title: Simple efficient load balancing algorithms for peer-to-peer systems

abstract: Load balancing is a critical issue for the efficient operation of peer-to-peer networks. We give two new load-balancing protocols whose provable performance guarantees are within a constant factor of optimal. Our protocols refine the consistent hashing data structure that underlies the Chord (and Koorde) P2P network. Both preserve Chord's logarithmic query time and near-optimal data migration cost.Consistent hashing is an instance of the distributed hash table (DHT) paradigm for assigning items to nodes in a peer-to-peer system: items and nodes are mapped to a common address space, and nodes have to store all items residing closeby in the address space.Our first protocol balances the distribution of the key address space to nodes, which yields a load-balanced system when the DHT maps items "randomly" into the address space. To our knowledge, this yields the first P2P scheme simultaneously achieving O(log n) degree, O(log n) look-up cost, and constant-factor load balance (previous schemes settled for any two of the three).Our second protocol aims to directly balance the distribution of items among the nodes. This is useful when the distribution of items in the address space cannot be randomized. We give a simple protocol that balances load by moving nodes to arbitrary locations "where they are needed." As an application, we use the last protocol to give an optimal implementation of a distributed data structure for range searches on ordered data.


title: The Abstract Structure of Quantum Algorithms

abstract: Quantum information brings together theories of physics and computer science. This synthesis challenges the basic intuitions of both fields. In this thesis, we show that adopting a unified and general language for process theories advances foundations and practical applications of quantum information. Our first set of results analyze quantum algorithms with a process theoretic structure. We contribute new constructions of the Fourier transform and Pontryagin duality in dagger symmetric monoidal categories. We then use this setting to study generalized unitary oracles and give a new quantum blackbox algorithm for the identification of group homomorphisms, solving the GROUPHOMID problem. In the remaining section, we construct a novel model of quantum blackbox algorithms in non-deterministic classical computation. Our second set of results concerns quantum foundations. We complete work begun by Coecke et al., definitively connecting the Mermin non-locality of a process theory with a simple algebraic condition on that theory's phase groups. This result allows us to offer new experimental tests for Mermin non-locality and new protocols for quantum secret sharing. In our final chapter, we exploit the shared process theoretic structure of quantum information and distributional compositional linguistics. We propose a quantum algorithm adapted from Weibe et al. to classify sentences by meaning. The clarity of the process theoretic setting allows us to recover a speedup that is lost in the naive application of the algorithm. The main mathematical tools used in this thesis are group theory (esp. Fourier theory on finite groups), monoidal category theory, and categorical algebra.


title: A hybrid model for IT project with Scrum

abstract: Many organizations are gradually catching up with the tide of adopting agile practices at workplace, but they seem to be struggling with how to choose the agile practices and mix them into their IT software project development and management. These organizations have already had their own development styles, many of which have adhered to the traditional plan-driven methods such as waterfall. The inherent corporate culture of resisting to change or hesitation to abandon what they have established for a whole new methodology hampers the process change. In this paper, we will review the current state of agile adoption in business organizations and propose a new approach to IT project development and management by blending Scrum, an agile method, into traditional plan-driven project development and management. The management activity involved in Scrum is discussed, the team and meeting composing of Scrum are investigated, the challenges and benefits of applying Scrum in traditional IT project development and management are analyzed, the blending structure is illustrated and discussed, and the iterative process with Scrum and planned process without Scrum are compared.


title: Experimental approach of X-band slotted microstrip patch antenna array with non-uniform current distribution

abstract: This paper presents an experimental approach of X-band slotted microstrip patch antenna array which is fed by a non-uniform current distribution. The use of a non-uniform current distribution aims to suppress the sidelobe level (SLL) which usually appears in the array of antenna. The proposed antenna array which is intended to work at frequency of 9.3GHz for X-band application consists of 4 × 4 microstrip patch antenna on a grounded 0.508mm thick RT/Duroid® 5880 dielectric substrate. From the characterization, it shows that the use of non-uniform current distribution can suppress the SLL up to 18dB in which this is 5dB better than the patch antenna array fed by a uniform current distribution.


Myelination of parvalbumin interneurons: a parsimonious locus of pathophysiological convergence in schizophrenia


title: Visual SLAM and Structure from Motion in Dynamic Environments: A Survey

abstract: In the last few decades, Structure from Motion (SfM) and visual Simultaneous Localization and Mapping (visual SLAM) techniques have gained significant interest from both the computer vision and robotic communities. Many variants of these techniques have started to make an impact in a wide range of applications, including robot navigation and augmented reality. However, despite some remarkable results in these areas, most SfM and visual SLAM techniques operate based on the assumption that the observed environment is static. However, when faced with moving objects, overall system accuracy can be jeopardized. In this article, we present for the first time a survey of visual SLAM and SfM techniques that are targeted toward operation in dynamic environments. We identify three main problems: how to perform reconstruction (robust visual SLAM), how to segment and track dynamic objects, and how to achieve joint motion segmentation and reconstruction. Based on this categorization, we provide a comprehensive taxonomy of existing approaches. Finally, the advantages and disadvantages of each solution class are critically discussed from the perspective of practicality and robustness.


title: Model patterns - The quest for the right level of abstraction

abstract: We know by now that evolution in software is inevitable. Given that is so, we should not just allow for but accommodate for change throughout the software lifecycle. The claim of this paper is that, in order to accommodate for change effectively, we need a modelling discipline with a built-in notion of refinement, so that domain concepts can be defined and understood on their appropriate level of abstraction, and change can be captured on that same level. Refinement serves to connect levels of abstraction within the same model, enabling a simultaneous understanding of that same model on different levels. We propose the term model pattern for the central concept in such a modelling discipline.


title: Exploiting hashtags for adaptive microblog crawling

abstract: Researchers have capitalized on microblogging services, such as Twitter, for detecting and monitoring real world events. Existing approaches have based their conclusions on data collected by monitoring a set of pre-defined keywords. In this paper, we show that this manner of data collection risks losing a significant amount of relevant information. We then propose an adaptive crawling model that detects emerging popular hashtags, and monitors them to retrieve greater amounts of highly associated data for events of interest. The proposed model analyzes the traffic patterns of the hashtags collected from the live stream to update subsequent collection queries. To evaluate this adaptive crawling model, we apply it to a dataset collected during the 2012 London Olympic Games. Our analysis shows that adaptive crawling based on the proposed Refined Keyword Adaptation algorithm collects a more comprehensive dataset than pre-defined keyword crawling, while only introducing a minimum amount of noise.


Postscript about NP-hard problems


title: The Validity of Interpersonal Skills Assessment via Situational Judgment Tests for Predicting Academic Success and Job Performance.

abstract: This study provides conceptual and empirical arguments why an assessment of applicants’ procedural knowledge about interpersonal behavior via a video-based situational judgment test might be valid for academic and postacademic success criteria. Four cohorts of medical students (N 723) were followed from admission to employment. Procedural knowledge about interpersonal behavior at the time of admission was valid for both internship performance (7 years later) and job performance (9 years later) and showed incremental validity over cognitive factors. Mediation analyses supported the conceptual link between procedural knowledge about interpersonal behavior, translating that knowledge into actual interpersonal behavior in internships, and showing that behavior on the job. Implications for theory and practice are discussed.


title: Design and development of autonomous omni-directional mobile robot with mecanum wheel

abstract: A variety of designs of mobile robot have been developed in recent years in order to improve their omni-directional maneuver and practical applications. Omni-directional mobile robot has vast advantages over conventional design likes differential drive in term of mobility in congested environments. The main purpose of this research is to design, develop and implement an omni-directional mobile robot with custom made mecanum wheel for autonomous navigation. Using these mecanum wheels, the mobile robot is provided with three degree of freedom (DOF) mobility. Attention also paid for the development of kinematics and dynamics model to analyze the mobile platform motion performances. Motor driver and robot controller were also developed. Motion control algorithm using BasicStamp Editor software was developed to test the capabilities of the system’s mobility performance. Experiments were performed to analyze the motion characteristic of the mobile robot motion in Y axis, X axis and differential drive capabilities. Using ultrasonic and line follower sensors, basic autonomous navigation had been develop and tested. Based on the experiment conducted, the autonomous navigation gave satisfactory result. The developed mobile robot will provide a test bed for advanced path planning and navigation projects in the future. © T his ite m is p rot ect ed by or igi na l c op yri gh t


title: An Approach to Evaluate Blurriness in Retinal Images with Vitreous Opacity for Cataract Diagnosis

abstract: Cataract is one of the leading causes of blindness in the world’s population. A method to evaluate blurriness for cataract diagnosis in retinal images with vitreous opacity is proposed in this paper. Three types of features are extracted, which include pixel number of visible structures, mean contrast between vessels and background, and local standard deviation. To avoid the wrong detection of vitreous opacity as retinal structures, a morphological method is proposed to detect and remove such lesions from retinal visible structure segmentation. Based on the extracted features, a decision tree is trained to classify retinal images into five grades of blurriness. The proposed approach was tested using 1355 clinical retinal images, and the accuracies of two-class classification and five-grade grading compared with that of manual grading are 92.8% and 81.1%, respectively. The kappa value between automatic grading and manual grading is 0.74 in five-grade grading, in which both variance and value are less than 0.001. Experimental results show that the grading difference between automatic grading and manual grading is all within 1 grade, which is much improvement compared with that of other available methods. The proposed grading method provides a universal measure of cataract severity and can facilitate the decision of cataract surgery.


title: An automated approach for classification of plant diseases towards development of futuristic Decision Support System in Indian perspective

abstract: The major cause for decrease in the quality and amount of agricultural productivity is plant diseases. Farmers encounter great difficulties in detecting and controlling plant diseases. Thus, it is of great importance to diagnose the plant diseases at early stages so that appropriate and timely action can be taken by the farmers to avoid further losses. The project focuses on the approach based on image processing for detection of diseases of soybean plants. The soybean images are captured using mobile camera having resolution greater than 2 mega pixels. The purpose of the proposed project is to provide inputs for the Decision Support System (DSS), which is developed for providing advice to the farmers as and when require over mobile internet. Our proposed work classifies the images of soybean leaves as healthy and diseased using Support Vector Machine (SVM). The algorithm comprises of four major steps: image acquisition, extracting the leaf from complex background, statistical analysis and classification. The pre-processing step includes conversion from RGB to HSV (Hue Saturation Value) color space. For extracting the region of interest (ROI) from the original image, multi-thresholding is used. The color based and cluster based methods are used for segmentation. The algorithm uses Scale Invariant Feature Transform (SIFT) technique which automatically recognizes the plant species based on the leaf shape. The SVM classifier proves its ability in automatic and accurate classification of images. Finally, it can be concluded from the experimental results that this approach can classify the leaves with an average accuracy of 93.79%. The proposed system will enable the farmers to get advice from the agricultural experts with minimal efforts.


title: Design and implementation of PC-based high performance automatic voltage regulator

abstract: This paper presents an overview of the currently employed AVRs (Automatic Voltage Regulators), the analogue and the digital, microprocessor based sampled data controlled, devices with respect to their control systems. The main design aims and constraints are examined in terms of performance specification concerning the speed, accuracy and stability of control. The contradiction between transient and dynamic stability requirements and its present day resolution by introducing Power System Stabilizing function into the AVR functions is explained. The design is implemented and tested on a 110 W synchronous generator in Power System Laboratory with an IBM personal computer acting as the real time controller. The results show that the proposed PC-based AVR are very effective for operation of a controller. These include its low cost, fast response, higher reliability and ruggedness.


title: A Billion-Scale Approximation Algorithm for Maximizing Benefit in Viral Marketing

abstract: Online social networks have been one of the most effective platforms for marketing and advertising. Through the “world-of-mouth” exchanges, so-called viral marketing, the influence and product adoption can spread from few key influencers to billions of users in the network. To identify those key influencers, a great amount of work has been devoted for the influence maximization ( IM ) problem that seeks a set of $k$ seed users that maximize the expected influence. Unfortunately, IM encloses two impractical assumptions: 1) any seed user can be acquired with the same cost and 2) all users are equally interested in the advertisement. In this paper, we propose a new problem, called cost-aware targeted viral marketing ( CTVM ), to find the most cost-effective seed users, who can influence the most relevant users to the advertisement. Since CTVM is NP-hard, we design an efficient $(1- 1/\sqrt {e}-\epsilon )$ -approximation algorithm, named Billion-scale Cost-award Targeted algorithm (BCT), to solve the problem in billion-scale networks. Comparing with IM algorithms, we show that BCT is both theoretically and experimentally faster than the state-of-the-arts while providing better solution quality. Moreover, we prove that under the linear threshold model, BCT is the first sub-linear time algorithm for CTVM (and IM ) in dense networks. We carry a comprehensive set of experiments on various real-networks with sizes up to several billion edges in diverse disciplines to show the absolute superiority of BCT on both CTVM and IM domains. Experiments on Twitter data set, containing 1.46 billions of social relations and 106 millions tweets, show that BCT can identify key influencers in trending topics in only few minutes.


title: The importance of vegetables in ensuring both food and nutritional security in attainment of the Millennium Development Goals

abstract: A nutritious and varied diet is a critical means by which good health can be maintained. Consumption of less than 200 g of vegetables per person per day in many countries today is common and this low amount, often in conjunction with poverty and poor medical services, is associated with unacceptable levels of mortality and malnutrition in preschool children and other vulnerable groups. An increase in the availability, affordability and consumption of nutrient-dense vegetables and pulses is one way malnutrition may be substantially reversed—yet nutritional security appears to be less valued than food security by key decision makers, and vegetable crops thus receive inadequate research investment. Opportunities exist for the poor to improve their access to vegetables, particularly if they are willing to grow home gardens. Research continues on defining appropriate nutrient-dense vegetables for such activities, but these efforts may be compromised by failure to adopt good agricultural practices, resulting in contamination and unhealthy produce for producers and consumers. The scientific community is ready to play its role in battling malnutrition and hunger, but unless the political resolve can be found to support the causes of both food and nutritional security together, it is unlikely that the Millennium Development Goals will be achieved in a timely fashion. Redressing the current imbalance in agricultural investment can improve efficiency in food production and ensure nutritious diets can be a reality for all people.


title: Design and performance analysis of rectangular microstrip patch antenna at 2.45 GHz

abstract: This paper presents simulation and performance analysis of a single element rectangular microstrip patch antenna (RMPA) and a 2×1 patch array for Industrial, Scientific and Medical (ISM) band (2.45 GHz). Both the antennas are designed over a Flame Retardant-4 (FR-4) substrate with dielectric constant (e0) of 3.8, thickness (h) of 1.5 mm and loss tangent of 0.02. The optimum design parameters of the antennas are selected to achieve the compact dimensions, as well as the best possible characteristics such as resonant frequency, high gain, high radiation efficiency etc. Return loss of single element and double element antenna is −38.5 dB and −37.67 dB respectively and gain is 5.49 dB and 8.0 dB respectively. Gain, directivity and bandwidth (BW) enhancement is obtained in two element patch array. Microstrip line feed is used, simulation results are obtained using CST Microwave Studio and the fabricated antennas are measured using Wave and Antenna Training System (WATS-2002).


title: Querying Factorized Probabilistic Triple Databases

abstract: An increasing amount of data is becoming available in the form of large triple stores, with the Semantic Web's linked open data cloud (LOD) as one of the most prominent examples. Data quality and completeness are key issues in many community-generated data stores, like LOD, which motivates probabilistic and statistical approaches to data representation, reasoning and querying. In this paper we address the issue from the perspective of probabilistic databases, which account for uncertainty in the data via a probability distribution over all database instances. We obtain a highly compressed representation using the recently developed RESCAL approach and demonstrate experimentally that efficient querying can be obtained by exploiting inherent features of RESCAL via sub-query approximations of deterministic views.


title: Can I trust you more? Model-Agnostic Hierarchical Explanations

abstract: Interactions such as double negation in sentences and scene interactions in images are common forms of complex dependencies captured by state-of-the-art machine learning models. We propose Mah\'e, a novel approach to provide Model-agnostic hierarchical \'explanations of how powerful machine learning models, such as deep neural networks, capture these interactions as either dependent on or free of the context of data instances. Specifically, Mah\'e provides context-dependent explanations by a novel local interpretation algorithm that effectively captures any-order interactions, and obtains context-free explanations through generalizing context-dependent interactions to explain global behaviors. Experimental results show that Mah\'e obtains improved local interaction interpretations over state-of-the-art methods and successfully explains interactions that are context-free.


title: Boosting the Guessing Attack Performance on Android Lock Patterns with Smudge Attacks

abstract: Android allows 20 consecutive fail attempts on unlocking a device. This makes it difficult for pure guessing attacks to crack user patterns on a stolen device before it permanently locks itself. We investigate the effectiveness of combining Markov model-based guessing attacks with smudge attacks on unlocking Android devices within 20 attempts. Detected smudges are used to pre-compute all the possible segments and patterns, significantly reducing the pattern space that needs to be brute-forced. Our Markov-model was trained using 70% of a real-world pattern dataset that consists of 312 patterns. We recruited 12 participants to draw the remaining 30% on Samsung Galaxy S4, and used smudges they left behind to analyze the performance of the combined attack. Our results show that this combined method can significantly improve the performance of pure guessing attacks, cracking 74.17% of patterns compared to just 13.33% when the Markov model-based guessing attack was performed alone---those results were collected from a naive usage scenario where the participants were merely asked to unlock a given device. Even under a more complex scenario that asked the participants to use the Facebook app for a few minutes---obscuring smudges were added as a result---our combined attack, at 31.94%, still outperformed the pure guessing attack at 13.33%. Obscuring smudges can significantly affect the performance of smudge-based attacks. Based on this finding, we recommend that a mitigation technique should be designed to help users add obscurity, e.g., by asking users to draw a second random pattern upon unlocking a device.


title: Toward Crowdsourcing-Based Road Pavement Monitoring by Mobile Sensing Technologies

abstract: In crowdsourcing applications, the quality of the crowdsourced data is decisive to the success of subsequent system-level mining processes. We proposed a smartphone probe car (SPC) system to monitor road pavement. An SPC is essentially an ordinary vehicle with a mounted smartphone that runs sensing programs to objectively assess bumping caused by road anomalies such as potholes and bumps. The proposed system has several features. First, to allow dynamic forming of SPCs, we develop a signal processing heuristic for the extraction of the vertical acceleration components from the accelerometer readings (upon which bumping detection and road surface anomaly assessment rely). By these means, the proposed system provides a driver-friendly environment, requiring neither complicated installation nor driver-assisted training processes, and thus is possible to achieve hassle-free mass deployment such that drivers would be willing to participate in crowdsourcing. Second, based on the underdamped oscillation model, we propose a road anomaly indexing heuristic that is representable for road anomalies rather than vehicle conditions. This will later facilitate the system-level data mining processes in the servers. Third, a prototype SPC system was implemented and extensive field tests were undertaken to verify the performance of our system framework. Furthermore, we experimentally adopted a DENCLUE-like algorithm to mine road anomaly information from reported events to demonstrate any potential benefit from future investigation of data mining process at the system level. We believe the research works introduced in this paper consist the first step toward building an “ecosystem” of SPC-based crowdsourcing traffic and road monitoring applications.


title: An FPGA implementation of a long short-term memory neural network

abstract: Our work proposes a hardware architecture for a Long Short-Term Memory (LSTM) Neural Network, aiming to outperform software implementations, by exploiting its inherent parallelism. The main design decisions are presented, along with the proposed network architecture. A description of the main building blocks of the network is also presented. The network is synthesized for various sizes and platforms, and the performance results are presented and analyzed. Our synthesized network achieves a 251 times speed-up over a custom-built software network, running on an i7–3770k Desktop computer, proving the benefits of parallel computation for this kind of network.


title: Challenges of studying and processing dialects in social media

abstract: Dialect features typically do not make it into formal writing, but flourish in social media. This enables largescale variational studies. We focus on three phonological features of African American Vernacular English and their manifestation as spelling variations on Twitter. We discuss to what extent our data can be used to falsify eight sociolinguistic hypotheses. To go beyond the spelling level, we require automatic analysis such as POS tagging, but social media language still challenges language technologies. We show how both newswire- and Twitter-adapted stateof-the-art POS taggers perform significantly worse on AAVE tweets, suggesting that large-scale dialect studies of language variation beyond the surface level are not feasible with out-ofthe-box NLP tools.


title: Modular Multilayer Perceptron for WLAN Based Localization

abstract: Location awareness is key capability of context-aware Ubiquitous environments. Received signal strength (RSS) based localization is increasingly popular choice especially for in-building scenarios after pervasive adoption of IEEE 802.11 wireless LAN. Fundamental requirement of such localization systems is to estimate location from RSS at a particular location. Multipath propagation effects make RSS to fluctuate in unpredictable manner, introducing uncertainty in location estimation. Moreover, in real life situations RSS values are not available at some locations all the time making the problem more difficult. We employ modular multi layer perceptron (MMLP) approach to effectively reduce the uncertainty in location estimation system. It provides better location estimation results than other approaches and systematically caters for unavailable signals at estimation time.


title: The SWELL Knowledge Work Dataset for Stress and User Modeling Research

abstract: This paper describes the new multimodal SWELL knowledge work (SWELL-KW) dataset for research on stress and user modeling. The dataset was collected in an experiment, in which 25 people performed typical knowledge work (writing reports, making presentations, reading e-mail, searching for information). We manipulated their working conditions with the stressors: email interruptions and time pressure. A varied set of data was recorded: computer logging, facial expression from camera recordings, body postures from a Kinect 3D sensor and heart rate (variability) and skin conductance from body sensors. The dataset made available not only contains raw data, but also preprocessed data and extracted features. The participants' subjective experience on task load, mental effort, emotion and perceived stress was assessed with validated questionnaires as a ground truth. The resulting dataset on working behavior and affect is a valuable contribution to several research fields, such as work psychology, user modeling and context aware systems.


title: Object discovery and grasp detection with a shared convolutional neural network

abstract: Grasp an object from a stack of objects in real-time is still a challenge in robotics. This requires the robot to have the ability of both fast object discovery and grasp detection: a target object should be picked out from the stack first and then a proper grasp configuration is applied to grasp the object. In this paper, we propose a shared convolutional neural network (CNN) which can simultaneously implement these two tasks in real-time. The processing speed of the model is about 100 frames per second on a GPU which largely satisfies the requirement. Meanwhile, we also establish a labeled RGBD dataset which contains scenes of stacked objects for robotic grasping. At last, we demonstrate the implementation of our shared CNN model on a real robotic platform and show that the robot can accurately discover a target object from the stack and successfully grasp it.


title: AGE AND POLYPHYLETIC ORIGINS OF HYBRID AND SPONTANEOUS PARTHENOGENETIC CAMPELOMA (GASTROPODA: VIVIPARIDAE) FROM THE SOUTHEASTERN UNITED STATES.

abstract: We used mtDNA sequence variation to assess the origin, age, and spatial patterns of sequence divergence of triploid hybrid and diploid spontaneous parthenogens from southeastern United States populations of the freshwater snail Campeloma. There was strong support for multiple origins of both parthenogens using likelihood-ratio tests, and we argue that parthenogens are recently derived from sexuals. Atlantic coastal populations of C. limum or C. floridense were the maternal ancestors of Gulf Coast triploid hybrids in the Florida Panhandle. Sequence divergence within monophyletic groups of both parthenogens is similar to within-population divergence found in sympatric sexuals, and monophyletic clades of hybrid and spontaneous parthenogens are geographically widespread throughout the Atlantic and Gulf Coast rivers. These patterns are consistent with higher dispersal rates and recent range expansion of parthenogens, which should reduce the effects of mutation accumulation or parasitism. Range expansion may have occurred through interdrainage transfer during Pleistocene glacial periods.


title: Multitask Learning and Benchmarking with Clinical Time Series Data

abstract: Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but progress in machine learning for healthcare research has been difficult to measure because of the absence of publicly available benchmark data sets. To address this problem, we propose four clinical prediction benchmarks using data derived from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database. These tasks cover a range of clinical problems including modeling risk of mortality, forecasting length of stay, detecting physiologic decline, and phenotype classification. We propose strong linear and neural baselines for all four tasks and evaluate the effect of deep supervision, multitask training and data-specific architectural modifications on the performance of neural models.


Composition of Compound Nouns Using Distributional Semantics.


TPad Fire : Surface Haptic Tablet


title: How to Track Your Data: The Case for Cloud Computing Provenance

abstract: Provenance, a meta-data describing the derivation history of data, is crucial for the uptake of cloud computing to enhance reliability, credibility, accountability, transparency, and confidentiality of digital objects in a cloud. In this paper, we survey current mechanisms that support provenance for cloud computing, we classify provenance according to its granularities encapsulating the various sets of provenance data for different use cases, and we summarize the challenges and requirements for collecting provenance in a cloud, based on which we show the gap between current approaches to requirements. Additionally, we propose our approach, Data PROVE, that aims to effectively and efficiently satisfy those challenges and requirements in cloud provenance, and to provide a provenance supplemented cloud for better integrity and safety of customers' data.


title: The international personality item pool and the future of public-domain personality measures ☆

abstract: Abstract Seven experts on personality measurement here discuss the viability of public-domain personality measures, focusing on the International Personality Item Pool (IPIP) as a prototype. Since its inception in 1996, the use of items and scales from the IPIP has increased dramatically. Items from the IPIP have been translated from English into more than 25 other languages. Currently over 80 publications using IPIP scales are listed at the IPIP Web site ( http://ipip.ori.org ), and the rate of IPIP-related publications has been increasing rapidly. The growing popularity of the IPIP can be attributed to five factors: (1) It is cost free; (2) its items can be obtained instantaneously via the Internet; (3) it includes over 2000 items, all easily available for inspection; (4) scoring keys for IPIP scales are provided; and (5) its items can be presented in any order, interspersed with other items, reworded, translated into other languages, and administered on the World Wide Web without asking permission of anyone. The unrestricted availability of the IPIP raises concerns about possible misuse by unqualified persons, and the freedom of researchers to use the IPIP in idiosyncratic ways raises the possibility of fragmentation rather than scientific unification in personality research.


title: The April 2007 earthquake swarm near Lake Trichonis and implications for active tectonics in western Greece

abstract: Abstract We investigate the properties of the April 2007 earthquake swarm (Mw 5.2) which occurred at the vicinity of Lake Trichonis (western Greece). First we relocated the earthquakes, using P- and S-wave arrivals to the stations of the Hellenic Unified Seismic Network (HUSN), and then we applied moment tensor inversion to regional broad-band waveforms to obtain the focal mechanisms of the strongest events of the 2007 swarm. The relocated epicentres, cluster along the eastern banks of the lake, and follow a distinct NNW–ESE trend. The previous strong sequence close to Lake Trichonis occurred in June–December 1975. We applied teleseismic body waveform inversion, to obtain the focal mechanism solution of the strongest earthquake of this sequence, i.e. the 31 December 1975 (Mw 6.0) event. Our results indicate that: a) the 31 December 1975 Mw 6.0 event was produced by a NW–SE normal fault, dipping to the NE, with considerable sinistral strike-slip component; we relocated its epicentre: i) using phase data reported to ISC and its coordinates are 38.486°N, 21.661°E; ii) using the available macroseismic data, and the coordinates of the macroseismic epicentre are 38.49°N, 21.63°E, close to the strongly affected village of Kato Makrinou; b) the earthquakes of the 2007 swarm indicate a NNW–SSE strike for the activated main structure, parallel to the eastern banks of Lake Trichonis, dipping to the NE and characterized by mainly normal faulting, occasionally combined with sinistral strike-slip component. The 2007 earthquake swarm did not rupture the well documented E–W striking Trichonis normal fault that bounds the southern flank of the lake, but on the contrary it is due to rupture of a NW–SE normal fault that strikes at a ∼ 45° angle to the Trichonis fault. The left-lateral component of faulting is mapped for the first time to the north of the Gulf of Patras which was previously regarded as the boundary for strike-slip motions in western Greece. This result signifies the importance of further investigations to unravel in detail the tectonics of this region.


Rare case of isolated true complete diphallus – Case report and review of literature


title: AffinityNet: semi-supervised few-shot learning for disease type prediction

abstract: While deep learning has achieved great success in computer vision and many other fields, currently it does not work very well on patient genomic data with the "big p, small N" problem (i.e., a relatively small number of samples with high-dimensional features). In order to make deep learning work with a small amount of training data, we have to design new models that facilitate few-shot learning. Here we present the Affinity Network Model (AffinityNet), a data efficient deep learning model that can learn from a limited number of training examples and generalize well. The backbone of the AffinityNet model consists of stacked k-Nearest-Neighbor (kNN) attention pooling layers. The kNN attention pooling layer is a generalization of the Graph Attention Model (GAM), and can be applied to not only graphs but also any set of objects regardless of whether a graph is given or not. As a new deep learning module, kNN attention pooling layers can be plugged into any neural network model just like convolutional layers. As a simple special case of kNN attention pooling layer, feature attention layer can directly select important features that are useful for classification tasks. Experiments on both synthetic data and cancer genomic data from TCGA projects show that our AffinityNet model has better generalization power than conventional neural network models with little training data. The code is freely available at this https URL .


title: At least nine ways to play: approaching gamer mentalities

abstract: Do digital games and play mean the same things for different people? This article presents the results of a three-year study in which we sought for new ways to approach digital games cultures and playing practices. First, we present the research process in brief and emphasise the importance of merging different kinds of methods and materials in the study of games cultures. Second, we introduce a gaming mentality heuristics that is not dedicated to a certain domain or genre of games, addressing light casual and light social gaming motivations as well as more dedicated ones in a joint framework. Our analysis reveals that, in contrast to common belief, the majority of digital gaming takes place between ‘casual relaxing’ and ‘committed entertaining’, where the multiplicity of experiences, feelings, and understandings that people have about their playing and digital games is wide-ranging. Digital gaming is thus found to be a multi-faceted social and cultural phenomenon which can be understood, practiced and used in various ways.


title: DrMP: Mixed Precision-Aware DRAM for High Performance Approximate and Precise Computing

abstract: Recent studies showed that DRAM restore time degrades as technology scales, which imposes large performance and energy overheads. This problem, prolonged restore time (PRT), has been identified by the DRAM industry as one of three major scaling challenges.This paper proposes DrMP, a novel fine-grained precision-aware DRAM restore scheduling approach, to mitigate PRT. The approach exploits process variations (PVs) within and across DRAM rows to save data with mixed precision. The paper describes three variants of the approach: DrMP-A, DrMP-P, and DrMP-U. DrMP-A supports approximate computing by mapping important data bits to fast row segments to reduce restore time for improved performance at a low application error rate. DrMP-P pairs memory rows together to reduce the average restore time for precise computing. DrMP-U combines DrMP-A and DrMP-P to better trade performance, energy consumption, and computation precision. Our experimental results show that, on average, DrMP achieves 20% performance improvement and 15% energy reduction over a precision-oblivious baseline. Further, DrMP achieves an error rate less than 1% at the application level for a suite of benchmarks, including applications that exhibit unacceptable error rates under simple approximation that does not differentiate the importance of different bits.


title: Keratins and skin disorders

abstract: The association of keratin mutations with genetic skin fragility disorders is now one of the best-established examples of cytoskeleton disorders. It has served as a paradigm for many other diseases and has been highly informative for the study of intermediate filaments and their associated components, in helping to understand the functions of this large family of structural proteins. The keratin diseases have shown unequivocally that, at least in the case of the epidermal keratins, a major function of intermediate filaments is to provide physical resilience for epithelial cells. This review article reflects on the variety of phenotypes arising from mutations in keratins and the reasons for this variation.


title: GradNets: Dynamic Interpolation Between Neural Architectures

abstract: In machine learning, there is a fundamental trade-off between ease of optimization and expressive power. Neural Networks, in particular, have enormous expressive power and yet are notoriously challenging to train. The nature of that optimization challenge changes over the course of learning. Traditionally in deep learning, one makes a static trade-off between the needs of early and late optimization. In this paper, we investigate a novel framework, GradNets, for dynamically adapting architectures during training to get the benefits of both. For example, we can gradually transition from linear to non-linear networks, deterministic to stochastic computation, shallow to deep architectures, or even simple downsampling to fully differentiable attention mechanisms. Benefits include increased accuracy, easier convergence with more complex architectures, solutions to test-time execution of batch normalization, and the ability to train networks of up to 200 layers.


title: Decoding of QR codes printed on spheres

abstract: In this paper, we deal with the situation wherein QR codes are printed on spheres. When an image is obtained from such a QR code, it can be seriously distorted. A scheme based on conic segmentation (CS) is proposed to rectify the distorted QR images.


title: Automatic part localization using 3D cuboid box for vehicle subcategory recognition

abstract: In this paper, we propose an efficient vehicle model recognition method which utilize 3D cuboid box for detection and Convolutional Neural Networks (CNN)-based classifier. Our method automatically localizes the unique part of the vehicle as features which enhance the classification performance. The proposed method is tested on the dataset called BoxCars which contain 63,750 images with 148 categories and the test results show 93.49%.


title: A framework for transient rendering

abstract: Recent advances in ultra-fast imaging have triggered many promising applications in graphics and vision, such as capturing transparent objects, estimating hidden geometry and materials, or visualizing light in motion. There is, however, very little work regarding the effective simulation and analysis of transient light transport, where the speed of light can no longer be considered infinite. We first introduce the transient path integral framework, formally describing light transport in transient state. We then analyze the difficulties arising when considering the light's time-of-flight in the simulation (rendering) of images and videos. We propose a novel density estimation technique that allows reusing sampled paths to reconstruct time-resolved radiance, and devise new sampling strategies that take into account the distribution of radiance along time in participating media. We then efficiently simulate time-resolved phenomena (such as caustic propagation, fluorescence or temporal chromatic dispersion), which can help design future ultra-fast imaging devices using an analysis-by-synthesis approach, as well as to achieve a better understanding of the nature of light transport.


title: Using Bayesian networks for cyber security analysis

abstract: Capturing the uncertain aspects in cyber security is important for security analysis in enterprise networks. However, there has been insufficient effort in studying what modeling approaches correctly capture such uncertainty, and how to construct the models to make them useful in practice. In this paper, we present our work on justifying uncertainty modeling for cyber security, and initial evidence indicating that it is a useful approach. Our work is centered around near real-time security analysis such as intrusion response. We need to know what is really happening, the scope and severity level, possible consequences, and potential countermeasures. We report our current efforts on identifying the important types of uncertainty and on using Bayesian networks to capture them for enhanced security analysis. We build an example Bayesian network based on a current security graph model, justify our modeling approach through attack semantics and experimental study, and show that the resulting Bayesian network is not sensitive to parameter perturbation.


Contain This , Unleashing Docker for HPC


title: NG-PON2 Technology and Standards

abstract: This paper provides a tutorial overview of the latest generation of passive optical network (PON) technology standards nearing completion in ITU-T. The system is termed NG-PON2 and offers a fiber capacity of 40 Gbit/s by exploiting multiple wavelengths at dense wavelength division multiplexing channel spacing and tunable transceiver technology in the subscriber terminals (ONUs). Here, the focus is on the requirements from network operators that are driving the standards developments and the technology selection prior to standardization. A prestandard view of the main physical layer optical specifications is also given, ahead of final ITU-T approval.


title: Simple Functional Encryption Schemes for Inner Products

abstract: Functional encryption is a new paradigm in public-key encryption that allows users to finely control the amount of information that is revealed by a ciphertext to a given receiver. Recent papers have focused their attention on constructing schemes for general functionalities at expense of efficiency. Our goal, in this paper, is to construct functional encryption schemes for less general functionalities which are still expressive enough for practical scenarios. We propose a functional encryption scheme for the inner-product functionality, meaning that decrypting an encrypted vector \(\mathbf {x}\) with a key for a vector \(\mathbf {y}\) will reveal only \(\langle \mathbf {x},\mathbf {y} \rangle \) and nothing else, whose security is based on the DDH assumption. Despite the simplicity of this functionality, it is still useful in many contexts like descriptive statistics. In addition, we generalize our approach and present a generic scheme that can be instantiated, in addition, under the LWE assumption and offers various trade-offs in terms of expressiveness and efficiency.


title: Cultural differences in the correction of social inferences: Does the dispositional rebound occur in an interdependent culture?

abstract: Although social observers have been found to rely heavily on dispositions in their causal analysis, it has been proposed that culture strongly affects this tendency. Recent research has shown that suppressing dispositional inferences during social judgment can lead to a dispositional rebound, that is relying more on dispositional information in subsequent judgments. In the present research, we investigated whether culture also affects this rebound tendency. First, Thai and Belgian participants took part in a typical attitude attribution paradigm. Next, dispositional rebound was assessed by having participants describe a series of pictures. The dispositional rebound occurred for both Belgian and Thai participants when confronted with a forced target, but disappeared for Thai participants when the situational constraints of the target were made salient. The findings are discussed in light of the current cultural models of attribution theory.


UNDERSTANDING PARTICIPATION IN SHARING ECONOMY: THE ROLES OF CONVENIENCE, RISK, AND REGULATORY FOC I


title: Impact of Dictionaries on Automated Content Analysis - The Use of Compound Concepts in Analysing Knowledge Management Research.

abstract: Within the knowledge management (KM) domain, we perceive an increasing number of publications. Considering this increase, content analysis (CA) is a popular and empirically established method to handle context-sensitive information and to achieve an improved understanding. While CA can be applied in an automated way by using software solutions, a problem concerns the analysis of compound concepts (e.g. “intellectual capital”). Whereas dictionaries (i.e. lists of compound concepts) have been suggested to solve this problem, lack of research exists concerning the impact of using such a dictionary. By focusing on the KM domain and using the example of 614 publications within the Journal of Knowledge Management (JoKM), this paper aims to evaluate the impact of dictionaries for automated CA. We perform CA applying the automated approach with and without using a self-developed KM dictionary. The results were compared in terms of result similarity as well as result relevance to the KM discipline. Our findings reveal that using a dictionary for automated CA can lead to an improved context understanding and time savings. However, these benefits are opposed by subjectivity that results from the manual extraction of compound concepts to be used in the dictionary.


title: www.elsevier.com/locate/jphysparis Computations in the early visual cortex

abstract: This paper reviews some of the recent neurophysiological studies that explore the variety of visual computations in the early visual cortex in relation to geometric inference, i.e. the inference of contours, surfaces and shapes. It attempts to draw connections between ideas from computational vision and findings from awake primate electrophysiology. In the classical feed-forward, modular view of visual processing, the early visual areas (LGN, V1 and V2) are modules that serve to extract local features, while higher extrastriate areas are responsible for shape inference and invariant object recognition. However, recent findings in primate early visual systems reveal that the computations in the early visual cortex are rather complex and dynamic, as well as interactive and plastic, subject to influence from global context, higher order perceptual inference, taskrequirement and behavioral experience. The evidence argues that the early visual cortex does not merely participate in the first stage of visual processing, but is involved in many levels of visual computation. � 2003 Elsevier Ltd. All rights reserved.


title: CEoptim: Cross-Entropy R Package for Optimization

abstract: The cross-entropy (CE) method is simple and versatile technique for optimization, based on Kullback-Leibler (or cross-entropy) minimization. The method can be applied to a wide range of optimization tasks, including continuous, discrete, mixed and constrained optimization problems. The new package CEoptim provides the R implementation of the CE method for optimization. We describe the general CE methodology for optimization and well as some useful modifications. The usage and efficacy of CEoptim is demonstrated through a variety of optimization examples, including model fitting, combinatorial optimization, and maximum likelihood estimation.


Platform Envelopment


title: Wound debridement: a clinical update.

abstract: This article aims to improve nurses’ knowledge of wound debridement through a review of different techniques and the related physiology of wound healing. Debridement has long been an established component of effective wound management. However, recent clinical developments have widened the choice of methods available. This article provides an overview of the physiology of wounds, wound bed preparation, methods of debridement and the important considerations for the practitioner in implementing effective, informed and patient-centred wound care


title: Lifestreams: an alternative to the desktop metaphor

abstract: We contend that managing one's own electronic world can be a frustrating task for most computer users, re- quiring too many separate applications, too many file transfers and format translations, the invention of too many pointless names and the construction of organi- zational hierarchies that too quickly become obsolete. What is needed is a metaphor and system for organiz- ing the electronic "bits of paper" we all so easily collect, whether we create them ourselves or they come to us in the form of email, downloaded images, web pages, or scheduling reminders. Lifestreams is such a system.


title: Self-* storage: Brick-based storage with automated administration

abstract: This white paper describes a new project exploring the design and implementation of “self-* storage systems:” self-organizing, self-configuring, self-tuning, self-healing, self-managing systems of storage bricks. Borrowing organizational ideas from corporate structure and automation technologies from AI and control systems, we hope to dramatically reduce the administrative burden currently faced by data center administrators. Further, compositions of lower cost components can be utilized, with available resources collectively used to achieve high levels of reliability, availability, and performance.


title: An extensive empirical study of feature selection metrics for text classification

abstract: Machine learning for text classification is the cornerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and more accurate. This paper presents an empirical comparison of twelve feature selection methods (e.g. Information Gain) evaluated on a benchmark of 229 text classification problem instances that were gathered from Reuters, TREC, OHSUMED, etc. The results are analyzed from multiple goal perspectives-accuracy, F-measure, precision, and recall-since each is appropriate in different situations. The results reveal that a new feature selection metric we call 'Bi-Normal Separation' (BNS), outperformed the others by a substantial margin in most situations. This margin widened in tasks with high class skew, which is rampant in text classification problems and is particularly challenging for induction algorithms. A new evaluation methodology is offered that focuses on the needs of the data mining practitioner faced with a single dataset who seeks to choose one (or a pair of) metrics that are most likely to yield the best performance. From this perspective, BNS was the top single choice for all goals except precision, for which Information Gain yielded the best result most often. This analysis also revealed, for example, that Information Gain and Chi-Squared have correlated failures, and so they work poorly together. When choosing optimal pairs of metrics for each of the four performance goals, BNS is consistently a member of the pair---e.g., for greatest recall, the pair BNS + F1-measure yielded the best performance on the greatest number of tasks by a considerable margin.


title: Minimal Gated Unit for Recurrent Neural Networks

abstract: Recently recurrent neural networks (RNN) has been very successful in handling sequence data. However, understanding RNN and finding the best practices for RNN is a difficult task, partly because there are many competing and complex hidden units (such as LSTM and GRU). We propose a gated unit for RNN, named as Minimal Gated Unit (MGU), since it only contains one gate, which is a minimal design among all gated hidden units. The design of MGU benefits from evaluation results on LSTM and GRU in the literature. Experiments on various sequence data show that MGU has comparable accuracy with GRU, but has a simpler structure, fewer parameters, and faster training. Hence, MGU is suitable in RNN's applications. Its simple architecture also means that it is easier to evaluate and tune, and in principle it is easier to study MGU's properties theoretically and empirically.


CONSUMERS ’ PURCHASE INTENTIONS TOWARDS NATURAL


title: Structure-aware QR Code abstraction

abstract: Quick Response Codes (QR Codes) have proved to be a great success in both automotive industry and general commercial use. However, in most cases, QR Codes are not identified as aesthetical, since the original motivation to invent QR Codes is to achieve high speed and high readability in scanning. This paper proposes a solution framework for QR Code abstraction, which produces machine readable QR Code that is visually similar to the input image. Unlike the existing schemes for QR Code abstraction, which produces QR Codes of low resolution, the framework aims to resolve the high computation complexity of producing halftone QR Codes of high resolution with both similarity and readability preserved. The solution framework has been implemented and tested on various input texts and images, and a user study was conducted to evaluate its performance in preserving similarity. The experimental results show that the framework can produce QR Codes of high resolution and high similarity without compromising readability.


title: Stock Price Forecasting: New Model for Uptrend Detecting and Downtrend Anticipating Based on Long Short-Term Memory

abstract: Stock price forecasting has tremendous importance, it is one of the most challenging tasks due to the high volatility of stock market data. Share market investment is often risky, hence the need for an accurate forecasting model to minimize this risk. This article aims to present a new model for forecasting stock prices for a given horizon. The model consists of four phases: the first phase is filtering out of all the stocks those for which we cannot reject the normal distribution hypothesis of the return. Note that the returns in our case reflect the evolution of the price compared to the moving average of n previous days. Then a second filtration to keep only the stocks whose returns has a high average, low standard deviation and the error type II is low enough for the normality test. In the third phase, we will build classes by comparing returns to given limits (8 given limits). The last phase is for training and testing the LSTM classifier. The main objective of the model is to anticipate class 0 and 1, which correspond to a medium and strong downtrend, and to predict class 7 and 8 which characterize the price increase. Experience shows that our proposed model gives strong results and its accuracy is quite high.


title: From Wearable Sensors to Smart Implants-–Toward Pervasive and Personalized Healthcare

abstract: OBJECTIVE ::: This paper discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. ::: ::: ::: METHODS ::: We provide an overview of some of the past milestones and recent developments, categorized into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multiomics data integration, and social media. ::: ::: ::: CONCLUSION ::: Technical advances have supported the evolution of the pervasive health paradigm toward preventative, predictive, personalized, and participatory medicine. ::: ::: ::: SIGNIFICANCE ::: The sensing technologies discussed in this paper and their future evolution will play a key role in realizing the goal of sustainable healthcare systems.


title: Instrumentation of a portable EMG sensor in a single PCB for human motion pattern detection

abstract: The objective of this work is to make a Surface Electromyography (EMG) signal detection sensor to test and analyze human motion pattern. EMG signal has a very small amplitude level and a frequency range of 20 Hz to 200 Hz. In order to realize the sensor, a circuit and its single layer PCB has been designed to amplify and filter the differential potential between Ag/AgCl electrodes at different points. After that the signal has been stored in computer using NI ELVIS II (DAQ) and LABVIEW at a sampling rate of 1000Hz. Then the signal has been used to calculate the stride length of a particular foot by a single threshold comparison method. The ultimate result shows that the calculated and actual stride length matches in 89% of the cases.


title: Impairment of social and moral behavior related to early damage in human prefrontal cortex

abstract: The long-term consequences of early prefrontal cortex lesions occurring before 16 months were investigated in two adults. As is the case when such damage occurs in adulthood, the two earlyonset patients had severely impaired social behavior despite normal basic cognitive abilities, and showed insensitivity to future consequences of decisions, defective autonomic responses to punishment contingencies and failure to respond to behavioral interventions. Unlike adult-onset patients, however, the two patients had defective social and moral reasoning, suggesting that the acquisition of complex social conventions and moral rules had been impaired. Thus early-onset prefrontal damage resulted in a syndrome resembling psychopathy.


title: Physiological responses to different WEB page designs

abstract: Physiological indicators of arousal have long been known to be sensitive to mental events such as positive and negative emotion, changes in attention and changes in workload. It has therefore been suggested that human physiology might be of use in the evaluation of software usability. To this, there are two main approaches or paradigms: (i) comparisons of physiological readings across periods of time to indicate different arousal levels under different circumstances, and (ii) the detection of short-term (occurring in seconds) physiological changes in response to specific events. Both approaches involve methodological, analytical and interpretational difficulties. Also, the tight experimental controls usually adopted in psychophysiological experimentation can be at odds with the needs of applied usability testing. This paper reports initial investigations of these approaches and difficulties in the evaluation of software interfaces. From exploratory data, a preliminary model is proposed which combines the two paradigms for identifying significant HCI events. Explorations of the model within the context of a web-related task are then discussed. These explorations suggest techniques and procedures for applied usability testing, and the results point to ways in which physiological data may be informative about software usability. However, further investigations involving variations in task and procedure are required.


title: Normal fetal nasal bone length at 14 to 28 weeks of gestation.

abstract: The fetal nasal bone length (FNBL) is a recent sonographic marker included in second trimester genetic sonography which varies with race and ethnicity. The importance of measuring FNBL is in the prenatal diagnosis of Down's syndrome (DS), the most common chromosomal abnormality. Nasal bone hypoplasia or absence is one of the frequent features of DS. Different studies conducted in different parts of the world have established FNBL in different races. But, reference values for normal fetal nasal bone length are yet to be established for Nepalese population. So, the aim of this study was to determine normal FNBL in second trimester. Objective was to create normal reference values for fetal nasal bone in Nepalese population. A cross sectional study was carried out on 150 second trimester pregnancy cases referred for antenatal ultrasound to Department of Radiology at Nepal Medical College and Teaching Hospital between May 2014 and July 2014. Cases included all the clinically normal second trimester pregnancy in which fetal nasal bone could be visualized in midsagittal plane. Table demonstrating normal FNBL corresponding to weeks of gestation was generated using IBM SPSS Statistics Version 20. There was linear increase in mean FNBL with gestational age. Minimum mean FNBL was 2.93 mm at 14 weeks and maximum was 7.89 mm at 27 weeks. Age of patients ranged from 17 to 35 years. Normal FNBL values in Nepalese population corroborates with those published in Western literature but with variation. Hence, reference values for local population become more relevant for antenatal ultrasound practices in Nepal.


title: X/Ku Band CMOS LNA Design Techniques

abstract: This paper reports two 11 GHz low-noise amplifiers (LNA) in 0.18?m CMOS technology. A cascade two stage LNA achieves 12 dB of power gain, 3.5 dB of noise figure, and an input/output match of ?15 dB/?27 dB at 11GHz, while consuming 28mA from 1.8V supply. The second LNA is a modified cascode amplifier and it achieves 8 dB of gain, 3.1 dB of noise figure, and an input/output match of ?12 dB/?15 dB at 11GHz, consuming 18mA from the 1.8V supply. The paper also discusses design considerations such the effects of layout on frequency tuning and noise.


title: Does working memory training work? The promise and challenges of enhancing cognition by training working memory

abstract: A growing body of literature shows that one’s working memory (WM) capacity can be expanded through targeted training. Given the established relationship between WM and higher cognition, these successful training studies have led to speculation that WM training may yield broad cognitive benefits. This review considers the current state of the emerging WM training literature, and details both its successes and limitations. We identify two distinct approaches to WM training, strategy training and core training, and highlight both the theoretical and practical motivations that guide each approach. Training-related increases in WM capacity have been successfully demonstrated across a wide range of subject populations, but different training techniques seem to produce differential impacts upon the broader landscape of cognitive abilities. In particular, core WM training studies seem to produce more far-reaching transfer effects, likely because they target domain-general mechanisms of WM. The results of individual studies encourage optimism regarding the value of WM training as a tool for general cognitive enhancement. However, we discuss several limitations that should be addressed before the field endorses the value of this approach.


title: Automatic assessment of expressive oral reading

abstract: We investigated the automatic assessment of expressive children's oral reading of grade level text passages using a standardized rubric. After a careful review of the reading literature and a close examination of the rubric, we designed a novel set of prosodic and lexical features to characterize fluent expressive reading. A number of complementary sources of information were used to design the features, each of them motivated by research on different components of reading fluency. Features are connected to the child's reading rate, to the presence and number of pauses, filled-pauses and word-repetitions, the correlation between punctuation marks and pauses, the length of word groupings, syllable stress and duration and the location of pitch peaks and contours. The proposed features were evaluated on a corpus of 783 one-minute reading sessions from 313 students reading grade-leveled passages without assistance (cold unassisted reading). Experimental results show that the proposed lexical and prosodic features provide complementary information and are able to capture the characteristics of expressive reading. The results showed that on both the 2-point and the 4-point expressiveness scales, computer-generated ratings of expressiveness agreed with human raters better than the human raters agreed with each other. The results of the study suggest that automatic assessment of expressive oral reading can be combined with automatic measures of word accuracy and reading rate to produce an accurate multidimensional estimate of children's oral reading ability.


title: The power of parallel prefix

abstract: The prefix computation problem is to compute all n initial products a1* . . . *a1,i=1, . . ., n of a set of n elements, where * is an associative operation. An O(((logn) log(2n/p))XI(n/p)) time deterministic parallel algorithm using p≤n processors is presented to solve the prefix computation problem, when the order of the elements is specified by a linked list. For p≤O(n1-e)(e〉0 any constant), this algorithm achieves linear speedup. Such optimal speedup was previously achieved only by probabilistic algorithms. This study assumes the weakest PRAM model, where shared memory locations can only be exclusively read or written (the EREW model).


title: A Bipolar High-Voltage Pulsed-Power Supply Based on Capacitor-Switch Voltage Multiplier

abstract: This paper presents a power electronics converter to generate bipolar high-voltage pulses using low-voltage dc source. Converter is designed based on charging of different capacitors by a predetermined switching pattern and their discharging on the load for pulsed power applications. Low number of power electronics devices and bulky transformer elimination are main advantages of the proposed converter. According to new findings, bipolar high-voltage pulses may lead to higher efficiency in many applications, such as food sterilization industry. The proposed pulse generator generates bipolar controllable pulses with a modular circuit structure based on a capacitor-switched network. Experimental results are presented to verify the analysis.


title: Software Engineering Technology COCOMO Suite Methodology and Evolution

abstract: n the late 1970s and the early 1980s assoftware engineering was starting totake shape, software managers found theyneeded a way to estimate the cost of soft-ware development and to explore optionswith respect to software project organiza-tion, characteristics, and cost/schedule.Along with a number of commercial andproprietary cost/schedule estimationmodels, one of the answers to this needwas the open-internal Constructive CostModel (COCOMO). This and other mod-els allowed users to


Residual Stress Analysis of Friction Stir Processed AA 6061


title: Experience with performance testing of software systems: issues, an approach, and case study

abstract: An approach to software performance testing is discussed. A case study describing the experience of using this approach for testing the performance of a system used as a gateway in a large industrial client/server transaction processing application is presented.


title: CoNLL 2016 Shared Task on Multilingual Shallow Discourse Parsing

abstract: The CoNLL-2016 Shared Task is the second edition of the CoNLL-2015 Shared Task, now on Multilingual Shallow discourse parsing. Similar to the 2015 task, the goal of the shared task is to identify individual discourse relations that are present in natural language text. Given a natural language text, participating teams are asked to locate the discourse connectives (explicit or implicit) and their arguments as well as predicting the sense of the discourse connectives. Based on the success of the previous year, we continued to ask participants to deploy their systems on TIRA, a web-based platform on which participants can run their systems on the test data for evaluation. This evaluation methodology preserves the integrity of the shared task. We have also made a few changes and additions in the 2016 shared task based on the feedback from 2015. The first is that teams could choose to carry out the task on Chinese texts, or English texts, or both. We have also allowed participants to focus on parts of the shared task (rather than the whole thing) as a typical system requires substantial investment of effort. Finally, we have modified the scorer so that it can report results based on partial matches of the arguments. 23 teams participated in this year’s shared task, using a wide variety of approaches. In this overview paper, we present the task definition, the training and test sets, and the evaluation protocol and metric used during this shared task. We also summarize the different approaches adopted by the participating teams, and present the evaluation results. The evaluation data sets and the scorer will serve as a benchmark for future research on shallow discourse parsing.


title: Computer graphics and image processing tools for visual servoing

abstract: This paper presents a project dealing with visual servoing applications. A general structure for the software system was developed starting from the ViSP architecture model [1]. Software modules were already implemented in beta versions: computer graphics procedures for robotic applications visualization and simulation and image processing procedures for visual servoing applications. Fuzzy algorithms and methods were used in order to support the implementation of the image processing procedures. The project is financed by Romanian National University Research Council CNCSIS.


title: Capped Lp-Norm Graph Embedding for Photo Clustering

abstract: Photos are a predominant source of information on a global scale. Cluster analysis of photos can be applied to situation recognition and understanding cultural dynamics. Graph-based learning provides a current approach for modeling data in clustering problems. However, the performance of this framework depends heavily on initial graph construction by input data. Data outliers degrade graph quality, leading to poor clustering results. We designed a new capped lp-norm graph-based model to reduce the impact of outliers. This is accomplished by allowing the data graph to self adjust as part of the graph embedding. Furthermore, we derive an iterative algorithm to solve the objective function optimization problem. Experiments on four real-world benchmark data sets and Yahoo Flickr Creative Commons data set show the effectiveness of this new graph-based capped lp-norm clustering method.


title: Communicating sequential processes

abstract: This paper suggests that input and output are basic primitives of programming and that parallel composition of communicating sequential processes is a fundamental program structuring method. When combined with a development of Dijkstra's guarded command, these concepts are surprisingly versatile. Their use is illustrated by sample solutions of a variety of a familiar programming exercises.


title: A Comparison of Knives for Bread Slicing

abstract: Vertical partitioning is a crucial step in physical database design in row-oriented databases. A number of vertical partitioning algorithms have been proposed over the last three decades for a variety of niche scenarios. In principle, the underlying problem remains the same: decompose a table into one or more vertical partitions. However, it is not clear how good different vertical partitioning algorithms are in comparison to each other. In fact, it is not even clear how to experimentally compare different vertical partitioning algorithms. In this paper, we present an exhaustive experimental study of several vertical partitioning algorithms. We categorize vertical partitioning algorithms along three dimensions. We survey six vertical partitioning algorithms and discuss their pros and cons. We identify the major differences in the use-case settings for different algorithms and describe how to make an apples-to-apples comparison of different vertical partitioning algorithms under the same setting. We propose four metrics to compare vertical partitioning algorithms. We show experimental results from the TPC-H and SSB benchmark and present four key lessons learned: (1) we can do four orders of magnitude less computation and still find the optimal layouts, (2) the benefits of vertical partitioning depend strongly on the database buffer size, (3) HillClimb is the best vertical partitioning algorithm, and (4) vertical partitioning for TPC-H-like benchmarks can improve over column layout by only up to 5%.


title: Association of Daytime and Nighttime Blood Pressure With Cardiovascular Disease Events Among African American Individuals

abstract: Importance Little is known regarding health outcomes associated with higher blood pressure (BP) levels measured outside the clinic among African American individuals. Objective To examine whether daytime and nighttime BP levels measured outside the clinic among African American individuals are associated with cardiovascular disease (CVD) and all-cause mortality independent of BP levels measured inside the clinic. Design, Setting, and Participants This prospective cohort study analyzed data from 1034 African American participants in the Jackson Heart Study who completed ambulatory BP monitoring at baseline (September 26, 2000, to March 31, 2004). Mean daytime and nighttime BPs were calculated based on measurements taken while participants were awake and asleep, respectively. Data were analyzed from July 1, 2017, to April 30, 2019. Main Outcomes and Measures Cardiovascular disease events, including coronary heart disease and stroke, experienced through December 31, 2014, and all-cause mortality experienced through December 31, 2016, were adjudicated. The associations of daytime BP and nighttime BP, separately, with CVD events and all-cause mortality were determined using Cox proportional hazards regression models. Results A total of 1034 participants (mean [SD] age, 58.9 [10.9] years; 337 [32.6%] male; and 583 [56.4%] taking antihypertensive medication) were included in the study. The mean daytime systolic BP (SBP)/diastolic BP (DBP) was 129.4/77.6 mm Hg, and the mean nighttime SBP/DBP was 121.3/68.4 mm Hg. During follow-up (median [interquartile range], 12.5 [11.1-13.6] years for CVD and 14.8 [13.7-15.6] years for all-cause mortality), 113 CVD events and 194 deaths occurred. After multivariable adjustment, including in-clinic SBP and DBP, the hazard ratios (HRs) for CVD events for each SD higher level were 1.53 (95% CI, 1.24-1.88) for daytime SBP (per 13.5 mm Hg), 1.48 (95% CI, 1.22-1.80) for nighttime SBP (per 15.5 mm Hg), 1.25 (95% CI, 1.02-1.51) for daytime DBP (per 9.3 mm Hg), and 1.30 (95% CI, 1.06-1.59) for nighttime DBP (per 9.5 mm Hg). Nighttime SBP was associated with all-cause mortality (HR per 1-SD higher level, 1.24; 95% CI, 1.06-1.45), but no association was present for daytime SBP (HR, 1.13; 95% CI, 0.97-1.33) and daytime (HR, 0.95; 95% CI, 0.81-1.10) and nighttime (HR, 1.06; 95% CI, 0.90-1.24) DBP. Conclusions and Relevance Among African American individuals, higher daytime and nighttime SBPs were associated with an increased risk for CVD events and all-cause mortality independent of BP levels measured in the clinic. Measurement of daytime and nighttime BP using ambulatory monitoring during a 24-hour period may help identify African American individuals who have an increased cardiovascular disease risk.


title: Understanding the Sharing Economy -- Drivers and Impediments for Participation in Peer-to-Peer Rental

abstract: As peer-to-peer coordinated products and services increasingly constitute competition for traditional businesses in many areas, understanding the underlying user motives of provision and consumption is key -- not only for business operators, but also for researchers investigating such markets. In this article, we develop a questionnaire for assessing motives for and against participating in what is casually referred to as the "Sharing Economy." More specifically, we focus on peer-to-peer rental services and investigate drivers and impediments for user activity in such markets by means of an online survey with over 600 participants.


title: Age and Gender Classification of Tweets Using Convolutional Neural Networks

abstract: Determining age and gender from a series of texts is useful for areas such as business intelligence and digital forensics. We explore the use of convolutional neural networks together with word2vec word embeddings for this task in comparison to handcrafted features. The network constructed consists of five layers and is trained using adadelta. It starts with an embedding layer where a word is represented by a vector, followed by a convolutional layer composed of three filters, each with 100 feature maps. It is followed by a max-over-time pooling layer which is done on each map and the resulting features are concatenated before a dropout layer and a softmax layer. The network was trained to classify age and gender for English and Spanish tweets. The predictions per tweet were aggregated using the majority prediction as the final prediction for the user who gave the tweets. The results outperform previous experiments. The highest English age and gender classification accuracy obtained are 49.6% and 72.1% respectively. The highest Spanish age and gender classification accuracy obtained on the other hand are 56.0% and 69.3% respectively.


title: Counterexamples in Probabilistic LTL Model Checking for Markov Chains

abstract: We propose a way of presenting and computing a counterexample in probabilistic LTL model checking for discrete-time Markov chains. In qualitative probabilistic model checking, we present a counterexample as a pair (*** ,*** ), where *** ,*** are finite words such that all paths that extend *** and have infinitely many occurrences of *** violate the specification. In quantitative probabilistic model checking, we present a counterexample as a pair (W ,R ), where W is a set of such finite words *** and R is a set of such finite words *** . Moreover, we suggest how the counterexample presented helps the user identify the underlying error in the system by means of an interactive game with the model checker.


title: Cloud computing data capsules for non-consumptiveuse of texts

abstract: As digital data sources grow in number and size, they pose an opportunity for computational investigation by means of text mining, natural language processing (NLP), and other text analysis techniques. In this paper we propose a virtual machine (VM) framework and methodology for non-consumptive text analysis. Using a remote VM model, the VM is configured with software and tooling for text analysis. When completed, the VM is wiped out and resources released for other users to share. Our approach extends the VM by turning it into a data capsules that prevents leakage of copyrighted content in the event that the VM is compromised. The HathiTrust Research Center Data Capsules has seen early use in application against the HathiTrust repository of digitized books from university libraries nationwide.


title: Spatial Cloaking for Anonymous Location-based Services in Mobile Peer-to-Peer Environments

abstract: This paper tackles a privacy breach in current location-based services (LBS) where mobile users have to report their exact location information to an LBS provider in order to obtain their desired services. For example, a user who wants to issue a query asking about her nearest gas station has to report her exact location to an LBS provider. However, many recent research efforts have indicated that revealing private location information to potentially untrusted LBS providers may lead to major privacy breaches. To preserve user location privacy, spatial cloaking is the most commonly used privacy-enhancing technique in LBS. The basic idea of the spatial cloaking technique is to blur a user's exact location into a cloaked area that satisfies the user specified privacy requirements. Unfortunately, existing spatial cloaking algorithms designed for LBS rely on fixed communication infrastructure, e.g., base stations, and centralized/distributed servers. Thus, these algorithms cannot be applied to a mobile peer-to-peer (P2P) environment where mobile users can only communicate with other peers through P2P multi-hop routing without any support of fixed communication infrastructure or servers. In this paper, we propose a spatial cloaking algorithm for mobile P2P environments. As mobile P2P environments have many unique limitations, e.g., user mobility, limited transmission range, multi-hop communication, scarce communication resources, and network partitions, we propose three key features to enhance our algorithm: (1) An information sharing scheme enables mobile users to share their gathered peer location information to reduce communication overhead; (2) A historical location scheme allows mobile users to utilize stale peer location information to overcome the network partition problem; and (3) A cloaked area adjustment scheme guarantees that our spatial cloaking algorithm is free from a "center-of-cloaked-area" privacy attack. Experimental results show that our P2P spatial cloaking algorithm is scalable while guaranteeing the user's location privacy protection.


title: Design and application of an active suspension system on a 6 DOF half vehicle model

abstract: In this study a novel active suspension system concept is investigated. A 6 DOF model is obtained for a half vehicle system by using Lagrange method. The proposed active suspension system is designed and applied on this half vehicle model. The modeling of both the vehicle system and the proposed controller is explained. The proposed controller is also compared with a classical PID controller in order to quantify the performance improvement. Additionally the energy consumption of the compared controllers are investigated. Finally an active suspension system is designed that requires measuring only the displacement of the front wheel in order to maintain both whole body suspension of the vehicle and suspension of the acceleration on the driver-passenger.


title: Enhancing the Symmetry and Proportion of 3D Face Geometry

abstract: We present an engine for enhancing the geometry of a 3D face mesh model while making the enhanced version share close similarity with the original. After obtaining the feature points of a given scanned 3D face model, we first perform a local and global symmetrization on the key facial features. We then apply an overall proportion optimization to the frontal face based on Neoclassical Canons and golden ratios. A nonlinear least-squares solution is adopted to adjust the feature points so that the face profile complies with the aesthetic criteria, which are derived from the profile cosmetology. Through the above processes, we obtain the optimized feature points, which will lead to a more attractive face. According to the original feature points and the optimized ones, we perform Laplacian deformation to adjust the remaining points of the face in order to preserve the geometric details. The analysis of user study in this paper validates the effectiveness of our 3D face geometry enhancement engine.


title: Knowledge Sharing on Enterprise Social Media: Practices to Cope With Institutional Complexity

abstract: This study examines the use of enterprise social media ESM for organizational knowledge sharing and shows that professionals face ambiguities because their knowledge sharing behavior is informed by an institutional complexity that consists of 2 dissimilar institutional logics: logics of the profession, and logics of the corporation. Our qualitative case study of an ESM at an IT consultancy organization shows that professionals find ways to manage the ambiguities they experience by engaging the affordances of ESM in such a way as to develop coping practices: connection management, reputation management, and information management. By complementing the affordance perspective with an institutional logics perspective, we are able to advance scholarly understanding on how ESM can facilitate but also frustrate knowledge sharing.


Recent Advances on Locomotion Mechanisms of Hybrid Mobile Robots


title: Frequent Pattern Mining Algorithms for Finding Associated Frequent Patterns for Data Streams: A Survey

abstract: Abstract Pattern recognition is seen as a major challenge within the field of data mining and knowledge discovery. For the work in this paper, we have analyzed a range of widely used algorithms for finding frequent patterns with the purpose of discovering how these algorithms can be used to obtain frequent patterns over large transactional databases. This has been presented in the form of a comparative study of the following algorithms: Apriori algorithm, Frequent Pattern (FP) Growth algorithm, Rapid Association Rule Mining (RARM), ECLAT algorithm and Associated Sensor Pattern Mining of Data Stream (ASPMS) frequent pattern mining algorithms. This study also focuses on each of the algorithm's strengths and weaknesses for finding patterns among large item sets in database systems.


title: Image Based Appraisal of Real Estate Properties

abstract: Real estate appraisal, which is the process of estimating the price for real estate properties, is crucial for both buyers and sellers as the basis for negotiation and transaction. Traditionally, the repeat sales model has been widely adopted to estimate real estate prices. However, it depends on the design and calculation of a complex economic-related index, which is challenging to estimate accurately. Today, real estate brokers provide easy access to detailed online information on real estate properties to their clients. We are interested in estimating the real estate price from these large amounts of easily accessed data. In particular, we analyze the prediction power of online house pictures, which is one of the key factors for online users to make a potential visiting decision. The development of robust computer vision algorithms makes the analysis of visual content possible. In this paper, we employ a recurrent neural network to predict real estate prices using the state-of-the-art visual features. The experimental results indicate that our model outperforms several other state-of-the-art baseline algorithms in terms of both mean absolute error and mean absolute percentage error.


title: 3D printed RF passive components by liquid metal filling

abstract: We present three-dimensional (3D) micro-scale electrical components and systems by means of 3D printing and a liquid-metal-filling technique. The 3D supporting polymer structures with hollow channels and cavities are fabricated from inkjet printing. Liquid metals made of silver particles suspension in this demonstration are then injected into the hollow paths and solidified to form metallic elements and interconnects with high electrical conductivity. In the proof-of-concept demonstrations, various radio-frequency (RF) passive components, including 3D-shaped inductors, capacitors and resistors are fabricated and characterized. High-Q inductors and capacitors up to 1 GHz have been demonstrated. This work establishes an innovative way to construct arbitrary 3D electrical systems with efficient and labor-saving processes.


title: Real-time detection of road markings for driving assistance applications

abstract: For a Driving Assistance System, which is designed to improve safety on the roads, knowledge about the type of lane border markings and other painted road objects is required. Furthermore, information about the position of the painted objects can be used by other systems to create a correct perception of the road. This paper describes a Lab VIEW based system for detection, measurement and classification of painted objects. The system is able to calibrate monocular video sources, define the features of the painted objects that we look for, and detect them in real time using geometric pattern matching and edge detection. The solution can be easily deployed on a dedicated real-time computing architecture, to be used as a standalone driving assistance sensor.


title: Wastewater treatment high rate algal ponds for biofuel production.

abstract: Abstract While research and development of algal biofuels are currently receiving much interest and funding, they are still not commercially viable at today’s fossil fuel prices. However, a niche opportunity may exist where algae are grown as a by-product of high rate algal ponds (HRAPs) operated for wastewater treatment. In addition to significantly better economics, algal biofuel production from wastewater treatment HRAPs has a much smaller environmental footprint compared to commercial algal production HRAPs which consume freshwater and fertilisers. In this paper the critical parameters that limit algal cultivation, production and harvest are reviewed and practical options that may enhance the net harvestable algal production from wastewater treatment HRAPs including CO 2 addition, species control, control of grazers and parasites and bioflocculation are discussed.


title: On multi-task learning for facial action unit detection

abstract: In this paper we investigate the use of Multitask Learning (MTL) methods to model the commonalities and variations across a set of facial action units (AUs) and also learn the classifiers for detection of multiple AUs simultaneously by exploiting their inner-relations. We studied three variants of MTL algorithms, the Regularized MTL (RMTL), the Multitask Feature Learning (MTFL) and the Alternating Multi-task Structure Learning (AMTSL). We used two databases to evaluate the performance of the MTL methods; the first one is the extended Cohn-Kanade (CK+) database with posed AUs while the second is the DISFA database consisting of spontaneous AUs. Compared with the canonical Support Vector Machine (SVM) which detects AUs individually without considering their relationships, the MTL-based methods show significant improvements in the F1 reliability measurement. In particular, the RMTL algorithm consistently outperforms the other investigated MTL-based classifiers as well as several state-of-the-art methods on the CK+ database while for spontaneous AUs on the DISFA database the MTFL approach achieves the best performance.


Profiling High Level Heterogeneous Programs: Using the SPOC GPGPU framework for OCaml


A Review of Related Work on Machine Learning in Semiconductor Manufacturing and Assembly Lines


title: A 2.45 GHz harmonic suppression array antenna for rectenna application

abstract: In this paper, a 2.45 GHz harmonic suppression capability of array antenna has been presented for wireless power transfer applications. Harmonic suppression up to a fourth order of the fundamental operating frequency has been achieved by using rectangular slot Defected Ground Structure (DGS). The designed antenna uses an FR4 material having thickness 1.6 mm as a substrate which has loss tangent tanδ) 0.02. The gain and radiation efficiency are 4.5dB, 75% respectively. This antenna is suitable for rectenna applications.


Classification of Malware by Using Structural Entropy on Convolutional Neural Networks.


title: A survey of analysis and classification of EEG signals for brain-computer interfaces

abstract: A Brain Computer-Interfaces (BCI) is a communication system that enables human brain to interact with machines or devices without involving physical contact by using EEG signals generated from brain activity. Selection of the processing technique of the EEG signals at each processing stage is very important to get the robust BCI system. The aim of this paper is to address the various techniques applied for BCI at each stage such as pre-processing, feature extraction and classification stage. This paper discussed the advantages, disadvantages and current trends of BCI at each stage. Finally, the initial experiment result at each BCI stage was discussed at the end of this paper which is different with previous survey paper.


Improving CTC-based Acoustic Model with Very Deep Residual Time-delay Neural Networks


title: An Innovative Framework for Supporting Cognitive-Based Big Data Analytics for Frequent Pattern Mining

abstract: The increasing size of modern applications and services produces huge volumes of a wide variety of valuable data of different veracity at a high velocity, which in turn leads to a new challenge to big data analytics. Researchers often use these 5V’s (volume, variety, value, veracity, and velocity) to describe the features of big data. The interest of discovering patterns from a large collection of data has risen in business for transforming goods into services. Rich sources of big data include complex sensing-centered service systems. Embedded in these big data are useful information and knowledge. In this paper, we present an innovative framework for supporting cognitive-based big data analytics for frequent pattern mining. Evaluation results show the applicability of our framework to support cognitive computing for big data analytics of frequent patterns.