title: Biped gait controller for large speed variations, combining reflexes and a central pattern generator in a neuromuscular model

text: Controllers based on neuromuscular models hold the promise of energy-efficient and human-like walkers. However, most of them rely on optimizations or cumbersome hand-tuning to find controller parameters which, in turn, are usually working for a specific gait or forward speed only. Consequently, designing neuromuscular controllers for a large variety of gaits is usually challenging and highly sensitive. In this contribution, we propose a neuromuscular controller combining reflexes and a central pattern generator able to generate gaits across a large range of speeds, within a single optimization. Applying this controller to the model of COMAN, a 95 cm tall humanoid robot, we were able to get energy-efficient gaits ranging from 0.4 m/s to 0.9 m/s. This covers normal human walking speeds once scaled to the robot height. In the proposed controller, the robot speed could be continuously commanded within this range by changing three high-level parameters as linear functions of the target speed. This allowed large speed transitions with no additional tuning. By combining reflexes and a central pattern generator, this approach can also predict when the next strike will occur and modulate the step length to step over a hole.


title: Using Argument Mining to Assess the Argumentation Quality of Essays

text: Argument mining aims to determine the argumentative structure of texts. Although it is said to be crucial for future applications such as writing support systems, the benefit of its output has rarely been evaluated. This paper puts the analysis of the output into the focus. In particular, we investigate to what extent the mined structure can be leveraged to assess the argumentation quality of persuasive essays. We find insightful statistical patterns in the structure of essays. From these, we derive novel features that we evaluate in four argumentation-related essay scoring tasks. Our results reveal the benefit of argument mining for assessing argumentation quality. Among others, we improve the state of the art in scoring an essay’s organization and its argument strength.


title: Ontology-Aware Token Embeddings for Prepositional Phrase Attachment

text: Type-level word embeddings use the same set of parameters to represent all instances of a word regardless of its context, ignoring the inherent lexical ambiguity in language. Instead, we embed semantic concepts (or synsets) as defined in WordNet and represent a word token in a particular context by estimating a distribution over relevant semantic concepts. We use the new, context-sensitive embeddings in a model for predicting prepositional phrase (PP) attachments and jointly learn the concept embeddings and model parameters. We show that using context-sensitive embeddings improves the accuracy of the PP attachment model by 5.4% absolute points, which amounts to a 34.4% relative reduction in errors.


title: Developing an Isolated Word Recognition System in MATLAB

text: The Development Workflow There are two major stages within isolated word recognition: a training stage and a testing stage. Training involves “teaching” the system by building its dictionary, an acoustic model for each word that the system needs to recognize. In our example, the dictionary comprises the digits ‘zero’ to ‘nine’. In the testing stage we use acoustic models of these digits to recognize isolated words using a classification algorithm. The development workflow consists of three steps: Speech acquisition • Speech analysis • User interface development •


title: Ruminating Reader: Reasoning with Gated Multi-Hop Attention

text: To answer the question in machine comprehension (MC) task, the models need to establish the interaction between the question and the context. To tackle the problem that the single-pass model cannot reflect on and correct its answer, we present Ruminating Reader. Ruminating Reader adds a second pass of attention and a novel information fusion component to the Bi-Directional Attention Flow model (BIDAF). We propose novel layer structures that construct a query aware context vector representation and fuse encoding representation with intermediate representation on top of BIDAF model. We show that a multi-hop attention mechanism can be applied to a bi-directional attention structure. In experiments on SQuAD, we find that the Reader outperforms the BIDAF baseline by 2.1 F1 score and 2.7 EM score. Our analysis shows that different hops of the attention have different responsibilities in selecting answers.


title: An investigation of current models of second language speech perception: the case of Japanese adults' perception of English consonants.

text: This study reports the results of two experiments with native speakers of Japanese. In experiment 1, near-monolingual Japanese listeners participated in a cross-language mapping experiment in which they identified English and Japanese consonants in terms of a Japanese category, then rated the identifications for goodness-of-fit to that Japanese category. Experiment 2 used the same set of stimuli in a categorial discrimination test. Three groups of Japanese speakers varying in English-language experience, and one group of native English speakers participated. Contrast pairs composed of two English consonants, two Japanese consonants, and one English and one Japanese consonant were tested. The results indicated that the perceived phonetic distance of second language (L2) consonants from the closest first language (L1) consonant predicted the discrimination of L2 sounds. In addition, this study investigated the role of experience in learning sounds in a second language. Some of the consonant contrasts tested showed evidence of learning (i.e., significantly higher scores for the experienced than the relatively inexperienced Japanese groups). The perceived phonetic distance of L1 and L2 sounds was found to predict learning effects in discrimination of L1 and L2 sounds in some cases. The results are discussed in terms of models of cross-language speech perception and L2 phonetic learning.


title: Resource Sharing in Continuous Sliding-Window Aggregates

text: We consider the problem of resource sharing when processing large numbers of continuous queries. We specifically address sliding-window aggregates over data streams, an important class of continuous operators for which sharing has not been addressed. We present a suite of sharing techniques that cover a wide range of possible scenarios: different classes of aggregation functions (algebraic, distributive, holistic), different window types (time-based, tuple-based, suffix, historical), and different input models (single stream, multiple substreams). We provide precise theoretical performance guarantees for our techniques, and show their practical effectiveness through experimental study.


title: Design of Wireless Sensor System for Neonatal Monitoring

text: In this paper, we present the application of wireless sensor technology and the advantages it will inherently have for neonatal care and monitoring at Neonatal Intensive Care Units (NICU). An electrocardiography (ECG) readout board and a wireless transceiver module developed by IMEC at the Holst Centre in the Netherlands are embedded in the proposed wireless sensor systems in combination with the signal processing and software interface developed at the Department of Industrial Design, Eindhoven University of Technology (TU/e). Through development of this prototype system, we opt to ensure correct data transmission, detection and display. The wireless system is designed to be suitable for integration into non-invasive monitoring platforms such as a smart neonatal jacket developed at TU/e. Experiments at Maxima Medical Centre (MMC) in Veldhoven, the Netherlands demonstrate the wireless transmission of ECG data from the smart jacket integrated with textile electrodes.


title: Sentiment Analysis and Subjectivity

text: Textual information in the world can be broadly categorized into two main types: facts and opinions. Facts are objective expressions about entities, events and their properties. Opinions are usually subjective expressions that describe people’s sentiments, appraisals or feelings toward entities, events and their properties. The concept of opinion is very broad. In this chapter, we only focus on opinion expressions that convey people’s positive or negative sentiments. Much of the existing research on textual information processing has been focused on mining and retrieval of factual information, e.g., information retrieval, Web search, text classification, text clustering and many other text mining and natural language processing tasks. Little work had been done on the processing of opinions until only recently. Yet, opinions are so important that whenever we need to make a decision we want to hear others’ opinions. This is not only true for individuals but also true for organizations.


title: Comparison of Classifier Fusion Methods for Classification in Pattern Recognition Tasks

text: This work presents a comparison of current research in the use of voting ensembles of classifiers in order to improve the accuracy of single classifiers and make the performance more robust against the difficulties that each individual classifier may have. Also, a number of combination rules are proposed. Different voting schemes are discussed and compared in order to study the performance of the ensemble in each task. The ensembles have been trained on real data available for benchmarking and also applied to a case study related to statistical description models of melodies for music genre recognition.


title: Unmanned Aerial Vehicle Path Following: A Survey and Analysis of Algorithms for Fixed-Wing Unmanned Aerial Vehicless

text: Unmanned aerial vehicles (UAVs) are mainly used by military and government organizations, but with low-cost sensors, electronics, and airframes there is significant interest in using low-cost UAVs among aircraft hobbyists, academic researchers, and industries. Applications such as mapping, search and rescue, patrol, and surveillance require the UAV to autonomously follow a predefined path at a prescribed height. The most commonly used paths are straight lines and circular orbits. Path-following algorithms ensure that the UAV will follow a predefined path in three or two dimensions at constant height. A basic requirement for these path-following algorithms is that they must be accurate and robust to wind disturbances.


title: Erratum to: Eye–hand coordination in a sequential target contact task

text: Most object manipulation tasks involve a series of actions demarcated by mechanical contact events, and gaze is typically directed to the locations of these events as the task unfolds. Here, we examined the timing of gaze shifts relative to hand movements in a task in which participants used a handle to contact sequentially five virtual objects located in a horizontal plane. This task was performed both with and without visual feedback of the handle position. We were primarily interested in whether gaze shifts, which in our task shifted from a given object to the next about 100 ms after contact, were predictive or triggered by tactile feedback related to contact. To examine this issue, we included occasional catch contacts where forces simulating contact between the handle and object were removed. In most cases, removing force did not alter the timing of gaze shifts irrespective of whether or not vision of handle position was present. However, in about 30% of the catch contacts, gaze shifts were delayed. This percentage corresponded to the fraction of contacts with force feedback in which gaze shifted more than 130 ms after contact. We conclude that gaze shifts are predictively controlled but timed so that the hand actions around the time of contact are captured in central vision. Furthermore, a mismatch between the expected and actual tactile information related to the contact can lead to a reorganization of gaze behavior for gaze shifts executed greater than 130 ms after a contact event.


title: Recognition with Local Features: the Kernel Recipe

text: Recent developments in computer vision have shown that local features can provide efficient representations suitable for robust object recognition. Support Vector Machines have been established as powerful learning algorithms with good generalization capabilities. In this paper, we combine these two approaches and propose a general kernel method for recognition with local features. We show that the proposed kernel satisfies the Mercer condition and that it is suitable for many established local feature frameworks. Large-scale recognition results are presented on three different databases, which demonstrate that SVMs with the proposed kernel perform better than standard matching techniques on local features. In addition, experiments on noisy and occluded images show that local feature representations significantly outperform global approaches.


title: Fast k-Means Based on k-NN Graph

text: In the big data era, k-means clustering has been widely adopted as a basic processing tool in various contexts. However, its computational cost could be prohibitively high when the data size and the cluster number are large. The processing bottleneck of k-means lies in the operation of seeking the closest centroid in each iteration. In this paper, a novel solution towards the scalability issue of k-means is presented. In the proposal, k-means is supported by an approximate k-nearest neighbors graph. In the k-means iteration, each data sample is only compared to clusters that its nearest neighbors reside. Since the number of nearest neighbors we consider is much less than k, the processing cost in this step becomes minor and irrelevant to k. The processing bottleneck is therefore broken. The most interesting thing is that k-nearest neighbor graph is constructed by calling the fast k-means itself. Compared with existing fast k-means variants, the proposed algorithm achieves hundreds to thousands times speed-up while maintaining high clustering quality. As it is tested on 10 million 512-dimensional data, it takes only 5.2 hours to produce 1 million clusters. In contrast, it would take 3 years for traditional k-means to fulfill the same scale of clustering.


title: Malassezia Intra-Specific Diversity and Potentially New Species in the Skin Microbiota from Brazilian Healthy Subjects and Seborrheic Dermatitis Patients

text: Malassezia yeasts are part of the resident cutaneous microbiota, and are also associated with skin diseases such as seborrheic dermatitis (SD). The role these fungi play in skin diseases and why they are pathogenic for only some individuals remain unclear. This study aimed to characterize Malassezia microbiota from different body sites in healthy and SD subjects from Brazil. Scalp and forehead samples from healthy, mild SD and severe SD subjects were collected. Non-scalp lesions from severe SD patients were also sampled. 5.8S rDNA/ITS2 amplicons from Malassezia sp. were analyzed by RFLP and sequencing. Results indicate that Malassezia microbiota did not group according to health condition or body area. Phylogenetic analysis revealed that three groups of sequences did not cluster together with any formally described species, suggesting that they might belong to potential new species. One of them was found in high proportions in scalp samples. A large variety of Malassezia subtypes were detected, indicating intra-specific diversity. Higher M. globosa proportions were found in non-scalp lesions from severe SD subjects compared with other areas, suggesting closer association of this species with SD lesions from areas other than scalp. Our results show the first panorama of Malassezia microbiota in Brazilian subjects using molecular techniques and provide new perspectives for further studies to elucidate the association between Malassezia microbiota and skin diseases.


title: ECISER: Efficient Clip-art Image SEgmentation by Re-rasterization

text: a r t i c l e i n f o


title: An integrated theory of the mind.

text: Adaptive control of thought-rational (ACT-R; J. R. Anderson & C. Lebiere, 1998) has evolved into a theory that consists of multiple modules but also explains how these modules are integrated to produce coherent cognition. The perceptual-motor modules, the goal module, and the declarative memory module are presented as examples of specialized systems in ACT-R. These modules are associated with distinct cortical regions. These modules place chunks in buffers where they can be detected by a production system that responds to patterns of information in the buffers. At any point in time, a single production rule is selected to respond to the current pattern. Subsymbolic processes serve to guide the selection of rules to fire as well as the internal operations of some modules. Much of learning involves tuning of these subsymbolic processes. A number of simple and complex empirical examples are described to illustrate how these modules function singly and in concert.


title: Compact Broadband Microstrip Crossover With Isolation Improvement and Phase Compensation

text: In this letter, we present a broadband microstrip crossover structure in two different configurations. Both structures have compact size, i.e., less than 10 mm × 10 mm. Across the measured band from dc to 10 GHz, the structure 1 has less than 1 dB insertion loss, more than 20 dB return loss and at least 23.3 dB isolation. Within the same band, the structure 2 has less than 1 dB insertion loss, more than 22 dB return loss and at least 18.9 dB isolation. Phase difference between two paths in both structures is kept in less than 5 °.


title: Efficient Local Search Heuristics for Packing Irregular Shapes in Two-Dimensional Heterogeneous Bins

text: In this paper we proposed a local search heuristic and a genetic algorithm to solve the two-dimensional irregular multiple bin-size bin packing problem. The problem consists of placing a set of pieces represented as 2D polygons in rectangular bins with different dimensions such that the total area of bins used is minimized. Most packing algorithms available in the literature for 2D irregular bin packing consider single size bins only. However, for many industries the material can be supplied in a number of standard size sheets, for example, metal, foam, plastic and timber sheets. For this problem, the cut plans must decide the set of standard size stock sheets as well as which pieces to cut from each bin and how to arrange them in order to minimise waste material. Moreover, the literature constrains the orientation of pieces to a single or finite set of angles. This is often an artificial constraint that makes the solution space easier to navigate. In this paper we do not restrict the orientation of the pieces. We show that the local search heuristic and the genetic algorithm can address all of these decisions and obtain good solutions, with the local search performing better. We also discuss the affect of different groups of stock sheet sizes.


title: Why are some problems hard? Evidence from Tower of Hanoi

text: This paper analyzes the causes for large differences in difficulty of various isomorphic versions of the Tower of Hanoi problem. Some forms of the problem take 16 times as long to solve, on average, as other versions. Since isomorphism rules out size of task domain as a determinant of relative difficulty, these experiments seek and find causes for the differences in features of the problem representation. Analysis of verbal protocols and the temporal patterning of moves allows the problem-solving behavior to be divided into exploratory and final-path phases. Entry into the final-path phase depends on acquisition of the ability to plan pairs of moves, an achievement made difficult by the working memory load it entails. This memory load can be reduced by automating the rules governing moves, either through problem exploration or training. Once automation has occurred, the solution is obtained very rapidly. Memory load is also proposed as the locus of other differences in difficulty found between various problem rep-


title: Problem-solving methods in artificial intelligence

text: Well, someone can decide by themselves what they want to do and need to do but sometimes, that kind of person will need some problem solving methods in artificial intelligence references. People with open minded will always try to seek for the new things and information from many sources. On the contrary, people with closed mind will always think that they can do it by their principals. So, what kind of person are you?


title: Product Geometric Crossover for the Sudoku Puzzle

text: Geometric crossover is a representation-independent definition of crossover based on the distance of the search space interpreted as a metric space. It generalizes the traditional crossover for binary strings and other important recombination operators for the most used representations. Using a distance tailored to the problem at hand, the abstract definition of crossover can be used to design new problem specific crossovers that embed problem knowledge in the search. In recent work, we have introduced the important notion of product geometric crossover that enables the construction of new geometric crossovers combining preexisting geometric crossovers in a simple way. In this paper, we use it to design an evolutionary algorithm to solve the Sudoku puzzle. The different types of constraints make Sudoku an interesting study case for crossover design. We conducted extensive experimental testing and found that, on medium and hard problems, the new geometric crossovers perform significantly better than hill-climbers and mutations alone.


title: GramFuzz: Fuzzing testing of web browsers based on grammar analysis and structural mutation

text: Fuzz testing is an automated black-box testing technique providing random data as input to a software system in the hope to find vulnerability. In order to be effective, the fuzzed input must be common enough to pass elementary consistency checks. Web Browser accepts JavaScript, CSS files as well as the html as input, which must be considered in fuzzing testing, while traditional fuzzing technology generates test cases using simple mutation strategies, ignoring the grammar and code structure. In this article, vulnerability patterns are summarized and a new fuzzing testing method are proposed based on grammar analysis of input data and mutation of code structure. Combining methods of generation and mutation, test cases will be more effective in the fuzzing testing of web browsers. Applied on the Mozilla and IE web browsers, it discovered a total of 36 new severe vulnerabilities(and thus became one of the top security bug bounty collectors within this period).


title: Clinical and radiological pictures of two newborn babies with manifestations of chondrodysplasia punctata and review of available literature

text:
BACKGROUND
Chondrodysplasia punctata (CDP) is a rare, heterogeneous congenital skeletal dysplasia, characterized by punctate or dot-like calcium deposits in cartilage observed on neonatal radiograms. A number of inborn metabolic diseases are associated with CDP, including peroxisomal and cholesterol biosynthesis dysfunction and other inborn errors of metabolism such as: mucolipidosis type II, mucopolysacharidosis type III, GM1 gangliosidosis. CDP is also related to disruption of vitamin K-dependent metabolism, causing secondary effects on the embryo, as well as fetal alcohol syndrome (FAS), chromosomal abnormalities that include trisomies 18 and 21, Turner syndrome.

CASE REPORT
This article presents clinical data and diagnostic imaging findings of two newborn babies with chondrodysplasia punctata. Children presented with skeletal and cartilage anomalies, dysmorphic facial feature, muscles tone abnormalities, skin changes and breathing difficulties. One of the patients demonstrated critical stenosis of spinal canal with anterior subluxation of C1 vertebra relative to C2. The aim of this article is to present cases and briefly describe current knowledge on etiopathogenesis as well as radiological and clinical symptoms of diseases coexisting with CDP.

CONCLUSIONS
Radiological diagnostic imaging allows for visualization of punctate focal mineralization in bone epiphyses during neonatal age and infancy. Determining the etiology of chondrodysplasia punctata requires performing various basic as well as additional examinations, including genetic studies.


title: Numerical Synthesis of Six-Bar Linkages for Mechanical Computation

text: This paper presents a design procedure for six-bar linkages that use eight accuracy points to approximate a specified input-output function. In the kinematic synthesis of linkages, this is known as the synthesis of a function generator to perform mechanical computation. Our formulation uses isotropic coordinates to define the loop equations of the Watt II, Stephenson II, and Stephenson III six-bar linkages. The result is 22 polynomial equations in 22 unknowns that are solved using the polynomial homotopy software Bertini. The bilinear structure of the system yields a polynomial degree of 705,432. Our first run of Bertini generated 92,736 nonsingular solutions, which were used as the basis of a parameter homotopy solution. The algorithm was tested on the design of the Watt II logarithmic function generator patented by Svoboda in 1944. Our algorithm yielded his linkage and 64 others, in 129 minutes of parallel computation on a Mac Pro with 12⇥2.93 GHz processors. Three additional examples are provided as well.


title: Skin Segmentation Using YCBCR and RGB Color Models

text: Face detection is one of the challenging problems in image processing. This report proposes a novel technique for detecting faces in color images using skin color model algorithm combined with skin likely-hood, skin Segmentation, Morphological operation and Template matching. Color images with skin color in the chromatic and pure color space YCrCb, which separates luminance and chrominance components. A Gaussian probability density is estimated from skin samples, collected from different ethnic groups, via the maximum-likelihood criterion. Adaptive thresholding for segmentation to localize the faces within the detected skin regions. Further, mathematical morphological operators are used to remove noisy regions and fill holes in the skin-color region, so that candidate human face regions can be extracted. These systems can achieve high detection accuracy, high detection speed and reduce the false detecting rate. The two methodology used for the Skin Segmentation is YCbCr and RGB Model. In YCbCr model both the skin colour and texture of the image can be used to identify the particular object in the image,where as in RGB model only the skin color has to be used for identification of the person.Hence the YCbCr model is better than the RGB model . From our analysis we conclude that the new approach in modeling skin color can achieve good detection success rate. The algorithm gives computationally a very efficient as well as an accurate approach for skin detection. The performance of different color space may be dependent on the method used to model the color for skin pixel. Keywords—Gaussian probability, YCrCb,RGB Model


title: Monte Carlo Filtering Using Kernel Embedding of Distributions

text: Recent advances of kernel methods have yielded a framework for representing probabilities using a reproducing kernel Hilbert space, called kernel embedding of distributions. In this paper, we propose a Monte Carlo filtering algorithm based on kernel embeddings. The proposed method is applied to state-space models where sampling from the transition model is possible, while the observation model is to be learned from training samples without assuming a parametric model. As a theoretical basis of the proposed method, we prove consistency of the Monte Carlo method combined with kernel embeddings. Experimental results on synthetic models and real vision-based robot localization confirm the effectiveness of the proposed approach.


title: Should I do that? using relational reinforcement learning and declarative programming to discover domain axioms

text: Robots assisting humans in complex domains need the ability to represent, reason with, and learn from, different descriptions of incomplete domain knowledge and uncertainty. This paper focuses on the challenge of incrementally and interactively discovering previously unknown axioms governing domain dynamics, and describes an architecture that integrates declarative programming and relational reinforcement learning to address this challenge. Answer Set Prolog (ASP), a declarative programming paradigm, is used to represent and reason with incomplete domain knowledge for planning and diagnostics. For any given goal, unexplained failure of plans created by ASP-based inference is taken to indicate the existence of unknown domain axioms. The task of discovering these axioms is formulated as a reinforcement learning problem, and a relational representation is used to incrementally generalize from specific axioms identified over time. These generic axioms are then added to the ASP-based representation for subsequent inference. The architecture's capabilities are demonstrated and evaluated in two domains, Blocks World and Robot Butler.


title: The ONE simulator for DTN protocol evaluation

text: Delay-tolerant Networking (DTN) enables communication in sparse mobile ad-hoc networks and other challenged environments where traditional networking fails and new routing and application protocols are required. Past experience with DTN routing and application protocols has shown that their performance is highly dependent on the underlying mobility and node characteristics. Evaluating DTN protocols across many scenarios requires suitable simulation tools. This paper presents the Opportunistic Networking Environment (ONE) simulator specifically designed for evaluating DTN routing and application protocols. It allows users to create scenarios based upon different synthetic movement models and real-world traces and offers a framework for implementing routing and application protocols (already including six well-known routing protocols). Interactive visualization and post-processing tools support evaluating experiments and an emulation mode allows the ONE simulator to become part of a real-world DTN testbed. We show sample simulations to demonstrate the simulator’s flexible support for DTN protocol evaluation.


title: Scene text extraction using stroke width transform for tourist translator on android platform

text: Scene text extraction is recent research area in the field of Computer Vision. Text component in an image is of particular interest, as it is easy to understand by both humans as well as machines and can be used in variety of applications like content-based image retrieval, aids for visually impaired people, translator for tourists. Although there exists a lot of research activities in this field, it is still remained as a challenging problem, mainly due to two issues: different variety of text patterns like fonts, colors, sizes, orientations; and presence of background outliers similar to text characters, such as windows, bricks. In this paper, an android application named as TravelMate is developed, which is a user-friendly application to assist the tourist navigation, while they are roaming in foreign countries. Proposed application is able to extract text from an image, which is captured by an android mobile camera. Extraction is performed using stroke width transform (SWT) approach and connected component analysis. Extracted texts are recognized using Google's open source optical character recognition (OCR) engine ‘Tesseract’ and translated to a target language using Google's translation. The entire result is displayed back onto the screen of the mobile Phone.


title: A Single-Channel Microstrip Electronic Tracking Antenna Array With Time Sequence Phase Weighting on Sub-Array

text: We have designed and tested a novel electronic tracking antenna array that is formed by 2 × 2 microstrip sub-arrays. Through time sequence phase weighting on each sub-array, the amplitude and phase on each sub-array can be recovered from the output of the resultant single channel. The amplitude and phase on each array can be used to produce the sum and difference radiation pattern by digital signal processing. In comparison with the monopulse system, the RF comparator is eliminated and the number of the receiver channels is reduced from 3 to 1. A proof-of-concept prototype was fabricated and tested. The measured results confirmed the validity and advantages of the proposed scheme. The procedure of channel correction is given.


title: Insights into the neural basis of response inhibition from cognitive and clinical neuroscience

text: Neural mechanisms of cognitive control enable us to initiate, coordinate and update behaviour. Central to successful control is the ability to suppress actions that are no longer relevant or required. In this article, we review the contribution of cognitive neuroscience, molecular genetics and clinical investigations to understanding how response inhibition is mediated in the human brain. In Section 1, we consider insights into the neural basis of inhibitory control from the effects of neural interference, neural dysfunction, and drug addiction. In Section 2, we explore the functional specificity of inhibitory mechanisms among a range of related processes, including response selection, working memory, and attention. In Section 3, we focus on the contribution of response inhibition to understanding flexible behaviour, including the effects of learning and individual differences. Finally, in Section 4, we propose a series of technical and conceptual objectives for future studies addressing the neural basis of inhibition.


title: Web science and human-computer interaction: when disciplines collide

text: Web Science and Human-Computer Interaction (HCI) are interdisciplinary arenas concerned with the intersection of people and technology. After introducing the two disciplines we discuss overlaps and notable differences between them, covering subject matter, scope and methodology. Given the longer history of HCI, we identify and discuss some potential lessons that the Web Science community may be able to take from this field. These concern: the division between interpretivist and positivist approaches; methods and methodology; evaluation; and design focus and methods. In summary, this paper clarifies the relationship between the communities, signposting complementary aspects and ways in which they might collaborate in future.


title: Summarization Evaluation Methods: Experiments and Analysis

text: Two methods are used for evaluation of summarization systems: an evaluation of generated summaries against an "ideal" summary and evaluation of how well summaries help a person perform in a task such as informa. tion retrieval. We carried out two large experiments to study the two evaluation methods. Our results show that different parameters of an experiment can (h-amatically affect how well a system scores. For example, summary length was found to affect both types of evaluations. For the "ideal" summary based evaluation, accuracy decreases as summary length increases, while for task based evaluations summary length and accuracy on an information retrieval task appear to correlate randomly. In this paper, we show how this parameter and others can affect evaluation results and describe how parameters can be controlled to produce a sound evaluation.


title: The 2 nd Workshop on Arabic Corpora and Processing Tools 2016 Theme :

text: Speech datasets and corpora are crucial for both developing and evaluating accurate Natural Language Processing systems. While Modern Standard Arabic has received more attention, dialects are drastically underestimated, even they are the most used in our daily life and the social media, recently. In this paper, we present the methodology of building an Arabic Speech Corpus for Algerian dialects, and the preliminary version of that dataset of dialectal arabic speeches uttered by Algerian native speakers selected from different Algeria’s departments. In fact, by means of a direct recording way, we have taken into acount numerous aspects that foster the richness of the corpus and that provide a representation of phonetic, prosodic and orthographic varieties of Algerian dialects. Among these considerations, we have designed a rich speech topics and content. The annotations provided are some useful information related to the speakers, time-aligned orthographic word transcription. Many potential uses can be considered such as speaker/dialect identification and computational linguistic for Algerian sub-dialects. In its preliminary version, our corpus encompasses 17 sub-dialects with 109 speakers and more than 6 K utterances.


title: Bike Angels: An Analysis of Citi Bike's Incentive Program

text:
Bike-sharing systems provide a sustainable and affordable transportation alternative in many American cities. However, they also face intricate challenges due to imbalance, caused by asymmetric traffic demand. That imbalance often-times leads to bike-sharing stations being empty (full), causing out-of-stock events for customers that want to rent (return) bikes at such stations. In recent years, the study of data-driven methods to help support the operation of such system, has developed as a popular research area.
 In this paper, we study the impact of Bike Angels, an incentive program New York City's Citi Bike system set up in 2015 to crowd-source some of its operational challenges related to imbalance. We develop a performance metric for both online- and offline-policies to set incentives within the system; our results indicate that though Citi Bike's original offline policy performed well in a regime in which incentives given to customers are not associated to costs, there is ample space for improvement when the costs of the incentives are taken into consideration. Motivated by these findings, we develop several online- and offline- policies to investigate the trade-offs between real-time and offline decision-making; one of our online policies has since been adopted by Citi Bike.


title: Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and internet of things technologies

text: Transportation is a necessary infrastructure for our modern society. The performance of transportation systems is of crucial importance for individual mobility, commerce, and for the economic growth of all nations. In recent years modern society has been facing more traffic jams, higher fuel prices, and an increase in CO2 emissions. It is imperative to improve the safety and efficiency of transportation. Developing a sustainable intelligent transportation system requires the seamless integration and interoperability with emerging technologies such as connected vehicles, cloud computing, and the Internet of Things. In this article we present and discuss some of the integration challenges that must be addressed to enable an intelligent transportation system to address issues facing the transportation sector such as high fuel prices, high levels of CO2 emissions, increasing traffic congestion, and improved road safety.


title: A Deep Dual-Branch Networks for Joint Blind Motion Deblurring and Super-Resolution

text: Image super-resolution is a fundamental pre-processing technique for the machine vision applications of robotics and other mobile platforms. Inevitably, images captured by the mobile camera tend to emerge severe motion blur and this degradation will deteriorate the performance of current state-of-the-art super-resolution methods. In this paper, we propose a deep dual-branch convolution neural network (CNN) to generate a clear high-resolution image from a single natural image with severe blurs. Compared to off-the-shelf methods, our method, called DB-SRN, can remove the complex non-uniform motion blurs and restore useful texture details simultaneously. By sharing the features from modified residual blocks (ResBlocks), the dual-branch design can promote the performances of both tasks other while retaining network simplicity. Extensive experiments demonstrate that our method produces remarkable deblurred and super-resolved images in terms of quality and quantity with high computational efficiency.


title: Energy-Aware Autonomic Resource Allocation in Multitier Virtualized Environments

text: With the increase of energy consumption associated with IT infrastructures, energy management is becoming a priority in the design and operation of complex service-based systems. At the same time, service providers need to comply with Service Level Agreement (SLA) contracts which determine the revenues and penalties on the basis of the achieved performance level. This paper focuses on the resource allocation problem in multitier virtualized systems with the goal of maximizing the SLAs revenue while minimizing energy costs. The main novelty of our approach is to address-in a unifying framework-service centers resource management by exploiting as actuation mechanisms allocation of virtual machines (VMs) to servers, load balancing, capacity allocation, server power state tuning, and dynamic voltage/frequency scaling. Resource management is modeled as an NP-hard mixed integer nonlinear programming problem, and solved by a local search procedure. To validate its effectiveness, the proposed model is compared to top-performing state-of-the-art techniques. The evaluation is based on simulation and on real experiments performed in a prototype environment. Synthetic as well as realistic workloads and a number of different scenarios of interest are considered. Results show that we are able to yield significant revenue gains for the provider when compared to alternative methods (up to 45 percent). Moreover, solutions are robust to service time and workload variations.


title: Extraction of Significant Patterns from Heart Disease Warehouses for Heart Attack Prediction

text: The diagnosis of diseases is a significant and tedious task in medicine. The detection of heart disease from various factors or symptoms is a multi-layered issue which is not free from false presumptions often accompanied by unpredictable effects. Thus the effort to utilize knowledge and experience of numerous specialists and clinical screening data of patients collected in databases to facilitate the diagnosis process is considered a valuable option. The healthcare industry gathers enormous amounts of heart disease data that regrettably, are not “mined” to determine concealed information for effective decision making by healthcare practitioners. In this paper, we have proposed an efficient approach for the extraction of significant patterns from the heart disease warehouses for heart attack prediction. Initially, the data warehouse is preprocessed to make it appropriate for the mining process. After preprocessing, the heart disease warehouse is clustered using the K-means clustering algorithm, which will extract the data relevant to heart attack from the warehouse. Subsequently the frequent patterns are mined from the extracted data, relevant to heart disease, using the MAFIA algorithm. Then the significant weightage of the frequent patterns are calculated. Further, the patterns significant to heart attack prediction are chosen based on the calculated significant weightage. These significant patterns can be used in the development of heart attack prediction system.


title: A survey of decision tree classifier methodology

text: Decision Tree Classifiers (DTC's) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition, to name only a few. Perhaps, the most important feature of DTC's is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. This paper presents a survey of current methods for DTC designs and the various existing issues. After considering potential advantages of DTC's over single stage classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed_ Finally, several remarks are made concerning possible future research directions.


title: Combination Data Mining Methods with New Medical Data to Predicting Outcome of Coronary Heart Disease

text: The prediction of survival of Coronary heart disease (CHD) has been a challenging research problem for medical society. The goal of this paper is to develop data mining algorithms for predicting survival of CHD patients based on 1000 cases .We carry out a clinical observation and a 6-month follow up to include 1000 CHD cases. The survival information of each case is obtained via follow up. Based on the data, we employed three popular data mining algorithms to develop the prediction models using the 502 cases. We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results indicated that the SVM is the best predictor with 92.1 % accuracy on the holdout sample artificial neural networks came out to be the second with91.0% accuracy and the decision trees models came out to be the worst of the three with 89.6% accuracy. The comparative study of multiple prediction models for survival of CHD patients along with a 10-fold cross-validation provided us with an insight into the relative prediction ability of different data.


title: Selected techniques for data mining in medicine

text: Widespread use of medical information systems and explosive growth of medical databases require traditional manual data analysis to be coupled with methods for efficient computer-assisted analysis. This paper presents selected data mining techniques that can be applied in medicine, and in particular some machine learning techniques including the mechanisms that make them better suited for the analysis of medical databases (derivation of symbolic rules, use of background knowledge, sensitivity and specificity of induced descriptions). The importance of the interpretability of results of data analysis is discussed and illustrated on selected medical applications.


title: Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks

text: -We give conditions ensuring that multilayer jeedJorward networks with as Jew as a single hidden layer and an appropriately smooth hidden layer activation fimction are capable o f arbitrarily accurate approximation to an arbitrao' function and its derivatives. In fact, these networks can approximate functions that are not dtifferentiable in the classical sense, but possess only a generalized derivative, as is the case jor certain piecewise dtlf[erentiable Junctions. The conditions imposed on the hidden layer activation function are relatively mild: the conditions imposed on the domain o f the fimction to be approximated have practical intplications. Our approximation results provide a previously missing theoretical justification jor the use of multilayer /~'ed/brward networks in applications requiring simultaneous approximation o f a function and its derivatives. Keywords--Approximation, Derivatives, Sobolev space, Feedforward networks. 1. I N T R O D U C T I O N The capability of sufficiently complex multilayer feedforward networks to approximate an unknown mapping./~. R' ----~ R arbitrarily well has been recently investigated by Cybenko (1989), Funahashi (1989), Hecht-Nielsen (1989), Hornik, Stinchcombe, and White (1989) (HSW) (all for sigmoid hidden layer activation functions) and Stinchcombe and White (1989) (SW) (non-sigmoid hidden layer activation functions). In applications, it may be desirable to approximate not only the unknown mapping, but also its unknown derivatives. This is the case in Jordan's (1989) recent investigation of robot learning of smooth movement . Jordan states: The Jacobian matrix 8z/Ox . . . is the matrix that relates small changes in the controller output to small changes in the task space results and cannot be assumed to be available a priori, or provided by the environment. However, all of the derivatives in the matrix are forward derivatives. They are easily obtained by differentiation if a forward model is available. The forward model itself must be learned, but this can be achieved directly by system idenAcknowledgements: We arc indebted to Angelo Melino for pressing us on the issue addressed here and to the referees for numerous helpful suggestions. White's participation was supported by NSF Grant SES-8806990. Requests for reprints should be sent to Halbert White, Department of Economics, D-008, University of California, San Diego. La Jolla, CA 92093. tification. Once the model is accurate over a particular domain, its derivatives provide a learning operator that allows the system to convert errors in task space into errors in articulatory space and thereby change the controller. Thus, learning an adequate approximation to the Jacobian matrix of an unknown mapping is a key component of Jordan 's approach to robot learning of smooth movement . Despite the success of Jordan 's experiments, there is no existing theoretical guarantee that multilayer feedforward networks generally have the capability to approximate an unknown mapping and its derivatives simultaneously. For example, a network with hard limiting hidden layer activations approximates unknown mappings with a piecewise-constant function, the first derivatives of which exist and are zero almost everywhere. Obviously, the derivatives of such a network output function cannot approximate the derivatives of an arbitrary function. Intuition suggests that networks having smooth hidden layer activation functions ought to have output function derivatives that will approximate the derivatives of an unknown mapping. However , the justification for this intuition is not obvious. Consider the class of single hidden layer feedforward networks having network output functions belonging to the set 2. "(G) -~ {g : x"-~ R[ g(x) : /:,G(_i';,,):


title: Domainless Adaptation by Constrained Decoding on a Schema Lattice

text: In many applications such as personal digital assistants, there is a constant need for new domains to increase the system’s coverage of user queries. A conventional approach is to learn a separate model every time a new domain is introduced. This approach is slow, inefficient, and a bottleneck for scaling to a large number of domains. In this paper, we introduce a framework that allows us to have a single model that can handle all domains: including unknown domains that may be created in the future as long as they are covered in the master schema. The key idea is to remove the need for distinguishing domains by explicitly predicting the schema of queries. Given permitted schema of a query, we perform constrained decoding on a lattice of slot sequences allowed under the schema. The proposed model achieves competitive and often superior performance over the conventional model trained separately per domain.


title: Some Basic Cryptographic Requirements for Chaos-Based Cryptosystems

text: In recent years, a large amount of work on chaos-based cryptosystems have been published. However many of the proposed schemes fail to explain or do not possess a number of features that are fundamentally important to all kind of cryptosystems. As a result, many proposed systems are difficult to implement in practice with a reasonable degree of security. Likewise, they are seldom accompanied by a thorough security analysis. Consequently, it is difficult for other researchers and end users to evaluate their security and performance. This work is intended to provide a common framework of basic guidelines that, if followed, every new cryptosystem would benefit from. The suggested guidelines address three main issues: implementation, key management, and security analysis, aiming at assisting designers of new cryptosystems to present their work in a more systematic and rigorous way to fulfill some basic cryptographic requirements. Meanwhile, several recommendations are made regarding some practical aspects of analog chaos-based secure communications, such as channel noise, limited bandwith, and attenuation.


title: A Framework for Cloud Forensic Readiness in Organizations

text: Many have argued that cloud computing is one of the fastest growing and most transformative technologies in the history of computing. It has radically changed the way in which information technologies can manage, access, deliver and create services. It has also brought numerous benefits to end-users and organizations. However, this rapid growth in cloud computing adoption has also seen it become a new arena for cybercrime. This has, in turn, led to new technical, legal and organizational challenges. In addition to the large number of attacks which affect cloud computing and the decentralized nature of data processing in the cloud, many concerns have been raised. One of these concerns is how to conduct a proper digital investigation in cloud environments and be ready to collect data proactively before an incident occurs in order to save time, money and effort. This paper proposes the technical, legal and organizational factors that influence digital forensic readiness for Infrastructure as a Service consumers.


title: Scalable Formal Concept Analysis algorithm for large datasets using Spark

text: In the process of knowledge discovery and representation in large datasets using formal concept analysis, complexity plays a major role in identifying all the formal concepts and constructing the concept lattice(digraph of the concepts). For identifying the formal concepts and constructing the digraph from the identified concepts in very large datasets, various distributed algorithms are available in the literature. However, the existing distributed algorithms are not very well suitable for concept generation because it is an iterative process. The existing algorithms are implemented using distributed frameworks like MapReduce and Open MP, these frameworks are not appropriate for iterative applications. Hence, in this paper we proposed efficient distributed algorithms for both formal concept generation and concept lattice digraph construction in large formal contexts using Apache Spark. Various performance metrics are considered for the evaluation of the proposed work, the results of the evaluation proves that the proposed algorithms are efficient for concept generation and lattice graph construction in comparison with the existing algorithms.


title: Mass-media coverage , its influence on public awareness of climate-change issues , and implications for Japan ’ s national campaign to reduce greenhouse gas emissions

text: We analyse Japanese newspaper coverage of global warming from January 1998 to July 2007 and how public opinion during parts of that period were influenced by newspaper coverage. We show that a dramatic increase in newspaper coverage of global warming from January 2007 correlated with an increase in public concern for the issue. Before January 2007, we find that coverage of global warming had an immediate but short-term influence on public concern. With such transitory high levels of media coverage we suggest that for more effective communication of climate change, strategies aimed at maintaining mass-media coverage of global warming are required. 2008 Elsevier Ltd. All rights reserved.


title: mmWave communications for 5G: implementation challenges and advances

text: The requirement of the fifth generation (5G) wireless communication for high throughput motivates the wireless industry to use the mmWave (millimeter wave) communications for its wide bandwidth advantage. To compensate the heavy path loss and increase the communications capacity, phased array beamforming and massive multiple-input multiple-output (MIMO) techniques are employed at both the user equipment (UE) and base stations (BS). Considering the commercial requirements, 5G mmWave large array systems should be implemented in an energy- and cost-efficient way with a small form factor. To address above issues and realize a reliable communications link, taking into account the particular characteristics of 5G mmWave systems, this paper firstly examines the design challenges and trade-offs in system implementations, then some of the design strategies are summarized. At last, recent advance in RF front-end circuits and receiver sub-systems is then highlighted.


title: A 6 Vpp, 52 dB, 30-dB Dynamic Range, 43 Gb/s InP DHBT Differential Limiting Amplifier

text: We report the design, fabrication and measurement of a 43 Gb/s differential limiting amplifier integrating a transimpedance amplifier (TIA) as input stage and a distributed amplifier (Driver) as output stage, realised in a 1.5-µm indium phosphide (InP) double heterojunction bipolar transistor (DHBT) technology. With a differential gain higher than 52 dB, the TIA-Driver provides a constant 6 Vpp differential output amplitude for an input amplitude ranging from 12.7 mVpp to 405 mVpp, achieving more than 30 dB of electrical dynamic range, for a power consumption of 2.7 W. Designed to be used between a photodiode and an electro-optical modulator, this circuit is well suited for optical-to-electrical-to-optical (O-E-O) wavelength converters at up to 43 Gb/s.


title: Stress Detection and Management: A Survey of Wearable Smart Health Devices

text: In today's world, many people feel stressed out from school, work, or other life events. Therefore, it is important to detect stress and manage it to reduce the risk of damage to an individual's well being. With the emergence of the Internet of Things (IoT), devices can be made to detect stress and manage it effectively by using cloudbased services and smartphone apps to aggregate and compute large data sets that track stress behavior over long periods of time. Additionally, there is added convenience via the connectivity and portability of these IoT devices. They allow individuals to seek intervention prior to elevated health risks and achieve a less stressful life.


title: Personal Archive Service System using Blockchain Technology: Case Study, Promising and Challenging

text: This paper is to explore applications of the blockchain technology to the concept of “proof of X” such as proof of identity, proof of property ownership, proof of specific transaction, proof of college degree, proof of medical records, proof of academic achievements, etc. It describes a novel approach of building a decentralized transparent immutable secure personal archive management and service system. Personal archive is defined as a collection of various artifacts that reflect personal portfolio as well as personal unique identifications. Personal portfolio is beyond of a statement of personal achievement. It is an evidentiary document designed to provide qualitative and quantitative chronically documents and examples. Subjects can tag their information with proof, that is, certified by trusted entities or organizations like universities. Such proofs are associated with confidentiality levels exposed in the public domains. Personal identifications include biometrics as well as other multi-factors such as something the subject “has”, the subject “knows” or the subject “acts”. Stack holders in a consortium oriented blockchain network serve as verifiers and /or miners that provide their trusted services the delegated proof of stake. Such personal archive based system can be exploited to various applications including professional network like Linkedin, instant credit approval like alipay or live human from social bots like internet social media. A prototype simulation shows that such personal portfolio management and service system is feasible and immune to many ID attacks.


title: Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing

text: Deep neural networks such as Convolutional Networks (ConvNets) and Deep Belief Networks (DBNs) represent the state-of-the-art for many machine learning and computer vision classification problems. To overcome the large computational cost of deep networks, spiking deep networks have recently been proposed, given the specialized hardware now available for spiking neural networks (SNNs). However, this has come at the cost of performance losses due to the conversion from analog neural networks (ANNs) without a notion of time, to sparsely firing, event-driven SNNs. Here we analyze the effects of converting deep ANNs into SNNs with respect to the choice of parameters for spiking neurons such as firing rates and thresholds. We present a set of optimization techniques to minimize performance loss in the conversion process for ConvNets and fully connected deep networks. These techniques yield networks that outperform all previous SNNs on the MNIST database to date, and many networks here are close to maximum performance after only 20 ms of simulated time. The techniques include using rectified linear units (ReLUs) with zero bias during training, and using a new weight normalization method to help regulate firing rates. Our method for converting an ANN into an SNN enables low-latency classification with high accuracies already after the first output spike, and compared with previous SNN approaches it yields improved performance without increased training time. The presented analysis and optimization techniques boost the value of spiking deep networks as an attractive framework for neuromorphic computing platforms aimed at fast and efficient pattern recognition.


title: Privacy protection based access control scheme in cloud-based services

text: With the rapid development of the computer technology, cloud-based services have become a hot topic. Cloud-based services not only provide users with convenience, but also bring many security issues. Therefore, the study of access control scheme to protect users' privacy in cloud environment is of great significance. In this paper, we present an access control system with privilege separation based on privacy protection (PS-ACS). In the PS-ACS scheme, we divide the users into personal domain (PSD) and public domain (PUD) logically. In the PSD, we set read and write access permissions for users respectively. The Key-Aggregate Encryption (KAE) is exploited to implement the read access permission which improves the access efficiency. A high degree of patient privacy is guaranteed simultaneously by exploiting an Improved Attribute-based Signature (IABS) which can determine the users' write access. For the users of PUD, a hierarchical attribute-based encryption (HABE) is applied to avoid the issues of single point of failure and complicated key distribution. Function and performance testing result shows that the PS-ACS scheme can achieve privacy protection in cloud-based services.


title: Input feature selection for classification problems

text: Feature selection plays an important role in classifying systems such as neural networks (NNs). We use a set of attributes which are relevant, irrelevant or redundant and from the viewpoint of managing a dataset which can be huge, reducing the number of attributes by selecting only the relevant ones is desirable. In doing so, higher performances with lower computational effort is expected. In this paper, we propose two feature selection algorithms. The limitation of mutual information feature selector (MIFS) is analyzed and a method to overcome this limitation is studied. One of the proposed algorithms makes more considered use of mutual information between input attributes and output classes than the MIFS. What is demonstrated is that the proposed method can provide the performance of the ideal greedy selection algorithm when information is distributed uniformly. The computational load for this algorithm is nearly the same as that of MIFS. In addition, another feature selection algorithm using the Taguchi method is proposed. This is advanced as a solution to the question as to how to identify good features with as few experiments as possible. The proposed algorithms are applied to several classification problems and compared with MIFS. These two algorithms can be combined to complement each other's limitations. The combined algorithm performed well in several experiments and should prove to be a useful method in selecting features for classification problems.


title: RF MEMS Actuated Reconfigurable Reflectarray Patch-Slot Element

text: This paper describes the design of a reconfigurable reflectarray element using commercially available radio frequency micro-electromechanical system (RF MEMS) switches. The element consists of a microstrip patch on the top surface and a slot with an actuated variable length in the ground plane. RF MEMS switches are mounted on the slot to electronically vary the slot length by actuating the switches and thus obtaining the desired phase response. Waveguide measurements and high frequency structure simulator (HFSS) simulations are used to characterize the reflectarray element. The four MEMS switches element gives 10 independent states with a phase swing of 150 deg and a loss variation from 0.4 dB to 1.5 dB at 2 GHz (more switches can provide larger phase shift). The loss is mainly attributed to the dielectric loss and the conductor loss, which occur due to the relatively strong electric fields in the substrate region below the patch and the large currents on the top surface of the patch, respectively, close to the patch resonance. Detailed analysis is performed to characterize the effect of the switches by taking into consideration the switch model and wire bonding effects.


title: Generalizing and Improving Weight Initialization

text: We propose a new weight initialization suited for arbitrary nonlinearities by generalizing previous weight initializations. The initialization corrects for the influence of dropout rates and an arbitrary nonlinearity’s influence on variance through simple corrective scalars. Consequently, this initialization does not require computing mini-batch statistics nor weight pre-initialization. This simple method enables improved accuracy over previous initializations, and it allows for training highly regularized neural networks where previous initializations lead to poor convergence.


title: Probabilistic Inductive Logic Programming

text: Probabilistic inductive logic programming aka. statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety of different formalisms and learning techniques have been developed. A unifying characterization of the underlying learning settings, however, is missing so far. In this chapter, we start from inductive logic programming and sketch how the inductive logic programming formalisms, settings and techniques can be extended to the statistical case. More precisely, we outline three classical settings for inductive logic programming, namely learning from entailment, learning from interpretations, and learning from proofs or traces, and show how they can be adapted to cover state-of-the-art statistical relational learning approaches.


title: Intuitive visualization of Pareto Frontier for multi-objective optimization in n-dimensional performance space

text: A visualization methodology is presented in which a Pareto Frontier can be visualized in an intuitive and straightforward manner for an n-dimensional performance space. Based on this visualization, it is possible to quickly identify ‘good’ regions of the performance and optimal design spaces for a multi-objective optimization application, regardless of space complexity. Visualizing Pareto solutions for more than three objectives has long been a significant challenge to the multi-objective optimization community. The Hyper-space Diagonal Counting (HSDC) method described here enables the lossless visualization to be implemented. The proposed method requires no dimension fixing. In this paper, we demonstrate the usefulness of visualizing n-f space (i.e. for more than three objective functions in a multiobjective optimization problem). The visualization is shown to aid in the final decision of what potential optimal design point should be chosen amongst all possible Pareto solutions.


title: MATCHINGS AND PAIRED DOMINATION IN FUZZY GRAPHS USING STRONG ARCS

text: The concepts of covering and matching in fuzzy graphs using strong arcs are introduced and obtained the relationship between them analogous to Gallai’s results in graphs. The notion of paired domination in fuzzy graphs using strong arcs is also studied. The strong paired domination number γspr of complete fuzzy graph and complete bipartite fuzzy graph is determined and obtained bounds for the strong paired domination number of fuzzy graphs. An upper bound for the strong paired domination number of fuzzy graphs in terms of strong independence number is also obtained.


title: Enabling internet banking adoption: An empirical examination with an augmented technology acceptance model (TAM)

text: Purpose – The integration of relevant antecedents into TAM would lead to better understanding of the decision factors which act as enablers for the adoption of internet banking. The purpose of the paper is to determine the influence of the antecedents subjective norm, image, banks initiative, internet banking self-efficacy, internet usage efficacy, trust, perceived risk, trialability and government support on the existing constructs of the technology acceptance model (TAM) and to test measurement invariance and the moderating effect of the demographic variables on the relationship between the latent constructs used in this augmented TAM. Design/methodology/approach – A survey questionnaire was administered on internet banking users and a total of 300 responses were collected. A two-step approach suggested by Hair et al. (2006) and Schumacker and Lomax (2004) was used in this study. The proposed model was assessed using the confirmatory factor analysis approach. The structural model was then tested in order to establish nomological validity. The data based on four demographic dimensions gender, age, income, education were divided into two groups for each of these demographic dimensions. The invariance test was first performed on the measurement model and then on the structural model. The measurement model and structural model were subjected to tests of equivalence of parameters across groups. Findings – To a large extent the results of the study supports the proposed model and thereby contributes to understand the influence of subjective norm, image, banks initiative, internet banking self-efficacy, internet usage efficacy, trust, perceived risk and government support on internet banking adoption. The predictor variables in the augmented TAM were able to explain 29.9 per cent of the variance in the actual usage of internet banking as compared to the TAM which was able to explain only 26.5 per cent variance in the actual usage of internet banking. A significant difference in the relationship between the different constructs of the model was observed when the model was subjected to multi-group invariance testing. Research limitations/implications – The study suffers from the same limitations as most other studies involving TAM. In this study self-reported measures about the usage were taken as the actual usage. The findings of the study can be of use to marketers for target-specific marketing by customizing the marketing campaign focussing on the factors that were found to be strong influencers leading to the usage of internet banking for each target audience. Originality/value – The main challenge in this study was to develop the conceptual model for the internet banking adoption by extending the TAM and to get a robust theoretical support from the extant literature for the relevant factors along with their relationship to uncover new insights about factors responsible for the internet banking adoption. The augmented model had an improved predictive capability and explanatory utility.


title: FeUdal Networks for Hierarchical Reinforcement Learning

text: We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical reinforcement learning. Our approach is inspired by the feudal reinforcement learning proposal of Dayan and Hinton, and gains power and efficacy by decoupling end-to-end learning across multiple levels – allowing it to utilise different resolutions of time. Our framework employs a Manager module and a Worker module. The Manager operates at a lower temporal resolution and sets abstract goals which are conveyed to and enacted by the Worker. The Worker generates primitive actions at every tick of the environment. The decoupled structure of FuN conveys several benefits – in addition to facilitating very long timescale credit assignment it also encourages the emergence of sub-policies associated with different goals set by the Manager. These properties allow FuN to dramatically outperform a strong baseline agent on tasks that involve long-term credit assignment or memorisation. We demonstrate the performance of our proposed system on a range of tasks from the ATARI suite and also from a 3D DeepMind Lab environment.


title: Parameter Space Noise for Exploration

text: Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent’s parameters, which can lead to more consistent exploration and a richer set of behaviors. Methods such as evolutionary strategies use parameter perturbations, but discard all temporal structure in the process and require significantly more samples. Combining parameter noise with traditional RL methods allows to combine the best of both worlds. We demonstrate that both offand on-policy methods benefit from this approach through experimental comparison of DQN, DDPG, and TRPO on high-dimensional discrete action environments as well as continuous control tasks. Our results show that RL with parameter noise learns more efficiently than traditional RL with action space noise and evolutionary strategies individually.


title: BOA: the Bayesian optimization algorithm

text: In this paper, an algorithm based on the concepts of genetic algorithms that uses an estimation of a probability distribution of promising solutions in order to generate new candidate solutions is proposed. To estimate the distribution, techniques for modeling multivariate data by Bayesian networks are used. The proposed algorithm identifies, reproduces and mixes building blocks up to a specified order. It is independent of the ordering of the variables in the strings representing the solutions. Moreover, prior information about the problem can be incorporated into the algorithm. However, prior information is not essential. Preliminary experiments show that the BOA outperforms the simple genetic algorithm even on decomposable functions with tight building blocks as a problem size grows.


title: Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

text: Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. One of the key difficulties is insufficient exploration, resulting in an agent being unable to learn robust policies. Intrinsically motivated agents can explore new behavior for their own sake rather than to directly solve external goals. Such intrinsic behaviors could eventually help the agent solve tasks posed by the environment. We present hierarchicalDQN (h-DQN), a framework to integrate hierarchical action-value functions, operating at different temporal scales, with goal-driven intrinsically motivated deep reinforcement learning. A top-level q-value function learns a policy over intrinsic goals, while a lower-level function learns a policy over atomic actions to satisfy the given goals. h-DQN allows for flexible goal specifications, such as functions over entities and relations. This provides an efficient space for exploration in complicated environments. We demonstrate the strength of our approach on two problems with very sparse and delayed feedback: (1) a complex discrete stochastic decision process with stochastic transitions, and (2) the classic ATARI game – ‘Montezuma’s Revenge’.


title: Cell biology of protein misfolding: The examples of Alzheimer's and Parkinson's diseases

text: The salutary intersection of fundamental cell biology with the study of disease is well illustrated by the emerging elucidation of neurodegenerative disorders. Novel mechanisms in cell biology have been uncovered through disease-orientated research; for example, the discovery of presenilin as an intramembrane aspartyl protease that processes many diverse proteins within the lipid bilayer. A common theme has arisen in this field: normally-soluble proteins accumulate, misfold and oligomerize, inducing cytotoxic effects that are particularly devastating in the post-mitotic milieu of the neuron.


title: Topological Data Analysis of Biological Aggregation Models

text: We apply tools from topological data analysis to two mathematical models inspired by biological aggregations such as bird flocks, fish schools, and insect swarms. Our data consists of numerical simulation output from the models of Vicsek and D'Orsogna. These models are dynamical systems describing the movement of agents who interact via alignment, attraction, and/or repulsion. Each simulation time frame is a point cloud in position-velocity space. We analyze the topological structure of these point clouds, interpreting the persistent homology by calculating the first few Betti numbers. These Betti numbers count connected components, topological circles, and trapped volumes present in the data. To interpret our results, we introduce a visualization that displays Betti numbers over simulation time and topological persistence scale. We compare our topological results to order parameters typically used to quantify the global behavior of aggregations, such as polarization and angular momentum. The topological calculations reveal events and structure not captured by the order parameters.


title: The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM

text: We introduce the Stochastic Asynchronous Proximal Alternating Linearized Minimization (SAPALM) method, a block coordinate stochastic proximal-gradient method for solving nonconvex, nonsmooth optimization problems. SAPALM is the first asynchronous parallel optimization method that provably converges on a large class of nonconvex, nonsmooth problems. We prove that SAPALM matches the best known rates of convergence — among synchronous or asynchronous methods — on this problem class. We provide upper bounds on the number of workers for which we can expect to see a linear speedup, which match the best bounds known for less complex problems, and show that in practice SAPALM achieves this linear speedup. We demonstrate state-of-the-art performance on several matrix factorization problems.


title: Characterization of Silicided Polysilicon Fuse Implemented in 65nm Logic CMOS Technology

text: NiSi electrically programmable fuses (eFUSE) were fabricated and investigated using 65 nm logic CMOS technology. The optimization of fuse program was achieved by analyzing electrical and physical responses of fuse bits for various conditions. Controlled electromigration of Ni during fuse program was identified as a key factor in achieving reliably high post-program fuse resistance.


title: Prediction of Microwave Absorption Behavior of Grading Honeycomb Composites Based on Effective Permittivity Formulas

text: Microwave absorbing honeycomb composites with grading coating thickness are prepared by impregnation of aramid paper frame within water solutions of conductive carbon blacks. Based on effective medium method, grading impedance in the composites can be theoretically expressed by a linear approximation of the inversely tapered effective permittivity at wave propagation direction, which provides a feasible way to optimize the design of microwave absorbing honeycomb structures. The consistent result of calculations and experimental measurements for positive impedance grading design confirms the broadband absorption effect in high frequencies.


title: New support vector algorithms with parametric insensitive/margin model

text: In this paper, a modification of v-support vector machines (v-SVM) for regression and classification is described, and the use of a parametric insensitive/margin model with an arbitrary shape is demonstrated. This can be useful in many cases, especially when the noise is heteroscedastic, that is, the noise strongly depends on the input value x. Like the previous v-SVM, the proposed support vector algorithms have the advantage of using the parameter 0 < or = v < or = 1 for controlling the number of support vectors. To be more precise, v is an upper bound on the fraction of training errors and a lower bound on the fraction of support vectors. The algorithms are analyzed theoretically and experimentally.


title: The feeling of effort during mental activity

text: The feeling of effort is familiar to most, if not all, humans. Prior research shows that the feeling of effort shapes judgments (e.g., of agency) and decisions (e.g., to quit the current task) in various ways, but the proximal causes of the feeling of effort are not well understood. In this research, I address these proximal causes. In particular, I conducted two preregistered experiments in which participants performed a difficult vs. easy cognitive task, while I measured effort-related phenomenology (feeling of effort) and physiology (pupil dilation) on a moment-to-moment basis. In both experiments, difficult tasks increased the feeling of effort; however, this effect could not be explained by concurrent increases in physiological effort. To explain these findings, I suggest that the feeling of effort during mental activity stems from the decision to exert physiological effort, rather than from physiological effort itself.


title: Temporal and Real-Time Databases: A Survey

text: • a short biography of John Doe. – born on April 3rd, 1975 in Smallville. His birth was registered on April 4th, 1975. – He went to live on his own in Bigtown. Although he moved out on August 26th, 1994, he forgot to register the change of address officially. – He updated his record on December 27, 1994. – John Doe was accidentally hit by a truck on April 1st, 2001. The coroner reported his date of death on the next day.


title: Conflict-Aware Event-Participant Arrangement and Its Variant for Online Setting

text: With the rapid development of Web 2.0 and Online To Offline (O2O) marketing model, various <italic>online <underline> e</underline>vent-<underline>b</underline>ased <underline>s</underline>ocial <underline>n</underline>etwork<underline>s </underline></italic> (EBSNs) are getting popular. An important task of EBSNs is to facilitate the most satisfactory event-participant arrangement for both sides, i.e., events enroll more participants and participants are arranged with personally interesting events. Existing approaches usually focus on the arrangement of each single event to a set of potential users, or ignore the conflicts between different events, which leads to infeasible or redundant arrangements. In this paper, to address the shortcomings of existing approaches, we first identify a more general and useful event-participant arrangement problem, called <italic><underline>G</underline>lobal <underline>E</underline> vent-participant <underline>A</underline>rrangement with <underline>C</underline>onflict and <underline>C</underline> apacity</italic> (<inline-formula><tex-math notation="LaTeX">$GEACC$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="chen-ieq1-2565468.gif"/></alternatives></inline-formula>) problem, focusing on the conflicts of different events and making event-participant arrangements in a global view. We find that the GEACC problem is NP-hard due to the conflicts among events. Thus, we design two approximation algorithms with provable approximation ratios and an exact algorithm with pruning technique to address this problem. In addition, we propose an online setting of GEACC, called OnlineGEACC, which is also practical in real-world scenarios. We further design an online algorithm with provable performance guarantee. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.


title: Interobserver variation in the assessment of pelvic organ prolapse

text: The aim of this study was to determine the interobserver agreement of two grading systems for pelvic organ prolapse: the vaginal profile and the International Continence Society (ICS) draft proposal. Forty-nine consecutive women referred for evaluation of urinary incontinence and/or pelvic organ prolapse were studied. Patients were first examined by a physician and a nurse clinician using the vaginal profile, followed by an examination according to the technique described in the ICS draft proposal for standardization of terminology (1994). κ statistic and Pearson's correlation coefficient were used to determine interobserver variability for the ICS system by overall stage, by stage-specific comparison, and by specific anatomic location. The vaginal profile was evaluated by obtaining a κ for overall degree of prolapse, stage-specific comparison and by anatomic area. The κ for the ICS stage was 0.79 (P<0.001), and the κ for the vaginal profile by area of greatest prolapse was 0.68 (P<0.001), indicating substantial interobserver agreement for both systems. The ICS system was noted to have substantial interobserver agreement by a stage-specific comparison. All anatomic locations of the ICS staging system were found to correlate significantly, and a high degree of interobserver precision was found. The vaginal profile also showed significant interobserver agreement by overall degree of prolapse, by specific degree of prolapse, and by anatomic area. It was concluded that both the proposed ICS staging system and the traditional vaginal profile show significant interobserver agreement both by overall stage, stage-specific analysis and specific location. The registered nurse examination correlated well with the physican examination, indicating that the most important factor in obtaining reproducible results may be definition and close attention to examination technique.


title: "Good job, you're so smart": The effects of inconsistency of praise type on young children's motivation.

text: Previous research has demonstrated that generic praise ("good drawer") is related to children giving up after failure because failure implies the lack of a critical trait (e.g., drawing ability). Conversely, nongeneric praise ("good job drawing") is related to mastery motivation because it implies that success is related to effort. Yet children may receive a mixture of these praise types (i.e., inconsistent praise), the effects of which are unclear. We tested how inconsistent praise influenced two components of motivation: self-evaluation and persistence. Kindergarteners (N=135) were randomly assigned to one of five conditions in which consistency of praise type was varied. After two failure scenarios, children reported self-evaluations and persistence. Results indicated that more nongeneric praise related linearly to greater motivation, yet self-evaluation and persistence were impacted differently by inconsistent praise types. Hearing even a small amount of generic praise reduced persistence, whereas hearing a small amount of nongeneric praise preserved self-evaluation.


title: Duration prediction using multi-level model for GPR-based speech synthesis

text: This paper introduces frame-based Gaussian process regression (GPR) into phone/syllable duration modeling for Thai speech synthesis. The GPR model is designed for predicting framelevel acoustic features using corresponding frame information, which includes relative position in each unit of utterance structure and linguistic information such as tone type and part of speech. Although the GPR-based prediction can be applied to a phone duration model, the use of phone duration model only is not always sufficient to generate natural sounding speech. Specifically, in some languages including Thai, syllable durations affect the perception of sentence structure. In this paper, we propose a duration prediction technique using a multi-level model which includes syllable and phone levels for prediction. In the technique, first, syllable durations are predicted, and then they are used as additional contexts in phone-level model to generate phone duration for synthesizing. Objective and subjective evaluation results show that GPR-based modeling with multi-level model for duration prediction outperforms the conventional HMM-based speech synthesis.


title: The effect of glycolic acid on the treatment of acne in Asian skin.

text:
BACKGROUND
Glycolic acid has become important and popular for treating acne.

OBJECTIVE
To evaluate the efficacy and safety of serial glycolic acid peels with glycolic acid home care products on facial acne lesions and other associated skin problems.

METHODS
We collected 40 Asian candidates with moderate to moderately severe acne. They were divided into two groups according to the degree of greasiness of their facial skin. The two groups' members were treated with four series of 35% and 50% glycolic acid peels, respectively. They also used 15% glycolic acid home care products during this study period. The improvement of acne as well as other associated problems were assessed by both the physicians and the patient themselves.

RESULTS
Significant resolution of comedones, papules, and pustules was found. The skin texture of each candidate was dramatically rejuvenated. Consistent and repetitive treatment with glycolic acid was needed for the apparent improvement of acne scars and cystic lesions. The follicular pores also became comparatively smaller. Furthermore, most of the candidates had much brighter and lighter looking skin. Only small percentage of patients (5.6%) developed side effects.

CONCLUSION
Glycolic acid has considerable therapeutic value for acne with minimal side effects even in Asian skin. It may be an ideal adjunctive treatment of acne.


title: Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews

text: In this paper, we propose a recommendation technique that not only can recommend items of interest to the user as traditional recommendation systems do but also specific aspects of consumption of the items to further enhance the user experience with those items. For example, it can recommend the user to go to a specific restaurant (item) and also order some specific foods there, e.g., seafood (an aspect of consumption). Our method is called Sentiment Utility Logistic Model (SULM). As its name suggests, SULM uses sentiment analysis of user reviews. It first predicts the sentiment that the user may have about the item based on what he/she might express about the aspects of the item and then identifies the most valuable aspects of the user's potential experience with that item. Furthermore, the method can recommend items together with those most important aspects over which the user has control and can potentially select them, such as the time to go to a restaurant, e.g. lunch vs. dinner, and what to order there, e.g., seafood. We tested the proposed method on three applications (restaurant, hotel, and beauty & spa) and experimentally showed that those users who followed our recommendations of the most valuable aspects while consuming the items, had better experiences, as defined by the overall rating.


title: Deep motion features for visual tracking

text: Robust visual tracking is a challenging computer vision problem, with many real-world applications. Most existing approaches employ hand-crafted appearance features, such as HOG or Color Names. Recently, deep RGB features extracted from convolutional neural networks have been successfully applied for tracking. Despite their success, these features only capture appearance information. On the other hand, motion cues provide discriminative and complementary information that can improve tracking performance. Contrary to visual tracking, deep motion features have been successfully applied for action recognition and video classification tasks. Typically, the motion features are learned by training a CNN on optical flow images extracted from large amounts of labeled videos. This paper presents an investigation of the impact of deep motion features in a tracking-by-detection framework. We further show that hand-crafted, deep RGB, and deep motion features contain complementary information. To the best of our knowledge, we are the first to propose fusing appearance information with deep motion features for visual tracking. Comprehensive experiments clearly suggest that our fusion approach with deep motion features outperforms standard methods relying on appearance information alone.


title: A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data

text: Detecting outliers which are grossly different from or inconsistent with the remaining dataset is a major challenge in real-world KDD applications. Existing outlier detection methods are ineffective on scattered real-world datasets due to implicit data patterns and parameter setting issues. We define a novel Local Distance-based Outlier Factor (LDOF) to measure the outlier-ness of objects in scattered datasets which addresses these issues. LDOF uses the relative location of an object to its neighbours to determine the degree to which the object deviates from its neighbourhood. Properties of LDOF are theoretically analysed including LDOF’s lower bound and its false-detection probability, as well as parameter settings. In order to facilitate parameter settings in real-world applications, we employ a top-n technique in our outlier detection approach, where only the objects with the highest LDOF values are regarded as outliers. Compared to conventional approaches (such as top-n KNN and top-n LOF), our method top-n LDOF is more effective at detecting outliers in scattered data. It is also easier to set parameters, since its performance is relatively stable over a large range of parameter values, as illustrated by experimental results on both real-world and synthetic datasets.


title: Unified Model for Contrast Enhancement and Denoising

text: In this paper, we attempt a challenging task to unify two important complementary operations, i.e. contrast enhancement and denoising, that is required in most image processing applications. The proposed method is implemented using practical analog circuit configurations that can lead to near real-time processing capabilities useful to be integrated with vision sensors. Metrics used for performance includes estimation of Residual Noise Level (RNL), Structural Similarity Index Measure (SSIM), Output-to-Input Contrast Ratio (CRo_i), and its combined score (SCD). The class of contrast stretching methods has resulted in higher noise levels (RNL ≥ 7) along with increased contrast measures (CRo-i ≥ eight times than that of the input image) and SSIM ≤ 0.52. Denoising methods generates images with lesser noise levels (RNL ≤ 0.2308), poor contrast enhancements (CRo-i ≤ 1.31) and with best structural similarity (SSIM ≥ 0.85). In contrast, the proposed model offers best contrast stretching (CRo-i = 5.83), least noise (RNL = 0.02), a descent structural similarity (SSIM = 0.6453) and the highest combined score (SCD = 169).


title: A Process Mining Driven Framework for Clinical Guideline Improvement in Critical Care

text: This paper presents a framework for process mining in critical care. The framework uses the CRISP-DM model, extended to incorporate temporal and multidimensional aspects (CRISP-TDM), combined with the Patient Journey Modeling Architecture (PaJMa), to provide a structured approach to knowledge discovery of new condition onset pathophysiologies in physiological data streams. The approach is based on temporal abstraction and mining of physiological data streams to develop process flow mappings that can be used to update patient journeys; instantiated in critical care within clinical practice guidelines. We demonstrate the framework within the neonatal intensive care setting, where we are performing clinical research in relation to pathophysiology within physiological streams of patients diagnosed with late onset neonatal sepsis. We present an instantiation of the framework for late onset neonatal sepsis, using CRISP-TDM for the process mining model and PaJMa for the knowledge representation.


title: Second to fourth digit ratio: a predictor of adult penile length.

text: The second to fourth digit ratio (2D:4D) has been proposed as a putative biomarker for prenatal testosterone and covaries with the sensitivity of the androgen receptor (AR). Both prenatal testosterone and the AR play a central role in penile growth. In this study, we investigated the relationship between digit ratio and penile length. Korean men who were hospitalized for urological surgery at a single tertiary academic centre were examined in this study, and 144 men aged 20 years or older who gave informed consent were prospectively enrolled. Right-hand second- and fourth-digit lengths were measured by a single investigator prior to measurement of penile length. Under anaesthesia, flaccid and stretched penile lengths were measured by another investigator who did not measure nor have any the information regarding the digit lengths. Univariate and multivariate analysis using linear regression models showed that only height was a significant predictive factor for flaccid penile length (univariate analysis: r=0.185, P=0.026; multivariate analysis: r=0.172, P=0.038) and that only digit ratio was a significant predictive factor for stretched penile length (univariate analysis:r=-0.216, P=0.009; multivariate analysis: r=-0.201, P=0.024; stretched penile length=-9.201×digit ratio + 20.577). Based on this evidence, we suggest that the digit ratio can predict adult penile size and that the effects of prenatal testosterone may in part explain the differences in adult penile length.