title: A Symbiotic View of Life: We Have Never Been Individuals

abstract: ABSTRACTThe notion of the “biological individual” is crucial to studies of genetics, immunology, evolution, development, anatomy, and physiology. Each of these biological subdisciplines has a specific conception of individuality, which has historically provided conceptual contexts for integrating newly acquired data. During the past decade, nucleic acid analysis, especially genomic sequencing and high-throughput RNA techniques, has challenged each of these disciplinary definitions by finding significant interactions of animals and plants with symbiotic microorganisms that disrupt the boundaries that heretofore had characterized the biological individual. Animals cannot be considered individuals by anatomical or physiological criteria because a diversity of symbionts are both present and functional in completing metabolic pathways and serving other physiological functions. Similarly, these new studies have shown that animal development is incomplete without symbionts. Symbionts also constitute a second mod...


title: Learning and evaluating classifiers under sample selection bias

abstract: Classifier learning methods commonly assume that the training data consist of randomly drawn examples from the same distribution as the test examples about which the learned model is expected to make predictions. In many practical situations, however, this assumption is violated, in a problem known in econometrics as sample selection bias. In this paper, we formalize the sample selection bias problem in machine learning terms and study analytically and experimentally how a number of well-known classifier learning methods are affected by it. We also present a bias correction method that is particularly useful for classifier evaluation under sample selection bias.


title: On the Complexity of CCG Parsing

abstract: We study the parsing complexity of Combinatory Categorial Grammar (CCG) in the formalism of Vijay-Shanker and Weir (1994). As our main result, we prove that any parsing algorithm for this formalism...


title: Representing Spatial Structure Through Maps and Language: Lord of the Rings Encodes the Spatial Structure of Middle Earth

abstract: Spatial mental representations can be derived from linguistic and non-linguistic sources of information. This study tested whether these representations could be formed from statistical linguistic frequencies of city names, and to what extent participants differed in their performance when they estimated spatial locations from language or maps. In a computational linguistic study, we demonstrated that co-occurrences of cities in Tolkien’s Lord of the Rings trilogy and The Hobbit predicted the authentic longitude and latitude of those cities in Middle Earth. In a human study, we showed that human spatial estimates of the location of cities were very similar regardless of whether participants read Tolkien’s texts or memorized a map of Middle Earth. However, text-based location estimates obtained from statistical linguistic frequencies better predicted the human text-based estimates than the human map-based estimates. These findings suggest that language encodes spatial structure of cities, and that human cognitive map representations can come from implicit statistical linguistic patterns, from explicit non-linguistic perceptual information, or from both.


title: MoViMash: online mobile video mashup

abstract: With the proliferation of mobile video cameras, it is becoming easier for users to capture videos of live performances and socially share them with friends and public. As an attendee of such live performances typically has limited mobility, each video camera is able to capture only from a range of restricted viewing angles and distance, producing a rather monotonous video clip. At such performances, however, multiple video clips can be captured by different users, likely from different angles and distances. These videos can be combined to produce a more interesting and representative mashup of the live performances for broadcasting and sharing. The earlier works select video shots merely based on the quality of currently available videos. In real video editing process, however, recent selection history plays an important role in choosing future shots. In this work, we present MoViMash, a framework for automatic online video mashup that makes smooth shot transitions to cover the performance from diverse perspectives. Shot transition and shot length distributions are learned from professionally edited videos. Further, we introduce view quality assessment in the framework to filter out shaky, occluded, and tilted videos. To the best of our knowledge, this is the first attempt to incorporate history-based diversity measurement, state-based video editing rules, and view quality in automated video mashup generations. Experimental results have been provided to demonstrate the effectiveness of MoViMash framework.


title: Identification and Classification of Bulk Paddy, Brown, and White Rice Cultivars with Colour Features Extraction using Image Analysis and Neural Network

abstract: Golpour I., Parian J.A., Chayjan R.A. (2014): Identification and classification of bulk paddy, brown, and white rice cultivars with colour features extraction using image analysis and neural network. Czech J. Food Sci., 32: 280–287. We identify five rice cultivars by mean of developing an image processing algorithm. After preprocessing operations, 36 colour features in RGB, HSI, HSV spaces were extracted from the images. These 36 colour features were used as inputs in back propagation neural network. The feature selection operations were performed using STEPDISC analysis method. The mean classification accuracy with 36 features for paddy, brown and white rice cultivars acquired 93.3, 98.8, and 100%, respectively. After the feature selection to classify paddy cultivars, 13 features were selected for this study. The highest mean classification accuracy (96.66%) was achieved with 13 features. With brown and white rice, 20 and 25 features acquired the highest mean classification accuracy (100%, for both of them). The optimised neural networks with two hidden layers and 36-6-5-5, 36-9-6-5, 36-6-6-5 topologies were obtained for the classification of paddy, brown, and white rice cultivars, respectively. These structures of neural network had the highest mean classification accuracy for bulk paddy, brown and white rice identification (98.8, 100, and 100%, respectively).


title: Design and evaluation of deep packet inspection system: A case study

abstract: An increasing number of Internet applications and services render network management more troublesome for bandwidth misuse and security concern. As a result, network traffic identification plays an increasingly important role in network management. Deep packet inspection (DPI) is one of the effective approaches. Conventional network devices lookup the header of a packet, but DPI means the network device is required to match a pattern in the payload of a packet. This study proposes a DPI system and WMT (Wu-Manber with trie) algorithm to classify popular network services; The Net-DPIS is developed based on Netfilter framework in Linux kernel. The authors show how to rearrange the rule policies to increase the performance of Net-DPIS. In the results, the authors show that WMT algorithm is faster than WM algorithm; Net-DPIS has higher average accuracy and performance than L7-filter.


title: Ifcowl: A case of transforming express schemas into ontologies

abstract: Ontologies have been successfully applied as a semantic enabler of communication between both users and applications in fragmented, heterogeneous multinational business environments. In this paper we discuss the underlying principles, their current implementation status, and most importantly, their applicability to problems in the building information modeling domain. We introduce the development of an ontology for the building and construction sector based on the industry foundation classes. We discuss several approaches of lifting modeling information that is based on the express family of languages for data modeling onto a logically rigid and semantically enhanced ontological level encoded in the W3C Ontology Web Language. We exemplify the added value of such formal notation of building models by providing several examples where generic query and reasoning algorithms can be applied to problems that otherwise have to be manually hard-wired into applications for processing building information. Furthermore, we show how the underlying resource description framework and the set of technologies evolving around it can be tailored to the need of distributed collaborative work in the building and construction industry.


title: The assessment and analysis of handedness: The Edinburgh inventory

abstract: Abstract The need for a simply applied quantitative assessment of handedness is discussed and some previous forms reviewed. An inventory of 20 items with a set of instructions and response- and computational-conventions is proposed and the results obtained from a young adult population numbering some 1100 individuals are reported. The separate items are examined from the point of view of sex, cultural and socio-economic factors which might appertain to them and also of their inter-relationship to each other and to the measure computed from them all. Criteria derived from these considerations are then applied to eliminate 10 of the original 20 items and the results recomputed to provide frequency-distribution and cumulative frequency functions and a revised item-analysis. The difference of incidence of handedness between the sexes is discussed.


title: Reducing falls in the inpatient hospital setting

abstract: Aim: This quality improvement project's objective was to introduce and assess a process change to potentially reduce fall rates on an inpatient medical–surgical unit. Methods: The Iowa Model of Evidence-Based Practice was used to guide the implementation of this project. This project was piloted over a 3-month period from October to December 2016 on a 38-bed medical–surgical unit at a community-based hospital in the southeastern United States. Prior to the implementation of this quality improvement project, staff received falls education using a modified pre-existing falls prevention protocol, with a focus on increasing the consistency of bed/chair alarm use for patients identified as being at risk for falling. Preintervention and postintervention fall rates were statistically compared to assess for change in practice. Results: The average monthly preintervention fall rate was 8.67 falls/1000 patient days, as compared with 5.07 falls postintervention, which resulted in an overall decrease of 44.5% in the average number of falls per month. Conclusion: This project illustrated that education and a consistent risk stratification for bed/chair alarm use may be an important component of an effective evidence-based falls prevention program.


title: MapReduce-based techniques for multiple object tracking in video analytics

abstract: Multiple Object Tracking (MOT) has been deployed effectively in various applications including automated video surveillance, self-driving vehicles, robotics, and medical imaging. Despite improvements in the qualitative performance (accuracy) of the existing state-of-the-art MOT methods through complex image analysis and global optimization techniques, a high computational cost is still a performance limitation. This paper focuses on achieving a high computational speed and proposes three parallel MOT techniques based on MapReduce. This paper introduces techniques that provide a parallel solution which effectively handles the challenges of time-dependencies among the various sections of the video file processed during MOT. Through performance analysis of a prototype deployed on the Amazon EC2 cloud, this paper shows that the proposed techniques provide a scalable solution for parallelizing the MOT methods and achieves an efficiency and speedup of up to 77% and 17 respectively, for a large video file, on a 20 node Hadoop cluster.


title: An intraday market risk management approach based on textual analysis

abstract: The management of financial risk is one of the most challenging tasks of financial institutions. In the last two decades, diverse quantitative models and approaches have been developed and refined to address the impact of volatile markets on business. Whereas existing approaches have intensively utilized structured data such as historical price series, little attention has been paid to unstructured (textual) data, which could be a large source of information in this context. Previous empirical research has shown that certain news stories, such as corporate disclosures, can cause abnormal price behavior subsequent to their publication. On the basis of a data set comprising such news stories as well as intraday stock prices, this paper explores the risk implications of information being newly available to market participants. After showing that such events can significantly drive stock price volatilities, this research aims at identifying among the textual data provided those disclosures that have resulted in most supranormal risk exposures. To this end, four different learners - Naive Bayes, k-Nearest Neighbour, Neural Network, and Support Vector Machine - have been applied in order to detect patterns in the textual data that could explain increased risk exposure. Two evaluations are presented in order to assess the learning capabilities of the approach in the context of risk management. First, ''classic'' data mining evaluation metrics are applied and, second, a newly developed simulation-based evaluation method is presented. Evaluation results provide strong evidence that unstructured (textual) data represents a valuable source of information also for financial risk management - a domain in which, in the past, little attention has been paid to unstructured data. With regard to classification performance, it is also shown that there exist significant differences between the applied learning techniques.


title: Compact electrodynamics MEMS-speaker

abstract: This paper describes the design, fabrication and characterization of an electrodynamic MEMS speaker. The miniaturized speaker consists of a deposited single turn microcoil on a 25 µm thick suspended polyimide diaphragm with a 2.5 mm diameter and a small volume permanent magnet Neodymium-Iron-Boron (Nd-Fe-B). A significant improvement of frequency response to sound pressure level of MEMS-speaker in sealed condition was achieved. The sealed measurement performed in a 1500 mm3 silicon rubber tube resulted in a peak amplitude of 90 dB-SPL at a frequency range of 1, 5 and 10 kHz and experienced magnitude boosting with a value of 25 to 30 dB at frequency below 1 kHz compared to unsealed condition. Peak magnitude level of 92.5 dB-SPL and 110 dB-SPL were achieved at the frequency of 200–500 Hz and 20–60 Hz respectively. The results shows that the fabricated MEMS speaker can be applied in ear canal applications such as hearing aids and music player earphones.


Noise Reduction on the Raw Signal of Emotiv EEG Neuroheadset


Evaluating 3D task performance for fish tank virtual worlds


title: What would other programmers do: suggesting solutions to error messages

abstract: Interpreting compiler errors and exception messages is challenging for novice programmers. Presenting examples of how other programmers have corrected similar errors may help novices understand and correct such errors. This paper introduces HelpMeOut, a social recommender system that aids the debugging of error messages by suggesting solutions that peers have applied in the past. HelpMeOut comprises IDE instrumentation to collect examples of code changes that fix errors; a central database that stores fix reports from many users; and a suggestion interface that, given an error, queries the database for a list of relevant fixes and presents these to the programmer. We report on implementations of this architecture for two programming languages. An evaluation with novice programmers found that the technique can suggest useful fixes for 47% of errors after 39 person-hours of programming in an instrumented environment.


abstract: In information retrieval, query auto completion (QAC), also known as type-ahead and auto-complete suggestion, refers to the following functionality: given a prex consisting of a number of characters entered into a search box, the user interface proposes alternative ways of extending the prex to a full query. QAC helps users to formulate their query when they have an intent in mind but not a clear way of expressing this in a query. It helps to avoid possible spelling mistakes, especially on devices with small screens. It saves keystrokes and cuts down the search duration of users which implies a lower load on the search engine, and results in savings in machine resources and maintenance. Because of the clear benets of QAC, a considerable number of algorithmic approaches to QAC have been proposed in the past few years. Query logs have proven to be a key asset underlying most of the recent research. This monograph surveys this research. It focuses on summarizing the literature on QAC and provides a general understanding of the wealth of QAC approaches that are currently available. A Survey of Query Auto Completion in Information Retrieval is an ideal reference on the topic. Its contributions can be summarized as follows: It provides researchers who are working on query auto completion or related problems in the eld of information retrieval with a good overview and analysis of state-of-the-art QAC approaches. In particular, for researchers new to the eld, the survey can serve as an introduction to the state-of-the-art. It also offers a comprehensive perspective on QAC approaches by presenting a taxonomy of existing solutions. In addition, it presents solutions for QAC under different conditions such as available high-resolution query logs, in-depth user interactions with QAC using eye-tracking, and elaborate user engagements in a QAC process. It also discusses practical issues related to QAC. Lastly, it presents a detailed discussion of core challenges and promising open directions in QAC.


Formal Semantics and Analysis of BPMN Process Models using Petri Nets


title: An Interleaved High-Power Flyback Inverter for Photovoltaic Applications

abstract: This paper presents analysis, design, and implementation of an isolated grid-connected inverter for photovoltaic (PV) applications based on interleaved flyback converter topology operating in discontinuous current mode. In today's PV inverter technology, the simple and the low-cost advantage of the flyback topology is promoted only at very low power as microinverter. Therefore, the primary objective of this study is to design the flyback converter at high power and demonstrate its practicality with good performance as a central-type PV inverter. For this purpose, an inverter system rated at 2 kW is developed by interleaving of only three flyback cells with added benefit of reduced size of passive filtering elements. A simulation model is developed in the piecewise linear electrical circuit simulator. Then, the design is verified and optimized for the best performance based on the simulation results. Finally, a prototype at rated power is built and evaluated under the realistic conditions. The efficiency of the inverter, the total harmonic distortion of the grid current, and the power factor are measured as 90.16%, 4.42%, and 0.998, respectively. Consequently, it is demonstrated that the performance of the proposed system is comparable to the commercial isolated PV inverters in the market, but it may have some cost advantage.


title: Automotive LIDAR sensor development scenarios for harsh weather conditions

abstract: This article focuses on development baseline for a novel LIDAR for future autonomous cars, which require perception not only in clear weather, but also under harsh weather conditions such as fog and rain. Development of automotive laser scanners is bound to the following requirements: maximize sensor performance, assess the performance level and keep the scanner component costs reasonable (<1000 €) even if more expensive optical and electronic components are needed. The objective of this article is to review the existing automotive laser scanners and their capabilities to pave the way for developing new scanner prototypes, which are more capable in harsh weather conditions. Testing of scanner capabilities has been conducted in the northern part of the Finland, at Sodankyla Airport, where fog creates a special problem. The scanner has been installed in the airport area for data gathering and analyzes if fog, snow or rain are visible in the scanner data. The results indicate that these conditions degrade sensor performance by 25%, and therefore, future work in software module development should take this into account with in-vehicle system performance estimations concerning the visual range of the scanner. This allows the vehicle to adapt speed, braking distance and stability control systems accordingly.


title: Conversation Trees: A Grammar Model for Topic Structure in Forums

abstract: Online forum discussions proceed differently from face-to-face conversations and any single thread on an online forum contains posts on different subtopics. This work aims to characterize the content of a forum thread as a conversation tree of topics. We present models that jointly perform two tasks: segment a thread into subparts, and assign a topic to each part. Our core idea is a definition of topic structure using probabilistic grammars. By leveraging the flexibility of two grammar formalisms, Context-Free Grammars and Linear Context-Free Rewriting Systems, our models create desirable structures for forum threads: our topic segmentation is hierarchical, links non-adjacent segments on the same topic, and jointly labels the topic during segmentation. We show that our models outperform a number of tree generation baselines.


Quantum Machine Learning Algorithms : Read the Fine Print


title: Electronic-Banking in Ethiopia- Practices, Opportunities and Challenges

abstract: The paper examines the practices, opportunities and challenges of E-banking services in Ethiopia. Ethiopian banking system is one of the most underdeveloped compared to the rest of the world. In Ethiopia cash is still the most dominant medium of exchange and electronic-banking is not well known, let alone used for transacting banking business. All banks in Ethiopia except Dashen Bank are too late to move with technological advancement and they should clearly chart out the time schedule for their integration and technological advancement.


title: Security Technology for Smart Grid Networks

abstract: There is virtually universal agreement that it is necessary to upgrade the electric grid to increase overall system efficiency and reliability. Much of the technology currently in use by the grid is outdated and in many cases unreliable. There have been three major blackouts in the past ten years. The reliance on old technology leads to inefficient systems, costing unnecessary money to the utilities, consumers, and taxpayers. To upgrade the grid, and to operate an improved grid, will require significant dependence on distributed intelligence and broadband communication capabilities. The access and communications capabilities require the latest in proven security technology for extremely large, wide-area communications networks. This paper discusses key security technologies for a smart grid system, including public key infrastructures and trusted computing.


title: An overview of business intelligence technology

abstract: BI technologies are essential to running today's businesses and this technology is going through sea changes.


title: Spherical skinning with dual quaternions and QTangents

abstract: Building upon previous work with Dual Quaternion skinning [KAVAN 2008], we will introduce a new format to represent tangent frames.


title: Physical layer security in 5G wireless networks for data protection

abstract: The fifth generation (5G) network would be a key facilitator to keeping up to the future wireless application demands such as ultra-high data rate, ultra-wide radio coverage, ultra — low latency, large number of devices connected etc. Security is a crucial issue in the 5G network as wireless transmissions have the innate quality of vulnerability towards unfriendly breach or hacks. In this paper, security issue in the 5G network, specifically the physical layer security, has been analyzed. The physical layer safeguards confidentiality of data by capitalizing on the inherent random nature of the communication medium. There are various technologies prevalent in 5G, the three most prominent ones-heterogeneous networks, massive multiple-input multiple-output, and millimeter wave have been considered and discussed. The potential areas for improvement and issues in each of the technologies have been identified and analyzed. This analysis would help in better understanding of physical layer security in the future.


Multi-biosignals Analysis - The Effect of Peripheral Nerve Stimulation on Skin Conductance and Heart Rate Variability


title: Learning Better Word Embedding by Asymmetric Low-Rank Projection of Knowledge Graph

abstract: Word embedding, which refers to low-dimensional dense vector representations of natural words, has demon-strated its power in many natural language processing tasks. However, it may suffer from the inaccurate and incomplete information contained in the free text corpus as training data. To tackle this challenge, there have been quite a few studies that leverage knowledge graphs as an additional information source to improve the quality of word embedding. Although these studies have achieved certain success, they have neglected some important facts about knowledge graphs: 1) many relationships in knowledge graphs are many-to-one, one-to-many or even many-to-many, rather than simply one-to-one; 2) most head entities and tail entities in knowledge graphs come from very different semantic spaces. To address these issues, in this paper, we propose a new algorithm named ProjectNet. ProjectNet models the relationships between head and tail entities after transforming them with different low-rank projection matrices. The low-rank projection can allow non one-to-one relationships between entities, while different projection matrices for head and tail entities allow them to originate in different semantic spaces. The experimental results demonstrate that ProjectNet yields more accurate word embedding than previous studies, and thus leads to clear improvements in various natural language processing tasks.


title: A survey of graphical languages for monoidal categories

abstract: This article is intended as a reference guide to various notions of monoidal categories and their associated string diagrams. It is hoped that this will be useful not just to mathematicians, but also to physicists, computer scientists, and others who use diagrammatic reasoning. We have opted for a somewhat informal treatment of topological notions, and have omitted most proofs. Nevertheless, the exposition is sufficiently detailed to make it clear what is presently known, and to serve as a starting place for more in-depth study. Where possible, we provide pointers to more rigorous treatments in the literature. Where we include results that have only been proved in special cases, we indicate this in the form of caveats.


title: Mechanical system and stable gait transformation of a leg-wheel hybrid transformable robot

abstract: This paper proposes a novel and mechanically decoupled leg and wheel hybrid transformable robot called HyTRo-I that combines the fast speed of wheeled vehicles on a flat ground and the high degree of flexibility of legged robots over irregular terrains. According to different terrain conditions, HyTRo-I can choose from three motion modes: wheeled rolling, quadrupedal walking mode and leg-wheel hybrid mode. By shifting among these moving patterns, the mobility of HyTRo-I over various surface conditions can be fully realized. While the control technology of actuating the wheeled vehicles is mature and simple, the control of quadruped walking is an area of active research. Therefore, we develop a statically stable gait controller for our robot. In addition, we study the locomotion mechanism of transformation that concerns the feasibility of three moving methods of HyTRo-I. By the mutual transformation gaits illustrated in details, HyTRo-I can be smoothly and reciprocally transformed between wheeled rolling mode and quadrupedal walking mode. Finally, we experimentally test the mode transformations of HyTRo-I.


title: Semi-Latent GAN: Learning to generate and modify facial images from attributes

abstract: Generating and manipulating human facial images using high-level attributal controls are important and interesting problems. The models proposed in previous work can solve one of these two problems (generation or manipulation), but not both coherently. This paper proposes a novel model that learns how to both generate and modify the facial image from high-level semantic attributes. Our key idea is to formulate a Semi-Latent Facial Attribute Space (SL-FAS) to systematically learn relationship between user-defined and latent attributes, as well as between those attributes and RGB imagery. As part of this newly formulated space, we propose a new model --- SL-GAN which is a specific form of Generative Adversarial Network. Finally, we present an iterative training algorithm for SL-GAN. The experiments on recent CelebA and CASIA-WebFace datasets validate the effectiveness of our proposed framework. We will also make data, pre-trained models and code available.


title: Origami-based earthworm-like locomotion robots.

abstract: Inspired by the morphology characteristics of the earthworms and the excellent deformability of origami structures, this research creates a novel earthworm-like locomotion robot through exploiting the origami techniques. In this innovation, appropriate actuation mechanisms are incorporated with origami ball structures into the earthworm-like robot 'body', and the earthworm's locomotion mechanism is mimicked to develop a gait generator as the robot 'centralized controller'. The origami ball, which is a periodic repetition of waterbomb units, could output significant bidirectional (axial and radial) deformations in an antagonistic way similar to the earthworm's body segment. Such bidirectional deformability can be strategically programmed by designing the number of constituent units. Experiments also indicate that the origami ball possesses two outstanding mechanical properties that are beneficial to robot development: one is the structural multistability in the axil direction that could contribute to the robot control implementation; and the other is the structural compliance in the radial direction that would increase the robot robustness and applicability. To validate the origami-based innovation, this research designs and constructs three robot segments based on different axial actuators: DC-motor, shape-memory-alloy springs, and pneumatic balloon. Performance evaluations reveal their merits and limitations, and to prove the concept, the DC-motor actuation is selected for building a six-segment robot prototype. Learning from earthworms' fundamental locomotion mechanism-retrograde peristalsis wave, seven gaits are automatically generated; controlled by which, the robot could achieve effective locomotion with qualitatively different modes and a wide range of average speeds. The outcomes of this research could lead to the development of origami locomotion robots with low fabrication costs, high customizability, light weight, good scalability, and excellent re-configurability.


title: Adafactor: Adaptive Learning Rates with Sublinear Memory Cost

abstract: In several recently proposed stochastic optimization methods (e.g. RMSProp, Adam, Adadelta), parameter updates are scaled by the inverse square roots of exponential moving averages of squared past gradients. Maintaining these per-parameter second-moment estimators requires memory equal to the number of parameters. For the case of neural network weight matrices, we propose maintaining only the per-row and per-column sums of these moving averages, and estimating the per-parameter second moments based on these sums. We demonstrate empirically that this method produces similar results to the baseline. Secondly, we show that adaptive methods can produce larger-than-desired updates when the decay rate of the second moment accumulator is too slow. We propose update clipping and a gradually increasing decay rate scheme as remedies. Combining these methods and dropping momentum, we achieve comparable results to the published Adam regime in training the Transformer model on the WMT 2014 English-German machine translation task, while using very little auxiliary storage in the optimizer. Finally, we propose scaling the parameter updates based on the scale of the parameters themselves.


title: Empirical research on consumers' initial trust and gender differences in B2C e-business

abstract: Based on the Technology Acceptance Model and related literatures, this paper proposes an integrated conceptual model of consumers' initial trust in B2C e-business. Empirical research results show that, perceived usefulness, perceived safety, perceived website reputation and propensity to trust significantly positively affect consumers' initial trust in e-vendor, which further positively influence consumers' intention to purchase online, by the complete mediation of attitude; however, the effect of perceived use of use on initial trust doesn't show the statistic significance. Furthermore, there are significant gender differences in the effects of perceived ease of use, perceived safety, perceived website reputation on consumers' initial trust.


title: An uncommon suicide method: Self-strangulation by vehicle-assisted ligature

abstract: Abstract Self-strangulation is a very uncommon method of suicide. Deaths by vehicle-assisted ligature are rarely published and mainly related to decapitation. We report an unusual case of self-strangulation where a body was found dead inside a car with a rope round his neck and tied to a bridge banister. The rope was broken at 20 meters from the vehicle while the victim was driving his car away. The manner of death was determined as suicide based on objective scene investigation, autopsy and witness testimony. This case is reported for its rarity due to the method of suicide employed by the victim and because it was not related to decapitation.


title: Building Language Models for Text with Named Entities

abstract: Text in many domains involves a significant amount of named entities. Predict- ing the entity names is often challenging for a language model as they appear less frequent on the training corpus. In this paper, we propose a novel and effective approach to building a discriminative language model which can learn the entity names by leveraging their entity type information. We also introduce two benchmark datasets based on recipes and Java programming codes, on which we evalu- ate the proposed model. Experimental re- sults show that our model achieves 52.2% better perplexity in recipe generation and 22.06% on code generation than the state-of-the-art language models.


title: Chirping Techniques to Maximize the Power-Handling Capability of Harmonic Waveguide Low-Pass Filters

abstract: This work was supported by the Spanish Ministry of Science and Innovation under Project TEC 2014-55735‐C3‐R.


title: 21st Century Search and Recommendation: Exploiting Personalisation and Social Media

abstract: Using the Internet to find information and interesting content is now one of the most common tasks performed on a computer. Up until recently, search algorithms returned only one-size-fits-all rankings, resulting in very poor performance for ambiguous search queries. Recent work has demonstrated that contextual information - such as the interests of the searcher - can be utilised to provide more accurate results which have been “personalised” and adapted to the user’s current information need and situation. Likewise, information about the user can be brought to bear to mitigate the problem of information overload and filter content so that users are only shown items they are likely to be interested in.


title: Convolutional neural networks (CNN) for indoor human activity recognition using Ubisense system

abstract: In order to improve the accuracy of Indoor Human Activity Recognition based on the spatial location information, we proposed a recognition method using the convolutional neural network(CNN). We pre-process the raw spatial location data and transfer them into motion feature, frequency feature and statistic feature. These features are input into the CNN to do local feature analysis. After that, we got the characteristic output items, which have to be processed by the Softmax classifier, which can recognize six activities, including walking, sitting, lying, standing, jogging and jumping. By comparing the experimental results, the best recognition rate of different experimenters is 86.7%, which shows its feasibility.


Creep and strength of frozen soils under combined stress


Weld : Rethinking the Interface Between Data-Intensive Libraries


Prediction of Low Birth Weight Infants and Its Risk Factors Using Data Mining Techniques Senthilkumar


title: Application of TiO2 Catalyst for Low-Temperature SCR of NOx in New Style PC Kiln

abstract: Low-temperature SCR denitration in new style PC kiln was conducted using TiO2 catalyst. First, the theoretical quantity of ammonia consumption was calculated and applied in this experiment. Maintain the volume of exhaust gas flow and ammonia consumption quantity, concentration of NOx reduced from initial 503 mg/Nm3 of 120°C to 57.5 mg/Nm3 of 170 °C after catalytic reaction. When excess ammonia was injected into SCR reactor, efficiency of the denigration increased linearly too with temperature increasing and achieving the maximum of 89.45% under conditions of 170 °C, ammonia flow rate of 4.00 SL/min. After 20h continuous operating, efficiency of the denigration fluctuated ± 1% only. Pore of TiO2 catalyst have not blocked by exhaust smoke, that is to say: Low-temperature SCR TiO2 catalyst can be long-term used in new style PC kiln.


title: TSP: mining top-K closed sequential patterns

abstract: Sequential pattern mining has been studied extensively in data mining community. Most previous studies require the specification of a minimum support threshold to perform the mining. However, it is difficult for users to provide an appropriate threshold in practice. To overcome this difficulty, we propose an alternative task: mining top-k frequent closed sequential patterns of length no less than min-l, where k is the desired number of closed sequential patterns to be mined, and minl, is the minimum length of each pattern. We mine closed patterns since they are compact representations of frequent patterns. We developed an efficient algorithm, called TSP, which makes use of the length constraint and the properties of top-k closed sequential patterns to perform dynamic support-raising and projected database-pruning. Our extensive performance study shows that TSP outperforms the closed sequential pattern mining algorithm even when the latter is running with the best tuned minimum support threshold.


title: Motion Profile Design to Reduce Residual Vibration of High-Speed Positioning Stages

abstract: Precision positioning machines are required to run with higher speed and higher accuracy. The high running speed can cause strong excitations on the machine structure, which results in severe structure vibration and long settling time. This paper presents a low-vibration motion profile generation method to reduce the residual vibration. The acceleration profile is designed by using a level-shifted sinusoidal waveform to have an s-shape in order to control its change rate. Simulation and experimental studies showed that in comparison with conventional trapezoidal profile and s-curve profile, the residual vibration by using the proposed motion profile can be reduced significantly.


title: Cannabis cultivation: Methodological issues for obtaining medical-grade product

abstract: Abstract As studies continue to reveal favorable findings for the use of cannabidiol in the management of childhood epilepsy syndromes and other disorders, best practices for the large-scale production of Cannabis are needed for timely product development and research purposes. The processes of two institutions with extensive experience in producing large-scale cannabidiol chemotype Cannabis crops—GW Pharmaceuticals and the University of Mississippi—are described, including breeding, indoor and outdoor growing, harvesting, and extraction methods. Such practices have yielded desirable outcomes in Cannabis breeding and production: GW Pharmaceuticals has a collection of chemotypes dominant in any one of eight cannabinoids, two of which—cannabidiol and cannabidivarin—are supporting epilepsy clinical trial research, whereas in addition to a germplasm bank of high-THC, high-CBD, and intermediate type cannabis varieties, the team at University of Mississippi has established an in vitro propagation protocol for cannabis with no detectable variations in morphologic, physiologic, biochemical, and genetic profiles as compared to the mother plants. Improvements in phytocannabinoid yields and growing efficiency are expected as research continues at these institutions. This article is part of a Special Issue entitled “Cannabinoids and Epilepsy”.


title: Gene Clustering Using Metaheuristic Optimization Algorithms

abstract: Gene clustering is a familiar step in the exploratory analysis of high dimensional biological data. It is the process of grouping genes of similar patterns in the same cluster and aims at analyzing the functions of gene that leads to the development of drugs and early diagnosis of diseases. In the recent years, much research has been proposed using nature inspired meta-heuristic algorithms. Cuckoo Search is one such optimization algorithm inspired from nature by breeding strategy of parasitic bird, the cuckoo. This paper proposes cuckoo search clustering and clustering using levy flight cuckoo search for grouping brain tumor gene expression dataset. A comparative study is made with genetic algorithm, PSO clustering, cuckoo search clustering and clustering using levy flight cuckoo search. Levy flight is an important property of levy distribution which covers the entire search space. Breeding pattern of cuckoo is associated with the genes that cause tumor to grow and affect other organs gradually. Clusters generated by these algorithms are validated to find the closeness among the genes in a cluster and separation of genes between clusters. Experimental results carried out in this paper show that cuckoo search clustering outperforms other clustering methods used for experimentation.


title: Electrically Small Metamaterial-Inspired Tri-Band Antenna With Meta-Mode

abstract: Two novel metamaterial-inspired antennas applied for 4G mobile communication are proposed in this letter. Antenna 1 takes a triangular electromagnetic resonator (TER) as its radiator fed by a coplanar waveguide (CPW). After the model building and simulation, three frequency bands of $1.78\sim 1.84~\hbox{GHz}$ , $2.34\sim 3.86~\hbox{GHz}$ and $5.75\sim 5.87~\hbox{GHz}$ are achieved. Each band is well matched except for the band 3. Therefore, Antenna 2 is further developed, which adds a complementary TER (CTER) on the ground, and the improved design has a good impedance matching in band 3 without influencing the first two bands. Both antennas have operational bands covering WiMAX in 1.8/3.5 GHz and WLAN in 5.8 GHz and omnidirectional radiation patterns during the operating bands. The proposed two metamaterials antennas have the advantages of simple fabrication, miniaturization and compactness, which can be applied to the 4G wireless mobile communication system.


title: Counterfactual thinking.

abstract: Counterfactuals are mental representations of alternatives to the past and produce consequences that are both beneficial and aversive to the individual. These apparently contradictory effects are integrated in a functionalist model of counterfactual thinking. The author reviews research in support of the assertions that (a) counterfactual thinking is activated automatically in response to negative affect, (b) the content of counterfactuals targets particularly likely causes of misfortune, (c) counterfactuals produce negative affective consequences through a contrast-effect mechanism and positive inferential consequences through a causal-inference mechanism, and (d) the net effect of counterfactual thinking is beneficial.


title: Improving Software Effort Estimation Using Neuro-Fuzzy Model with SEER-SEM

abstract: The aims of our research are to evaluate the prediction performance of the proposed neuro-fuzzy model with System Evaluation and Estimation of Resource Software Estimation Model (SEER-SEM) in software estimation practices and to apply the proposed architecture that combines the neuro-fuzzy technique with different algorithmic models. In this paper, an approach combining the neuro-fuzzy technique and the SEER-SEM effort estimation algorithm is described. This proposed model possesses positive characteristics such as learning ability, decreased sensitivity, effective generalization, and knowledge integration for introducing the neuro-fuzzy technique. Moreover, continuous rating values and linguistic values can be inputs of the proposed model for avoiding the large estimation deviation among similar projects. The performance of the proposed model is accessed by designing and conducting evaluation with published projects and industrial data. The evaluation results indicate that estimation with our proposed neuro-fuzzy model containing SEER-SEM is improved in comparison with the estimation results that only use SEER-SEM algorithm. At the same time, the results of this research also demonstrate that the general neuro-fuzzy framework can function with various algorithmic models for improving the performance of software effort estimation.


Chapter 5 . 3 : Data Security in Cloud Computing


title: Interpreting traffic dynamics using ubiquitous urban data

abstract: Given a large collection of urban datasets, how can we find their hidden correlations? For example, New York City (NYC) provides open access to taxi data from year 2012 to 2015 with about half million taxi trips generated per day. In the meantime, we have a rich set of urban data in NYC including points-of-interest (POIs), geo-tagged tweets, weather, vehicle collisions, etc. Is it possible that these ubiquitous datasets can be used to explain the city traffic? Understanding the hidden correlation between external data and traffic data would allow us to answer many important questions in urban computing such as: If we observe a high traffic volume at Madison Square Garden (MSG) in NYC, is it because of the regular peak hour or a big event being held at MSG? If a disaster weather such as a hurricane or a snow storm hits the city, how would the traffic be affected? Most of existing studies on traffic dynamics focus only on traffic data itself and do not seek for external datasets to explain traffic. In this paper, we present our results in attempts to understand taxi traffic dynamics in NYC from multiple external data sources. We use four real-world ubiquitous urban datasets, including POIs, weather, geo-tagged tweets, and collision records. To address the heterogeneity of ubiquitous urban data, we present carefully-designed feature representations for these datasets. Our analysis suggests that POIs can well describe the regular traffic patterns. In addition, geo-tagged tweets can be used to explain irregular traffic caused by big events, and weather may account for abnormal traffic drops.


title: Gain and bandwidth improvement of microstrip antenna using RIS and FPC resonator

abstract: Improvement in gain as well as bandwidth of microstrip antenna using reactive impedance surface and Fabry Perot cavity resonator is proposed. Suspended microstrip antenna (MSA) is designed on reactive impedance surface (RIS) backed FR4 substrate. RIS improves bandwidth, gain and efficiency of antenna due to reduced coupling between MSA and ground plane. This MSA backed RIS is placed in a FPC resonator for further improving the gain of MSA. 1×1 to 4×4 square patch arrays are designed on a dielectric layer which is energized by the radiations from RIS backed MSA. The PRS layer is placed at about λ 0 /2 from ground. The S11 is 20 dB and > 70 % antenna efficiency. The measured results of prototype structure are in agreement with the simulation results.


title: Structure and thermal/mechanical properties of poly(l-lactide)-clay blend

abstract: Organophilic montmorillonite was obtained by the reaction of montmorillonite (MON) and distearyldimethylammonium chloride (DSAC). The modified clay and poly(l-lactide), (PLLA), were solvent-cast blended using chloroform as cosolvent. The structure and properties of the PLLA-clay blends were investigated. Thermal measurements revealed that cold crystallization took place in the as-cast PLLA, and that the clay served as a nucleating agent. From small and wide-angle x-ray scattering measurements, it was found that silicate layers forming the clay could not be individually well dispersed in the PLLA-clay blends prepared by the solvent-cast method. In other words, the clay existed in the form of tactoids, which consist of several stacked silicate monolayers. However, these tactoids formed a remarkable geometrical structure in the blend films. That is, their surfaces lay almost parallel to the film surface, and were stacked with the insertion of PLLA crystalline lamellae in the thickness direction of the film. During the blend drawing process, fibrillation took place with the formation of plane-like voids developed on the plane parallel to the film surface. Furthermore, delamination of the silicate layers did not occur even under the application of a shearing force. Finally, Young's modulus of the blend increased with the addition of a small amount of the clay. © 1997 John Wiley & Sons, Inc.


title: Parallel subgraph listing in a large-scale graph

abstract: Subgraph listing is a fundamental operation to many graph and network analyses. The problem itself is computationally expensive and is well-studied in centralized processing algorithms. However, the centralized solutions cannot scale well to large graphs. Recently, several parallel approaches are introduced to handle the large graphs. Unfortunately, these parallel approaches still rely on the expensive join operations, thus cannot achieve high performance. In this paper, we design a novel parallel subgraph listing framework, named PSgL. The PSgL iteratively enumerates subgraph instances and solves the subgraph listing in a divide-and-conquer fashion. The framework completely relies on the graph traversal, and avoids the explicit join operation. Moreover, in order to improve its performance, we propose several solutions to balance the workload and reduce the size of intermediate results. Specially, we prove the problem of partial subgraph instance distribution for workload balance is NP-hard, and carefully design a set of heuristic strategies. To further reduce the enormous intermediate results, we introduce three independent mechanisms, which are automorphism breaking of the pattern graph, initial pattern vertex selection based on a cost model, and a pruning method based on a light-weight index. We have implemented the prototype of PSgL, and run comprehensive experiments of various graph listing operations on diverse large graphs. The experiments clearly demonstrate that PSgL is robust and can achieve performance gain over the state-of-the-art solutions up to 90%.


title: Filter technologies for wireless base stations

abstract: Phenomenal growth in the telecommunication industry in recent years has brought significant advances in filter technology as new communication systems emerged, demanding more stringent filter characteristics. In particular, the growth of the wireless communication industry has spurred tremendous activity in the area of microwave filter miniaturization and has been responsible for many advances made in this field. The filters that are currently being used in wireless base stations can be divided into two main categories: coaxial cavity resonator filters and dielectric resonator (DR) filters. While coaxial cavity filters have limited quality factor (Q) values, they offer the lowest cost design and are still being widely employed, particularly in wide bandwidth applications. With increased demands for high performance wireless systems, dielectric resonator filters are emerging as the baseline design for wireless base stations. Over the next five years, dielectric resonator filters are expected to have a significant share of the overall wireless base station filter market. High-temperature superconductor (HTS) filters are also expected to have a share of this market, particularly for systems, which have very stringent requirements for out-of-band interference. In this article, we begin by reviewing the main filter requirements, highlighting the technologies that are being currently employed. Emerging filter technologies that have the potential to replace the existing technologies are then described.


title: Comparison of 2.3-kV Medium-Voltage Multilevel Converters for Industrial Medium-Voltage Drives

abstract: This paper compares the expense of power semiconductors and passive components of a (2.3 kV, 2.4 MVA) two-level, three-level neutral-point-clamped, three-level flying-capacitor, four-level flying-capacitor, and five-level series-connected H-bridge voltage source converter on the basis of the state-of-the-art 6.5-, 3.3-, 2.5-, and 1.7-kV insulated gate bipolar transistors for industrial medium-voltage drives. The power semiconductor losses, the loss distribution, the installed switch power, the design of flying capacitors, and the components of an sine filter for retrofit applications are considered.


Fuzzy Based Control Using Lab view For Temperature Process


title: Autonomous driving in semi-structured environments: Mapping and planning

abstract: We consider the problem of autonomous driving in semi-structured environments (e.g., parking lots). Such environments have strong topological structure (graphs of drivable lanes), but maneuvers with significant deviations from those graphs are valid and frequent. We address two main challenges of operating in such environments: i) detection of topological structure from sensor data, and ii) using that structure to guide path planning. We present experimental results on both of these topics, demonstrating robust estimation of lane networks in parking lots and the benefits of using these topological networks to guide path planning.


title: Implementation intentions: Strong effects of simple plans.

abstract: When people encounter problems in translating their goals into action (e.g., failing to get started, becoming distracted, or falling into bad habits), they may strategically call on automatic processes in an attempt to secure goal attainment. This can be achieved by plans in the form of implementation intentions that link anticipated critical situations to goal-directed responses ("Whenever situation x arises, I will initiate the goal-directed response y!"). Implementation intentions delegate the control of goal-directed responses to anticipated situational cues, which (when actually encountered) elicit these responses automatically. A program of research demonstrates that implementation intentions further the attainment of goals, and it reveals the underlying processes.


title: Human Motor Plasticity Induced by Mirror Visual Feedback

abstract: The clinical use of mirror visual feedback (MVF) was initially introduced to alleviate phantom pain, and has since been applied to the improvement of hemiparesis following stroke. However, it is not known whether MVF can restore motor function by producing plastic changes in the human primary motor cortex (M1). Here, we used transcranial magnetic stimulation to test whether M1 plasticity is a physiological substrate of MVF-induced motor behavioral improvement. MVF intervention in normal volunteers using a mirror box improved motor behavior and enhanced excitatory functions of the M1. Moreover, behavioral and physiological measures of MVF-induced changes were positively correlated with each other. Improved motor performance occurred after observation of a simple action, but not after repetitive motor training of the nontarget hand without MVF, suggesting the crucial importance of visual feedback. The beneficial effects of MVF were disrupted by continuous theta burst stimulation (cTBS) over the M1, but not the control site in the occipital cortex. However, MVF following cTBS could further improve the motor functions. Our findings indicate that M1 plasticity, especially in its excitatory connections, is an essential component of MVF-based therapies.


title: Genome-wide Association Mapping of Cold Tolerance Genes at the Seedling Stage in Rice

abstract: BackgroundRice is a temperature-sensitive crop and its production is severely affected by low temperature in temperate and sub-tropical regions. To understand the genetic basis of cold tolerance in rice, we evaluated the cold tolerance at the seedling stage (CTS) of 295 rice cultivars in the rice diversity panel 1 (RDP1), these cultivars were collected from 82 countries.ResultsThe evaluations revealed that both temperate and tropical japonica rice cultivars are more tolerant to cold stress than indica and AUS cultivars. Using the cold tolerance phenotypes and 44 K SNP chip dataset of RDP1, we performed genome-wide association mapping of quantitative trait loci (QTLs) for CTS. The analysis identified 67 QTLs for CTS that are located on 11 chromosomes. Fifty-six of these QTLs are located in regions without known cold tolerance-related QTLs.ConclusionOur study has provided new information on the genetic architecture of rice cold tolerance and has also identified highly cold tolerant cultivars and CTS-associated SNP markers that will be useful rice improvement.


IMAGE TO LATEX VIA NEURAL NETWORKS A Project


title: Poster Abstract: Privacy in Blockchain-Enabled IoT Devices

abstract: Over past two decades, the idea of Internet of Things has been adopted widely as a solution to many societal problems in different areas. These areas include but are not limited to healthcare, transportation, environment, etc. Low cost overhead of Internet connectivity feature has been the main contributing factor in the widespread use of such devices in building different IoT solutions. The original stovepipe architecture of IoT systems limits the possibility of sharing the hardware infrastructure of IoT solutions and therefore is the main barrier against novel solutions. In recent year, however, there have been efforts to come up with solutions for sharing the hardware infrastructure and therefore pave the way for innovative solutions by amortizing the capital cost of setting up the hardware. In this work, we propose an architectural guideline for blockchain enabled IoT devices which facilitates sharing them between multiple blockchain ecosystems and at the same time, ensures the exclusive access to them seamlessly through blockchain smart contracts.


title: hyperdoc2vec: Distributed Representations of Hypertext Documents

abstract: Hypertext documents, such as web pages and academic papers, are of great importance in delivering information in our daily life. Although being effective on plain documents, conventional text embedding methods suffer from information loss if directly adapted to hyper-documents. In this paper, we propose a general embedding approach for hyper-documents, namely, hyperdoc2vec, along with four criteria characterizing necessary information that hyper-document embedding models should preserve. Systematic comparisons are conducted between hyperdoc2vec and several competitors on two tasks, i.e., paper classification and citation recommendation, in the academic paper domain. Analyses and experiments both validate the superiority of hyperdoc2vec to other models w.r.t. the four criteria.


title: High-rate turbo equalization modem on GNU Radio and USRP

abstract: High-rate data communication is defined in relation to the available bandwidth. In some particularly wireless applications this bandwidth is only limited to voiceband, making high-rate links (traditionally above 2400 bits per second) very challenging using practical waveforms. Turbo equalizers perform well in the receiver part of such modems, with a fundamental weakness of complexity. In this paper, we propose some simplifications to make these great receivers more feasible in these channels. Additionally, software-defined radio (SDR) is chosen to implement the modem as a real-world radio system with the GNU Radio and USRP as its software and hardware platforms, respectively. Test results show that the proposed methods can dramatically decline the main complexity and they also include the performance of a typical modem in a standard channel model.


title: Shape Completion from a Single RGBD Image

abstract: We present a novel approach for constructing a complete 3D model for an object from a single RGBD image. Given an image of an object segmented from the background, a collection of 3D models of the same category are non-rigidly aligned with the input depth, to compute a rough initial result. A volumetric-patch-based optimization algorithm is then performed to refine the initial result to generate a 3D model that not only is globally consistent with the overall shape expected from the input image but also possesses geometric details similar to those in the input image. The optimization with a set of high-level constraints, such as visibility, surface confidence and symmetry, can achieve more robust and accurate completion over state-of-the art techniques. We demonstrate the efficiency and robustness of our approach with multiple categories of objects with various geometries and details, including busts, chairs, bikes, toys, vases and tables.


title: Psychology will be a much better science when we change the way we analyze data

abstract: Jn 1964, 1 entered the field of psychology because 1 believed that within it dwelt some o f the most fund a m e nta l a nd ch a lJe n g ing proble ms of the exta nt sciences. Who could not be intrigu ed, for example, by the rela tion between consciou sness and be ha vior, o r the rules guiding interactions in social situations, o r the processes tha t underlie d evelopme nt from infancy to ma turity? Tod ay, in 1996, my fascination with these proble ms is undiminis hed . But I have deve loped a certain an gst ove r the interve ning 30-something years-a constant, nagging feeling that our fie ld spe nds a lo t of time spinning its wheels without really ma king much progress. This problem s hows up in obvious waysfor insta nce, in the regulari ty with which findings seem no t to replicate. 1t also s hows up in subtler ways-for instance, one d oes not o ften hea r psychologists saying, " Well, this proble m is solved now; let's move on to the next one" (as, e.g., Johannes Kepler must have said m ore tha n three centuries


title: Toward Privacy-Assured Health Insurance Claims

abstract: According to HIPAA (Health Insurance Portability and Accountability Act), the medical insurance claim process is carried out by healthcare providers, insurance companies, and clearinghouses. The clearinghouse coordinates the medical insurance claims between providers and insurance companies. As centralized communication hubs, clearinghouses may maliciously or unintentionally leak patient information. In this work, we propose a distributed solution to replace the role of clearinghouses during health insurance claim process and mitigate the risk of data leakage among parties in Healthcare sector. Our solution enhances the patients' privacy protection through developing a HIPAA compliance system for the medical insurance claim process in a decentralized manner using blockchain technology. Blockchain ensures transaction integrity and anonymity of cryptocurrencies by using distributed immutable ledgers. We first design data structures for patient information, medical service record, insurance payment, and insurance agreements within the ledger. We then focus on defining smart contracts for privacy assurance, as well as automating the insurance claim process. We implement and evaluate the proposed framework with Hyperledger Fabric, showing promising performance and response time.


title: A distributed frequent itemset mining algorithm based on Spark

abstract: Frequent itemset mining is an important step of association rules mining. Traditional frequent itemset mining algorithms have certain limitations. For example Apriori algorithm has to scan the input data repeatedly, which leads to high I/O load and low performance, and the FP-Growth algorithm is limited by the capacity of computer's inner stores because it needs to build a FP-tree and mine frequent itemset on the basis of the FP-tree in memory. With the coming of the Big Data era, these limitations are becoming more prominent when confronted with mining large-scale data. In this paper, DPBM, a distributed matrix-based pruning algorithm based on Spark, is proposed to deal with frequent itemset mining. DPBM can greatly reduce the amount of candidate itemset by introducing a novel pruning technique for matrix-based frequent itemset mining algorithm, an improved Apriori algorithm which only needs to scan the input data once. In addition, each computer node reduces greatly the memory usage by implementing DPBM under a latest distributed environment-Spark, which is a lightning-fast distributed computing. The experimental results show that DPBM have better performance than MapReduce-based algorithms for frequent itemset mining in terms of speed and scalability.


title: Sex differences in the gut microbiome–brain axis across the lifespan

abstract: In recent years, the bidirectional communication between the gut microbiome and the brain has emerged as a factor that influences immunity, metabolism, neurodevelopment and behaviour. Cross-talk between the gut and brain begins early in life immediately following the transition from a sterile in utero environment to one that is exposed to a changing and complex microbial milieu over a lifetime. Once established, communication between the gut and brain integrates information from the autonomic and enteric nervous systems, neuroendocrine and neuroimmune signals, and peripheral immune and metabolic signals. Importantly, the composition and functional potential of the gut microbiome undergoes many transitions that parallel dynamic periods of brain development and maturation for which distinct sex differences have been identified. Here, we discuss the sexually dimorphic development, maturation and maintenance of the gut microbiome-brain axis, and the sex differences therein important in disease risk and resilience throughout the lifespan.


title: Brain disorders and the biological role of music

abstract: Despite its evident universality and high social value, the ultimate biological role of music and its connection to brain disorders remain poorly understood. Recent findings from basic neuroscience have shed fresh light on these old problems. New insights provided by clinical neuroscience concerning the effects of brain disorders promise to be particularly valuable in uncovering the underlying cognitive and neural architecture of music and for assessing candidate accounts of the biological role of music. Here we advance a new model of the biological role of music in human evolution and the link to brain disorders, drawing on diverse lines of evidence derived from comparative ethology, cognitive neuropsychology and neuroimaging studies in the normal and the disordered brain. We propose that music evolved from the call signals of our hominid ancestors as a means mentally to rehearse and predict potentially costly, affectively laden social routines in surrogate, coded, low-cost form: essentially, a mechanism for transforming emotional mental states efficiently and adaptively into social signals. This biological role of music has its legacy today in the disordered processing of music and mental states that characterizes certain developmental and acquired clinical syndromes of brain network disintegration.


title: Conceptual Privacy Framework for Health Information on Wearable Device

abstract: Wearable health tech provides doctors with the ability to remotely supervise their patients' wellness. It also makes it much easier to authorize someone else to take appropriate actions to ensure the person's wellness than ever before. Information Technology may soon change the way medicine is practiced, improving the performance, while reducing the price of healthcare. We analyzed the secrecy demands of wearable devices, including Smartphone, smart watch and their computing techniques, that can soon change the way healthcare is provided. However, before this is adopted in practice, all devices must be equipped with sufficient privacy capabilities related to healthcare service. In this paper, we formulated a new improved conceptual framework for wearable healthcare systems. This framework consists of ten principles and nine checklists, capable of providing complete privacy protection package to wearable device owners. We constructed this framework based on the analysis of existing mobile technology, the results of which are combined with the existing security standards. The approach also incorporates the market share percentage level of every app and its respective OS. This framework is evaluated based on the stringent CIA and HIPAA principles for information security. This evaluation is followed by testing the capability to revoke rights of subjects to access objects and ability to determine the set of available permissions for a particular subject for all models Finally, as the last step, we examine the complexity of the required initial setup.


title: The file drawer problem and tolerance for null results.

abstract: For any given research area, one cannot tell how many studies have been conducted but never reported. The extreme view of the "file drawer problem" is that journals are filled with the 5% of the studies that show Type I errors, while the file drawers are filled with the 95% of the studies that show nonsignificant results. Quantitative procedures for computing the tolerance for filed and future null results are reported and illustrated, and the implications are discussed. (15 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)


title: A survey on Arabic character segmentation

abstract: Arabic character segmentation is a necessary step in Arabic Optical Character Recognition (OCR). The cursive nature of Arabic script poses challenging problems in Arabic character recognition; however, incorrectly segmented characters will cause misclassifications of characters which in turn may lead to wrong results. Therefore, off-line Arabic character segmentation is a difficult research problem and little research has been achieved in this area in the past few decades. This is due to both the cursive nature of Arabic writing in both printed and handwritten forms and the scarcity of Arabic databases and dictionaries. Most of the character recognition methods used in the recognition of Arabic characters are adopted from available methods used on handwritten Latin and Chinese characters; however, other methods are developed only for Arabic character segmentation. This survey presents the description of the Arabic script characteristics with an overview on OCR systems and a comprehensive review mainly on off-line printed Arabic character segmentation techniques.


title: Killer Applications in Digital Humanities

abstract: The emerging discipline of 'digital humanities' has been plagued by a perceived neglect on the part of the broader humanities community. The community as a whole tends not to be aware of the tools developed by DH practitioners (as documented by the recent surveys by Siemens et al.), and tends not to take seriously many of the results of scholarship obtained by DH methods and tools. This article argues for a focus on deliverable results in the form of useful solutions to common problems that humanities scholars share, instead of simply new representations. The question to address is what needs the humanities community has that can be dealt with using DH tools and techniques, or equivalently what incentive humanists have to take up and to use new methods. This can be treated in some respects like the computational quest for the 'killer application'-a need of the user group that can be filled, and by filling it, create an acceptance of that tool and the supporting methods/results. Some definitions and examples are provided both to illustrate the idea and to support why this is necessary. The apparent alternative is the status quo, where digital research tools are brilliantly developed, only to languish in neglect and disuse.


title: Technological impact of magnetic hard disk drives on storage systems

abstract: Magnetic hard disk drives have undergone vast technological improvements since their introduction as storage devices over 45 years ago, and these improvements have had a marked influence on how disk drives are applied and what they can do. Areal density increases have exceeded the traditional semiconductor development trajectory and have yielded higher-capacity, higher-performance, and smaller-form-factor disk drives, enabling desktop and mobile computers to store multi-gigabytes of data easily. Server systems containing large numbers of drives have achieved unparalleled reliability, performance, and storage capacity. All of these characteristics have been achieved at rapidly declining disk costs. This paper relates advances in disk drives to corresponding trends in storage systems and projects where these trends may lead in the future.


title: Two-Stream Convolutional Networks for Action Recognition in Videos

abstract: We investigate architectures of discriminatively trained deep Convolutional Networks (ConvNets) for action recognition in video. The challenge is to capture the complementary information on appearance from still frames and motion between frames. We also aim to generalise the best performing hand-crafted features within a data-driven learning framework. ::: ::: Our contribution is three-fold. First, we propose a two-stream ConvNet architecture which incorporates spatial and temporal networks. Second, we demonstrate that a ConvNet trained on multi-frame dense optical flow is able to achieve very good performance in spite of limited training data. Finally, we show that multitask learning, applied to two different action classification datasets, can be used to increase the amount of training data and improve the performance on both. Our architecture is trained and evaluated on the standard video actions benchmarks of UCF-101 and HMDB-51, where it is competitive with the state of the art. It also exceeds by a large margin previous attempts to use deep nets for video classification.


title: Self-medication amongst university students of Karachi: prevalence, knowledge and attitudes.

abstract: Objective: To determine the prevalence, attitude and knowledge of self-medication amongst university students of Karachi, Pakistan. Methods: This cross-sectional, study was conducted from Jan-Feb 2007. A convenience sample was taken from 2 medical and 2 non-medical universities of the city of Karachi, Pakistan. Data was analyzed using SPSS v 14 and associations were tested using the Chi square test. Results: Of the 572 participants (mean age=21 ± 1.8 years, Male: Female ratio=1:1.5), 295 were medical and 277 were non-medical students. The prevalence of self-medication was 76%. Forty three percent students stated that they alter the regimen of prescribed medicines while 61.9% stated that they stop taking a prescribed medicine without consulting a doctor. The most common reason for self-medication was previous experience (50.1 %) and the most common symptoms were headache (72.4%), flu (65.5%), and fever (55.2%). Commonly used medicines were analgesics (88.3%), antipyretics (65.1 %) and antibiotics (35.2%). Eighty seven percent of students thought self-medication could be harmful and 82.5% students thought that it was necessary to consult a doctor before taking a new medicine. There was no significant difference between the self medication practices of medical and non medical students (p = 0.8) Conclusion: Prevalence of self-medication is high in the educated youth, despite majority being aware of its harmful effects. There is a need to educate the youth to ensure safe practices. Strict policies heed to be implemented on the advertising and selling of medications to prevent this problem from escalating.


title: LSTM Neural Reordering Feature for Statistical Machine Translation

abstract: Artificial neural networks are powerful models, which have been widely applied into many aspects of machine translation, such as language modeling and translation modeling. Though notable improvements have been made in these areas, the reordering problem still remains a challenge in statistical machine translations. In this paper, we present a novel neural reordering model that directly models word pairs and alignment. By utilizing LSTM recurrent neural networks, much longer context could be learned for reordering prediction. Experimental results on NIST OpenMT12 Arabic-English and Chinese-English 1000-best rescoring task show that our LSTM neural reordering feature is robust and achieves significant improvements over various baseline systems.


title: A Systematic Assessment of the Security of Full Disk Encryption

abstract: Organizations as well as private users frequently report the loss and theft of mobile devices such as laptops and smartphones. The threat of data exposure in such scenarios can be mitigated by protection mechanisms based on encryption. Full disk encryption (FDE) is an effective method to protect data against unauthorized access. FDE can generally be classified into software- and hardware-based solutions. We assess the practical security that users can expect from these FDE solutions regarding physical access threats. We assume that strong cryptography like AES cannot be broken but focus on vulnerabilities arising from practical FDE implementations. We present the results of a comprehensive and systematic comparison of the security of software- and hardware-based FDE. Thereby, we exhibit attacks on widespread FDE standards in many common scenarios and different system configurations. As a result, we show that neither software- nor hardware-based FDE provides perfect security, nor is one clearly superior to the other.


title: Performance Evaluation of Cyber-Physical Intrusion Detection on a Robotic Vehicle

abstract: Intrusion detection systems designed for conventional computer systems and networks are not necessarily suitable for mobile cyber-physical systems, such as robots, drones and automobiles. They tend to be geared towards attacks of different nature and do not take into account mobility, energy consumption and other physical aspects that are vital to a mobile cyber-physical system. We have developed a decision tree-based method for detecting cyber attacks on a small-scale robotic vehicle using both cyber and physical features that can be measured by its on-board systems and processes. We evaluate it experimentally against a variety of scenarios involving denial of service, command injection and two types of malware attacks. We observe that the addition of physical features noticeably improves the detection accuracy for two of the four attack types and reduces the detection latency for all four.


title: The use of personality measures in personnel selection: What does current research support?

abstract: With an eye toward research and practice, this article reviews and evaluates main trends that have contributed to the increasing use of personality assessment in personnel selection. Research on the ability of personality to predict job performance is covered, including the Five Factor Model of personality versus narrow personality measures, meta-analyses of personality–criterion relationships, moderator effects, mediator effects, and incremental validity of personality over other selection testing methods. Personality and team performance is also covered. Main trends in contemporary research on the extent to which applicant “faking” of personality tests poses a serious threat are explicated, as are promising approaches for contending with applicant faking such as the “faking warning” and the forced-choice method of personality assessment. Finally, internet-based assessment of personality and computer adaptive personality testing are synopsized.


title: Variable-speed quadrupedal bounding using impulse planning: Untethered high-speed 3D Running of MIT Cheetah 2

abstract: This paper introduces a bounding gait control algorithm that allows a variable-speed running in the MIT Cheetah 2. A simple impulse planning algorithm is proposed to design vertical and horizontal force profiles which make net impulse on the system during one cycle zero. This design of force profiles leads to the conservation of linear momentum over a complete step, providing periodicity in horizontal and vertical velocity. When designed profiles are applied to the system, periodic orbits with an ability to change running speed are obtained. A virtual compliance control in the horizontal and vertical direction has been added onto the designed force profiles to stabilize the periodic orbits. The experimental results show that the algorithm successfully achieved untethered 3D running of the MIT Cheetah 2, with speeds ranging from 0 m/sec to 4.5 m/sec on treadmills as well as on grassy fields.


title: Histopathology Stain-Color Normalization Using Deep Generative Models

abstract: Performance of designed CAD algorithms for histopathology image analysis is affected by the amount of variations in the samples such as color and intensity of stained images. Stain-color normalization is a well-studied technique for compensating such effects at the input of CAD systems. In this paper, we introduce unsupervised generative neural networks for performing stain-color normalization. For color normalization in stained hematoxylin and eosin (H&E) images, we present three methods based on three frameworks for deep generative models: variational auto-encoder (VAE), generative adversarial networks (GAN) and deep convolutional Gaussian mixture models (DCGMM). Our contribution is defining the color normalization as a learning generative model that is able to generate various color copies of the input image through a nonlinear parametric transformation. In contrast to earlier generative models proposed for stain-color normalization, our approach does not need any labels for data or any other assumptions about the H&E image content. Furthermore, our models learn a parametric transformation during training and can convert the color information of an input image to resemble any arbitrary reference image. This property is essential in time-critical CAD systems in case of changing the reference image, since our approach does not need retraining in contrast to other proposed generative models for stain-color normalization. Experiments on histopathological H&E images with high staining variations, collected from different laboratories, show that our proposed models outperform quantitatively state-of-the-art methods in the measure of color constancy with at least 10-15%, while the converted images are visually in agreement with this performance improvement.


title: Conflict Management: Difficult Conversations with Difficult People

abstract: Conflict occurs frequently in any workplace; health care is not an exception. The negative consequences include dysfunctional team work, decreased patient satisfaction, and increased employee turnover. Research demonstrates that training in conflict resolution skills can result in improved teamwork, productivity, and patient and employee satisfaction. Strategies to address a disruptive physician, a particularly difficult conflict situation in healthcare, are addressed.


title: A dynamic routing strategy for the traffic control of AGVs in automatic warehouses

abstract: In this paper we propose a novel algorithm for the dynamic routing of a group of Autonomous Guided Vehicles (AGVs) used for transporting goods in automatic warehouses. Our strategy allows to improve the efficiency of a fleet of AGVs in terms of delivery time and its computational burden is sufficiently small to be embedded in standard industrial traffic management system. The algorithm is validated in a small-scale automatic warehouse with real AGVs.


title: Power and energy capacity requirements of storages providing frequency control reserves

abstract: Due to their fast response time and high ramp rates, storage systems are capable of providing frequency control reserves. However, the limit in energy capacity poses difficulties as frequency control signals are not unbiased. We describe a scheme to recharge or discharge the storage without impeding the quality of the provided service, and formulate an analyzing method to investigate the resulting size of the storage. We show that even small storage sizes are sufficient to provide continuous and reliable primary and secondary frequency control reserves to the grid.


title: Semantic tag cloud generation via DBpedia

abstract: Many current recommender systems exploit textual annotations (tags) provided by users to retrieve and suggest online contents. The text-based recommendation provided by these systems could be enhanced (i) using unambiguous identifiers representative of tags and (ii) exploiting semantic relations among tags which are impossible to be discovered by traditional textual analysis. In this paper we concentrate on annotation and retrieval of web content, exploiting semantic tagging with DBpedia. We use semantic information stored in the DBpedia dataset and propose a new hybrid ranking system to rank keywords and to expand queries formulated by the user. Inputs of our ranking system are (i) the DBpedia dataset; (ii) external information sources such as classical search engine results and social tagging systems. We compare our approach with other RDF similarity measures, proving the validity of our algorithm with an extensive evaluation involving real users.


title: FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record

abstract: Electronic health record (EHR) data is collected by individual institutions and often stored across locations in silos. Getting access to these data is difficult and slow due to security, privacy, regulatory, and operational issues. We show, using ICU data from 58 different hospitals, that machine learning models to predict patient mortality can be trained efficiently without moving health data out of their silos using a distributed machine learning strategy. We propose a new method, called Federated-Autonomous Deep Learning (FADL) that trains part of the model using all data sources in a distributed manner and other parts using data from specific data sources. We observed that FADL outperforms traditional federated learning strategy and conclude that balance between global and local training is an important factor to consider when design distributed machine learning methods , especially in healthcare.


title: Approximation algorithms for scheduling unrelated parallel machines

abstract: We consider the following scheduling problem. There arem parallel machines andn independent jobs. Each job is to be assigned to one of the machines. The processing of jobj on machinei requires timepij. The objective is to find a schedule that minimizes the makespan.Our main result is a polynomial algorithm which constructs a schedule that is guaranteed to be no longer than twice the optimum. We also present a polynomial approximation scheme for the case that the number of machines is fixed. Both approximation results are corollaries of a theorem about the relationship of a class of integer programming problems and their linear programming relaxations. In particular, we give a polynomial method to round the fractional extreme points of the linear program to integral points that nearly satisfy the constraints.In contrast to our main result, we prove that no polynomial algorithm can achieve a worst-case ratio less than 3/2 unlessP = NP. We finally obtain a complexity classification for all special cases with a fixed number of processing times.


title: Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering

abstract: Under the framework of graph-based learning, the key to robust subspace clustering and subspace learning is to obtain a good similarity graph that eliminates the effects of errors and retains only connections between the data points from the same subspace (i.e., intra-subspace data points). Recent works achieve good performance by modeling errors into their objective functions to remove the errors from the inputs. However, these approaches face the limitations that the structure of errors should be known prior and a complex convex problem must be solved. In this paper, we present a novel method to eliminate the effects of the errors from the projection space (representation) rather than from the input space. We first prove that $\ell_1$-, $\ell_2$-, $\ell_{\infty}$-, and nuclear-norm based linear projection spaces share the property of Intra-subspace Projection Dominance (IPD), i.e., the coefficients over intra-subspace data points are larger than those over inter-subspace data points. Based on this property, we introduce a method to construct a sparse similarity graph, called L2-Graph. The subspace clustering and subspace learning algorithms are developed upon L2-Graph. Experiments show that L2-Graph algorithms outperform the state-of-the-art methods for feature extraction, image clustering, and motion segmentation in terms of accuracy, robustness, and time efficiency.


title: Emergent: a novel data-set for stance classification

abstract: We present Emergent, a novel data-set derived from a digital journalism project for rumour debunking. The data-set contains 300 rumoured claims and 2,595 associated news articles, collected and labelled by journalists with an estimation of their veracity (true, false or unverified). Each associated article is summarized into a headline and labelled to indicate whether its stance is for, against, or observing the claim, where observing indicates that the article merely repeats the claim. Thus, Emergent provides a real-world data source for a variety of natural language processing tasks in the context of fact-checking. Further to presenting the dataset, we address the task of determining the article headline stance with respect to the claim. For this purpose we use a logistic regression classifier and develop features that examine the headline and its agreement with the claim. The accuracy achieved was 73% which is 26% higher than the one achieved by the Excitement Open Platform (Magnini et al., 2014).


title: Direct Multi-Target Tracking and Multisensor Fusion Using Antenna Arrays.

abstract: This paper investigates a direct Target Motion Analysis (TMA) estimator for the problem of calculating the states (i.e. source positions, velocities, etc.) of multiple sources from measurements made with multiple (fixed or moving) antenna arrays. We use the novel Subspace Data Fusion (SDF) approach and extend it to the multisensor case. In the SDF approach, subspaces are formed in a first pre-processing step from the raw antenna outputs. Then, the parameters of interest are estimated directly from a single cost function, which results from fusing all subspaces. This approach requires only a single low-dimensional optimization and completely circumvents the bearing data association problem inherent in traditional TMA approaches. We derive the Cramer-Rao Bound (CRB) for the direct multitarget tracking problem. In Monte Carlo simulations we find that the SDF estimator approaches the CRB and always performs better than or equal to the traditional TMA approach. We show that the state estimation accuracy can be improved by using multiple antenna arrays.


title: Laboratories of Oligarchy? How the Iron Law Extends to Peer Production

abstract: Peer production projects like Wikipedia have inspired voluntary associations, collectives, social movements, and scholars to embrace open online collaboration as a model of democratic organization. However, many peer production projects exhibit entrenched leadership and deep inequalities, suggesting that they may not fulfill democratic ideals. Instead, peer production projects may conform to Robert Michels' “iron law of oligarchy,” which proposes that democratic membership organizations become increasingly oligarchic as they grow. Using exhaustive data of internal processes from a sample of 683 wikis, we construct empirical measures of participation and test for increases in oligarchy associated with growth in wikis' contributor bases. In contrast to previous studies, we find support for Michels' iron law and conclude that peer production entails oligarchic organizational forms.


title: K-Automorphism: A General Framework For Privacy Preserving Network Publication

abstract: The growing popularity of social networks has generated interesting data management and data mining problems. An important concern in the release of these data for study is their privacy, since social networks usually contain personal information. Simply removing all identifiable personal information (such as names and social security number) before releasing the data is insufficient. It is easy for an attacker to identify the target by performing different structural queries. In this paper we propose k-automorphism to protect against multiple structural attacks and develop an algorithm (called KM) that ensures k-automorphism. We also discuss an extension of KM to handle "dynamic" releases of the data. Extensive experiments show that the algorithm performs well in terms of protection it provides.


title: Joint Mention Extraction and Classification with Mention Hypergraphs

abstract: We present a novel model for the task of joint mention extraction and classification. Unlike existing approaches, our model is able to effectively capture overlapping mentions with unbounded lengths. The model is highly scalable, with a time complexity that is linear in the number of words in the input sentence and linear in the number of possible mention classes. Our model can be extended to additionally capture mention heads explicitly in a joint manner under the same time complexity. We demonstrate the effectiveness of our model through extensive experiments on standard datasets.


title: Citation analysis and mapping of nanoscience and nanotechnology: identifying the scope and interdisciplinarity of research

abstract: Diversification and fragmentation of scientific exploration brings an increasing need for integration, for example through interdisciplinary research. The field of nanoscience and nanotechnology appears to exhibit strong interdisciplinary characteristics. Our objective was to explore the structure of the field and ascertain how different research areas within this field reflect interdisciplinarity through citation patterns. The complex relations between the citing and cited articles were examined through schematic visualization. Examination of WOS categories assigned to journals shows the scatter of nano studies across a wide range of research topics. We identified four distinctive groups of categories each showing some detectable shared characteristics. Three alternative measures of similarity were employed to delineate these groups. These distinct groups enabled us to assess interdisciplinarity within the groups and relationships between the groups. Some measurable levels of interdisciplinarity exist in all groups. However, one of the groups indicated that certain categories of both citing as well as cited articles aggregate mostly in the framework of physics, chemistry, and materials. This may suggest that the nanosciences show characteristics of a distinct discipline. The similarity in citing articles is most evident inside the respective groups, though, some subgroups within larger groups are also related to each other through the similarity of cited articles.


title: A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis

abstract: Clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. In particular, their main goal is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics. There is a vast body of knowledge in the area of clustering and there has been attempts to analyze and categorize them for a larger number of applications. However, one of the major issues in using clustering algorithms for big data that causes confusion amongst practitioners is the lack of consensus in the definition of their properties as well as a lack of formal categorization. With the intention of alleviating these problems, this paper introduces concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as providing a comparison, both from a theoretical and an empirical perspective. From a theoretical perspective, we developed a categorizing framework based on the main properties pointed out in previous studies. Empirically, we conducted extensive experiments where we compared the most representative algorithm from each of the categories using a large number of real (big) data sets. The effectiveness of the candidate clustering algorithms is measured through a number of internal and external validity metrics, stability, runtime, and scalability tests. In addition, we highlighted the set of clustering algorithms that are the best performing for big data.


Incremental Self-Improvement For Life-Time Multi-Agent Reinforcement Learning