title: MaskGAN: Better Text Generation via Filling in the ______

abstract: Neural text generation models are often autoregressive language models or seq2seq models. These models generate text by sampling words sequentially, with each word conditioned on the previous word, and are state-of-the-art for several machine translation and summarization benchmarks. These benchmarks are often defined by validation perplexity even though this is not a direct measure of the quality of the generated text. Additionally, these models are typically trained via maxi- mum likelihood and teacher forcing. These methods are well-suited to optimizing perplexity but can result in poor sample quality since generating text requires conditioning on sequences of words that may have never been observed at training time. We propose to improve sample quality using Generative Adversarial Networks (GANs), which explicitly train the generator to produce high quality samples and have shown a lot of success in image generation. GANs were originally designed to output differentiable values, so discrete language generation is challenging for them. We claim that validation perplexity alone is not indicative of the quality of text generated by a model. We introduce an actor-critic conditional GAN that fills in missing text conditioned on the surrounding context. We show qualitatively and quantitatively, evidence that this produces more realistic conditional and unconditional text samples compared to a maximum likelihood trained model.


title: Open learner models and learning analytics dashboards: a systematic review

abstract: This paper aims to link student facing Learning Analytics Dashboards (LADs) to the corpus of research on Open Learner Models (OLMs), as both have similar goals. We conducted a systematic review of literature on OLMs and compared the results with a previously conducted review of LADs for learners in terms of (i) data use and modelling, (ii) key publication venues, (iii) authors and articles, (iv) key themes, and (v) system evaluation. We highlight the similarities and differences between the research on LADs and OLMs. Our key contribution is a bridge between these two areas as a foundation for building upon the strengths of each. We report the following key results from the review: in reports of new OLMs, almost 60% are based on a single type of data; 33% use behavioral metrics; 39% support input from the user; 37% have complex models; and just 6% involve multiple applications. Key associated themes include intelligent tutoring systems, learning analytics, and self-regulated learning. Notably, compared with LADs, OLM research is more likely to be interactive (81% of papers compared with 31% for LADs), report evaluations (76% versus 59%), use assessment data (100% versus 37%), provide a comparison standard for students (52% versus 38%), but less likely to use behavioral metrics, or resource use data (33% against 75% for LADs). In OLM work, there was a heightened focus on learner control and access to their own data.


title: Active Learning by Label Uncertainty for Acoustic Emotion Recognition

abstract: Speech data is in principle available in large amounts for the training of acoustic emotion recognisers. However, emotional labelling is usually not given and the distribution is heavily unbalanced, as most data is ‘rather neutral’ than truly ‘emotional’. In the ‘hay stack’ of speech data, Active Learning automatically identifies the ‘needles’, i.e., the more informative instances to reduce human labelling effort when building a classifier, e.g., for acoustic emotion recognition. The critical issue thus is the determination and quantification of informativeness. To this end, we suggest to exploit the reliability of the usual ambiguity of emotional labels, i.e., we propose a novel approach based on label uncertainty. By building a certainty model and predicting the candidate instances, informativeness is thus based on labeller agreement. In addition, we consider class sparseness. The results of extensive test runs under well standardised conditions show the method’s great potential in reducing labelling costs while boosting performance.


title: The M2 splice isoform of pyruvate kinase is important for cancer metabolism and tumour growth

abstract: Many tumour cells have elevated rates of glucose uptake but reduced rates of oxidative phosphorylation. This persistence of high lactate production by tumours in the presence of oxygen, known as aerobic glycolysis, was first noted by Otto Warburg more than 75 yr ago. How tumour cells establish this altered metabolic phenotype and whether it is essential for tumorigenesis is as yet unknown. Here we show that a single switch in a splice isoform of the glycolytic enzyme pyruvate kinase is necessary for the shift in cellular metabolism to aerobic glycolysis and that this promotes tumorigenesis. Tumour cells have been shown to express exclusively the embryonic M2 isoform of pyruvate kinase. Here we use short hairpin RNA to knockdown pyruvate kinase M2 expression in human cancer cell lines and replace it with pyruvate kinase M1. Switching pyruvate kinase expression to the M1 (adult) isoform leads to reversal of the Warburg effect, as judged by reduced lactate production and increased oxygen consumption, and this correlates with a reduced ability to form tumours in nude mouse xenografts. These results demonstrate that M2 expression is necessary for aerobic glycolysis and that this metabolic phenotype provides a selective growth advantage for tumour cells in vivo.


title: Writing abilities in intellectual disabilities: A comparison between Down and Williams syndrome

abstract: Abstract Writing is a complex task that requires the integration of multiple cognitive, linguistic, and motor abilities. Until now, only a few studies investigated writing abilities in individuals with Intellectual Disability (ID). The aim of the present exploratory study was to provide knowledge on the organization of writing in two populations with ID, Down syndrome (DS) and Williams syndrome (WS), trying to disentangle different components of the process. A battery tapping diverse writing demands as low-level transcription skills as well as high-level writing skills was proposed to 13 individuals with WS, 12 individuals with DS and 11 mental-age-matched typically developing (TD) children. Results showed that the two groups with genetic syndromes did not differ from TD in writing a list of objects placed in bedroom, in the number of errors in the text composition, in a text copying task and in kind of errors made. However, in a word dictation task, individuals with DS made more errors than individuals with WS and TD children. In a pseudoword dictation task, both individuals with DS and WS showed more errors than TD children. Our results showed good abilities in individuals with ID in different aspects of writing, involving not only low-level transcription skills but also high-level composition skills. Contrary to the pessimistic view, considering individuals with ID vulnerable for failure, our results indicate that the presence of ID does not prevent the achievement of writing skills.


title: Novel low cost compact printed antenna CPW-fed for GSM, GPS and PCS applications

abstract: In this paper, a novel study on the design and analysis of a compact printed antenna for multi-band applications are presented. The structure of the entire area is 45×45.5 mm2 and is printed on an FR-4 epoxy substrate. It is suitable for GSM/GPS/PCS applications by using an M-shaped antenna which is formed of different arms and an L-shaped shorted strip connected between the feeding line and the ground plane. Simulation results of the antenna input impedance bandwidths for, S11 ≤ (−10) dB, show that it covers the GSM, GPS and PCS bands. Also, a stable radiation pattern and an average antenna “Advanced Design System”. Also, we have conducted another study by using CST-MW to compare the results obtained with ADS which give good agreement between the both EM solvers.


title: On Studying Organizational Cultures

abstract: The article presents an overview of the longitudinal-processual theory of organizational formation. The ideological underpinnings of the method are described, highlighting its central focus on the factors and elements of an organization's creation and formation rather than its existent structures or practices. Speculative discussion is given regarding the processes involved in organizational culture, based off of an empirical school administration case study. Additional topics discussed include the influence of "dramatic" events which broadly change the social structure of the environment, the influence of social entrepreneurs, and the specific definition of organizational culture as an entity.


title: Processing of Topological BIM Queries using Boundary Representation Based Methods

abstract: Display Omitted We discuss topological operators as part of a query language for Building Information Models.We present novel boundary representation based algorithms implementing these operators.We achieve efficient processing of spatial queries by combining the developed BRep methods with spatial indexing.The methods are designed for processing complex data sets with high element counts and detailed geometry. Building Information Models (BIM) are comprehensive digital representations of buildings, which provide a large set of information originating from the different disciplines involved in the design, construction and operation processes. Moreover, accessing the data needed for a specific downstream application scenario is a challenging task in large-scale BIM projects. Several researchers recently proposed using formal query languages for specifying the desired information in a concise, well-defined manner. One of the main limitations of the languages introduced so far, however, is the inadequate treatment of geometric information. This is a significant drawback, as buildings are inherently spatial objects and qualitative spatial relationships accordingly play an important role in the analysis and verification of building models. In addition, the filters needed in specific data exchange scenarios for selecting the information required can be built by spatial objects and their relations. The lack of spatial functionality in BIM query languages is filled by the Query Language for Building Information Models (QL4BIM) which provides metric, directional and topological operators for defining filter expressions with qualitative spatial semantics. This paper focuses on the topological operators provided by the language. In particular, it presents a new implementation method based on the boundary representation of the operands which outperforms the previously presented octree-based approaches. The paper discusses the developed algorithms in detail and presents extensive performance tests.


title: Action Recognition in Still Images With Minimum Annotation Efforts

abstract: We focus on the problem of still image-based human action recognition, which essentially involves making prediction by analyzing human poses and their interaction with objects in the scene. Besides image-level action labels (e.g., riding, phoning), during both training and testing stages, existing works usually require additional input of human bounding boxes to facilitate the characterization of the underlying human–object interactions. We argue that this additional input requirement might severely discourage potential applications and is not very necessary. To this end, a systematic approach was developed in this paper to address this challenging problem of minimum annotation efforts, i.e., to perform recognition in the presence of only image-level action labels in the training stage. Experimental results on three benchmark data sets demonstrate that compared with the state-of-the-art methods that have privileged access to additional human bounding-box annotations, our approach achieves comparable or even superior recognition accuracy using only action annotations in training. Interestingly, as a by-product in many cases, our approach is able to segment out the precise regions of underlying human–object interactions.


title: Forecasting the Subway Passenger Flow Under Event Occurrences With Social Media

abstract: Subway passenger flow prediction is strategically important in metro transit system management. The prediction under event occurrences turns into a very challenging task. In this paper, we adopt a new kind of data source—social media—to tackle this challenge. We develop a systematic approach to examine social media activities and sense event occurrences. Our initial analysis demonstrates that there exists a moderate positive correlation between passenger flow and the rates of social media posts. This finding motivates us to develop a novel approach for improved flow forecast. We first develop a hashtag-based event detection algorithm. Furthermore, we propose a parametric and convex optimization-based approach, called optimization and prediction with hybrid loss function (OPL), to fuse the linear regression and the results of seasonal autoregressive integrated moving average (SARIMA) model jointly. The OPL hybrid model takes advantage of the unique strengths of linear correlation in social media features and SARIMA model in time series prediction. Experiments on events nearby a subway station show that OPL reports the best forecasting performance compared with other state-of-the-art techniques. In addition, an ensemble model is developed to leverage the weighted results from OPL and support vector machine regression together. As a result, the prediction accuracy and the robustness further increase.


title: Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering

abstract: Building a successful recommender system depends on understanding both the dimensions of people's preferences as well as their dynamics. In certain domains, such as fashion, modeling such preferences can be incredibly difficult, due to the need to simultaneously model the visual appearance of products as well as their evolution over time. The subtle semantics and non-linear dynamics of fashion evolution raise unique challenges especially considering the sparsity and large scale of the underlying datasets. In this paper we build novel models for the One-Class Collaborative Filtering setting, where our goal is to estimate users' fashion-aware personalized ranking functions based on their past feedback. To uncover the complex and evolving visual factors that people consider when evaluating products, our method combines high-level visual features extracted from a deep convolutional neural network, users' past feedback, as well as evolving trends within the community. Experimentally we evaluate our method on two large real-world datasets from Amazon.com, where we show it to outperform state-of-the-art personalized ranking measures, and also use it to visualize the high-level fashion trends across the 11-year span of our dataset.


title: Artificial Sense of Slip—A Review

abstract: Slip sensing is important in robotic dexterous manipulation and in advanced upper limb prosthetics because it provides useful information for conveniently adapting manipulation forces. In this paper the physical phenomena involved in slip occurrence are briefly examined, as well as the physiological bases of the human “sense of slip.” Transduction principles and technological approaches, exploited over the years for reproducing the “artificial sense of slip,” are analyzed, with the final aim of identifying the most relevant open issues, as well as research trends.


title: Torque control of a brushless DC motor using multivariable sliding mode extremum seeking PI tuning

abstract: In this paper, a novel Multivariable Sliding-mode Extremum Seeking (MSES) Proportional-Integral (PI) tuning method is proposed and its application is studied for torque control in a Permanent Magnet Synchronous Motor (PMSM). To this end, the stator three-phase currents are characterized by their maximum amplitude which directly controls the shaft torque. Hence, a PI controller is applied for controlling the maximum amplitude of the stator three phase currents. Due to requiring only one control loop to control the stator phase currents, the computational and implemental costs of the system reduce significantly when compared with conventional current controllers. Here, multivariable sliding-mode extremum seeking method is proposed as an optimization technique to tune parameters of the PI controller. This makes the PI current controller more efficient in terms of disturbance rejection and transient conditions. Furthermore, rotor position detection is conducted by applying Hall Effect sensors and using a continuous estimation method. This affects the switching sequence of a three-phase inverter connected to PMSM. The simulation results demonstrate the advantages of the proposed controller in terms of fast and precise convergence and robust performance in face of disturbances and uncertainties.


Document Information as Side Constraints for Improved Neural Patent Translation


title: Low-cost EEG based electric wheelchair with advanced control features

abstract: Brain-computer interface can have a profound impact on the life of paralyzed or elderly citizens as they offer control over various devices without any necessity of movement of the body parts. This technology has come a long way and opened new dimensions in improving our life. Use of electroencephalogram (EEG wave) based control schemes can change the shape of the lives of the disabled citizens if incorporated with an electric wheelchair through a wearable device. Electric wheelchairs are nowadays commercially available which provides mobility to the disabled persons with relative ease. But most of the commercially available products are much expensive and controlled through the joystick, hand gesture, voice command, etc. which may not be viable control scheme for severely disabled or paralyzed persons. In our research work, we have developed a low-cost electric wheelchair using locally available cheap parts and incorporated brain-computer interface considering the affordability of people from developing countries. So, people who have lost their control over their limbs or have the inability to drive a wheelchair by any means can control the proposed wheelchair only by their attention and willingness to blink. To acquire the signal of attention and blink, single channel electroencephalogram (EEG wave) was captured by a wearable Neurosky MindWave Mobile. One of the salient features of the proposed scheme is ‘Destination Mapping’ by which the wheelchair develops a virtual map as the user moves around and autonomously reaches desired positions afterward by taking command from a smart interface based on EEG signal. From the experiments that were carried out at different stages of the development, it was exposed that, such a wheelchair is easy to train and calibrate for different users and offers a low cost and smart alternative especially for the elderly people in developing countries.


title: Genomics of Speciation in Temperate and Boreal Angiosperm Trees

abstract: Understanding how species form and persist has been a central objective of evolutionary biology since the time of Darwin. Species emerge where two or more populations develop reproductive barriers yielding phenotypic and genetic divergence. This definition has led some to question the reality of species boundaries in plants. In contrast to animals, in which behavioral traits may allow sympatric groups to establish and maintain reproductive isolation (RI), plants do not benefit from mate selection in quite the same way. As a result, the definition of plant species has long confounded evolutionary biologists, as have the ecological and genetic processes that underlie their formation. Temperate and boreal angiosperm trees make an interesting case study in this regard, where a lack of RI often leads to difficulties in defining closely related species. With widespread and overlapping distributions, many tree species must resist gene flow from congeners, often in the absence of pre- and post-zygotic barriers, in order to maintain adaptations to their particular niche. While high rates of intraspecific gene flow likely promote species cohesion, angiosperm trees also show extensive interspecific hybridization in contact zones. In this chapter we highlight recent work to understand the pattern and process of speciation in angiosperm trees, and how contemporary sequencing technologies are providing insights into the genomic mechanisms that underlie genomic porosity on the one hand, and isolation on the other.


title: Compact 28 GHz antenna array with full polarization flexibility under yaw, pitch, roll motions

abstract: Novel antenna design technologies are devised at 28 GHz to realize vertical and horizontal polarizations and its combined radiation characteristics using ultra-thin printed circuit board (PCB) substrates. Details of the design methodologies, simulation and measurement results are presented and discussed in relation to the targeted mmWave 5G mobile application.


title: A survey on indexing techniques for big data: taxonomy and performance evaluation

abstract: The explosive growth in volume, velocity, and diversity of data produced by mobile devices and cloud applications has contributed to the abundance of data or `big data.' Available solutions for efficient data storage and management cannot fulfill the needs of such heterogeneous data where the amount of data is continuously increasing. For efficient retrieval and management, existing indexing solutions become inefficient with the rapidly growing index size and seek time and an optimized index scheme is required for big data. Regarding real-world applications, the indexing issue with big data in cloud computing is widespread in healthcare, enterprises, scientific experiments, and social networks. To date, diverse soft computing, machine learning, and other techniques in terms of artificial intelligence have been utilized to satisfy the indexing requirements, yet in the literature, there is no reported state-of-the-art survey investigating the performance and consequences of techniques for solving indexing in big data issues as they enter cloud computing. The objective of this paper is to investigate and examine the existing indexing techniques for big data. Taxonomy of indexing techniques is developed to provide insight to enable researchers understand and select a technique as a basis to design an indexing mechanism with reduced time and space consumption for BD-MCC. In this study, 48 indexing techniques have been studied and compared based on 60 articles related to the topic. The indexing techniques' performance is analyzed based on their characteristics and big data indexing requirements. The main contribution of this study is taxonomy of categorized indexing techniques based on their method. The categories are non-artificial intelligence, artificial intelligence, and collaborative artificial intelligence indexing methods. In addition, the significance of different procedures and performance is analyzed, besides limitations of each technique. In conclusion, several key future research topics with potential to accelerate the progress and deployment of artificial intelligence-based cooperative indexing in BD-MCC are elaborated on.


title: The development of a clothing fit evaluation system under virtual environment

abstract: This study proposes an innovative clothing fit evaluation method for virtual try- on using 3D digital human model. The clothing patterns are first designed and subsequently sewn together around a human model. Because complex cloth shapes are difficult to fit onto the human body, this study proposes a 3D human model dressed using virtual clothes according to the model's body shape. Different clothing sizes are tested to fit onto a human body to assure satisfactory fit. Several test subjects were recruited in this study. Representations of several different sized clothing were compared between an actual human body (RB) and virtual model (VM) to evaluate the virtual try-on clothing fit. The clothing fit of specified sizes was evaluated quantitatively by calculating the vacant space area between the clothes and human body. The vacant space measure is used to perform real and virtual body fit evaluations. The clothing fit information obtained in this study can be applied to the development of clothing products in the apparel industry.


title: The Stanford LittleDog: A learning and rapid replanning approach to quadruped locomotion

abstract: Legged robots have the potential to navigate a wide variety of terrain that is inaccessible to wheeled vehicles. In this paper we consider the planning and control tasks of navigating a quadruped robot over challenging terrain, including terrain that it has not seen until run-time. We present a software architecture that makes use of both static and dynamic gaits, as well as specialized dynamic maneuvers, to accomplish this task. Throughout the paper we highlight two themes that have been central to our approach: (1) the prevalent use of learning algorithms, and (2) a focus on rapid recovery and replanning techniques; we present several novel methods and algorithms that we developed for the quadruped and that illustrate these two themes. We evaluate the performance of these different methods, and also present and discuss the performance of our system on the official Learning Locomotion tests.


title: High Performance Convolutional Neural Networks for Document Processing

abstract: Convolutional neural networks (CNNs) are well known for producing state-of-the-art recognizers for document processing [1]. However, they can be difficult to implement and are usually slower than traditional multi-layer perceptrons (MLPs). We present three novel approaches to speeding up CNNs: a) unrolling convolution, b) using BLAS (basic linear algebra subroutines), and c) using GPUs (graphic processing units). Unrolled convolution converts the processing in each convolutional layer (both forward-propagation and back-propagation) into a matrix-matrix product. The matrix-matrix product representation of CNNs makes their implementation as easy as MLPs. BLAS is used to efficiently compute matrix products on the CPU. We also present a pixel shader based GPU implementation of CNNs. Results on character recognition problems indicate that unrolled convolution with BLAS produces a dramatic 2.4X−3.0X speedup. The GPU implementation is even faster and produces a 3.1X−4.1X speedup.


Understanding Jordanian Consumers ’ Online Purchase Intentions : Integrating Trust to the UTAUT 2 Framework


title: Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation

abstract: This paper reports on the development of an instrument designed to measure the various perceptions that an individual may have of adopting an information technology IT innovation. This instrument is intended to be a tool for the study of the initial adoption and eventual diffusion of IT innovations within organizations. While the adoption of information technologies by individuals and organizations has been an area of substantial research interest since the early days of computerization, research efforts to date have led to mixed and inconclusive outcomes. The lack of a theoretical foundation for such research and inadequate definition and measurement of constructs have been identified as major causes for such outcomes. In a recent study examining the diffusion of new end-user IT, we decided to focus on measuring the potential adopters' perceptions of the technology. Measuring such perceptions has been termed a "classic issue" in the innovation diffusion literature, and a key to integrating the various findings of diffusion research. The perceptions of adopting were initially based on the five characteristics of innovations derived by Rogers 1983 from the diffusion of innovations literature, plus two developed specifically within this study. Of the existing scales for measuring these characteristics, very few had the requisite levels of validity and reliability. For this study, both newly created and existing items were placed in a common pool and subjected to four rounds of sorting by judges to establish which items should be in the various scales. The objective was to verify the convergent and discriminant validity of the scales by examining how the items were sorted into various construct categories. Analysis of inter-judge agreement about item placement identified both bad items as well as weaknesses in some of the constructs' original definitions. These were subsequently redefined. Scales for the resulting constructs were subjected to three separate field tests. Following the final test, the scales all demonstrated acceptable levels of reliability. Their validity was further checked using factor analysis, as well as conducting discriminant analysis comparing responses between adopters and nonadopters of the innovation. The result is a parsimonious, 38-item instrument comprising eight scales which provides a useful tool for the study of the initial adoption and diffusion of innovations. A short, 25 item, version of the instrument is also suggested.


title: System Design Perspective for Human-Level Agents Using Deep Reinforcement Learning: A Survey

abstract: Reinforcement learning (RL) has distinguished itself as a prominent learning method to augment the efficacy of autonomous systems. Recent advances in deep learning studies have complemented existing RL methods and led to a crucial breakthrough in the effort of applying RL to automation and robotics. Artificial agents based on deep RL can take selective and intelligent actions comparable with those of a human to maximize the feedback reward from the interactive environment. In this paper, we survey recent developments in the literature regarding deep RL methods for building human-level agents. As a result, prominent studies that involve modeling every aspect of a human-level agent will be examined. We also provide an overview of constructing a framework for prospective autonomous systems. Moreover, various toolkits and frameworks are suggested to facilitate the development of deep RL methods. Finally, we open a discussion that potentially raises a range of future research directions in deep RL.


title: Cell biology: Detached membrane bending

abstract: Cells use various protein complexes to remodel membrane-bound organelles. In vitro reconstitution of the activity of one such complex, ESCRT-III, shows that it promotes membrane bending in an unconventional way.


title: RD-Connect: An Integrated Platform Connecting Databases, Registries, Biobanks and Clinical Bioinformatics for Rare Disease Research

abstract: Research into rare diseases is typically fragmented by data type and disease. Individual efforts often have poor interoperability and do not systematically connect data across clinical phenotype, genomic data, biomaterial availability, and research/trial data sets. Such data must be linked at both an individual-patient and whole-cohort level to enable researchers to gain a complete view of their disease and patient population of interest. Data access and authorization procedures are required to allow researchers in multiple institutions to securely compare results and gain new insights. Funded by the European Union's Seventh Framework Programme under the International Rare Diseases Research Consortium (IRDiRC), RD-Connect is a global infrastructure project initiated in November 2012 that links genomic data with registries, biobanks, and clinical bioinformatics tools to produce a central research resource for rare diseases.


A PDTB-Styled End-to-End Discourse Parser


title: Wideband and Low-Profile Omnidirectional Circularly Polarized Patch Antenna

abstract: A wideband low-profile omnidirectional circularly polarized (CP) patch antenna is proposed and investigated in this communication. The design utilizes two monopolar modes of a circular patch which connects to a modified ground plane by a set of conductive vias, to achieve wide-band impedance matching. The curved branches introduced at the edge of circular ground plane excite a degenerate mode and create CP fields. To verify the design, a prototype operating at 2.4 GHz-WLAN band has been fabricated and measured. Measured reflection coefficient, axial ratio (AR), radiation pattern, and antenna gain agree well with simulation results. The proposed prototype has a low profile of 0.024 λ0, a 10-dB impedance bandwidth of 19.8% and a 3-dB AR bandwidth of 19.3%. To further characterize the design concept, a parametric study of the proposed antenna is carried out using HFSS, and general design guidelines are provided.


title: K.M.: Natural language processing to the rescue?: Extracting’Situational awareness’ tweets during mass emergency

abstract: In times of mass emergency, vast amounts of data are generated via computer-mediated communication (CMC) that are difficult to manually cull and organize into a coherent picture. Yet valuable information is broadcast, and can provide useful insight into time- and safety-critical situations if captured and analyzed properly and rapidly. We describe an approach for automatically identifying messages communicated via Twitter that contribute to situational awareness, and explain why it is beneficial for those seeking information during mass emergencies. We collected Twitter messages from four different crisis events of varying nature and magnitude and built a classifier to automatically detect messages that may contribute to situational awareness, utilizing a combination of hand-annotated and automatically-extracted linguistic features. Our system was able to achieve over 80% accuracy on categorizing tweets that contribute to situational awareness. Additionally, we show that a classifier developed for a specific emergency event performs well on similar events. The results are promising, and have the potential to aid the general public in culling and analyzing information communicated during times of mass emergency.


title: Associative memory design via perceptron learning

abstract: In the present paper, a new synthesis approach is developed for associative memories based on the perceptron learning algorithm. The design (synthesis) problem of feedback neural networks for associative memories is formulated as a set of linear inequalities such that the use of perceptron learning is evident. The perceptron learning in the synthesis algorithms is guaranteed to converge. To demonstrate the applicability of the present results, a specific example is considered.


title: Space Efficient Hash Tables with Worst Case Constant Access Time

abstract: We generalize Cuckoo Hashing [16] to d-ary Cuckoo Hashing and show how this yields a simple hash table data structure that stores n elements in (1 + e) n memory cells, for any constant e > 0. Assuming uniform hashing, accessing or deleting table entries takes at most d = O(In)) probes and the expected amortized insertion time is constant. This is the first dictionary that has worst case constant access time and expected constant update time, works with (1 + e) n space, and supports satellite information. Experiments indicate that d = 4 choices suffice for e ≃ 0.03. We also describe a hash table data structure using explicit constant time hash functions, using at most d = O(ln 2 ) probes in the worst case. A corollary is an expected linear time algorithm for finding maximum cardinality matchings in a rather natural model of sparse random bipartite graphs.


Impact Of The Self-heating Effect On Circuit Performance Estimation Using DC Model Parameters


title: Mechanism study for micro surgical robotic system that can induce multisensory illusion

abstract: In recent years, robotic technology has been introduced in surgery in the form of master slave system and its applicable surgical area is widely spreading. Minimally invasive surgery and microsurgery are the representing applications of the robotic surgery that the surgeon performs the dexterous and fine operations in the deep or narrow surgical area in micro-scale. The intuitiveness is thus highly desirable to perform the highly elaborated surgical tasks such as suturing for surgical master slave systems. To increase the operability, time-delay of system response, haptic feedback and eye-hand coordination issues have mainly been discussed in the field of robotics. In addition to these approaches, we propose a surgical robotic system that induces multisensory illusion. In our previous study, we reported that the robotic instruments enhances the multisensory illusion. In this paper, we determine the requirements for inducing the multisensory illusion on a multi-DOF master slave system and the first stage of prototype implementation based on the given requirements is described.


title: The design and implementation of SPIRIT: a spatially aware search engine for information retrieval on the Internet

abstract: Much of the information stored on the web contains geographical context, but current search engines treat such context in the same way as all other content. In this paper we describe the design, implementation and evaluation of a spatially aware search engine which is capable of handling queries in the form of the triplet of 〈theme〉〈spatial relationship〉〈location〉. The process of identifying geographic references in documents and assigning appropriate footprints to documents, to be stored together with document terms in an appropriate indexing structure allowing real-time search, is described. Methods allowing users to query and explore results which have been relevance-ranked in terms of both thematic and spatial relevance have been implanted and a usability study indicates that users are happy with the range of spatial relationships available and intuitively understand how to use such a search engine. Normalised precision for 38 queries, containing four types of spatial relationships, is significantly higher (p<0.001) for searches exploiting spatial information than pure text search.


title: A scalable hash ripple join algorithm

abstract: Recently, Haas and Hellerstein proposed the hash ripple join algorithm in the context of online aggregation. Although the algorithm rapidly gives a good estimate for many join-aggregate problem instances, the convergence can be slow if the number of tuples that satisfy the join predicate is small or if there are many groups in the output. Furthermore, if memory overflows (for example, because the user allows the algorithm to run to completion for an exact answer), the algorithm degenerates to block ripple join and performance suffers. In this paper, we build on the work of Haas and Hellerstein and propose a new algorithm that (a) combines parallelism with sampling to speed convergence, and (b) maintains good performance in the presence of memory overflow. Results from a prototype implementation in a parallel DBMS show that its rate of convergence scales with the number of processors, and that when allowed to run to completion, even in the presence of memory overflow, it is competitive with the traditional parallel hybrid hash join algorithm.


title: Every smart phone is a backscatter reader: Modulated backscatter compatibility with Bluetooth 4.0 Low Energy (BLE) devices

abstract: In this work, we show how modulated backscatter signals can be crafted to yield channelized band-pass signals akin to those transmitted by many conventional wireless devices. As a result, conventional wireless devices can receive these backscattered signals without any modification (neither hardware nor software) to the conventional wireless device. We present a proof of concept using the Bluetooth 4.0 Low Energy, or BLE, standard widely available on smart phones and mobile devices. Our prototype backscatter tag produces three-channel bandpass frequency shift keying (FSK) packets at 1 Mbps that are indistinguishable from conventional BLE advertising packets. An unmodified Apple iPad is shown to correctly receive and display these packets at a range of over 9.4 m using its existing iOS Bluetooth stack with no changes whatsoever. We create all three BLE channels by backscattering a single incident CW carrier using a novel combination of fundamentalmode and harmonic-mode backscatter subcarrier modulation, with two of the band-pass channels generated by the fundamental mode and one of the band-pass channels generated by the second harmonic mode. The backscatter modulator consumes only 28.4 pJ/bit, compared with over 10 nJ/bit for conventional BLE transmitters. The backscatter approach yields over 100X lower energy per bit than a conventional BLE transmitter, while retaining compatibility with billions of existing Bluetooth enabled smartphones and mobile devices.


title: Future Directions of Precision Agriculture

abstract: Precision Agriculture is advancing but not as fast as predicted 5 years ago. The development of proper decision-support systems for implementing precision decisions remains a major stumbling block to adoption. Other critical research issues are discussed, namely, insufficient recognition of temporal variation, lack of whole-farm focus, crop quality assessment methods, product tracking and environmental auditing. A generic research programme for precision agriculture is presented. A typology of agriculture countries is introduced and the potential of each type for precision agriculture discussed.


title: Laser and light based treatments of acne.

abstract: Medical treatments for acne vulgaris include a variety of topical and oral medications. Poor compliance, lack of durable remission, and potential side effects are common drawbacks to these treatments. Therefore, there is a growing demand for a fast, safe, and side-effect-free novel therapy. Acne often improves after exposure to sunlight, and this has led to the development of laser and other light therapies resulting in the overall ease of treatment, with minimal adverse effects. A variety of light and laser devices has been used for the treatment of acne, including the potassium titanyl phosphate laser, the 585- and 595-nm pulsed dye lasers, the 1450-nm diode laser, radiofrequency devices, intense pulsed light sources, and photodynamic therapy using 5-aminolevulinic acid and indocyanine green. These devices are thought to target the underlying pathogenic factors such as propionibacterium acnes colonization, increased sebaceous gland activity, and the cutaneous inflammatory response. In this article, we review the current status of light- and laser-based treatment of acne.


title: High-Quality Ultra-Compact Grid Layout of Grouped Networks

abstract: Prior research into network layout has focused on fast heuristic techniques for layout of large networks, or complex multi-stage pipelines for higher quality layout of small graphs. Improvements to these pipeline techniques, especially for orthogonal-style layout, are difficult and practical results have been slight in recent years. Yet, as discussed in this paper, there remain significant issues in the quality of the layouts produced by these techniques, even for quite small networks. This is especially true when layout with additional grouping constraints is required. The first contribution of this paper is to investigate an ultra-compact, grid-like network layout aesthetic that is motivated by the grid arrangements that are used almost universally by designers in typographical layout. Since the time when these heuristic and pipeline-based graph-layout methods were conceived, generic technologies (MIP, CP and SAT) for solving combinatorial and mixed-integer optimization problems have improved massively. The second contribution of this paper is to reassess whether these techniques can be used for high-quality layout of small graphs. While they are fast enough for graphs of up to 50 nodes we found these methods do not scale up. Our third contribution is a large-neighborhood search meta-heuristic approach that is scalable to larger networks.


title: Expertise-related deactivation of the right temporoparietal junction during musical improvisation

abstract: Abstract Musical training has been associated with structural changes in the brain as well as functional differences in brain activity when musicians are compared to nonmusicians on both perceptual and motor tasks. Previous neuroimaging comparisons of musicians and nonmusicians in the motor domain have used tasks involving prelearned motor sequences or synchronization with an auditorily presented sequence during the experiment. Here we use functional magnetic resonance imaging (fMRI) to examine expertise-related differences in brain activity between musicians and nonmusicians during improvisation – the generation of novel musical–motor sequences – using a paradigm that we previously used in musicians alone. Despite behaviorally matched performance, the two groups showed significant differences in functional brain activity during improvisation. Specifically, musicians deactivated the right temporoparietal junction (rTPJ) during melodic improvisation, while nonmusicians showed no change in activity in this region. The rTPJ is thought to be part of a ventral attentional network for bottom-up stimulus-driven processing, and it has been postulated that deactivation of this region occurs in order to inhibit attentional shifts toward task-irrelevant stimuli during top-down, goal-driven behavior. We propose that the musicians' deactivation of the rTPJ during melodic improvisation may represent a training-induced shift toward inhibition of stimulus-driven attention, allowing for a more goal-directed performance state that aids in creative thought.


Analogy-Based Reasoning with Memory Networks for Future Prediction


title: Recounting the Courts? Toward A Text-Centered Computational Approach to

abstract: This paper explores the potential uses of computational linguistics techniques for analyzing Supreme Court briefs and opinions. To do so, we focused on advocacy documents associated with the two recent University of Michigan affirmative action cases (Gratz v. Bollinger and Grutter v. Bollinger). The cases attracted more than one hundred amicus briefs, which provide a rich textual database for such an exploratory study. The goal of our preliminary work is to model the linguistic contents of the arguments presented by the petitioners and respondents, as captured in the original litigants’ briefs and the amici briefs submitted in these two cases. In particular, we are interested in the types of words and phrases used by both sides in order to forward their arguments. These linguistic cues may provide us with insight into the policy and ideological inclinations of the parties involved. We utilize and compare two analytical methods, an adaptation of the Wordscoring technique first developed by Laver, Benoit, and Garry (2003) and a Naive Bayes’ classifier to identify commonalities in documents and group them accordingly. We find the methods to be quite competent at detecting amici brief positions, clustering petitioner and respondent briefs into well-spaced separate normal distributions. Additionally, we find it quite useful as an aid to qualitative content analysis. We identify distinctive rhetorical styles utilized by the respondents and petitioners and suggest how this type of analysis can improve our understanding of how and why different groups, ideologies, actors, interests, and the like, conceptualize issues.


title: Document Image Retrieval With Improvements In Database Quality

abstract: Modern technology has made it possible to produce, process, transmit and store digital images efficiently. Consequently, the amount of visual information is increasing at an accelerating rate in many diverse application areas. To fully exploit this new content-based image retrieval techniques are required. Document image retrieval systems can be utilized in many organizations which are using document image databases extensively. This thesis presents document image retrieval techniques and new approaches to improve database content. The goal of the thesis is to develop a functional retrieval system and to demonstrate that better retrieval results can be achieved with the proposed database generation methods. Retrieval system architecture, a document data model, and tools for querying document image databases are introduced. The retrieval framework presented allows users to interactively define, construct and combine queries using document or image properties: physical (structural), semantic, textual and visual image content. A technique for combining primitive features like color, shape and texture into composite features is presented. A novel search base reduction technique which uses structural and content properties of documents is proposed for speeding up the query process. A new model for database generation within the image retrieval system is presented. An approach for automated document image defect detection and management is presented to build high quality and retrievable database objects. In image database population, image feature profiles and their attributes are manipulated automatically to better match with query requirements determined by the available query methods, the application environment and the user. Experiments were performed with multiple image databases containing over one thousand images. They comprised a range of document and scene images from different categories, properties and condition. The results show that better recall and accuracy for retrieval is achieved with the proposed optimization techniques. The search base reduction technique results in a considerable speed-up in overall query processing. The constructed document image retrieval system performs well in different retrieval scenarios and provides a consistent basis for algorithm development. The proposed modular system structure and interfaces facilitate its usage in a wide variety of document image retrieval applications.


title: Survey of consensus protocols on blockchain applications

abstract: Blockchain is a distributed, transparent, immutable ledger. Consensus protocol forms the core of blockchain. They decide how a blockchain works. With the advent of new possibilities in blockchain technology, researchers are keen to find a well-optimized Byzantine fault tolerant consensus protocol. Creating a global consensus protocol or tailoring a cross-platform plug and play software application for implementation of various consensus protocols are ideas of huge interest. Stellar Consensus Protocol (SCP) is considered to be a global consensus protocol and promises to be Byzantine Fault Tolerant (BFT) by bringing with it the concept of quorum slices and federated byzantine fault tolerance. This consensus's working and its comparison with other protocols that were earlier proposed are analyzed here. Also, hyperledger an open-source project by Linux Foundation which includes implementing the concept of practical byzantine fault tolerance and also a platform where various other consensus protocols and blockchain applications can be deployed in a plug and play manner is also being discussed here. This paper focuses on analyzing these consensus protocols already proposed and their feasibility and efficiency in meeting the characteristics they propose to provide.


title: Newborn infant skin: physiology, development, and care.

abstract: Abstract Infant skin is critical to the newborn child's transition from the womb environment to the journey to self-sufficiency. This review provides an integrative perspective on the skin development in full term and premature infants. There is a particular focus on the role of vernix caseosa and on the implications of skin development for epidermal penetration of exogenous compounds. Healthy full-term newborn skin is well-developed and functional at birth, with a thick epidermis and well-formed stratum corneum (SC) layers. Transepidermal water loss is very low at birth, equal to, or lower than adults, indicating a highly effective skin barrier. Vernix facilitates SC development in full-term infants through a variety of mechanisms including physical protection from amniotic fluid and enzymes, antimicrobial effects, skin surface pH lowering, provision of lipids, and hydration. Premature infants, particularly those of very low birth weight, have a poor skin barrier with few cornified layers and deficient dermal proteins. They are at increased risk for skin damage, increased permeability to exogenous agents and infection. The SC barrier develops rapidly after birth but complete maturation requires weeks to months. The best methods for caring for infant skin, particularly in the diaper region, are described and related to these developmental changes.


title: Retinal vascular changes in malignant melanoma of the choroid.

abstract: A review of fluorescein angiograms from 41 patients with malignant melanoma of the choroid showed that 11 patients (27%) had evidence of retinal vascular abnormalities. Abnormal retinal capillaries were found in eight cases, areas of capillary nonperfusion in seven cases, and microaneurysms in four cases. Lipid exudation, retinal neovascularization, and arteriovenous communication were noted in one case each. Histopathologic studies in eight cases showed extensive degeneration of the overlaying retina in all eight cases and extension through Bruch's membrane by the tumor in four cases. Abnormal capillary architecture was demonstrated by trypsin digest preparations from four cases.


title: Rectovaginal fistula: A common diagnostic error with significant consequences in girls with anorectal malformations

abstract: Background/Purpose: Rectovaginal fistula is a term that continues to be used frequently to describe girls with anorectal malformations. This study attempts to evaluate the true frequency of this anomaly and the consequences of its misdiagnosis. Methods: A retrospective review of all girls with anorectal malformations treated by the senior author from 1980 through September 2000 was performed, and the pertinent literature was reviewed. Results: Of the 617 patients identified, only 6 were found to have a true rectovaginal fistula, an incidence of 1%. A total of 139 of the 617 patients were referred after a previous repair. Of these, 42 had a diagnosis of recto-vaginal fistula originally. The diagnosis was incorrect in all 42. Twelve patients had a rectovestibular fistula, and 30 had a cloaca. The 30 cloaca patients, because of the misconception that they had a rectovaginal fistula, underwent an initial repair of only the rectal component of the malformation, leaving all patients with a urogenital sinus necessitating a second complete repair. Conclusions: The use of the term rectovaginal fistula is common, despite a true incidence of 1% in girls with anorectal malformations. The majority of girls will have either a rectovestibular fistula or a cloaca. Such diagnostic errors may lead to the use of inappropriate surgical techniques, incomplete repair, and unnecessary morbidity.


title: RF Localization in Indoor Environment

abstract: In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained.


Approximate Inference for Deep Latent Gaussian Mixtures


title: An analysis of color demosaicing in plenoptic cameras

abstract: A plenoptic camera captures the 4D radiance about a scene. Recent practical solutions mount a microlens array on top of a commodity SLR to directly acquire these rays. However, they suffer from low resolution as hundreds of thousands of views need to be captured in a single shot. In this paper, we develop a simple but effective technique for improving the image resolution of the plenoptic camera by maneuvering the demosaicing process. We first show that the traditional solution by demosaicing each individual microlens image and then blending them for view synthesis is suboptimal. In particular, this demosaicing process often suffers from aliasing artifacts, and it damages high frequency information recorded by each microlens image hence degrades the image quality. We instead propose to de-mosaic the synthesized view at the rendering stage. Specifically, we first transform the radiance to the desired focal plane and then apply frequency domain plenoptic resampling. A full resolution color filtered image is then created by performing a 2D integral projection from the reparam-eterized radiance. Finally, we conduct demosacing to obtain the color result. We show that our solution can achieve visible resolution enhancement on dynamic refocusing and depth-assisted deep focus rendering.


title: Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees

abstract: Abstract The purpose of this study was to investigate the use of deep learning for coniferous/deciduous classification of individual trees segmented from airborne LiDAR data. To enable processing by a deep convolutional neural network (CNN), we designed two discrete representations using leaf-off and leaf-on LiDAR data: a digital surface model with four channels (DSM × 4) and a set of four 2D views (4 × 2D). A training dataset of tree crowns was generated via segmentation of tree crowns, followed by co-registration with field data. Potential mislabels due to GPS error or tree leaning were corrected using a statistical ensemble filtering procedure. Because the training data was heavily unbalanced (~8% conifers), we trained an ensemble of CNNs on random balanced sub-samples. Benchmarked against multiple traditional shallow learning methods using manually designed features, the CNNs improved accuracies up to 14%. The 4 × 2D representation yielded similar classification accuracies to the DSM × 4 representation (~82% coniferous and ~90% deciduous) while converging faster. Further experimentation showed that early/late fusion of the channels in the representations did not affect the accuracies in a significant way. The data augmentation that was used for the CNN training improved the classification accuracies, but more real training instances (especially coniferous) likely results in much stronger improvements. Leaf-off LiDAR data were the primary source of useful information, which is likely due to the perennial nature of coniferous foliage. LiDAR intensity values also proved to be useful, but normalization yielded no significant improvement. As we observed, large training data may compensate for the lack of a subset of important domain data. Lastly, the classification accuracies of overstory trees (~90%) were more balanced than those of understory trees (~90% deciduous and ~65% coniferous), which is likely due to the incomplete capture of understory tree crowns via airborne LiDAR. In domains like remote sensing and biomedical imaging, where the data contain a large amount of information and are not friendly to human visual system, human-designed features may become suboptimal. As exemplified by this study, automatic, objective derivation of optimal features via deep learning can improve prediction tasks in such domains.


title: An Algorithmic Framework to Control Bias in Bandit-based Personalization

abstract: Personalization is pervasive in the online space as it leads to higher efficiency and revenue by allowing the most relevant content to be served to each user. However, recent studies suggest that personalization methods can propagate societal or systemic biases and polarize opinions; this has led to calls for regulatory mechanisms and algorithms to combat bias and inequality. Algorithmically, bandit optimization has enjoyed great success in learning user preferences and personalizing content or feeds accordingly. We propose an algorithmic framework that allows for the possibility to control bias or discrimination in such bandit-based personalization. Our model allows for the specification of general fairness constraints on the sensitive types of the content that can be displayed to a user. The challenge, however, is to come up with a scalable and low regret algorithm for the constrained optimization problem that arises. Our main technical contribution is a provably fast and low-regret algorithm for the fairness-constrained bandit optimization problem. Our proofs crucially leverage the special structure of our problem. Experiments on synthetic and real-world data sets show that our algorithmic framework can control bias with only a minor loss to revenue.


title: Crowdsourcing Tools for Disaster Management: A Review of Platforms and Methods

abstract: Recent advances on information technologies and communications, coupled with the advent of the social media applications have fuelled a new landscape of emergency and disaster response systems by enabling affected citizens to generate georeferenced real time information on critical events. The identification and analysis of such events is not straightforward and the application of crowdsourcing methods or automatic tools is needed for that purpose. Whereas crowdsourcing makes emphasis on the resources of people to produce, aggregate, or filter original data, automatic tools make use of information retrieval techniques to analyze publicly available information. This paper reviews a set of online tools and platforms implemented in recent years which are currently being applied in the area of emergency management and proposes a taxonomy for its categorization.


title: Swoop: A Web Ontology Editing Browser

abstract: In this paper, we describe Swoop, a hypermedia inspired Ontology Browser and Editor based on OWL, the recently standardized Web-oriented ontology language. After discussing the design rationale and architecture of Swoop, we focus mainly on its features, using illustrative examples to highlight its use. We demonstrate that with its Web-metaphor, adherence to OWL recommendations and key unique features, such as Collaborative Annotation using Annotea, Swoop acts as a useful and efficient Web Ontology development tool. We conclude with a list of future plans for Swoop, that should further increase its overall appeal and accessibility.


title: Understanding of Antecedents to Achieve Customer Trust and Customer Intention to Purchase E-Commerce in Social Media, an Empirical Assessment

abstract: This study aims to analyze empirically three factors antecedents of trust they are system quality, information quality, and service quality. Customer trust is used in determining customer intention to purchase of e-commerce in social media (facebook). A number of respondents were 451. The results of this study concluded that three factors antecedents of trust directly had a positive impact to customer trust and indirectly had positive impact on customer intention to purchase in e-commerce transactions on social media.


title: Twenty years experiences of interprofessional education in Linkoping--ground-breaking and sustainable.

abstract: A pioneering and ground-breaking effort to organize interprofessional education (IPE) was initiated in 1986 at the Faculty of Health Sciences at Linkoping University in Sweden. The so-called "Linkoping IPE model" has now yielded practical experience and development of curricula for over 20 years. The basic idea of this model is that it is favorable for the development of students' own professional identity to meet other health and social professions already into their undergraduate studies. Interprofessional learning is a process over time that requires several integrated stages to gain interprofessional competence, i.e., the skills required to work together interprofessionally in practice. We believe that defined IPE modules early in the curriculum combined with student-training ward placement as the final module is an encouraging example of how to implement undergraduate IPE among health science students. It is strengthened by problem based learning (PBL) in small groups and student-centered learning. Based on these experiences, this paper aims to contribute to the discussion on how to implement and achieve the aims of IPE and to keep it sustainable. It is not a description of "how to do it" but rather a summarizing of our experiences for successful performance of IPE. The article presents how the Linkoping model was developed, the outcomes, experiences and some outlines for future challenges.


title: The Human Kernel

abstract: Bayesian nonparametric models, such as Gaussian processes, provide a compelling framework for automatic statistical modelling: these models have a high degree of flexibility, and automatically calibrated complexity. However, automating human expertise remains elusive; for example, Gaussian processes with standard kernels struggle on function extrapolation problems that are trivial for human learners. In this paper, we create function extrapolation problems and acquire human responses, and then design a kernel learning framework to reverse engineer the inductive biases of human learners across a set of behavioral experiments. We use the learned kernels to gain psychological insights and to extrapolate in human-like ways that go beyond traditional stationary and polynomial kernels. Finally, we investigate Occam's razor in human and Gaussian process based function learning.


title: Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks

abstract: This paper presents a simple yet effective supervised deep hash approach that constructs binary hash codes from labeled data for large-scale image search. We assume that the semantic labels are governed by several latent attributes with each attribute on or off , and classification relies on these attributes. Based on this assumption, our approach, dubbed supervised semantics-preserving deep hashing (SSDH), constructs hash functions as a latent layer in a deep network and the binary codes are learned by minimizing an objective function defined over classification error and other desirable hash codes properties. With this design, SSDH has a nice characteristic that classification and retrieval are unified in a single learning model. Moreover, SSDH performs joint learning of image representations, hash codes, and classification in a point-wised manner, and thus is scalable to large-scale datasets. SSDH is simple and can be realized by a slight enhancement of an existing deep architecture for classification; yet it is effective and outperforms other hashing approaches on several benchmarks and large datasets. Compared with state-of-the-art approaches, SSDH achieves higher retrieval accuracy, while the classification performance is not sacrificed.


title: Measuring Phase Synchrony in Brain Signals

abstract: This article presents, for the first time, a practical method for the direct quantification of frequency-specific synchronization (i.e., transient phase-locking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronies as a putative mechanism for long-range neural integration during cognitive tasks. The method, called phase-locking statistics (PLS), measures the significance of the phase covariance between two signals with a reasonable time-resolution (,100 ms). Unlike the more traditional method of spectral coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration. To validate synchrony values against background fluctuations, PLS uses surrogate data and thus makes no a priori assumptions on the nature of the experimental data. We also apply PLS to investigate intracortical recordings from an epileptic patient performing a visual discrimination task. We find large-scale synchronies in the gamma band (45 Hz), e.g., between hippocampus and frontal gyrus, and local synchronies, within a limbic region, a few cm apart. We argue that whereas long-scale effects do reflect cognitive processing, short-scale synchronies are likely to be due to volume conduction. We discuss ways to separate such conduction effects from true signal synchrony. Hum Brain Mapping 8:194-208, 1999. r 1999 Wiley-Liss, Inc.


title: (Bandit) Convex Optimization with Biased Noisy Gradient Oracles

abstract: Algorithms for bandit convex optimization and online learning often rely on constructing noisy gradient estimates, which are then used in appropriately adjusted first-order algorithms, replacing actual gradients. Depending on the properties of the function to be optimized and the nature of “noise” in the bandit feedback, the bias and variance of gradient estimates exhibit various tradeoffs. In this paper we propose a novel framework that replaces the specific gradient estimation methods with an abstract oracle. With the help of the new framework we unify previous works, reproducing their results in a clean and concise fashion, while, perhaps more importantly, the framework also allows us to formally show that to achieve the optimal root-n rate either the algorithms that use existing gradient estimators, or the proof techniques used to analyze them have to go beyond what exists today.


title: Pervasive Decentralisation of Digital Infrastructures: A Framework for Blockchain enabled System and Use Case Analysis

abstract: Technological innovation and consequential decentralisation are driving forces in the ongoing evolution and increasing openness of digital infrastructures and services. One of the most discussed and allegedly disruptive innovations is the distributed database technology referred to as blockchain. Although it is still in its technological infancy, experimental adoption and customization seem to be in full progress in various potential fields of application ranging from decentralized grids for computation and storage to global financial services. However, the technology and its path of development still entail a lot of common unknowns for practitioners and researchers alike. Especially regarding the question how the technology could amend or be incorporated into the existing landscape of digital services, processes and infrastructures. Hence, in this article we develop an ontology that (1) clearly delineates common terminology, core concepts and components, their relationships as well as innovative features of blockchain technology. It further (2) connects these insights with implications for relevant types of digital market models. Our framework is of high theoretical and practical value as it provides researchers and practitioners a common basis for communication and means for guided analysis of blockchain applicability.


title: Energy Flow Optimization in Multicarrier Systems

abstract: In this paper, a generalized heuristic approach is proposed to solve the optimal power flow problem in multicarrier energy systems. This technique omits the use of any extra variable, such as dispatch factors or dummy variables required for conventional techniques. The unified proposed approach can be utilized with all evolutionary algorithms. Modeling hub devices with constant efficiency may produce a considerable error in finding the actual optimal operating point of the whole network. However, using variable efficiency model adds complexity to the conventional methods while increasing the computation–demand of these techniques, but this target can be simply implemented by the proposed scheme. A multicarrier energy system consists of an electrical, a natural gas, and a district heating network is analyzed by the proposed algorithm using the modified teaching–learning-based optimization method. Results validate the utilized approach and show that it can successfully reach the global optimal solution of the problem.


title: Nanostructured MnO2 as Electrode Materials for Energy Storage

abstract: Manganese dioxides, inorganic materials which have been used in industry for more than a century, now find great renewal of interest for storage and conversion of energy applications. In this review article, we report the properties of MnO2 nanomaterials with different morphologies. Techniques used for the synthesis, structural, physical properties, and electrochemical performances of periodic and aperiodic frameworks are discussed. The effect of the morphology of nanosized MnO2 particles on their fundamental features is evidenced. Applications as electrodes in lithium batteries and supercapacitors are examined.


title: Ensuring sustainability of PV systems for a given climate region in South Africa

abstract: A number of software simulation packages for alternative energy systems exist which may be used to determine PV system installation parameters (e.g. tilt and orientation angles) and operating limits (e.g. maximum drain current per day). However, climate change has contributed to a series of variations in weather patterns, resulting in either prolonged rain periods or lengthy heat waves. Both these variable climatic conditions exert a negative influence on the sustainability of PV systems, resulting either in system downtime or system lifetime reduction. This paper aims to contrast the results of a specific software package (RETScreen) for PV systems to experimental results obtained for a given climatic region in South Africa. The results indicate that the RETScreen software package's results should be seen as a guide in the design and installation of sustainable PV systems.


title: Analysing Wikipedia and Gold-Standard Corpora for NER Training

abstract: Named entity recognition (ner) for English typically involves one of three gold standards: muc, conll, or bbn, all created by costly manual annotation. Recent work has used Wikipedia to automatically create a massive corpus of named entity annotated text. ::: ::: We present the first comprehensive cross-corpus evaluation of ner. We identify the causes of poor cross-corpus performance and demonstrate ways of making them more compatible. Using our process, we develop a Wikipedia corpus which outperforms gold standard corpora on cross-corpus evaluation by up to 11%.


title: An inverted suspended microstrip antenna array integrated with ratrace comparator at X-Band for monopulse tracking

abstract: In this paper, an inverted suspended microstrip antenna array with ratrace comparator at X-band for monopulse tracking radar applications has been designed and simulated using IE3D software based on method of moments (MoM). The radiating part of the monopulse antenna consists of hybrid feeding technique to achieve better side lobe level and high gain. The bandwidth of 8.6% has been achieved for VSWR < 2 from 8.96 GHz to 9.76 GHz. The maximum gain of 25.1 dB has been achieved with approximately 1 dB variations over the entire bandwidth. The sum radiation pattern shows a side lobe level of better than 17.5 dB in both azimuth as well as elevation planes. The difference radiation pattern shows a null depth of better than 28 dB. The radiation pattern is symmetric with HPBW of 6.3° and 7.1° in azimuth and elevation planes, respectively.


title: BubbleRank: Safe Online Learning to Rerank

abstract: We study the problem of online learning to re-rank, where users provide feedback to improve the quality of displayed lists. Learning to rank has been traditionally studied in two settings. In the offline setting, rankers are typically learned from relevance labels of judges. These approaches have become the industry standard. However, they lack exploration, and thus are limited by the information content of offline data. In the online setting, an algorithm can propose a list and learn from the feedback on it in a sequential fashion. Bandit algorithms developed for this setting actively experiment, and in this way overcome the biases of offline data. But they also tend to ignore offline data, which results in a high initial cost of exploration. We propose BubbleRank, a bandit algorithm for re-ranking that combines the strengths of both settings. The algorithm starts with an initial base list and improves it gradually by swapping higher-ranked less attractive items for lower-ranked more attractive items. We prove an upper bound on the n-step regret of BubbleRank that degrades gracefully with the quality of the initial base list. Our theoretical findings are supported by extensive numerical experiments on a large real-world click dataset.


title: Cutaneous histopathological findings of AicardiGoutires syndrome, overlap with chilblain lupus

abstract: We report a 2-year-old girl with developmental delay who, from the age of 1 year, developed perniotic lesions of the hands and feet initially diagnosed as chilblain lupus. Histological examination showed features of epidermal necrosis with intraepidermal bulla formation, interface dermatitis, lymphocytic vasculitis with fibrinoid necrosis and thrombi formation, both superficial and deep dermal lymphocytic infiltrate, lymphocytic eccrine hidradenitis and absence of marked dermal edema. Subsequent investigations suggested a clinical diagnosis of Aicardi-Goutieres syndrome (AGS), a rare genetic leukoencephalopathy. Recently, both AGS and familial chilblain lupus, an autosomal dominant form of systemic lupus erythematosus (SLE), have been shown to be allelic thus suggesting a common pathogenic basis. In addition, a phenotypic overlap is apparent between SLE and AGS. To our knowledge, this is the first comprehensive dermatopathological report of the cutaneous lesions seen in AGS, and our paper highlights the importance of considering AGS in the differential diagnosis of perniosis and chilblain lupus.


title: Exploiting Spectral-Spatial Correlation for Coded Hyperspectral Image Restoration

abstract: Conventional scanning and multiplexing techniques for hyperspectral imaging suffer from limited temporal and/or spatial resolution. To resolve this issue, coding techniques are becoming increasingly popular in developing snapshot systems for high-resolution hyperspectral imaging. For such systems, it is a critical task to accurately restore the 3D hyperspectral image from its corresponding coded 2D image. In this paper, we propose an effective method for coded hyperspectral image restoration, which exploits extensive structure sparsity in the hyperspectral image. Specifically, we simultaneously explore spectral and spatial correlation via low-rank regularizations, and formulate the restoration problem into a variational optimization model, which can be solved via an iterative numerical algorithm. Experimental results using both synthetic data and real images show that the proposed method can significantly outperform the state-of-the-art methods on several popular coding-based hyperspectral imaging systems.


title: Halftone QR codes

abstract: QR code is a popular form of barcode pattern that is ubiquitously used to tag information to products or for linking advertisements. While, on one hand, it is essential to keep the patterns machine-readable; on the other hand, even small changes to the patterns can easily render them unreadable. Hence, in absence of any computational support, such QR codes appear as random collections of black/white modules, and are often visually unpleasant. We propose an approach to produce high quality visual QR codes, which we call halftone QR codes, that are still machine-readable. First, we build a pattern readability function wherein we learn a probability distribution of what modules can be replaced by which other modules. Then, given a text tag, we express the input image in terms of the learned dictionary to encode the source text. We demonstrate that our approach produces high quality results on a range of inputs and under different distortion effects.


title: This is how we do it: Answer Reranking for Open-domain How Questions with Paragraph Vectors and Minimal Feature Engineering

abstract: We present a simple yet powerful approach to non-factoid answer reranking whereby question-answer pairs are represented by concatenated distributed representation vectors and a multilayer perceptron is used to compute the score for an answer. Despite its simplicity, our approach achieves state-of-the-art performance on a public dataset of How questions, outperforming systems which employ sophisticated feature sets. We attribute this good performance to the use of paragraph instead of word vector representations and to the use of suitable data for training these representations.


title: Decentralized Semantic Identity

abstract: This paper examines a semantic approach for identity management, namely the W3C WebID, as a representation of personal information, and the WebID-TLS as a decentralized authentication protocol, allowing individuals to manage their own identities and data privacy. The paper identifies a set of important usability, privacy and security issues that needs to be addressed, and proposes an end to end authentication mechanism based on WebID, JSON Web Tokens (JWT) and the blockchain. The WebID includes a personal profile with its certificate, and the social relationship information described as the RDF-based FOAF ontology. The JWT is a standardized container format to encode personal related information in a secure way using "claims". The distributed, irreversible, undeletable, and immutable nature of the blockchain has appropriate attributes for distributed credential storage and decentralized identity management.


title: A novel approach to online social influence maximization

abstract: Online social networks are becoming a true growth point of Internet. As individuals constantly desire to interact with each other, the ability for Internet to deliver this networking influence becomes much stronger. In this paper, we study the online social influence maximization problem, which is to find a small group of influential users that maximizes the spread of influence through networks. After a thorough analysis of existing models, especially two classical ones, namely Independent cascade and linear thresholds, we argue that their idea that each user can only be activated by its active neighbors is not applicable to online social networks, since in many applications there is no clear concept for the issue of "activation". In our proposed influence model, if there is a social influence path linking two nonadjacent individuals, the value of influence between these two individuals can be evaluated along the existing social path based on the influence transitivity property under certain constraints. To compute influence probabilities between two neighbors, we also develop a new method which leverages both structure of networks and historical data. With reasonably learned influence probabilities, we recast the problem of online influence maximization to a weighted maximum cut problem which analyzes the influence flow among graph vertices. By running a semidefinite program-based (GW) algorithm on a complete influence graph, an optimal seed set can be identified effectively. We also provide experimental results on real online social networks, showing that our algorithm significantly outperforms greedy methods.


title: Statistical Procedures for Forecasting Criminal Behavior: A Comparative Assessment

abstract: Research Summary ::: ::: ::: A substantial and powerful literature in statistics and computer science has clearly demonstrated that modern machine learning procedures can forecast more accurately than conventional parametric statistical models such as logistic regression. Yet, several recent studies have claimed that for criminal justice applications, forecasting accuracy is about the same. In this article, we address the apparent contradiction. Forecasting accuracy will depend on the complexity of the decision boundary. When that boundary is simple, most forecasting tools will have similar accuracy. When that boundary is complex, procedures such as machine learning, which proceed adaptively from the data, will improve forecasting accuracy, sometimes dramatically. Machine learning has other benefits as well, and effective software is readily available. ::: ::: ::: ::: Policy Implications ::: ::: The complexity of the decision boundary will in practice be unknown, and there can be substantial risks to gambling on simplicity. Criminal justice decision makers and other stakeholders can be seriously misled with rippling effects going well beyond the immediate offender. There seems to be no reason for continuing to rely on traditional forecasting tools such as logistic regression.


title: Layout-aware text extraction from full-text PDF of scientific articles

abstract: BackgroundThe Portable Document Format (PDF) is the most commonly used file format for online scientific publications. The absence of effective means to extract text from these PDF files in a layout-aware manner presents a significant challenge for developers of biomedical text mining or biocuration informatics systems that use published literature as an information source. In this paper we introduce the ‘Layout-Aware PDF Text Extraction’ (LA-PDFText) system to facilitate accurate extraction of text from PDF files of research articles for use in text mining applications.ResultsOur paper describes the construction and performance of an open source system that extracts text blocks from PDF-formatted full-text research articles and classifies them into logical units based on rules that characterize specific sections. The LA-PDFText system focuses only on the textual content of the research articles and is meant as a baseline for further experiments into more advanced extraction methods that handle multi-modal content, such as images and graphs. The system works in a three-stage process: (1) Detecting contiguous text blocks using spatial layout processing to locate and identify blocks of contiguous text, (2) Classifying text blocks into rhetorical categories using a rule-based method and (3) Stitching classified text blocks together in the correct order resulting in the extraction of text from section-wise grouped blocks. We show that our system can identify text blocks and classify them into rhetorical categories with Precision1 = 0.96% Recall = 0.89% and F1 = 0.91%. We also present an evaluation of the accuracy of the block detection algorithm used in step 2. Additionally, we have compared the accuracy of the text extracted by LA-PDFText to the text from the Open Access subset of PubMed Central. We then compared this accuracy with that of the text extracted by the PDF2Text system, 2commonly used to extract text from PDF. Finally, we discuss preliminary error analysis for our system and identify further areas of improvement.ConclusionsLA-PDFText is an open-source tool for accurately extracting text from full-text scientific articles. The release of the system is available at http://code.google.com/p/lapdftext/.


Optimal Economic Operation of Electric Power Systems


title: Txt msg n school literacy: does texting and knowledge of text abbreviations adversely affect children's literacy attainment?

abstract: This paper reports on two studies which investigated the relationship between children’s texting behaviour, their knowledge of text abbreviations and their school attainment in written language skills. In Study One, 11–12-year-old children provided information on their texting behaviour. They were also asked to translate a standard English sentence into a text message and vice versa. The children’s standardised verbal and nonverbal reasoning scores were also obtained. Children who used their mobiles to send three or more text messages a day had significantly lower scores than children who sent none. However, the children who, when asked to write a text message, showed greater use of text abbreviations (‘textisms’) tended to have better performance on a measure of verbal reasoning ability, which is highly associated with Key Stage 2 (KS2) and 3 English scores. In Study Two, children’s performance on writing measures was examined more specifically. Ten to eleven-year-old children were asked to complete another English to text message translation exercise. Spelling proficiency was also assessed, and KS2 Writing scores were obtained. Positive correlations between spelling ability and performance on the translation exercise were found, and group-based comparisons based on the children’s writing scores also showed that good writing attainment was associated with greater use of textisms, although the direction of this association is nor clear. Overall, these findings suggest that children’s knowledge of textisms is not associated with poor written language outcomes for children in this age range.


title: Matched-Routing Common-Centroid 3-D MOM Capacitors for Low-Power Data Converters

abstract: As technology advances, smaller feature size enables layout precision in the lateral direction. In contrast to traditional metal–insulator–metal capacitors, metal–oxide–metal (MOM) capacitors are generally of higher density and consume less area, because they adopt a 3-D topology over several metal layers to use lateral field capacitance. To achieve little area consumption, MOM capacitors require new layout methods with routing even before placement. Since the routing wires are of comparable size to unit capacitors in MOM design, routing-induced parasitic capacitance must also be considered. To obtain a higher yield, common-centroid layout style and high dispersion of cells are desired. This paper introduces a new vertical bars structure for binary weighted MOM capacitor arrays and presents a new method to generate common-centroid regular-structured layouts with mismatch reduction and routing-parasitic-matching consideration. Experimental results show that the presented approach generates high-density, low-powered, and highly accurate ratioed capacitors.


title: Linear (Hull) and Algebraic Cryptanalysis of the Block Cipher PRESENT

abstract: The contributions of this paper include the first linear hull and a revisit of the algebraic cryptanalysis of reduced-round variants of the block cipher PRESENT, under known-plaintext and ciphertext-only settings. We introduce a pure algebraic cryptanalysis of 5-round PRESENT and in one of our attacks we recover half of the bits of the key in less than three minutes using an ordinary desktop PC. The PRESENT block cipher is a design by Bogdanov et al. , announced in CHES 2007 and aimed at RFID tags and sensor networks. For our linear attacks, we can attack 25-round PRESENT with the whole code book, 296.68 25-round PRESENT encryptions, 240 blocks of memory and 0.61 success rate. Further we can extend the linear attack to 26-round with small success rate. As a further contribution of this paper we computed linear hulls in practice for the original PRESENT cipher, which corroborated and even improved on the predicted bias (and the corresponding attack complexities) of conventional linear relations based on a single linear trail.


title: E-Learning 3.0: Anyone, Anywhere, Anytime, and AI

abstract: The concept of e-Learning 2.0 has become well established and widely accepted. Just like how e-Learning 2.0 replaced its predecessor, we are again on the verge of a transformation. Both previous generations of e-Learning 1.0 and 2.0 closely parody the prevalent technologies available in their kin Web versions 1.0 and 2.0, respectively. In order to acquire a better perspective to assess what technologies will be available in the Web 3.0 and therefore e-Learning 3.0, we take a historical glance at the previous generations of e-Learning and theWeb. We then survey some existing predictions for e-Learning 3.0 and finally provide our own. Previous surveys tend to identify educational needs for e-Learning, and then discuss what technologies are required to satisfy these needs. Educational needs are an important factor, but the required technologies may not reach fruition. Gauging past trends we take the reverse approach by first identifying technologies that are likely to be brought forth by the Web 3.0, and only then looking at how these technologies could be utilized in the learning domain. In particular, we pin-point Artificial Intelligence more specifically Machine Learning and Data Mining as a major driving force behind the Web 3.0. We therefore examine the influence that AI might exert on the development of e-Learning 3.0.


title: Incidental or influential? - Challenges in automatically detecting citation importance using publication full texts

abstract: This work looks in depth at several studies that have attempted to automate the process of citation importance classification based on the publications’ full text. We analyse a range of features that have been previously used in this task. Our experimental results confirm that the number of in-text references are highly predictive of influence. Contrary to the work of Valenzuela et al. (2015) [1], we find abstract similarity one of the most predictive features. Overall, we show that many of the features previously described in literature are not particularly predictive. Consequently, we discuss challenges and potential improvements in the classification pipeline, provide a critical review of the performance of individual features and address the importance of constructing a large scale gold-standard reference dataset.


title: Clustering of contingency table and mixture model

abstract: Basing cluster analysis on mixture models has become a classical and powerful approach. It enables some classical criteria such as the well-known k-means criterion to be explained. To classify the rows or the columns of a contingency table, an adapted version of k-means known as Mndki2, which uses the chi-square distance, can be used. Unfortunately, this simple, effective method which can be used jointly with correspondence analysis based on the same representation of the data, cannot be associated with a mixture model in the same way as the classical k-means algorithm. In this paper we show that the Mndki2 algorithm can be viewed as an approximation of a classifying version of the EM algorithm for a mixture of multinomial distributions. A comparison of the algorithms belonging in this context are experimentally investigated using Monte Carlo simulations.


title: Relationship between Employee Motivation and Job Performance: A Study at Universiti Teknologi MARA (Terengganu)

abstract: Motivation is considered as a predictor of job performance. In other words, the determinants of employee job performance were motivation, aptitudes and skill. Thus, motivated employees with high levels of job involvement are considered as important elements to an organization. In this paper, it attempts to measure the effect of employee motivation towards job performance among non-academic staff in Universiti Teknologi MARA Terengganu (UiTMT). A total of 169 respondents were selected from non-academic staff of departments in UiTMT. Three predictors such as individual needs, personal preferences and work environment were found to have medium to strong correlation with the dependent variables namely job performance. The analysis shows that individual needs, personal preferences and work environment are positively and significantly related to job performance. The outcome of the study indicates that UiTMT needs to focus on their motivation aspect, where it can boost up their level of job performance. Finally, research and practical implications of the study are discussed. DOI: 10.5901/mjss.2015.v6n4s2p632


Do stop losses work? The disposition effect, stop losses and investor demographics


title: LCCT: A Semi-supervised Model for Sentiment Classification

abstract: Analyzing public opinions towards products, services and social events is an important but challenging task. An accurate sentiment analyzer should take both lexicon-level information and corpus-level information into account. It also needs to exploit the domainspecific knowledge and utilize the common knowledge shared across domains. In addition, we want the algorithm being able to deal with missing labels and learning from incomplete sentiment lexicons. This paper presents a LCCT (Lexicon-based and Corpus-based, Co-Training) model for semi-supervised sentiment classification. The proposed method combines the idea of lexicon-based learning and corpus-based learning in a unified cotraining framework. It is capable of incorporating both domain-specific and domainindependent knowledge. Extensive experiments show that it achieves very competitive classification accuracy, even with a small portion of labeled data. Comparing to state-ofthe-art sentiment classification methods, the LCCT approach exhibits significantly better performances on a variety of datasets in both English and Chinese.


Insects of Citrus and Other Subtropical Fruits


title: Ethical regulations on robotics in Europe

abstract: There are only a few ethical regulations that deal explicitly with robots, in contrast to a vast number of regulations, which may be applied. We will focus on ethical issues with regard to “responsibility and autonomous robots”, “machines as a replacement for humans”, and “tele-presence”. Furthermore we will examine examples from special fields of application (medicine and healthcare, armed forces, and entertainment). We do not claim to present a complete list of ethical issue nor of regulations in the field of robotics, but we will demonstrate that there are legal challenges with regard to these issues.