title: Social signals and algorithmic trading of Bitcoin

abstract: The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behaviour offer a great potential to drive trading strategies. We contribute to this by providing a consistent approach that integrates various datasources in the design of algorithmic traders. This allows us to derive insights into the principles behind the profitability of our trading strategies. We illustrate our approach through the analysis of Bitcoin, a cryptocurrency known for its large price fluctuations. In our analysis, we include economic signals of volume and price of exchange for USD, adoption of the Bitcoin technology and transaction volume of Bitcoin. We add social signals related to information search, word of mouth volume, emotional valence and opinion polarization as expressed in tweets related to Bitcoin for more than 3 years. Our analysis reveals that increases in opinion polarization and exchange volume precede rising Bitcoin prices, and that emotional valence precedes opinion polarization and rising exchange volumes. We apply these insights to design algorithmic trading strategies for Bitcoin, reaching very high profits in less than a year. We verify this high profitability with robust statistical methods that take into account risk and trading costs, confirming the long-standing hypothesis that trading-based social media sentiment has the potential to yield positive returns on investment.


Using Sentiment Analysis to Detect Customer Attitudes in Social Media Comments


title: Children, Wired: For Better and for Worse

abstract: Children encounter technology constantly at home and in school. Television, DVDs, video games, the Internet, and smart phones all play a formative role in children's development. The term "technology" subsumes a large variety of somewhat independent items, and it is no surprise that current research indicates causes for both optimism and concern depending upon the content of the technology, the context in which the technology immerses the user, and the user's developmental stage. Furthermore, because the field is still in its infancy, results can be surprising: video games designed to be reasonably mindless result in widespread enhancements of various abilities, acting, we will argue, as exemplary learning tools. Counterintuitive outcomes like these, besides being practically relevant, challenge and eventually lead to refinement of theories concerning fundamental principles of brain plasticity and learning.


title: Incremental indexing and distributed image search using shared randomized vocabularies

abstract: We present a cooperative framework for content-based image retrieval for the realistic setting where images are distributed across multiple cooperating servers. The proposed method is in line with bag-of-features approaches but uses fully data-independent, randomized structures, shared by the cooperating servers, to map image features to common visual words. A coherent, global image similarity measure (which is a kernel) is computed in a distributed fashion over visual words, by only requiring a small amount of data transfers between nodes. Our experiments on various image types show that this framework is a very promising step towards large-scale, distributed content-based image retrieval.


title: A low power Class D audio amplifier with discrete time loop filter compensation

abstract: This brief describes a new Class D amplifier topology with digital input and a digital compensation loop filter to improve linearity. The core of the system is a high resolution hybrid multi-bit ΣΔ-Pulse Width Modulation (PWM) modulator. The modulator does not require dynamic element matching nor signal processing to correct for distortion arising from the discrete PWM process. A system prototype based on a FPGA and commercial 16b analog to digital converter (ADC) validate the system design before implementation in 0.13µm CMOS. Measurement results at 1kHz yield a Total Harmonic Distortion (THD) better than 0.03% and greater than 30dB of noise rejection and linearity enhancement. The system is inherently scalable to deep submicron processes due to its predominately digital architecture.


title: Data from eye-tracking corpora as evidence for theories of syntactic processing complexity

abstract: We evaluate the predictions of two theories of syntactic processing complexity, dependency locality theory (DLT) and surprisal, against the Dundee Corpus, which contains the eye-tracking record of 10 participants reading 51,000 words of newspaper text. Our results show that DLT integration cost is not a significant predictor of reading times for arbitrary words in the corpus. However, DLT successfully predicts reading times for nouns. We also find evidence for integration cost effects at auxiliaries, not predicted by DLT. For surprisal, we demonstrate that an unlexicalized formulation of surprisal can predict reading times for arbitrary words in the corpus. Comparing DLT integration cost and surprisal, we find that the two measures are uncorrelated, which suggests that a complete theory will need to incorporate both aspects of processing complexity. We conclude that eye-tracking corpora, which provide reading time data for naturally occurring, contextualized sentences, can complement experimental evidence as a basis for theories of processing complexity.


title: An On-line Truthful and Individually Rational Pricing Mechanism for Ride-sharing

abstract: Ride-sharing has the potential of addressing many socioeconomic challenges related to transportation. The rising popularity of ride-sharing platforms (e.g., Uber, Lyft, DiDi) in addition to the emergence of new applications like food delivery and grocery shopping which use a similar platform, calls for an in-depth and detailed evaluation of various aspects of this problem. Auction frameworks and mechanism design, have been widely used for modeling ride-sharing platforms. A key challenge in these approaches is preventing the involving parties from manipulating the platform for their personal gain which in turn, can result in a less satisfactory experience for other parties and/or loss of profit for the platform provider. We introduce a latent space transition model for ride-sharing platforms which drivers can exploit and predict the future supply of the drivers (i.e., available drivers) to their own advantage. Following, we propose a pricing model for ride-sharing platforms which is both truthful and individually rational based on Vickery auctions and show how we can manage the loss of revenue in this approach. We compare our predicting model and pricing model with competing approaches through experiments on New York City's taxi dataset. Our results show that our model can accurately learn the transition patterns of people's ride requests. Furthermore, our pricing mechanism forces drivers to be truthful and takes away any unfair advantage the drivers can achieve by bidding untruthfully. More importantly, our pricing model forces truthfulness without sacrificing much profit unlike what is typical with second-price auction schemes.


title: A cognitive architecture for a humanoid robot: a first approach

abstract: Future life pictures humans having intelligent humanoid robotic systems taking part in their everyday life. Thus researchers strive to supply robots with an adequate artificial intelligence in order to achieve a natural and intuitive interaction between human being and robotic system. Within the German Humanoid Project we focus on learning and cooperating multimodal robotic systems. In this paper we present a first cognitive architecture for our humanoid robot: The architecture is a mixture of a hierarchical three-layered form on the one hand and a composition of behaviour-specific modules on the other hand. Perception, learning, planning of actions, motor control, and human-like communication play an important role in the robotic system and are embedded step by step in our architecture


title: Automatic facial emotion recognition using weber local descriptor for e-Healthcare system

abstract: For an e-Healthcare system, detecting the emotion of a patient is vital for initial assessment of the patient. This paper proposes an emotion recognition system from face using for an e-Healthcare system. For features, Weber local descriptors (WLD) are utilized. In the proposed system, a static facial image is subdivided into many blocks. A multi-scale WLD is applied to each of the blocks to obtain a WLD histogram for the image. The significant bins of the histogram are found by using Fisher discrimination ratio. These bin values represent the descriptors of the face. The face descriptors are then input to a support vector machine based classifier to recognition the emotion. Two publicly available databases are used in the experiments. Experimental results demonstrate that the proposed WLD based system achieves a very high recognition rate, where the highest recognition accuracy reaches up to 99.28 % in case of Cohn---Kanade database.


title: A Sentiment Analysis System of Spanish Tweets and Its Application in Colombia 2014 Presidential Election

abstract: What people say on social media has turned into a rich source of information to understand social behavior. Sentiment analysis of Twitter data has been widely used to capture trends in public opinion regarding important events such as political elections. However, current research in social media analysis in political domains faces two major problems, namely: sentiment analysis methods implemented are often too simple, and most of the works assume that all users and their tweets are trustworthy. This research is aimed at dealing with these problems to achieve more reliable public opinion measurements. First, a dataset of 513K tweets referring to Colombia 2014 presidential election was collected. To distinguish spammer accounts from non-spammer ones, a supervised learning technique was implemented on a labeled collection of users. Next, a sentiment analysis system was developed by following a supervised classification approach. Lastly, the system was applied in the Colombian election to investigate the potential of social media for voting intention inference. Experimental results show that inference methods based on Twitter data are not consistent, despite obtaining the lowest mean absolute error and correctly ranking the highest-polling candidates in the first round election with the proposed inference method.


title: Recognizing driver inattention by convolutional neural networks

abstract: Driver inattention has long been recognized as the main contributing factors in traffic accidents. Development of intelligent driver assistance systems with embedded functionality of driver vigilance monitoring is therefore an urgent and challenging task. This paper presents a novel system which applies convolutional neural network to automatically learn and predict the state of driver's eye, mouth and ear. The initial inspiration is to predict driver fatigue and distraction by analysing these states. In our works, a CNN model was trained with six classes of labeled data. The Approach was verified using self-specified Driving Dataset, which comprised of four activities, including normal driving, responding to a cell phone call, eating and falling asleep. Experiment results demonstrate that our design achieves a promising performance with a overall accuracy of 95.56% in classifying six states of the driver's eye, mouth and ear.


title: Opioid dosing trends and mortality in Washington State workers' compensation, 1996-2002.

abstract: BACKGROUND ::: The use of opioids for chronic non-cancer pain has increased in the United States since state laws were relaxed in the late 1990s. These policy changes occurred despite scanty scientific evidence that chronic use of opioids was safe and effective. ::: ::: ::: METHODS ::: We examined opiate prescriptions and dosing patterns (from computerized databases, 1996 to 2002), and accidental poisoning deaths attributable to opioid use (from death certificates, 1995 to 2002), in the Washington State workers' compensation system. ::: ::: ::: RESULTS ::: Opioid prescriptions increased only modestly between 1996 and 2002. However, prescriptions for the most potent opioids (Schedule II), as a percentage of all scheduled opioid prescriptions (II, III, and IV), increased from 19.3% in 1996 to 37.2% in 2002. Among long-acting opioids, the average daily morphine equivalent dose increased by 50%, to 132 mg/day. Thirty-two deaths were definitely or probably related to accidental overdose of opioids. The majority of deaths involved men (84%) and smokers (69%). ::: ::: ::: CONCLUSIONS ::: The reasons for escalating doses of the most potent opioids are unknown, but it is possible that tolerance or opioid-induced abnormal pain sensitivity may be occurring in some workers who use opioids for chronic pain. Opioid-related deaths in this population may be preventable through use of prudent guidelines regarding opioid use for chronic pain.


title: Data mining with Temporal Abstractions: learning rules from time series

abstract: A large volume of research in temporal data mining is focusing on discovering temporal rules from time-stamped data. The majority of the methods proposed so far have been mainly devoted to the mining of temporal rules which describe relationships between data sequences or instantaneous events and do not consider the presence of complex temporal patterns into the dataset. Such complex patterns, such as trends or up and down behaviors, are often very interesting for the users. In this paper we propose a new kind of temporal association rule and the related extraction algorithm; the learned rules involve complex temporal patterns in both their antecedent and consequent. Within our proposed approach, the user defines a set of complex patterns of interest that constitute the basis for the construction of the temporal rule; such complex patterns are represented and retrieved in the data through the formalism of knowledge-based Temporal Abstractions. An Apriori-like algorithm looks then for meaningful temporal relationships (in particular, precedence temporal relationships) among the complex patterns of interest. The paper presents the results obtained by the rule extraction algorithm on a simulated dataset and on two different datasets related to biomedical applications: the first one concerns the analysis of time series coming from the monitoring of different clinical variables during hemodialysis sessions, while the other one deals with the biological problem of inferring relationships between genes from DNA microarray data.


title: A comprehensive portrait of cilia and ciliopathies from a CRISPR-based screen for Hedgehog signaling

abstract: The primary cilium organizes Hedgehog signaling, shapes embryonic development and is the unifying cause of the ciliopathies. We conducted a functional genomic screen for Hedgehog signaling by engineering antibiotic-based selection of Hedgehog-responsive cells and applying genome-wide CRISPR-mediated gene disruption. The screen robustly identifies factors required for ciliary signaling with few false positives or false negatives. Characterization of hit genes uncovers novel components of several ciliary structures including a protein complex containing ε- and δ- tubulin that is required for centriole maintenance. The screen also provides an unbiased tool for classifying ciliopathies and reveals that many forms of congenital heart defects are ciliopathies. Collectively, this screen enables a systematic analysis of ciliary function and of ciliopathies and also defines a versatile platform for dissecting signaling pathways through CRISPR-based screening.


title: Faster Smith-Waterman database searches with inter-sequence SIMD parallelisation

abstract: Background ::: The Smith-Waterman algorithm for local sequence alignment is more sensitive than heuristic methods for database searching, but also more time-consuming. The fastest approach to parallelisation with SIMD technology has previously been described by Farrar in 2007. The aim of this study was to explore whether further speed could be gained by other approaches to parallelisation.


Manipulation Matters: Isolating the Impact of Lecture vs. Lab Experience in an Undergraduate Engineering Controls Class


title: A meta-plugin for bespoke data management in wordpress

abstract: WordPress is a powerful and extensible platform for web-based information publishing and management. While the WordPress core is targeted to the publication of chronologically ordered textual articles typical of blogs, users have developed plugins as well as themes to support the data management requirements of specific domains such as e-commerce or e-learning. However, the creation of such plugins requires development skills and effort. We present a meta-plugin that automatically generates bespoke plugins for data management based on user-defined ER models. We illustrate the approach using an example of creating a WordPress site for managing information about courses.


title: A Dual-Resonant Shorted Patch Antenna for Wearable Application in 430 MHz Band

abstract: In this communication, a novel approach to expand the bandwidth for the ultra-low profile wearable shorted patch is proposed and fabricated. It introduces dual-resonance to acquire 5.2% bandwidth within 0.358λ×0.358λ×0.011λ dimension at 430 MHz without increasing the structure complexity. By using EVA (ethylene-vinyl acetate) foam and copper gauze as the material, this dual-resonant shorted patch is light, flexible, and rigid enough for wearable application. SAR (special absorption rate) and performance after bending are simulated and analyzed to validate its reliability in wearable condition. The measurement result verifies the design concept, which can provide consistent radiation character, along with acceptable gain and efficiency in the whole working band.


title: Dynamic Self-modifying Code Detection Based on Backward Analysis

abstract: Self-modifying code (SMC) is widely used in obfuscated program for enhancing the difficulty in reverse engineering. The typical mode of self-modifying code is restore-execute-hide, it drives program to conceal real behaviors at most of the time, and only under actual running will the real code be restored and executed. In order to locate the SMC and further recover the original logic of code for guiding program analysis, dynamic self-modifying code detecting method based on backward analysis is proposed. Our method first extracts execution trace such as instructions and status through dynamic analysis. Then we maintain a memory set to store the memory address of execution instructions, the memory set will update dynamically while backward searching the trace, and simultaneously will we check the memory write address to match with current memory set in order to identify the mode "modify then execute". By means of validating self-modifying code which is identified via above procedures, we can easily deobfuscate the program which use self-modifying code and achieve its original logic. A prototype that can be applied in self-modifying code detection is designed and implemented. The evaluation results show our method can trace the execution of program effectively, and can reduce the consumption in time and space.


title: The Synthesis of Complex Audio Spectra by Means of Frequency Modulation

abstract: A new application of the well-known process of frequency modulation is shown to result in a surprising control of audio spectra. The technique provides a means of great simplicity to control the spectral components and their evolution in time. Such dynamic spectra are diverse in their subjective impressions and include sounds both known and unknown. In natural sounds the amplitudes of the frequency components of the spectrum are time-variant, or dynamic. The energy of the components often evolves in complicated ways, in particular, during the attack and decay portions of the sound. The temporal evolution of the spectrum is in some cases easily followed as with bells, whereas in other cases not, because the evolution occurs in a very short time period, but it is nevertheless perceived and is an important cue in the recognition of timbre. Many natural sounds seem to have characteristic spectral evolutions that, in addition to providing their "signature," are largely responsible for what we judge to be their lively quality. In contrast, it is largely the fixed proportion spectrum of most synthesized sounds that so readily imparts to the listener the electronic cue and lifeless quality. The special application of the equation for frequency modulation, described below, allows the production of complex spectra with very great simplicity. The fact that the temporal evolution of the frequency components of the spectrum can be easily controlled is perhaps the most striking attribute of the technique, for dynamic spectra are achieved only with considerable difficulty using current techniques of synthesis. At the end of this paper some simulations of brass, woodwind, and percussive sounds are given. The importance of these simulations is as much in their elegance and simplicity as it is in their accuracy. This frequency modulation technique, although not a physical model for natural sound, is shown to be a very powerful perceptual model for at least some.


title: Survey on resource allocation policy and job scheduling algorithms of cloud computing1

abstract: Cloud computing is the product of the evolution of calculation. It is a new distributed computing model. As more and more people put into the research and applications on cloud computing, the technology of computing becomes more and more widely used. Cloud computing has a huge user group. It has to deal with a large number of tasks. How to make appropriate decisions when allocating hardware resources to the tasks and dispatching the computing tasks to resource pool has become the main issue in cloud computing. This paper is based on the current situation of resource allocation policy and job scheduling algorithms under cloud circumstance. It summarizes some methods to improve the performance, including dynamic resource allocation strategy based on the law of failure, dynamic resource assignment on the basis of credibility, ant colony optimization algorithm for resource allocation, dynamic scheduling algorithm based on threshold, optimized genetic algorithm with dual fitness and improved ant colony algorithm for job scheduling.


title: Miniaturized and Reconfigurable CPW Square-Ring Slot Antenna Loaded With Ferroelectric BST Thin Film Varactors

abstract: A novel miniaturized and reconfigurable coplanar waveguide (CPW) square-ring slot antenna is presented in this paper. The miniaturization is achieved via a hybrid approach including ferroelectric varactor loadings, high dielectric constant materials, and tuning stub for impedance matching. For the first time, nine shunt ferroelectric (FE) BST (Ba(1-x)SrxTiO3) thin film varactors are integrated with the CPW antenna structure achieving both antenna miniaturization and reconfiguration at the same time. The size of the miniaturized antenna is reduced to 0.067λ0 0.067λ0 without ground, and 0.1λ0 0.1λ0 with ground. The resonant frequency of the miniaturized antenna can be reconfigured from 5.3 GHz to 5.8 GHz by applying a DC voltage. Measured E-plane and H-plane co-polarized patterns are presented.


title: T2-based segmentation of periventricular volumes for quantification of proton magnetic resonance spectra of multiple sclerosis lesions

abstract: Partial volume averaging of signal from multiple sclerosis lesions influences biexponential fitting of the water T2 relaxation as used for tissue/CSF segmentation of spectroscopic volumes. Periventricular volumes-of-interest comprising CSF, lesion and normal-appearing white matter in varying proportion were studied. The relaxation of the localized water signal was measured at 12 echo times (STEAM, geometric spacing from 30 ms to 2000 ms, least-squares fit). Magnetization transfer (MT) was applied to identify contributions of tissue signal to the CSF component. The T2 of the long-lived component (T2long=433–1782 ms) and its prolongation after MT were inversely correlated to the MT ratio. Hence, short T2long is associated with overestimation of CSF partial volume, and thus metabolite concentrations. A T2-correction for the CSF partial volume was suggested and applied to the quantification of MR spectra of large MS lesions. The T2 of bulk CSF (2280±87 ms) and the influence of the TE sampling scheme were also studied.


title: Pulse transit time: validation of blood pressure measurement under positive airway pressure ventilation

abstract: Purpose ::: The objective of the present study was to validate noninvasive, continuous blood pressure (BP) measurement using pulse transit time (PTT) to represent absolute values and detect BP changes under continuous positive airway pressure (CPAP) treatment.


title: Loginson: a transform and load system for very large scale log analysis in large IT infrastructures

abstract: Nowadays, most systems and applications produce log records that are useful for security and monitoring purposes such as debugging programming errors, checking system status, and detecting configuration problems or even attacks. To this end, a log repository becomes necessary whereby logs can be accessed and visualized in a timely manner. This paper presents Loginson, a high-performance log centralization system for large-scale log collection and processing in large IT infrastructures. Besides log collection, Loginson provides high-level analytics through a visual interface for the purpose of troubleshooting critical incidents. We note that Loginson outperforms all of the other log centralization solutions by taking full advantage of the vertical scalability, and therefore decreasing Capital Expenditure (CAPEX) and Operating Expense (OPEX) costs for deployment scenarios with a huge volume of log data.


title: Deep Feature Selection: Theory and Application to Identify Enhancers and Promoters

abstract: Abstract Sparse linear models approximate target variable(s) by a sparse linear combination of input variables. Since they are simple, fast, and able to select features, they are widely used in classification and regression. Essentially they are shallow feed-forward neural networks that have three limitations: (1) incompatibility to model nonlinearity of features, (2) inability to learn high-level features, and (3) unnatural extensions to select features in a multiclass case. Deep neural networks are models structured by multiple hidden layers with nonlinear activation functions. Compared with linear models, they have two distinctive strengths: the capability to (1) model complex systems with nonlinear structures and (2) learn high-level representation of features. Deep learning has been applied in many large and complex systems where deep models significantly outperform shallow ones. However, feature selection at the input level, which is very helpful to understand the nature of a complex system, is stil...


title: Region Representation Learning via Mobility Flow

abstract: Increasing amount of urban data are being accumulated and released to public; this enables us to study the urban dynamics and address urban issues such as crime, traffic, and quality of living. In this paper, we are interested in learning vector representations for regions using the large-scale taxi flow data. These representations could help us better measure the relationship strengths between regions, and the relationships can be used to better model the region properties. Different from existing studies, we propose to consider both temporal dynamics and multi-hop transitions in learning the region representations. We propose to jointly learn the representations from a flow graph and a spatial graph. Such a combined graph could simulate individual movements and also addresses the data sparsity issue. We demonstrate the effectiveness of our method using three different real datasets.


title: Hardware acceleration of BWA-MEM genomic short read mapping for longer read lengths

abstract: We present our work on hardware accelerated genomics pipelines, using either FPGAs or GPUs to accelerate execution of BWA-MEM, a widely-used algorithm for genomic short read mapping. The mapping stage can take up to 40% of overall processing time for genomics pipelines. Our implementation offloads the Seed Extension function, one of the main BWA-MEM computational functions, onto an accelerator. Sequencers typically output reads with a length of 150 base pairs. However, read length is expected to increase in the near future. Here, we investigate the influence of read length on BWA-MEM performance using data sets with read length up to 400 base pairs, and introduce methods to ameliorate the impact of longer read length. For the industry-standard 150 base pair read length, our implementation achieves an up to two-fold increase in overall application-level performance for systems with at most twenty-two logical CPU cores. Longer read length requires commensurately bigger data structures, which directly impacts accelerator efficiency. The two-fold performance increase is sustained for read length of at most 250 base pairs. To improve performance, we perform a classification of the inefficiency of the underlying systolic array architecture. By eliminating idle regions as much as possible, efficiency is improved by up to +95%. Moreover, adaptive load balancing intelligently distributes work between host and accelerator to ensure use of an accelerator always results in performance improvement, which in GPU-constrained scenarios provides up to +45% more performance.


title: Visualizing SAT Instances and Runs of the DPLL Algorithm

abstract: SAT-solvers have turned into essential tools in many areas of applied logic like, for example, hardware verification or satisfiability checking modulo theories. However, although recent implementations are able to solve problems with hundreds of thousands of variables and millions of clauses, much smaller instances remain unsolved. What makes a particular instance hard or easy is at most partially understood --- and is often attributed to the instance's internal structure. By converting SAT instances into graphs and applying established graph layout techniques, this internal structure can be visualized and thus serve as the basis of subsequent analysis. Moreover, by providing tools that animate the structure during the run of a SAT algorithm, dynamic changes of the problem instance become observable. Thus, we expect both to gain new insights into the hardness of the SAT problem and to help in teaching SAT algorithms.


title: Towards a Better Understanding of Context and Context-Awareness

abstract: When humans talk with humans, they are able to use implicit situational information, or context, to increase the conversational bandwidth. Unfortunately, this ability to convey ideas does not transfer well to humans interacting with computers. In traditional interactive computing, users have an impoverished mechanism for providing input to computers. By improving the computer’s access to context, we increase the richness of communication in human-computer interaction and make it possible to produce more useful computational services. The use of context is increasingly important in the fields of handheld and ubiquitous computing, where the user?s context is changing rapidly. In this panel, we want to discuss some of the research challenges in understanding context and in developing context-aware applications.


title: Deep Learning for Human Activity Recognition in Mobile Computing

abstract: By leveraging advances in deep learning, challenging pattern recognition problems have been solved in computer vision, speech recognition, natural language processing, and more. Mobile computing has also adopted these powerful modeling approaches, delivering astonishing success in the field’s core application domains, including the ongoing transformation of human activity recognition technology through machine learning.


title: Improved Random Forest for Classification

abstract: We propose an improved random forest classifier that performs classification with a minimum number of trees. The proposed method iteratively removes some unimportant features. Based on the number of important and unimportant features, we formulate a novel theoretical upper limit on the number of trees to be added to the forest to ensure improvement in classification accuracy. Our algorithm converges with a reduced but important set of features. We prove that further addition of trees or further reduction of features does not improve classification performance. The efficacy of the proposed approach is demonstrated through experiments on benchmark data sets. We further use the proposed classifier to detect mitotic nuclei in the histopathological data sets of breast tissues. We also apply our method on the industrial data set of dual-phase steel microstructures to classify different phases. Results of our method on different data sets show significant reduction in an average classification error compared with a number of competing methods.


title: A mood- and situation-based model for developing intuitive Pop music recommendation systems

abstract: Traditionally, a per-song-purchased base recommendation system is used on most music websites, but this method produces unsatisfactory results under various situational practices. This study proposes a hybrid procedure that includes both an expert-attributes selection capability and a mood/situation-attributes categorization functionality. This procedure fosters the development of a so-called MoMusic model as an unlimited online streaming service to replace current systems and artfully provide music to interested parties. This study employs a dataset consisting of 821 songs from the 2005-2010 annual music rankings as well as songs from the top artists from 2009 to 2010 from Taiwan's popular KKBOX music streaming service. The experimental dataset was assessed and coded by domain experts, and the expert-attributes selections and mood/situation-attributes categorizations were used to produce recommendation lists. These recommendation lists were then paired with questionnaire-derived music preferences from experienced users. The experimental results conclusively show that the MoMusic model is approximately twice as accurate as the random selection-based lists and the KKBOX-like recommendation lists and performs better than the two listed recommendation systems. The MoMusic model scores 0.889 on the usefulness evaluation, whereas the system satisfaction is 0.96. The MoMusic model has the advantages of intuitive use and high performance.


title: DAAL: Deep Activation-based Attribute Learning for Action Recognition in Depth Videos

abstract: Abstract In this paper, we propose a joint semantic preserving action attribute learning framework for action recognition from depth videos, which is built on multi-stream deep neural networks. More specifically, this paper describes the idea to explore action attributes learned from deep activations. Multiple stream deep neural networks rather than conventional hand-crafted low-level features are employed to learn the deep activations. An undirected graph is utilized to model the complex semantics among action attributes and is integrated into our proposed joint action attribute learning algorithm. Experiments on several public datasets for action recognition demonstrate that 1) the deep activations achieve the state-of-the-art discriminative performance as feature vectors and 2) the attribute learner can produce generic attributes, and thus obtains decent performance on zero-shot action recognition.


title: Research paper recommender system evaluation: a quantitative literature survey

abstract: Over 80 approaches for academic literature recommendation exist today. The approaches were introduced and evaluated in more than 170 research articles, as well as patents, presentations and blogs. We reviewed these approaches and found most evaluations to contain major shortcomings. Of the approaches proposed, 21% were not evaluated. Among the evaluated approaches, 19% were not evaluated against a baseline. Of the user studies performed, 60% had 15 or fewer participants or did not report on the number of participants. Information on runtime and coverage was rarely provided. Due to these and several other shortcomings described in this paper, we conclude that it is currently not possible to determine which recommendation approaches for academic literature are the most promising. However, there is little value in the existence of more than 80 approaches if the best performing approaches are unknown.


title: Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection

abstract: Recently, feature selection and dimensionality reduction have become fundamental tools for many data mining tasks, especially for processing high-dimensional data such as gene expression microarray data. Gene expression microarray data comprises up to hundreds of thousands of features with relatively small sample size. Because learning algorithms usually do not work well with this kind of data, a challenge to reduce the data dimensionality arises. A huge number of gene selection are applied to select a subset of relevant features for model construction and to seek for better cancer classification performance. This paper presents the basic taxonomy of feature selection, and also reviews the state-of-the-art gene selection methods by grouping the literatures into three categories: supervised, unsupervised, and semi-supervised. The comparison of experimental results on top 5 representative gene expression datasets indicates that the classification accuracy of unsupervised and semi-supervised feature selection is competitive with supervised feature selection.


title: Autonomous Ramp Merge Maneuver Based on Reinforcement Learning with Continuous Action Space

abstract: Ramp merging is a critical maneuver for road safety and traffic efficiency. Most of the current automated driving systems developed by multiple automobile manufacturers and suppliers are typically limited to restricted access freeways only. Extending the automated mode to ramp merging zones presents substantial challenges. One is that the automated vehicle needs to incorporate a future objective (e.g. a successful and smooth merge) and optimize a long-term reward that is impacted by subsequent actions when executing the current action. Furthermore, the merging process involves interaction between the merging vehicle and its surrounding vehicles whose behavior may be cooperative or adversarial, leading to distinct merging countermeasures that are crucial to successfully complete the merge. In place of the conventional rule-based approaches, we propose to apply reinforcement learning algorithm on the automated vehicle agent to find an optimal driving policy by maximizing the long-term reward in an interactive driving environment. Most importantly, in contrast to most reinforcement learning applications in which the action space is resolved as discrete, our approach treats the action space as well as the state space as continuous without incurring additional computational costs. Our unique contribution is the design of the Q-function approximation whose format is structured as a quadratic function, by which simple but effective neural networks are used to estimate its coefficients. The results obtained through the implementation of our training platform demonstrate that the vehicle agent is able to learn a safe, smooth and timely merging policy, indicating the effectiveness and practicality of our approach.


Joint Event Extraction via Recurrent Neural Networks


title: A Critical Review of the Integration of Geographic Information System and Building Information Modelling at the Data Level

abstract: The benefits brought by the integration of Building Information Modelling (BIM) and Geographic Information Systems (GIS) are being proved by more and more research. The integration of the two systems is difficult for many reasons. Among them, data incompatibility is the most significant, as BIM and GIS data are created, managed, analyzed, stored, and visualized in different ways in terms of coordinate systems, scope of interest, and data structures. The objective of this paper is to review the relevant research papers to (1) identify the most relevant data models used in BIM/GIS integration and understand their advantages and disadvantages; (2) consider the possibility of other data models that are available for data level integration; and (3) provide direction on the future of BIM/GIS data integration.


title: Ultra low loss and high linearity SPMT antenna switch using SOI CMOS process

abstract: Low loss and high linearity antenna switch is designed and implemented using 0.18 µm silicon-on-insulator(SOI) CMOS process for GSM/WCDMA mobile phone front-ends. Body-contacts(BC) NFETs from partially depleted (PD) SOI were chosen to implement low loss and high power antenna switch. The antenna switch employs a conventional multi-stacking FET structure to distribute voltage stress toward each FETs. Measurement results demonstrate less than 0.5 dB insertion loss up to 2 GHz and 80 dBc harmonics at all bands in the case of a single pole four throw(SP4T) switch design. This SP4T switch integrates controller to be used in the Tx module for GPRS/EDGE application. The controller operates from 3 V to 4.5 V. The design of antenna switches using multi-stack FETs can be extended by SP10T switch design with exceptional low loss and high linearity.


title: Energy management and control algorithms for integration of energy storage within microgrid

abstract: This paper discusses energy management and control algorithms of microgrid with energy storage. Specific management framework within microgrid presented here includes: a) help prioritizing the renewable energy sources (RES) to provide power to local loads, b) manage battery to avoid deep-discharging and overcharging, c) maintain dc link voltage at a specified value, and d) regulate ac voltage buses at nominal voltage and frequency. The system analyzed here is adopted from Consortium for Electric Reliability Technology Solutions (CERTS) microgrid test bed. While the generation priority is from RES, the system also has a thermal generation unit to compensate for renewable energy variability and intermittency. Depending on battery state of charge (SOC) and power difference between photovoltaic (PV) and the load, the energy management can work with several modes: charging, discharging, export power, import power, and halt. The system is simulated in PSCAD platform and shows that energy management and control algorithms work as expected.


title: Fault-Tolerant Routing for Exascale Supercomputer: The BXI Routing Architecture

abstract: BXI, Bull eXascale Interconnect, is the new inter-connection network developed by Atos for High Performance Computing. It has been designed to meet the requirements of exascale supercomputers. At such scale, faults have to be expected and dealt with transparently so that applications remain unaffected by them. BXI features various mechanisms for this purpose, one of which is the BXI routing component presented in this paper. The BXI routing module computes the full routing tables for a 64k nodes fat-tree in a few minutes. But with partial re-computation it can withstand numerous inter-router link failures without any noticeable impact on running applications.


title: Learning to Track at 100 FPS with Deep Regression Networks

abstract: Machine learning techniques are often used in computer vision due to their ability to leverage large amounts of training data to improve performance. Unfortunately, most generic object trackers are still trained from scratch online and do not benefit from the large number of videos that are readily available for offline training. We propose a method for offline training of neural networks that can track novel objects at test-time at 100 fps. Our tracker is significantly faster than previous methods that use neural networks for tracking, which are typically very slow to run and not practical for real-time applications. Our tracker uses a simple feed-forward network with no online training required. The tracker learns a generic relationship between object motion and appearance and can be used to track novel objects that do not appear in the training set. We test our network on a standard tracking benchmark to demonstrate our tracker's state-of-the-art performance. Further, our performance improves as we add more videos to our offline training set. To the best of our knowledge, our tracker is the first neural-network tracker that learns to track generic objects at 100 fps.


title: Conflict and Mediation Event Observations (CAMEO): A New Event Data Framework for the Analysis of Foreign Policy Interactions

abstract: The Conflict and Mediation Events Observations (CAMEO) framework is a new event data coding scheme optimized for the study of third-party mediation in international disputes. We have developed and implemented this system using the TABARI automated coding program and have generated data sets for the Balkans (1989-2002; N=69,620), Levant (1979-2002; N=146,283), and West Africa (1989-2002; N=17,468) from Reuters and Agence France Presse reports. In this paper, we describe why we decided to develop a new coding system, rather than continuing to use the World Events Interaction Survey (WEIS) framework that we have used in earlier work. Our decision involved both known weaknesses in the WEIS system and some additional problems that we have found occur when WEIS is coded using automated methods. We have addressed these problems in constructing CAMEO and have produced much more completed documentation than has been available for WEIS. In the second half of the paper, we make several statistical comparisons of CAMEO-coded and WEIScoded data in the three geographical regions. When the data are aggregated to a general behavioral level—verbal cooperation, material cooperation, verbal conflict and material conflict—most of the data sets show a high correlation (r>0.90) in the number of WEIS and CAMEO events coded per month. However, as we expected, CAMEO consistently picks up a greater number of events involving material cooperation. Finally, there is a very significant correlation (r>0.57) between the count of CAMEO events specifically dealing with mediation and negotiation and a pattern-based measure of mediation we developed earlier from WEIS data. Appendices in the paper show the WEIS and CAMEO coding framework and examples from the CAMEOcodebook.


title: Information Systems Strategy. Theory, Practice, and Challenges for Future Research

abstract: The paper depicts the current state of IS strategy research with the aim of identifying research needs as well as appropriate ways to study IS strategy in the future. To this end, the paper introduces the results of an extensive analysis of academic literature on IS-strategy. In addition, it sheds light on current practice as uncovered in case-study research and through in-depth interviews with IS strategy professionals. A comparison reveals that the issues prevalent in practice and the ones traditionally focused on in the academic debate on IS strategy often vary considerably. A conspicuous attempt to fill this is the so-called “Strategizing” framework put forward by Robert Galliers. This framework, which is receiving increasing attention in the current academic debate, calls for a practice turn in IS strategy research in that it treats strategy as something people or, more precisely, IS strategists do. In addition, by identifying the challenges and problems IS strategists are faced with, the framework intends to better reflect the new planning conditions which are often seen as characteristics of the information age. The framework distinguishes three general problem domains of IS strategizing: exploration, exploitation, as well as implementation and change management. Copyright Springer Fachmedien Wiesbaden 2013


title: A lane detection algorithm based on reliable lane markings

abstract: This paper proposes a robust and effective vision-based lane detection approach. First, two binary images are obtained from the region of interest of gray-scale images. The obtained binary images are merged by a novel neighborhood AND operator and then transformed to a bird's eye view (BEV) via inverse perspective mapping. Then, gaussian probability density functions are fit to the left and right regions of a histogram image acquired from the BEV. Finally, a polynomial lane model is estimated from the identified regions. Experimental results show that the proposed method accurately detects lanes in complex situations including worn-out and curved lanes.


title: TextTopicNet - Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces

abstract: The immense success of deep learning based methods in computer vision heavily relies on large scale training datasets. These richly annotated datasets help the network learn discriminative visual features. Collecting and annotating such datasets requires a tremendous amount of human effort and annotations are limited to popular set of classes. As an alternative, learning visual features by designing auxiliary tasks which make use of freely available self-supervision has become increasingly popular in the computer vision community. In this paper, we put forward an idea to take advantage of multi-modal context to provide self-supervision for the training of computer vision algorithms. We show that adequate visual features can be learned efficiently by training a CNN to predict the semantic textual context in which a particular image is more probable to appear as an illustration. More specifically we use popular text embedding techniques to provide the self-supervision for the training of deep CNN. Our experiments demonstrate state-of-the-art performance in image classification, object detection, and multi-modal retrieval compared to recent self-supervised or naturally-supervised approaches.


title: Fishbone-inspired soft robotic glove for hand rehabilitation with multi-degrees-of-freedom

abstract: Soft robotic gloves have shown great potential in accelerating the rehabilitation process of individuals with hand pathologies. However, most of the existing soft assistive devices allow a single degree-of-freedom (DoF) movement for each finger while independent motion of finger joints plays a crucial role in hand rehabilitation. Trying to address these challenges, a novel fishbone-inspired soft actuator with multi-DoFs is proposed in this article for the first time, to the best knowledge of the authors, and a preliminary soft glove is developed. With the assistance of the glove, the Metacarpophalangeal (MCP) and the proximal interphalangeal (PIP) joints of human fingers can bend or extend independently. In this paper, the basic concept of the actuators is illustrated in detail and the structural parameters are determined with FEM models. Additionally, several groups of experiments are conducted to demonstrate the varied motion patterns of the actuators and the bending curvature of each segment in different patterns is calculated. Lastly, the efficacy as well as the dexterity of the proposed soft glove is further validated by performing complicated gestures and conducting functional grasping tests.


Execution-Guided Neural Program Decoding.


title: Two Birds with One Stone: Two-Factor Authentication with Security Beyond Conventional Bound

abstract: As the most prevailing two-factor authentication mechanism, smart-card-based password authentication has been a subject of intensive research in the past two decades, and hundreds of this type of schemes have wave upon wave been proposed. In most of these studies, there is no comprehensive and systematical metric available for schemes to be assessed objectively, and the authors present new schemes with assertions of the superior aspects over previous ones, while overlooking dimensions on which their schemes fare poorly. Unsurprisingly, most of them are far from satisfactory—either are found short of important security goals or lack of critical properties, especially being stuck with the security-usability tension. To overcome this issue, in this work we first explicitly define a security model that can accurately capture the practical capabilities of an adversary and then suggest a broad set of twelve properties framed as a systematic methodology for comparative evaluation, allowing schemes to be rated across a common spectrum. As our main contribution, a new scheme is advanced to resolve the various issues arising from user corruption and server compromise, and it is formally proved secure under the harshest adversary model so far. In particular, by integrating “honeywords”, traditionally the purview of system security, with a “fuzzy-verifier”, our scheme hits “two birds”: it not only eliminates the long-standing security-usability conflict that is considered intractable in the literature, but also achieves security guarantees beyond the conventional optimal security bound.


Rejuvenating Dysfunctional Public Spaces Using Augmented Reality Systems (ARS)


title: IMMUNOLOGIC STUDIES OF HEART TISSUE

abstract: Reactions of normal and pathologic sera with heart tissue have been investigated by the immunofluorescent method, with particular reference to presumptive autoantibodies to heart and their differentiation from blood group isoantibodies and Wassermann antibody. In the heart, blood group substances A and B were found distributed in capillary walls, vascular endothelium, and interstitial connective tissue. In surveys of randomly selected sera, isoimmune reactions against tissue blood group substances A and B were noted infrequently. This finding was considered related to the limited sensitivity of fluorescent antibody methods. Heart tissue from blood group O individuals was used for screening of pathologic sera for presence of tissue-reactive factors. Wassermann antibody was found reactive with constituents of myocardial sarcoplasm, of which the major reactant was cardiolipin. Wassermann-positive sera absorbed with beef cardiolipin gave evidence of reaction with other constituents of myofiber sarcoplasm. Sera of patients with rheumatic fever, rheumatic heart disease, rheumatoid arthritis, disseminated lupus, and liver disease frequently showed a marked reactivity with constituents of myofiber sarcoplasm. These serum factors were differentiated from Wassermann antibody. At least three patterns of immunofluorescent staining could be differentiated by the distribution of reactants in the myofiber sarcoplasm. These reactants were extractable with ethanol and methanol but not by acetone. Sera found reactive by immunofluorescence frequently gave positive flocculation and complement-fixation tests with alcohol extracts of human heart. Immunofluorescent tests were best correlated with flocculation reactions. Sarcoplasmic-reactive factors were associated in some sera with 19S gamma globulin as demonstrated by the use of fluorescent anti-19S gamma globulin. Serologic reactions with homologous or autologous heart were observed particularly frequently with sera from rheumatic patients approximately 2 weeks following cardiac surgery, as well as in some non-rheumatic patients following cardiac or thoracic surgery or acute myocardial infarction. The pathogenetic significance of these presumptive autoantibodies to heart is unknown. As yet, no definite conclusions may be drawn regarding their relationship to bound gamma globulin in rheumatic hearts or to the postcardiotomy and post-infarction syndromes.


title: Alternating direction method for image inpainting in wavelet domain

abstract: Image inpainting in wavelet domains refers to the recovery of an image from incomplete and/or inaccurate wavelet coefficients. To reconstruct the image, total variation (TV) models have been widely used in the literature, and they produce high-quality reconstructed images. In this paper, we consider an unconstrained, TV-regularized, $\ell_2$-data-fitting model to recover the image. The model is solved by the alternating direction method (ADM). At each iteration, the ADM needs to solve three subproblems, all of which have closed-form solutions. The per-iteration computational cost of the ADM is dominated by two Fourier transforms and two wavelet transforms, all of which admit fast computation. Convergence of the ADM iterative scheme is readily obtained. We also discuss extensions of this ADM scheme to solving two closely related constrained models. We present numerical results to show the efficiency and stability of the ADM for solving wavelet domain image inpainting problems. Numerical results comparing the ADM with some recent algorithms are also reported.


title: The relation between language, culture, and thought

abstract: The relationship between culture, language, and thought has long been one of the most important topics for those who wish to understand the nature of human cognition. This issue has been investigated for decades across a broad range of research disciplines. However, there has been scant communication across these different disciplines, a situation largely arising through differences in research interests and discrepancies in the definitions of key terms such as ‘culture,’ ‘language,’ and ‘thought.’ This article reviews recent trends in research on the relation between language, culture and thought to capture how cognitive psychology and cultural psychology have defined ‘language’ and ‘culture,’ and how this issue was addressed within each research discipline. We then review recent research conducted in interdisciplinary perspectives, which directly compared the roles of culture and language. Finally, we highlight the importance of considering the complex interplay between culture and language to provide a comprehensive picture of how language and culture affect thought.


title: How to separate between Machine-Printed/Handwritten and Arabic/Latin Words?

abstract: This paper gathers some contributions to script and its nature identification. Different sets of features have been employed successfully for discriminating between handwritten and machine-printed Arabic and Latin scripts. They include some well established features, previously used in the literature, and new structural features which are intrinsic to Arabic and Latin scripts. The performance of such features is studied towards this paper. We also compared the performance of three classifiers: Bayes (AODEsr), k-Nearest Neighbor (k-NN) and Decision Tree (J48) used to identify the script at word level. These classifiers have been chosen enough different to test the feature contributions. Experiments have been conducted with handwritten and machine-printed words, covering a wide range of fonts. Experimental results show the capability of the proposed features to capture differences between scripts and the effectiveness of the three classifiers. An average identification precision and recall rates of 98.72% was achieved, using a set of 58 features and AODEsr classifier, which is slightly better than those reported in similar works.


title: Diversity in Faces

abstract: Face recognition is a long standing challenge in the field of Artificial Intelligence (AI). The goal is to create systems that accurately detect, recognize, verify, and understand human faces. There are significant technical hurdles in making these systems accurate, particularly in unconstrained settings due to confounding factors related to pose, resolution, illumination, occlusion, and viewpoint. However, with recent advances in neural networks, face recognition has achieved unprecedented accuracy, largely built on data-driven deep learning methods. While this is encouraging, a critical aspect that is limiting facial recognition accuracy and fairness is inherent facial diversity. Every face is different. Every face reflects something unique about us. Aspects of our heritage - including race, ethnicity, culture, geography - and our individual identify - age, gender, and other visible manifestations of self-expression, are reflected in our faces. We expect face recognition to work equally accurately for every face. Face recognition needs to be fair. As we rely on data-driven methods to create face recognition technology, we need to ensure necessary balance and coverage in training data. However, there are still scientific questions about how to represent and extract pertinent facial features and quantitatively measure facial diversity. Towards this goal, Diversity in Faces (DiF) provides a data set of one million annotated human face images for advancing the study of facial diversity. The annotations are generated using ten well-established facial coding schemes from the scientific literature. The facial coding schemes provide human-interpretable quantitative measures of facial features. We believe that by making the extracted coding schemes available on a large set of faces, we can accelerate research and development towards creating more fair and accurate facial recognition systems.


title: Discursive Meaning Creation in Crowdfunding: A Socio-material Perspective Research-in-Progress

abstract: Crowdfunding is an exciting new phenomenon with the potential to disrupt early-stage capital markets. Enabled through specialized internet websites and social media, entrepreneurs now have a new source for start-up capital (estimated at $2.8 billion in 2012). Currently, entrepreneurs need to network through intermediaries to have access to wealthy investors. Crowdfunding bypasses these intermediaries and brings the ability to raise capital to the crowd. Consequently, decisions to fund an entrepreneurial endeavor are not made through ‘who you know’ and back-room deals, but through the discourse that occurs through the crowdfunding project page. The purpose of this research is to analyze and understand this discourse and the meaning it creates over the course of a crowdfunding campaign. The lens of sociomateriality in conjunction with discourse analysis is used to identify how meaning is created and its influence on the IS artifact.


title: Generation of OAM radio waves with three polarizations using circular horn antenna array

abstract: This paper provides an effective solution of generating OAM-carrying radio beams with all three polarizations: the linear, the left-hand circular, and the right-hand circular polarizations. Through reasonable configuration of phased antenna array using elements with three polarizations, the OAM radio waves with three polarizations for different states can be generated. The vectors of electric fields with different OAM states for all three polarizations are presented and analyzed in detail. The superposition of two coaxial OAM states is also carried out, and the general conclusion is provided.


title: Developmental changes in personal goal orientation from young to late adulthood: From striving for gains to maintenance and prevention of losses.

abstract: Using a multimethod approach, the authors conducted 4 studies to test life span hypotheses about goal orientations across adulthood. Confirming expectations, in Studies 1 and 2 younger adults reported a primary growth orientation in their goals, whereas older adults reported a stronger orientation toward maintenance and loss prevention. Orientation toward prevention of loss correlated negatively with well-being in younger adults. In older adults, orientation toward maintenance was positively associated with well-being. Studies 3 and 4 extend findings of a self-reported shift in goal orientation to the level of behavioral choice involving cognitive and physical fitness goals. Studies 3 and 4 also examine the role of expected resource demands. The shift in goal orientation is discussed as an adaptive mechanism to manage changing opportunities and constraints across adulthood.


title: NOVA: a microhypervisor-based secure virtualization architecture

abstract: The availability of virtualization features in modern CPUs has reinforced the trend of consolidating multiple guest operating systems on top of a hypervisor in order to improve platform-resource utilization and reduce the total cost of ownership. However, today's virtualization stacks are unduly large and therefore prone to attacks. If an adversary manages to compromise the hypervisor, subverting the security of all hosted operating systems is easy. We show how a thin and simple virtualization layer reduces the attack surface significantly and thereby increases the overall security of the system. We have designed and implemented a virtualization architecture that can host multiple unmodified guest operating systems. Its trusted computing base is at least an order of magnitude smaller than that of existing systems. Furthermore, on recent hardware, our implementation outperforms contemporary full virtualization environments.


title: Automatically locating salutation and signature blocks in emails

abstract: This paper focuses on the problem of automatically locating salutation and signature blocks in the body of plain-text emails. Texts of salutation and signature block in an email usually contain identity information about the email's sender or recipients. The analysis of locating and extracting salutation and signature blocks from emails has many potential applications, such as entity attributes extracting, person entity based email social network analysis, anonymization of email corpora, improving automatic content-based email classifiers and email threading. Our approach is based on the statistical method and the rules restricted method, which can greatly improve the locating efficiency and at the same time promise a relatively high accuracy of the extracted blocks. We use the statistical method to roughly estimate the number of lines in salutation and signature blocks, and introduce some restriction rules to refine the lines located by the statistical method. Results on the public subset of the Enron corpus prove the high performance of our approach with the average F1 value above 94%.


title: Zein film: Effects of dielectric barrier discharge atmospheric cold plasma

abstract: Dielectric barrier discharge atmospheric plasma is a novel nonthermal technology for the food and packaging industry. The effects of dielectric barrier discharge plasma on the surface, structural, thermal, and moisture sorption properties of edible zein films have been examined. Plasma treatment increased the surface roughness and equillibrium moisture content of the zein film in a direct relationship with the applied voltage level. No significant difference in the thermal stability of the zein film is also observed after plasma treatment. Dielectric barrier discharge plasma treatments of zein film lead to a change in the protein conformation which is confirmed by X-ray diffraction and Fourier transform Infrared spectroscopy. The evaluation of films modifications by plasma discharge will contribute to enhance the in-package decontamination studies of food products by plasma. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014, 131, 40803.


title: VANet security challenges and solutions: A survey

abstract: Abstract VANET is an emergent technology with promising future as well as great challenges especially in its security. In this paper, we focus on VANET security frameworks presented in three parts. The first presents an extensive overview of VANET security characteristics and challenges as well as requirements. These requirements should be taken into consideration to enable the implementation of secure VANET infrastructure with efficient communication between parties. We give the details of the recent security architectures and the well-known security standards protocols. The second focuses on a novel classification of the different attacks known in the VANET literature and their related solutions. The third is a comparison between some of these solutions based on well-known security criteria in VANET. Then we draw attention to different open issues and technical challenges related to VANET security, which can help researchers for future use.


title: Chronic Constipation with Megacolon

abstract: A 61-year-old female with long-standing constipation presented with increasing abdominal distention, pain, nausea and weight loss. She had been previously treated with intermittent fiber supplements and osmotic laxatives for chronic constipation. She did not use medications known to cause delayed bowel transit. Examination revealed a distended abdomen, hard stool in the rectum, and audible heart sounds throughout the abdomen. A CT scan showed severe colonic distention from stool (Fig. 1). She had no mechanical, infectious, metabolic, or endocrine-related etiology for constipation. After failing conservative management including laxative suppositories, enemas, manual disimpaction, methylnaltrexone and neostigmine, the patient underwent a colectomy with Hartmann pouch and terminal ileostomy. The removed colon measured 25.5 cm in largest diameter and weighed over 15 kg (Fig. 2). The histopathological examination demonstrated no neuronal degeneration, apoptosis or agangliosis to suggest Hirschprung’s disease or another intrinsic neuro-muscular disorder. Idiopathic megacolon is a relatively uncommon condition usually associated with slow-transit constipation. Although medical therapy is frequently ineffective, rectal laxatives, gentle enemas, and manual disimpaction can be attempted. Oral osmotic or secretory laxatives as well as unprepped lower endoscopy are relative contraindications as they may precipitate a perforation. Surgical therapy is often required as most cases are refractory to medical therapy. ::: ::: ::: ::: Figure 1 ::: ::: CT scan showing severe colonic distention from stool. ::: ::: ::: ::: ::: ::: Figure 2 ::: ::: Surgical specimen (colon).


title: Design and development of precision agriculture system using wireless sensor network

abstract: Crop farming in India is labour intensive and obsolete. Farming is still dependent on techniques which were evolved hundreds of years ago and doesn't take care of conservation of resources. The newer scenario of decreasing water tables, drying up of rivers and tanks, unpredictable environment present an urgent need of proper utilization of water. We have the technology to bridge the gap between water usage and water wastage. Technology used in some developed countries is too expensive and complicated for a common farmer to understand. Our project is to give cheap, reliable, cost efficient and easy to use technology which would help in conservation of resources such as water and also in automatizing farms. We proposed use of temperature and moisture sensor at suitable locations for monitoring of crops. The sensing system is based on a feedback control mechanism with a centralized control unit which regulates the flow of water on to the field in the real time based on the instantaneous temperature and moisture values. The sensor data would be collected in a central processing unit which would take further action. Thus by providing right amount of water we would increase the efficiency of the farm. The farmer can also look at the sensory data and decide course of action himself. We have made the interface of our project keeping in view the educational and financial background of average Indian farmer. In this paper we are proposed a low cost and efficient wireless sensor network technique to acquire the soil moisture and temperature from various locations of farm and as per the need of crop controller take the decision to make irrigation ON or OFF.


title: Controlled mixed culture fermentation: a new perspective on the use of non-Saccharomyces yeasts in winemaking

abstract: Mixed fermentations using controlled inoculation of Saccharomyces cerevisiae starter cultures and non-Saccharomyces yeasts represent a feasible way towards improving the complexity and enhancing the particular and specific characteristics of wines. The profusion of selected starter cultures has allowed the more widespread use of inoculated fermentations, with consequent improvements to the control of the fermentation process, and the use of new biotechnological processes in winemaking. Over the last few years, as a consequence of the re-evaluation of the role of non-Saccharomyces yeasts in winemaking, there have been several studies that have evaluated the use of controlled mixed fermentations using Saccharomyces and different non-Saccharomyces yeast species from the wine environment. The combined use of different species often results in unpredictable compounds and/or different levels of fermentation products being produced, which can affect both the chemical and the aromatic composition of wines. Moreover, possible synergistic interactions between different yeasts might provide a tool for the implementation of new fermentation technologies. Thus, knowledge of the Saccharomyces and non-Saccharomyces wine yeast interactions during wine fermentation needs to be improved. To reach this goal, further investigations into the genetic and physiological background of such non-Saccharomyces wine yeasts are needed, so as to apply '-omics' approaches to mixed culture fermentations.


title: Torque-Maximizing Design of Double-Stator, Axial-Flux, PM Machines Using Simple Non-Linear Magnetic Analysis

abstract: This paper presents a torque-maximizing design of a double-stator, axial-flux PM machine using a simple non-linear magnetic analysis. The proposed analysis consists of a geometric- flux-tube-based equivalent-magnetic-circuit model. The model can take into account not only the magnetic saturation but also the three-dimensional(3D) flux distribution. The proposed analysis has realized a dramatic reduction in the computation time compared to 3D-FEA, keeping reasonable analytical accuracy. At first, the validity of the proposed analysis is examined for the various design parameters. After verifying the accuracy of the torque computation of the proposed analysis through the comparisons with 3D-FEA, an 8-pole axial-flux PM machine with double-stator-single-rotor is optimally designed. The performance of the optimized motor are finally verified by 3D-FEA.


title: Software Defined networking based routing firewall

abstract: A router's main function is to allow communication between different networks as quickly as possible and in efficient manner. The communication can be between LAN or between LAN and WAN. A firewall's function is to restrict unwanted traffic. In big networks, routers and firewall tasks are performed by different network devices. But in small networks, we want both functions on same device i.e. one single device performing both routing and firewalling. We call these devices as routing firewall. In Traditional networks, the devices are already available. But the next generation networks will be powered by Software Defined Networks. For wide adoption of SDN, we need northbound SDN applications such as routers, load balancers, firewalls, proxy servers, Deep packet inspection devices, routing firewalls running on OpenFlow based physical and virtual switches. But the SDN is still in early stage, so still there is very less availability of these applications. There already exist simple L3 Learning application which provides very elementary router function and also simple stateful firewalls providing basic access control. In this paper, we are implementing one SDN Routing Firewall Application which will perform both the routing and firewall function.


Studying Cities to Learn about Minds How Geometric Intuitions Shape Urban Space and Make It Work


title: Exploiting coarse-grained task, data, and pipeline parallelism in stream programs

abstract: As multicore architectures enter the mainstream, there is a pressing demand for high-level programming models that can effectively map to them. Stream programming offers an attractive way to expose coarse-grained parallelism, as streaming applications (image, video, DSP, etc.) are naturally represented by independent filters that communicate over explicit data channels.In this paper, we demonstrate an end-to-end stream compiler that attains robust multicore performance in the face of varying application characteristics. As benchmarks exhibit different amounts of task, data, and pipeline parallelism, we exploit all types of parallelism in a unified manner in order to achieve this generality. Our compiler, which maps from the StreamIt language to the 16-core Raw architecture, attains a 11.2x mean speedup over a single-core baseline, and a 1.84x speedup over our previous work.


title: Physiological signals based human emotion Recognition: a review

abstract: Recent research in the field of Human Computer Interaction aims at recognizing the user's emotional state in order to provide a smooth interface between humans and computers. This would make life easier and can be used in vast applications involving areas such as education, medicine etc. Human emotions can be recognized by several approaches such as gesture, facial images, physiological signals and neuro imaging methods. Most of the researchers have developed user dependent emotion recognition system and achieved maximum classification rate. Very few researchers have tried to develop a user independent system and obtained lower classification rate. Efficient emotion stimulus method, larger data samples and intelligent signal processing techniques are essential for improving the classification rate of the user independent system. In this paper, we present a review on emotion recognition using physiological signals. The various theories on emotion, emotion recognition methodology and the current advancements in emotion research are discussed in subsequent topics. This would provide an insight on the current state of research and its challenges on emotion recognition using physiological signals, so that research can be advanced to obtain better recognition.


title: Laughter Research: A Review of the ILHAIRE Project

abstract: Laughter is everywhere. So much so that we often do not even notice it. First, laughter has a strong connection with humour. Most of us seek out laughter and people who make us laugh, and it is what we do when we gather together as groups relaxing and having a good time. But laughter also plays an important role in making sure we interact with each other smoothly. It provides social bonding signals that allow our conversations to flow seamlessly between topics; to help us repair conversations that are breaking down; and to end our conversations on a positive note.


title: Multi-Scale DenseNet-Based Electricity Theft Detection

abstract: Electricity theft detection issue has drawn lots of attention during last decades. Timely identification of the electricity theft in the power system is crucial for the safety and availability of the system. Although sustainable efforts have been made, the detection task remains challenging and falls short of accuracy and efficiency, especially with the increase of the data size. Recently, convolutional neural network-based methods have achieved better performance in comparison with traditional methods, which employ handcrafted features and shallow-architecture classifiers. In this paper, we present a novel approach for automatic detection by using a multi-scale dense connected convolution neural network (multi-scale DenseNet) in order to capture the long-term and short-term periodic features within the sequential data. We compare the proposed approaches with the classical algorithms, and the experimental results demonstrate that the multiscale DenseNet approach can significantly improve the accuracy of the detection. Moreover, our method is scalable, enabling larger data processing while no handcrafted feature engineering is needed.


title: Text Generation for Abstractive Summarization

abstract: We have begun work on a framework for abstractive summarization and decided to focus on a module for text generation. For TAC 2010, we thus move away from sentence extraction. Each sentence in the summary we generate is based on a document sentence but it usually contains a smaller amount of information and uses fewer words. The system uses the output of a syntactic parser for a sentence and then regenerates part of the sentence using a Natural Language Generation engine. The sentences of the summary are selected among regenerated sentences based on the document frequency of contained words, while avoiding redundancy. Date and location were handled and generated especially for cluster categories 1 and 2. Even though our initial scores were not outstanding, we intend to continue work on this approach in the coming years.


title: Original article Does Online Harassment Constitute Bullying? An Exploration of Online Harassment by Known Peers and Online-Only Contacts

abstract: Purpose: To shed light on the nature of online harassment and the extent to which it may be bullying by examining differences in the characteristics of harassed youth, online harassment incidents, and distressing online harassment based on the identity of online harassers (known peer vs. online-only contact). Methods: A telephone survey of a nationally representative sample of 1500 youth Internet users, ages 10 to 17, conducted between March and June 2005. Results: Nine percent (n 129) of youth were harassed online in the past year, 43% (n 56) by known peers and 57% (n 73) by people they met online and did not know in person (online-only contacts). Most online harassment incidents did not appear to meet the standard definition of bullying used in school-based research and requiring aggression, repetition, and power imbalance. Only 25% of incidents by known peers and 21% by online-only contacts involved both repeated incidents and either distress to targets or adult intervention. Conclusions: In many cases, the concept of "bullying" or "cyber-bullying" may be inappropriate for online interpersonal offenses. We suggest using "online harassment," with disclaimers that it does not constitute bullying unless it is part of or related to offline bullying. This would include incidents perpetrated by peers that occur entirely online, but arise from school-related events or relationships and have school-related consequences for targets. The Internet provides opportunities for the extension of conventional school bullying to new venues. Those who study conventional school bullying should include online forms of the behavior in research, prevention, and intervention paradigms. © 2007 Society for Adolescent Medicine. All rights reserved.


title: Cyber-Sexual Harassment: The Development of the Cyber-Sexual Experiences Questionnaire

abstract: Research on sexual harassment has shown that although half of all women experience unwanted sex-related behaviors in work and school, only 4% to 20% label those experiences as sexual harassment (Magley, Hulin, Fitzgerald, & DeNardo, 1999; Stockdale, Vaux, & Cashin, 1995). Moreover, while research and literature on face-to-face sexual harassment continues to grow, little research has examined sexual harassment in an Internet context. Because of this, much of what is known about Internet-based sexual harassment comes from research conducted on face-to-face sexual harassment. The current study establishes a foundation of understanding and develops a measure of experiences of sexual harassment on the Internet (i.e., cyber-sexual harassment). Cyber-sexual harassers use the Internet as a way to connect with acquaintances from off-line social interactions (i.e., work and school), or complete strangers, to perpetrate their victims through gender harassment, the exhibition of unwanted sexual attention, and/or the use of sexual coercion. In a British survey, 41% of female regular Internet users reported being sent unsolicited pornographic materials, harassed, or stalked on the Internet (Griffiths, 2000). Such a large number of women encountering offensive sex-related experiences on the Internet prompts further understanding of these experiences and others like them. The purpose of the current study is to add to the developing body of literature on cyber-sexual harassment by creating a measure of cyber-sexual harassment. Twenty-four female undergraduate students participated in focus groups of two to six people. Guided by predetermined questions, these groups discussed their positive and negative experiences on the Internet. Information gathered was used to create the Cyber-Sexual Experiences Questionnaire, modeled after the Sexual Experiences Questionnaire (Fitzgerald, Gelfand, & Drasgow, 1995). The 21-item measure consists of questions related to the participant’s cyber-sexual harassment experiences and whether the participant labels her experiences as cyber-sexual harassment. Responses for the frequency of each item range between one (never) to five (most of the time). Cyber-Sexual Harassment While face-to-face sexual harassment research and literature continues to expand, little research has examined sexual harassment in an Internet context. Because of this, much of what is known about Internet-based sexual harassment comes from research conducted on face-to-face sexual harassment, as well as other related Internet behavior. The current study establishes a foundation of understanding and develops a measure of experiences of sexual harassment on the Internet (i.e., cyber-sexual harassment). The Pew Internet and American Life Project found that e-mail was the most popular form of communication in the year 2004, when over 90% of people using the Internet reported e-mail use (2005). With so many people using the Internet on a regular basis, acts of deviance are likely to occur at increasing rates. One such deviant act is cyber-sexual harassment. In a British survey, 41% of females who regularly use the Internet reported being sent unsolicited pornographic materials, harassed, or stalked on the Internet (Griffiths, 2000). In another study, Whitty (2004) found that 17% of women reported being harassed through e-mail in the workplace, while 49% reported having received offensive e-mails. Although this research suggests widespread use of the Internet and experiences linked to what may potentially be identified as cyber-sexual harassment, little known research has empirically Samantha Schenk


title: X band septum polarizer as feed for parabolic antenna

abstract: Contemporary RHCP and LHCP wave occurs in several applications of microwave communication and measurement system. From this point of view the septum polarizer can be useful. The septum polarizer is a four-port waveguide device. The square waveguide at one end constitutes two ports because it can support two orthogonal modes. A slopping (or stepped) septum divides the square waveguide into two standard rectangular waveguides sharing a common broad-wall. The size of the septum as well as two versions of the waveguides excitation were analyzed and are described in this paper.


title: Discovering Reader's Emotions Triggered from News Articles

abstract: The study of sentiment analysis aims to explore the occurrences of opinion words in a document, and their orientation and strength. Most existing researches focus on writer's emotions, which are the feelings that authors were expressing. In contrast, news articles are usually described in objective terms, in which opinion words are rare. In this paper, we propose to discover the reader's feelings when they read news articles. First, different feature units are extracted such as unigrams, bigrams, and segmented words. We compare several feature selection methods including document frequency variation, information gain, mutual information, and chi-square test, to select the candidate features for sentiment classification. Then, the performance for multi-class classification and multiple binary classification are evaluated. The experimental results on real news articles show the effectiveness of the proposed method for discovering reader's emotions triggered from news articles. Further investigation is needed to validate the performance in larger scales.


title: Arabic Natural Language Processing from Software Engineering to Complex Pipeline

abstract: Arabic Natural Language Processing (ANLP) has known an important development during the last decade. Nowadays, Several ANLP tools are developed such as morphological analyzers, syntactic parsers, etc. These tools are characterized by their diversity in terms of development languages used, inputs/outputs manipulated, internal and external representations of results, etc. This is mainly due to the lack of models and standards that govern their implementations. This diversity does not favor interoperability between these tools or their reuse in new advanced projects. In this article, we propose APIs and models for three types of tools namely: stemmers, morphological analyzers and syntactic parsers, using SAFAR platform. Our proposal is a step for standardizing all aspects shared by tools of the same type. We review also the issue of interoperability between these tools. Finally, we discuss pipeline processes.


title: PLC control and Matlab/Simulink simulations. A translation approach

abstract: This paper proposes a translation methodology to emulate PLC control program in the Matlab/Simulink environment. The translation package automatically translates the PLC control program into Matlab/Simulink software language. The PLC control program is translated into a Matlab function block, within the Matlab/Simulink environment, that will control the model of the industrial process as long as the simulation runs. The translation package inputs are: the type of PLC, the PLC's number of inputs and outputs and the PLC control program file. The translation package output will be a Matlab/Simulink compatible m-file with the correspondent PLC control program translation. Universidade de Evora students are successfully using this package, so that they can test their PLC control programs together with Matlab/Simulink process models.


title: Adolescent Alcohol Exposure Persistently Impacts Adult Neurobiology and Behavior

abstract: Adolescence is a developmental period when physical and cognitive abilities are optimized, when social skills are consolidated, and when sexuality, adolescent behaviors, and frontal cortical functions mature to adult levels. Adolescents also have unique responses to alcohol compared with adults, being less sensitive to ethanol sedative-motor responses that most likely contribute to binge drinking and blackouts. Population studies find that an early age of drinking onset correlates with increased lifetime risks for the development of alcohol dependence, violence, and injuries. Brain synapses, myelination, and neural circuits mature in adolescence to adult levels in parallel with increased reflection on the consequence of actions and reduced impulsivity and thrill seeking. Alcohol binge drinking could alter human development, but variations in genetics, peer groups, family structure, early life experiences, and the emergence of psychopathology in humans confound studies. As adolescence is common to mammalian species, preclinical models of binge drinking provide insight into the direct impact of alcohol on adolescent development. This review relates human findings to basic science studies, particularly the preclinical studies of the Neurobiology of Adolescent Drinking in Adulthood (NADIA) Consortium. These studies focus on persistent adult changes in neurobiology and behavior following adolescent intermittent ethanol (AIE), a model of underage drinking. NADIA studies and others find that AIE results in the following: increases in adult alcohol drinking, disinhibition, and social anxiety; altered adult synapses, cognition, and sleep; reduced adult neurogenesis, cholinergic, and serotonergic neurons; and increased neuroimmune gene expression and epigenetic modifiers of gene expression. Many of these effects are specific to adolescents and not found in parallel adult studies. AIE can cause a persistence of adolescent-like synaptic physiology, behavior, and sensitivity to alcohol into adulthood. Together, these findings support the hypothesis that adolescent binge drinking leads to long-lasting changes in the adult brain that increase risks of adult psychopathology, particularly for alcohol dependence.


title: Recurrent Inference Machines for Solving Inverse Problems

abstract: Inverse problems are typically solved by first defining a model and then choosing an inference procedure. With this separation of modeling from inference, inverse problems can be framed in a modular way. For example, variational inference can be applied to a broad class of models. The modularity, however, typically goes away after model parameters have been trained under a chosen inference procedure. During training, model and inference often interact in a way that the model parameters will ultimately be adapted to the chosen inference procedure, posing the two components inseparable after training. But if model and inference become inseperable after training, why separate them in the first place? ::: ::: We propose a novel learning framework which abandons the dichotomy between model and inference. Instead, we introduce Recurrent Inference Machines (RIM), a class of recurrent neural networks (RNN) that directly learn to solve inverse problems. ::: ::: We demonstrate the effectiveness of RIMs in experiments on various image reconstruction tasks. We show empirically that RIMs exhibit the desirable convergence behavior of classical inference procedures, and that they can outperform state-of- the-art methods when trained on specialized inference tasks. ::: ::: Our approach bridges the gap between inverse problems and deep learning, providing a framework for fast progression in the field of inverse problems.


title: Extracting significant signal of news consumption from social networks: the case of Twitter in Italian political elections

abstract: Abstract According to the Eurobarometer report about EU media use of May 2018, the number of European citizens who consult on-line social networks for accessing information is considerably increasing. In this work we analyse approximately 106 tweets exchanged during the last Italian elections held on March 4, 2018. Using an entropy-based null model discounting the activity of the users, we first identify potential political alliances within the group of verified accounts: if two verified users are retweeted more than expected by the non-verified ones, they are likely to be related. Then, we derive the users’ affiliation to a coalition measuring the polarisation of unverified accounts. Finally, we study the bipartite directed representation of the tweets and retweets network, in which tweets and users are collected on the two layers. Users with the highest out-degree identify the most popular ones, whereas highest out-degree posts are the most “viral”. We identify significant content spreaders with a procedure that allows to statistically validate the connections that cannot be explained by users’ tweeting activity and posts’ virality, using an entropy-based null model as benchmark. The analysis of the directed network of validated retweets reveals signals of the alliances formed after the elections, highlighting commonalities of interests before the event of the national elections.


title: A 500 mW ADC-Based CMOS AFE With Digital Calibration for 10 Gb/s Serial Links Over KR-Backplane and Multimode Fiber

abstract: This paper presents the design of an analog-front-end (AFE) integrated into a DSP-based transceiver for both serial 10 Gbps KR-backplane and long-reach-multimode-fiber (LRM) applications. The receiver consists of a programmable gain amplifier (PGA) and a 6-bit 4-way time-interleaved ADC, which is digitally calibrated to compensate for the offset, gain and phase mismatches between the interleaved channels. With a 5 GHz input signal, the ADC achieves overall SNDR of 29 dB, while the measured SNDR of flash sub-ADC is 31.6 dB. The power efficiency FoM of the complete interleaved ADC is 1.4 pJ per conversion step. The PLL uses a calibrated LC-VCO and the TX features a full-rate 3-tap de-emphasis at the output. Inductively tuned buffers connected in tandem are employed to distribute the 10 GHz clock. Random and deterministic jitter measured at the TX output are 0.38 psrms and 2.65 pspp, respectively. Implemented in 65 nm CMOS technology, the AFE occupies an area of 3 mm2 and consumes 500 mW from a 1 V supply. BER of less than 10-15 is measured over legacy backplanes with 26 dB loss at Nyquist and the measured transceiver optical sensitivity is less than -13 dBm for all four LRM stressors, exceeding both the KR and the LRM specifications.


title: Classifying Dialogue Acts in One-on-One Live Chats

abstract: We explore the task of automatically classifying dialogue acts in 1-on-1 online chat forums, an increasingly popular means of providing customer service. In particular, we investigate the effectiveness of various features and machine learners for this task. While a simple bag-of-words approach provides a solid baseline, we find that adding information from dialogue structure and inter-utterance dependency provides some increase in performance; learners that account for sequential dependencies (CRFs) show the best performance. We report our results from testing using a corpus of chat dialogues derived from online shopping customer-feedback data.


title: Online normalizer calculation for softmax

abstract: The Softmax function is ubiquitous in machine learning, multiple previous works suggested faster alternatives for it. In this paper we propose a way to compute classical Softmax with fewer memory accesses and hypothesize that this reduction in memory accesses should improve Softmax performance on actual hardware. The benchmarks confirm this hypothesis: Softmax accelerates by up to 1.3x and Softmax+TopK combined by up to 5x.


ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings