential speed steering control, the effectiveness of the proposed control strategy is demonstrated.


title: Deep complementary bottleneck features for visual speech recognition

abstract: Deep bottleneck features (DBNFs) have been used successfully in the past for acoustic speech recognition from audio. However, research on extracting DBNFs for visual speech recognition is very limited. In this work, we present an approach to extract deep bottleneck visual features based on deep autoencoders. To the best of our knowledge, this is the first work that extracts DBNFs for visual speech recognition directly from pixels. We first train a deep autoencoder with a bottleneck layer in order to reduce the dimensionality of the image. Then the autoencoder's decoding layers are replaced by classification layers which make the bottleneck features more discriminative. Discrete Cosine Transform (DCT) features are also appended in the bottleneck layer during training in order to make the bottleneck features complementary to DCT features. Long-Short Term Memory (LSTM) networks are used to model the temporal dynamics and the performance is evaluated on the OuluVS and AVLetters databases. The extracted complementary DBNF in combination with DCT features achieve the best performance resulting in an absolute improvement of up to 5% over the DCT baseline.


title: Clustering with Missing Values: No Imputation Required,” Classification

abstract: Clustering algorithms can identify groups in large data sets, such as star catalogs and hyperspectral images. In general, clustering methods cannot analyze items that have missing data values. Common solutions either fill in the missing values (imputation) or ignore the missing data (marginalization). Imputed values are treated as being just as reliable as the observed data, but they are only as good as the assumptions used to create them. In contrast, we present a method for encoding partially observed features as a set of supplemental soft constraints and introduce the KSC algorithm, which incorporates constraints into the clustering process. In experiments on artificial data and data from the Sloan Digital Sky Survey, we show that soft constraints are an effective way to enable clustering with missing values.


title: Design and implementation of a quadrotor tail-sitter VTOL UAV

abstract: We present the design and implementation of a quadrotor tail-sitter Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV). The VTOL UAV combines the advantage of a quadrotor, vertical take-off and landing and hovering at a stationary point, with that of a fixed-wing, efficient level flight. We describe our vehicle design with special considerations on fully autonomous operation in a real outdoor environment where the wind is present. The designed quadrotor tail-sitter UAV has insignificant vibration level and achieves stable hovering and landing performance when a cross wind is present. Wind tunnel test is conducted to characterize the full envelope aerodynamics of the aircraft, based on which a flight controller is designed, implemented and tested. MATLAB simulation is presented and shows that our vehicle can achieve a continuous transition from hover flight to level flight. Finally, both indoor and outdoor flight experiments are conducted to verify the performance of our vehicle and the designed controller.


Crowdfunding the next hit : Microfunding online experience goods


Question Answering on SQuAD


Appropriate Intervention Strategies for Weight Loss and Prevention of Weight Regain for Adults


title: Medium frequency induction melting furnace as a load on the power system

abstract: The variable frequency operation of the coreless induction melting furnace has been investigated in detail both theoretically and experimentally. The time variations of the operational impedance value of the induction melting furnace, owing to the time-varying resonance frequency and crucible conditions, have been derived for typical melting cycles by using a variable, parallel RLC model derived from extensive field measurements. The work coil and its crucible is supplied at variable frequency from a multi-pulse rectifier and a current-fed, load resonant inverter cascade. Uncharacteristic current harmonics caused by cross modulation phenomenon across the AC-DC-AC link have been derived theoretically, and verified by field tests. These theoretical results and extensive field measurements on a sample induction melting furnace installation have shown that i. the major power quality problem of an induction melting furnace as a load on the power system, is the injection of various characteristic and uncharacteristic current harmonics to the grid, ii. line currents on the supply side are nearly balanced, iii. flicker contribution is below the limits specified in the standards, iv. reactive power demand is relatively low and the variations in the magnitude and rate of change of active power are not drastic, and v. no voltage sag and swell phenomena arising from furnace operation occur.


title: Smart wireless healthcare monitoring for drivers community

abstract: Smart healthcare monitoring systems have been given more attention in recent research efforts as they are not only used for patients but also recommended for old age people, sports persons, drivers' community and home makers. For example, smart healthcare support is introduced in some expensive cars like Mercedes-Benz. In developing countries like India, some road accidents may happen due to driver's poor health condition like heart stroke while driving, over stress due to continuous work, etc. Hence, if a smart healthcare monitoring system is available at an affordable cost, it can be used for drivers' community to take some preventive measures, thus road accidents may be reduced. In this work, a healthcare monitoring system is designed to monitor the health status periodically and abnormal values are informed to both transport office and healthcare provider. The primary function of the system is to monitor a driver's physiological parameters such as, pulse rate, body temperature, etc., using sensors and transferred to a Smartphone using Bluetooth communication. Then, the proposed healthcare application in a Smartphone verifies the criticality. If there is any significant difference with normal value details are communicated to both healthcare service providers and transport office via cellular networks. Healthcare professionals take necessary action using ambulance service and Transport Corporation ensures driver's health status or arranges an alternative driver. The proposed system prototype is implemented and tested in our Network Lab.


Improving your data transformations: Applying the Box-Cox transformation


Color Dog - Guiding the Global Illumination Estimation to Better Accuracy


title: Cross-lingual transfer learning during supervised training in low resource scenarios

abstract: In this study, transfer learning techniques are presented for cross-lingual speech recognition to mitigate the effects of limited availability of data in a target language using data from richly resourced source languages. First, a maximum likelihood (ML) based regularization criterion is used to learn context-dependent Gaussian mixture model (GMM) based hidden Markov model (HMM) parameters for phones in target language using data from both target and source languages. Recognition results indicate improved HMM state alignments. Second, the hidden layers of a deep neural network (DNN) are initialized using unsupervised pre-training of a multilingual deep belief network (DBN). The DNN is fine-tuned jointly using a modified cross entropy criterion that uses HMM state alignments from both target and source languages. Third, another DNN fine-tuning technique is explored where the training is performed in a sequential manner source language followed by the target language. Experiments conducted using varying amounts of target data indicate further improvements in performance can be obtained using joint and sequential training of the DNN compared to existing techniques. Turkish and English were chosen to be the target and source languages respectively.


title: Data analysis and visualization using spectral decomposition and feature selection

abstract: The aim of this study is to develop a technique to mine time series data and extract patterns from it which help it to be visualized easily. These visualizations can be static or dynamic while showing the long term dominant trends in data and subduing the short term recurring features. The focus is on time series data from economics and business.


title: A comparative study of feature selection and machine learning techniques for sentiment analysis

abstract: Sentiment analysis is performed to extract opinion and subjectivity knowledge from user generated text content. This is contextually different from traditional topic based text classification since it involves classifying opinionated text according to the sentiment conveyed by it. Feature selection is a critical task in sentiment analysis and effectively selected representative features from subjective text can improve sentiment based classification. This paper explores the applicability of five commonly used feature selection methods in data mining research (DF, IG, GR, CHI and Relief-F) and seven machine learning based classification techniques (Naive Bayes, Support Vector Machine, Maximum Entropy, Decision Tree, K-Nearest Neighbor, Winnow, Adaboost) for sentiment analysis on online movie reviews dataset. The paper demonstrates that feature selection does improve the performance of sentiment based classification, but it depends on the method adopted and the number of feature selected. The experimental results presented in this paper show that Gain Ratio gives the best performance for sentimental feature selection, and SVM performs better than other techniques for sentiment based classification.


title: Taking Advantage of Sparsity in Multi-Task Learning

abstract: We study the problem of estimating multiple linear regression equations for the purpose of both prediction and variable selection. Following recent work on multi-task learning Argyriou et al. [2008], we assume that the regression vectors share the same sparsity pattern. This means that the set of relevant predictor variables is the same across the different equations. This assumption leads us to consider the Group Lasso as a candidate estimation method. We show that this estimator enjoys nice sparsity oracle inequalities and variable selection properties. The results hold under a certain restricted eigenvalue condition and a coherence condition on the design matrix, which naturally extend recent work in Bickel et al. [2007], Lounici [2008]. In particular, in the multi-task learning scenario, in which the number of tasks can grow, we are able to remove completely the effect of the number of predictor variables in the bounds. Finally, we show how our results can be extended to more general noise distributions, of which we only require the variance to be finite.


title: Product development performance measures in manufacturing firm

abstract: Product development is becoming more and more importance for manufacturing firm. A well-designed performance measurement system can demonstrate the impacts of product development on the whole firm and be utilized for motivation of people, especially product development staff. At the same time, suitable product development measures provide valuable information for the controlling of activities and continuous improvement of the product development process. This paper reviews the evolution of manufacturing sectors, analyzes the role of performance measures and product development performance measure proposals in manufacturing, especially from the standpoint of manufacturing firms in China.


title: Dispositional trust — Do we trust autonomous cars?

abstract: The 21st century is the age of automatization. While before the millennium day-to-day applications were semi-autonomous since they necessitated human intervention in achieving their goals, now we are living in a society that expects trustworthy autonomous systems in the industrial and business life as well as in their private life that can attain goals independently without any human intervention [1]. The automatons might be on the verge of completion, but are the people ready for them, or will they hesitate to use them because of lack of trust? Present article endeavours the tackle the issue of trust in automatic systems on the basis of a primary research on attitudes toward (semi) automated vehicles.


title: A methodology for evaluating algorithms for table understanding in PDF documents

abstract: This paper presents a methodology for the evaluation of table understanding algorithms for PDF documents. The evaluation takes into account three major tasks: table detection, table structure recognition and functional analysis. We provide a general and flexible output model for each task along with corresponding evaluation metrics and methods. We also present a methodology for collecting and ground-truthing PDF documents based on consensus-reaching principles and provide a publicly available ground-truthed dataset.


title: An Efficient Participant’s Selection Algorithm for Crowdsensing

abstract: With the advancement of mobile technology the use of Smartphone is greatly increased. Everyone has the mobile phones and it becomes the necessity of life. Today, smart devices are flooding the internet data at every time and in any form that cause the mobile crowdsensing (MCS). One of the key challenges in mobile crowd sensing system is how to effectively identify and select the well-suited participants in recruitments from a large user pool. This research work presents the concept of crowdsensing along with the selection process of participants from a large user pool. MCS provides the efficient selection process for participants that how well suited participant’s selects/recruit from a large user pool. For this, the proposed selection algorithm plays our role in which the recruitment of participants takes place with the availability status from the large user pool. At the end, the graphical result presented with the suitable location of the participants and their time slot.


title: Predicting ICU mortality risk by grouping temporal trends from a multivariate panel of physiologic measurements

abstract: ICU mortality risk prediction may help clinicians take effective interventions to improve patient outcome. Existing machine learning approaches often face challenges in integrating a comprehensive panel of physiologic variables and presenting to clinicians interpretable models. We aim to improve both accuracy and interpretability of prediction models by introducing Subgraph Augmented Non-negative Matrix Factorization (SANMF) on ICU physiologic time series. SANMF converts time series into a graph representation and applies frequent subgraph mining to automatically extract temporal trends. We then apply non-negative matrix factorization to group trends in a way that approximates patient pathophysiologic states. Trend groups are then used as features in training a logistic regression model for mortality risk prediction, and are also ranked according to their contribution to mortality risk. We evaluated SANMF against four empirical models on the task of predicting mortality or survival 30 days after discharge from ICU using the observed physiologic measurements between 12 and 24 hours after admission. SANMF outperforms all comparison models, and in particular, demonstrates an improvement in AUC (0.848 vs. 0.827, p<0.002) compared to a state-of-the-art machine learning method that uses manual feature engineering. Feature analysis was performed to illuminate insights and benefits of subgraph groups in mortality risk prediction.


title: Analysis of the Loaded Gait Subjected to the Trunk Flexion Change

abstract: In the paper, the energetic features of the loaded gait are newly analyzed depending on the trunk flexion change. To investigate the loaded gait, walking experiments are performed for five subjects and, the ground reaction forces and kinematic data are measured. Based on these information, we compute the impulse, momentum and mechanical works done on the center of body mass, through the trunk flexion change. As a result, it is shown that the trunk flexion change does not affect the impulses and momentums during the step-to-step transition as well. However, the direction of the pre-collision momentum does change depending on the trunk flexion change, which is degenerated just after (or during) the collision period. Keywords—Loaded gait, collision, impulse, gravity, heel strike, push-off, gait analysis.


Performance Measurement and Management ( PMM ) for SMEs : a literature review and a reference framework for PMM design


title: Benchmarking of Document Image Analysis Tasks for Palm Leaf Manuscripts from Southeast Asia

abstract: This paper presents a comprehensive test of the principal tasks in document image analysis (DIA), starting with binarization, text line segmentation, and isolated character/glyph recognition, and continuing on to word recognition and transliteration for a new and challenging collection of palm leaf manuscripts from Southeast Asia. This research presents and is performed on a complete dataset collection of Southeast Asian palm leaf manuscripts. It contains three different scripts: Khmer script from Cambodia, and Balinese script and Sundanese script from Indonesia. The binarization task is evaluated on many methods up to the latest in some binarization competitions. The seam carving method is evaluated for the text line segmentation task, compared to a recently new text line segmentation method for palm leaf manuscripts. For the isolated character/glyph recognition task, the evaluation is reported from the handcrafted feature extraction method, the neural network with unsupervised learning feature, and the Convolutional Neural Network (CNN) based method. Finally, the Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) based method is used to analyze the word recognition and transliteration task for the palm leaf manuscripts. The results from all experiments provide the latest findings and a quantitative benchmark for palm leaf manuscripts analysis for researchers in the DIA community.


title: On the Design of a Blockchain Platform for Clinical Trial and Precision Medicine

abstract: This paper proposes a blockchain platform architecture for clinical trial and precision medicine and discusses various design aspects and provides some insights in the technology requirements and challenges. We identify 4 new system architecture components that are required to be built on top of traditional blockchain and discuss their technology challenges in our blockchain platform: (a) a new blockchain based general distributed and parallel computing paradigm component to devise and study parallel computing methodology for big data analytics, (b) blockchain application data management component for data integrity, big data integration, and integrating disparity of medical related data, (c) verifiable anonymous identity management component for identity privacy for both person and Internet of Things (IoT) devices and secure data access to make possible of the patient centric medicine, and (d) trust data sharing management component to enable a trust medical data ecosystem for collaborative research.


title: Towards Smart Farming and Sustainable Agriculture with Drones

abstract: The use of drones in agriculture is becoming more and more popular. The paper presents a novel approach to distinguish between different field's plowing techniques by means of an RGB-D sensor. The presented system can be easily integrated in commercially available Unmanned Aerial Vehicles (UAVs). In order to successfully classify the plowing techniques, two different measurement algorithms have been developed. Experimental tests show that the proposed methodology is able to provide a good classification of the field's plowing depths.


title: A Quantum Random Walk Search Algorithm

abstract: Quantum random walks on graphs have been shown to display many interesting properties, including exponentially fast hitting times when compared with their classical counterparts. However, it is still unclear how to use these novel properties to gain an algorithmic speedup over classical algorithms. In this paper, we present a quantum search algorithm based on the quantum random-walk architecture that provides such a speedup. It will be shown that this algorithm performs an oracle search on a database of N items with O({radical}(N)) calls to the oracle, yielding a speedup similar to other quantum search algorithms. It appears that the quantum random-walk formulation has considerable flexibility, presenting interesting opportunities for development of other, possibly novel quantum algorithms.


title: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

abstract: Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at relatively low computational cost. Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network. This raises the question of whether there are any benefit in combining the Inception architecture with residual connections. Here we give clear empirical evidence that training with residual connections accelerates the training of Inception networks significantly. There is also some evidence of residual Inception networks outperforming similarly expensive Inception networks without residual connections by a thin margin. We also present several new streamlined architectures for both residual and non-residual Inception networks. These variations improve the single-frame recognition performance on the ILSVRC 2012 classification task significantly. We further demonstrate how proper activation scaling stabilizes the training of very wide residual Inception networks. With an ensemble of three residual and one Inception-v4, we achieve 3.08 percent top-5 error on the test set of the ImageNet classification (CLS) challenge


title: Authenticity, life satisfaction, and distress: a longitudinal analysis.

abstract: The purpose of the present study was to examine the reciprocal relationships between authenticity and measures of life satisfaction and distress using a 2-wave panel study design. Data were collected from 232 college students attending 2 public universities. Structural equation modeling was used to analyze the data. The results of the cross-lagged panel analysis indicated that after controlling for temporal stability, initial authenticity (Time 1) predicted later distress and life satisfaction (Time 2). Specifically, higher levels of authenticity at Time 1 were associated with increased life satisfaction and decreased distress at Time 2. Neither distress nor life satisfaction at Time 1 significantly predicted authenticity at Time 2. However, the relationship between Time 1 distress and Time 2 authenticity was not significantly different from the relationship between Time 1 authenticity and Time 2 distress. Results are discussed in light of humanistic-existential theories and the empirical research on well-being.


title: Enhanced Network Anomaly Detection Based on Deep Neural Networks

abstract: Due to the monumental growth of Internet applications in the last decade, the need for security of information network has increased manifolds. As a primary defense of network infrastructure, an intrusion detection system is expected to adapt to dynamically changing threat landscape. Many supervised and unsupervised techniques have been devised by researchers from the discipline of machine learning and data mining to achieve reliable detection of anomalies. Deep learning is an area of machine learning which applies neuron-like structure for learning tasks. Deep learning has profoundly changed the way we approach learning tasks by delivering monumental progress in different disciplines like speech processing, computer vision, and natural language processing to name a few. It is only relevant that this new technology must be investigated for information security applications. The aim of this paper is to investigate the suitability of deep learning approaches for anomaly-based intrusion detection system. For this research, we developed anomaly detection models based on different deep neural network structures, including convolutional neural networks, autoencoders, and recurrent neural networks. These deep models were trained on NSLKDD training data set and evaluated on both test data sets provided by NSLKDD, namely NSLKDDTest+ and NSLKDDTest21. All experiments in this paper are performed by authors on a GPU-based test bed. Conventional machine learning-based intrusion detection models were implemented using well-known classification techniques, including extreme learning machine, nearest neighbor, decision-tree, random-forest, support vector machine, naive-bays, and quadratic discriminant analysis. Both deep and conventional machine learning models were evaluated using well-known classification metrics, including receiver operating characteristics, area under curve, precision-recall curve, mean average precision and accuracy of classification. Experimental results of deep IDS models showed promising results for real-world application in anomaly detection systems.


title: Prevalence and frequency of self-perceived systemic features in people with joint hypermobility syndrome/Ehlers-Danlos syndrome hypermobility type

abstract: Some commonly reported systemic features of joint hypermobility syndrome (JHS)/Ehlers-Danlos syndrome hypermobility type (EDS-HT) are absent from nosologies due to insufficient validity. The primary aim was to examine the hypothesised high prevalence and frequency of orthostatic intolerance, easy bruising, and urinary incontinence in adults with JHS/EDS-HT and secondarily to determine the association between extent of generalised joint hypermobility (GJH) and these systemic features. A cross-sectional cohort study was conducted via online recruitment of medically diagnosed JHS/EDS-HT patients. A survey collected demographic data and clinical history. A subgroup of participants underwent physical testing of GJH using the Beighton score and Lower Limb Assessment Score (LLAS). Descriptive analysis was performed on demographic data and self-reported non-musculoskeletal systemic features. Correlation of GJH scores and systemic features were performed using Spearman’s rank correlation. The survey was completed by 116 individuals (95% female; 16–68 years) with 57 (93% female) also participating in the physical assessment. The most prevalent systemic feature was orthostatic intolerance (98%), followed by easy bruising and urinary incontinence (97% and 84% respectively). Of those reporting symptoms of orthostatic intolerance, easy bruising, and urinary incontinence, 58%, 40%, and 18% described them as very highly frequent respectively (frequency > 75%). No significant correlations were found between the extent of systemic features and GJH scores as measured by either the Beighton score or the LLAS. The high prevalence and frequency of the systemic features found in this study, which are omitted in diagnostic classification criteria, suggest that further research on their diagnostic accuracy is warranted.


title: Partnering to Analyze Selection of Resources by Medical Students for Case-Based Small Group Learning: A Collaboration between Librarians and Medical Educators.

abstract: Librarians and medical educators analyzed the quality of information resources used by first- and second-year medical students in their case-based small group learning summaries. Librarians provided instruction on using library resources and gave formal feedback to students about appropriate resources for basic science and clinical questions. The team found that students used a high number of clinical and basic science journal articles and textbooks with a number of factors influencing their resource choices. The study demonstrates numerous areas where librarians can play a key role in assisting students to find and assess information to answer clinical questions.


title: A Model for Temporal Dependencies in Event Streams

abstract: We introduce the Piecewise-Constant Conditional Intensity Model, a model for learning temporal dependencies in event streams. We describe a closed-form Bayesian approach to learning these models, and describe an importance sampling algorithm for forecasting future events using these models, using a proposal distribution based on Poisson superposition. We then use synthetic data, supercomputer event logs, and web search query logs to illustrate that our learning algorithm can efficiently learn nonlinear temporal dependencies, and that our importance sampling algorithm can effectively forecast future events.


EXISTENTIAL RISK PREVENTION AS THE MOST IMPORTANT TASK FOR HUMANITY


title: 7th Open Challenge on Question Answering over Linked Data (QALD-7)

abstract: The past years have seen a growing amount of research on question answering (QA) over Semantic Web data, shaping an interaction paradigm that allows end users to profit from the expressive power of Semantic Web standards while, at the same time, hiding their complexity behind an intuitive and easy-to-use interface. On the other hand, the growing amount of data has led to a heterogeneous data landscape where QA systems struggle to keep up with the volume, variety and veracity of the underlying knowledge.


title: Measuring query privacy in location-based services

abstract: The popularity of location-based services leads to serious concerns on user privacy. A common mechanism to protect users' location and query privacy is spatial generalisation. As more user information becomes available with the fast growth of Internet applications, e.g., social networks, attackers have the ability to construct users' personal profiles. This gives rise to new challenges and reconsideration of the existing privacy metrics, such as k-anonymity. In this paper, we propose new metrics to measure users' query privacy taking into account user profiles. Furthermore, we design spatial generalisation algorithms to compute regions satisfying users' privacy requirements expressed in these metrics. By experimental results, our metrics and algorithms are shown to be effective and efficient for practical usage.


title: The neural systems that mediate human perceptual decision making

abstract: Perceptual decision making is the act of choosing one option or course of action from a set of alternatives on the basis of available sensory evidence. Thus, when we make such decisions, sensory information must be interpreted and translated into behaviour. Neurophysiological work in monkeys performing sensory discriminations, combined with computational modelling, has paved the way for neuroimaging studies that are aimed at understanding decision-related processes in the human brain. Here we review findings from human neuroimaging studies in conjunction with data analysis methods that can directly link decisions and signals in the human brain on a trial-by-trial basis. This leads to a new view about the neural basis of human perceptual decision-making processes.


title: Automatic detection and correction of purple fringing using the gradient information and desaturation

abstract: This paper proposes a method to automatically detect and correct purple fringing that is one of the color artifacts due to characteristics of charge coupled device sensors in a digital camera. The proposed method consists of two steps. In the first step, we detect purple fringed regions that satisfy specific properties: hue characteristics around highlight regions with large gradient magnitudes. In the second step, color correction of the purple fringed regions is made by desaturating the pixels in the detected regions. The proposed method is able to detect purple fringe artifacts more precisely than Kang's method. It can be used as a post processing in a digital camera.