title: Angular Triplet-Center Loss for Multi-view 3D Shape Retrieval

abstract: How to obtain the desirable representation of a 3D shape, which is discriminative across categories and polymerized within classes, is a significant challenge in 3D shape retrieval. Most existing 3D shape retrieval methods focus on capturing strong discriminative shape representation with softmax loss for the classification task, while the shape feature learning with metric loss is neglected for 3D shape retrieval. In this paper, we address this problem based on the intuition that the cosine distance of shape embeddings should be close enough within the same class and far away across categories. Since most of 3D shape retrieval tasks use cosine distance of shape features for measuring shape similarity, we propose a novel metric loss named angular triplet-center loss, which directly optimizes the cosine distances between the features. It inherits the triplet-center loss property to achieve larger inter-class distance and smaller intra-class distance simultaneously. Unlike previous metric loss utilized in 3D shape retrieval methods, where Euclidean distance is adopted and the margin design is difficult, the proposed method is more convenient to train feature embeddings and more suitable for 3D shape retrieval. Moreover, the angle margin is adopted to replace the cosine margin in order to provide more explicit discriminative constraints on an embedding space. Extensive experimental results on two popular 3D object retrieval benchmarks, ModelNet40 and ShapeNetCore 55, demonstrate the effectiveness of our proposed loss, and our method has achieved state-ofthe-art results on various 3D shape datasets.


ICU Mortality Prediction: A Classification Algorithm for Imbalanced Datasets.


title: IEEE 802.11ac TXOP sharing technique: performance evaluation

abstract: Sharing Transmission Opportunity period (TXOP) technique is recently defined by IEEE 802.11ac to allow simultaneous downlink transmissions. In this paper, we focus on the basis of this technique and the behavior of the access point (AP) to manage the multi-user access. We discuss the different steps required to allow simultaneous downlink transmissions. Based on simulation results, we prove that TXOP sharing technique has a significant positive impact on delay performance of different access categories (ACs).


title: Argos: practical many-antenna base stations

abstract: Multi-user multiple-input multiple-output theory predicts manyfold capacity gains by leveraging many antennas on wireless base stations to serve multiple clients simultaneously through multi-user beamforming (MUBF). However, realizing a base station with a large number antennas is non-trivial, and has yet to be achieved in the real-world. We present the design, realization, and evaluation of Argos, the first reported base station architecture that is capable of serving many terminals simultaneously through MUBF with a large number of antennas (M >> 10). Designed for extreme flexibility and scalability, Argos exploits hierarchical and modular design principles, properly partitions baseband processing, and holistically considers real-time requirements of MUBF. Argos employs a novel, completely distributed, beamforming technique, as well as an internal calibration procedure to enable implicit beamforming with channel estimation cost independent of the number of base station antennas. We report an Argos prototype with 64 antennas and capable of serving 15 clients simultaneously. We experimentally demonstrate that by scaling from 1 to 64 antennas the prototype can achieve up to 6.7 fold capacity gains while using a mere 1/64th of the transmission power.


title: Highlighting interventions and user differences: informing adaptive information visualization support

abstract: There is increasing evidence that the effectiveness of information visualization techniques can be impacted by the particular needs and abilities of each user. This suggests that it is important to investigate information visualization systems that can dynamically adapt to each user. In this paper, we address the question of how to adapt. In particular, we present a study to evaluate a variety of visual prompts, called "interventions", that can be performed on a visualization to help users process it. Our results show that some of the tested interventions perform better than a condition in which no intervention is provided, both in terms of task performance as well as subjective user ratings. We also discuss findings on how intervention effectiveness is influenced by individual differences and task complexity.


title: Revisiting virtual memory for high performance computing on manycore architectures: a hybrid segmentation kernel approach

abstract: Page-based memory management (paging) is utilized by most of the current operating systems (OSs) due to its rich features such as prevention of memory fragmentation and fine-grained access control. Page-based virtual memory, however, stores virtual to physical mappings in page tables that also reside in main memory. Because translating virtual to physical addresses requires walking the page tables, which in turn implies additional memory accesses, modern CPUs employ translation lookaside buffers (TLBs) to cache the mappings. Nevertheless, TLBs are limited in size and applications that consume a large amount of memory and exhibit little or no locality in their memory access patterns, such as graph algorithms, suffer from the high overhead of TLB misses. This paper proposes a new hybrid kernel design targeting many-core CPUs, which manages the application's memory space by segmentation and offloads kernel services to dedicated CPU cores where paging is utilized. The method enables applications to run on top of the low-cost segmented memory management while allows the kernel to use the rich features of paging. We present the design and implementation of our kernel and demonstrate that segmentation can provide superior performance compared to both regular and large page based virtual memory. For example, running Graph500 on top of our segmentation design over Intel's Xeon Phi chip can yield up to 81% and 9% improvement compared to utilizing 4kB and 2MB pages in MPSS Linux, respectively.


title: Pupil: an open source platform for pervasive eye tracking and mobile gaze-based interaction

abstract: In this paper we present Pupil -- an accessible, affordable, and extensible open source platform for pervasive eye tracking and gaze-based interaction. Pupil comprises 1) a light-weight eye tracking headset, 2) an open source software framework for mobile eye tracking, as well as 3) a graphical user interface to playback and visualize video and gaze data. Pupil features high-resolution scene and eye cameras for monocular and binocular gaze estimation. The software and GUI are platform-independent and include state-of-the-art algorithms for real-time pupil detection and tracking, calibration, and accurate gaze estimation. Results of a performance evaluation show that Pupil can provide an average gaze estimation accuracy of 0.6 degree of visual angle (0.08 degree precision) with a processing pipeline latency of only 0.045 seconds.


title: Modeling Retail Transaction Data for Personalized Shopping Recommendation

abstract: Retail transaction data conveys rich preference information on brands and goods from customers. How to mine the transaction data to provide personalized recommendation to customers becomes a critical task for retailers. Previous recommendation methods either focus on the user-product matrix and ignore the transactions, or only use the partial information of transactions, leading to inferior performance in recommendation. Inspired by association rule mining, we introduce association pattern as a basic unit to capture the correlation between products from both intra- and intertransactions. A Probabilistic model over the Association Patterns (PAP for short) is then employed to learn the potential shopping interests and also to provide personalized recommendations. Experimental results on two real world retail data sets show that our proposed method can outperform the state-of-the-art recommendation methods.


title: Whispers in the dark: analysis of an anonymous social network

abstract: Social interactions and interpersonal communication has undergone significant changes in recent years. Increasing awareness of privacy issues and events such as the Snowden disclosures have led to the rapid growth of a new generation of anonymous social networks and messaging applications. By removing traditional concepts of strong identities and social links, these services encourage communication between strangers, and allow users to express themselves without fear of bullying or retaliation. Despite millions of users and billions of monthly page views, there is little empirical analysis of how services like Whisper have changed the shape and content of social interactions. In this paper, we present results of the first large-scale empirical study of an anonymous social network, using a complete 3-month trace of the Whisper network covering 24 million whispers written by more than 1 million unique users. We seek to understand how anonymity and the lack of social links affect user behavior. We analyze Whisper from a number of perspectives, including the structure of user interactions in the absence of persistent social links, user engagement and network stickiness over time, and content moderation in a network with minimal user accountability. Finally, we identify and test an attack that exposes Whisper users to detailed location tracking. We have notified Whisper and they have taken steps to address the problem.


title: Multi-task Learning for Financial Forecasting

abstract: Financial forecasting is challenging and attractive in machine learning. There are many classic solutions, as well as many deep learning based methods, proposed to deal with it yielding encouraging performance. Stock time series forecasting is the most representative problem in financial forecasting. Due to the strong connections among stocks, the information valuable for forecasting is not only included in individual stocks, but also included in the stocks related to them. However, most previous works focus on one single stock, which easily ignore the valuable information in others. To leverage more information, in this paper, we propose a jointly forecasting approach to process multiple time series of related stocks simultaneously, using multi-task learning framework. Compared to the previous works, we use multiple networks to forecast multiple related stocks, using the shared and private information of them simultaneously through multi-task learning. Moreover, we propose an attention method learning an optimized weighted combination of shared and private information based on the idea of Capital Asset Pricing Model (CAPM) to help forecast. Experimental results on various data show improved forecasting performance over baseline methods.


The Business Impact Of Social Media Analytics


title: Data mining techniques for better decisions in human resource management systems

abstract: This paper presents the role of data mining in Human Resource Management Systems (HRMS). A deep understanding of the knowledge hidden in Human Resource (HR) data is vital to a firm's competitive position and organisational decision making. Analysing the patterns and relationships in HR data is quite rare. The HR data is usually treated to answer queries. Because HR data primarily concerns transactional processing getting data into the system, recording it for reporting purposes it is necessary for HRMS to become more concerned with the quantifiable data. We show how data mining discovers and extracts useful patterns from this large data set to find observable patterns in HR. The paper demonstrates the ability of data mining in improving the quality of the decision-making process in HRMS and gives propositions regarding whether data-mining capabilities should lead to increased performance to sustain competitive advantage.


title: Beamforming techniques for enabling spatial-reuse in MCCA 802.11s networks

abstract: We address the problem of co-channel interference (CCI) in wireless mesh networks based on the IEEE802.11s extension. The carrier sensing mechanism deployed in those networks insufficiently addresses the CCI problem, causing the hidden and exposed node problems; consequently degrading the throughput and latency. In this paper, we show how beamforming techniques can be implemented on top of the IEEE802.11s medium access control protocol and, using the information readily available, cancel the interference to mitigate this inefficiency of carrier sense and improve the spatial-reuse gain. In addition, we propose the signal-to-jamming-noise ratio (SJNR) beamformer and show that it significantly improves the spatial-reuse gain compared to the simple zero-forcing (ZF) beamformer and the basic IEEE802.11s access scheme. We derive the ergodic capacity of the ZF beamformer and the basic IEEE802.11s access scheme and simulate the performance of the various schemes. We show that improvements of up to 85% are achieved as function of the scenario simulated and the beamforming technique used and that the SJNR scheme outperforms the standard ZF beamformer.


title: A Decision Support System for Performance Appraisal

abstract: Intense competition is forcing companies to develop innovative strategies to capture customer needs and improve customer satisfaction in the external business environment. Performance appraisal and employee satisfaction is high on the agenda of many companies around the world and with the growing number of improvement models now available. Employee care has to be taken to adopt an approach that will yield the modest attractive return on investment in an internal business environment. Employee performance appraisal is a subject of great interest in most organizations. This is because performance appraisal decisions have effects and consequences on workers' compensation and recognition. To deal with this performance appraisal business process, the authors proposed a Decision Support System (DSS) which consists of a mathematical model to evaluate the performance appraisal for individual and for departments in an organization. The findings of this paper provide important direction for the development of employee relation in an organization.


title: Analytical model to estimate the subthreshold swing of SOI FinFET

abstract: This paper presents an analytical model to approximate the subthreshold swing of a SOI-FinFET device. The model considers only the capacitive coupling inside the structure of the device and does not take the doping attenuation of the channel into consideration because the channel of SOI-FinFET is either undoped or lightly doped. The devolved model can be used to analyze the impacts of various device parameters on the subthreshold behavior of SOI-FinFET. It is deducted that by optimizing the internal geometric parameters it is possible to achieve a subthreshold swing (S) or inverse subthreshold slope value close to 60mv/decade for SOI-FinFET. For the existing and emerging MOS devices the value of S is much higher than 60mv/decade (the theoretical minimum for silicon devices).


title: Web Crawlers: Taxonomy, Issues & Challenges

abstract: with increase in the size of Web, the search engine relies on Web Crawlers to build and maintain the index of billions of pages for efficient searching. The creation and maintenance of Web indices is done by Web crawlers, the crawlers recursively traverses and downloads Web pages on behalf of search engines. The exponential growth of Web poses many challenges for crawlers.This paper makes an attempt to classify all the existing crawlers on certain parameters and also identifies the various challenges to web crawlers.


title: Exploring Biological Motion Regularities of Human Actions: A New Perspective on Video Analysis

abstract: The ability to detect potentially interacting agents in the surrounding environment is acknowledged to be one of the first perceptual tasks developed by humans, supported by the ability to recognise biological motion. The precocity of this ability suggests that it might be based on rather simple motion properties, and it can be interpreted as an atomic building block of more complex perception tasks typical of interacting scenarios, as the understanding of non-verbal communication cues based on motion or the anticipation of others’ action goals. In this article, we propose a novel perspective for video analysis, bridging cognitive science and machine vision, which leverages the use of computational models of the perceptual primitives that are at the basis of biological motion perception in humans. Our work offers different contributions. In a first part, we propose an empirical formulation for the Two-Thirds Power Law, a well-known invariant law of human movement, and thoroughly discuss its readability in experimental settings of increasing complexity. In particular, we consider unconstrained video analysis scenarios, where, to the best of our knowledge, the invariant law has not found application so far. The achievements of this analysis pave the way for the second part of the work, in which we propose and evaluate a general representation scheme for biological motion characterisation to discriminate biological movements with respect to non-biological dynamic events in video sequences. The method is proposed as the first layer of a more complex architecture for behaviour analysis and human-machine interaction, providing in particular a new way to approach the problem of human action understanding.


title: Polarimetric Radar Barcode: A Novel Chipless RFID Concept With High Data Capacity and Ultimate Tag Robustness

abstract: This paper presents a novel chipless radio frequency identification (RFID) concept where the information is encoded on the tag with a 2-D polarimetric radar barcode. We achieved a data capacity with this technique that is outstanding for chipless RFID. Even though these chipless RFID tags are simple, they are extremely robust in harsh environments. The polarimetric radar barcode is formed by a set of geometric structures like small dihedral mirrors, plane surface elements, or dipole elements with different polarimetric backscattering behaviors. These structures are arranged in a 2-D array to create a specific polarimetric 2-D radar barcode. The information is read by polarimetric millimeter-wave imaging radar. The lateral resolution of the radar determines the minimum resolvable element size and is therefore critical for the maximum data capacity of the tag. We show that, theoretically, a data capacity of more than 800 bit on a surface less than the size of a credit card can be achieved if a millimeter-wave multiple-input multiple-output radar is used as reader. The polarimetric encoded reflectors can be formed by metal or reflecting ceramic structures, and the tag can consist of a single material. Thus, tag robustness is limited only by the robustness and temperature resistance of the material used. We verify the performance and functionality of the proposed chipless RFID concept with measurements taken in a laboratory test setup with a 20-bit tag and a millimeter-wave radar system with 77.5-GHz center frequency.


title: Determining Factors in the Acceptance of Social Shopping Websites

abstract: While many businesses seek to seize the momentum of social networking sites, the combination of technology-enabled social networks and e-commerce may offer significant opportunities for retailers. Social shopping sites emerged as the latest developments to leverage the power of social networking with online shopping. Despite enormous business interests and potentials, little is known about whether users will adopt such systems. This paper extended the Technology Acceptance Model (TAM) to understand the adoption of social shopping sites. Besides perceived ease of use and perceived usefulness as proposed in TAM, this study explores three additional constructs: an online shopper’s tendency to social comparison, social presence, and perceived enjoyment in using the website. Results suggest that perceived usefulness and perceived enjoyment both affect behavioral intention to adopt social shopping sites. Users’ tendency to social comparison and perception of social presence are also significant antecedents in the model. Directions for future research are discussed.


N-soliton collision in the Manakov model


title: Stochastic nonlinear model predictive control with probabilistic constraints

abstract: Stochastic uncertainties are ubiquitous in complex dynamical systems and can lead to undesired variability of system outputs and, therefore, a notable degradation of closed- loop performance. This paper investigates model predictive control of nonlinear dynamical systems subject to probabilistic parametric uncertainties. A nonlinear model predictive control framework is presented for control of the probability dis- tribution of system states while ensuring the satisfaction of constraints with some desired probability levels. To obtain a computationally tractable formulation for real control applica- tions, polynomial chaos expansions are utilized to propagate the probabilistic parametric uncertainties through the system model. The paper considers individual probabilistic constraints, which are converted explicitly into convex second-order cone constraints for a general class of probability distributions. An algorithm is presented for receding horizon implementation of the finite-horizon stochastic optimal control problem. The capability of the stochastic model predictive control approach in terms of shaping the probability distribution of system states and fulfilling state constraints in a stochastic setting is demon- strated for optimal control of polymorphic transformation in batch crystallization.


title: Communication in Distributed Agile Development: A Case Study

abstract: Distributed software development is an increasingly important development approach for software companies as it brings tempting opportunities. Distributed development is already burdened with several problems and agile methods bring further challenges in the form of their reliance on verbal communication and volatile requirements. There is little empirical knowledge on distributed agile software development. We conducted two distributed agile software development case studies and compared our findings against existing recommendations about communication in distributed agile development. Our findings along with existing literature conclude that presented recommendations are worthwhile considering in distributed agile development, but with some caution. Our empirically based findings indicate that the role of a well-defined customer is the key recommendation. The lack of a well-defined customer able to meet responsibilities, as well as volatile requirements and inefficient communication, can cause severe problems even in small-scale distributed agile software development projects. Discussed recommendations are complemented with an additional recommendation.


Handbook of psychophysiology


title: The Work Life of Developers: Activities, Switches and Perceived Productivity

abstract: Many software development organizations strive to enhance the productivity of their developers. All too often, efforts aimed at improving developer productivity are undertaken without knowledge about how developers spend their time at work and how it influences their own perception of productivity. To fill in this gap, we deployed a monitoring application at 20 computers of professional software developers from four companies for an average of 11 full work day in situ . Corroborating earlier findings, we found that developers spend their time on a wide variety of activities and switch regularly between them, resulting in highly fragmented work. Our findings extend beyond existing research in that we correlate developers’ work habits with perceived productivity and also show productivity is a personal matter. Although productivity is personal, developers can be roughly grouped into morning, low-at-lunch and afternoon people. A stepwise linear regression per participant revealed that more user input is most often associated with a positive, and emails, planned meetings and work unrelated websites with a negative perception of productivity. We discuss opportunities of our findings, the potential to predict high and low productivity and suggest design approaches to create better tool support for planning developers’ work day and improving their personal productivity.


title: Techniques for the production of point-light and fully illuminated video displays from identical recordings

abstract: Illumination of only a few key points on a moving human body or face is enough to convey a compelling perception of human motion. A full understanding of the perception ofbiological motion from point-light displays requires accurate comparison with the perception of motion in normal, fully illuminated versions of the same images. Traditionally, these two types of stimuli (point-light and fully illuminated) have been filmed separately, allowing the introduction of uncontrolled variation across recordings. This is undesirable for accurate comparison of perceptual performance across the two types of display. This article describes simple techniques, using proprietary software, that allow production of point-light and fully illuminated video displays from identical recordings. These techniques are potentially useful for many studies of motion perception, by permitting precise comparison of perceptual performances across point-light displays and their fully illuminated counterparts with accuracy and comparative ease.


title: A Computational Model of Linguistic Humor in Puns

abstract: Humor plays an essential role in human interactions. Precisely what makes something funny, however, remains elusive. While research on natural language understanding has made significant advancements in recent years, there has been little direct integration of humor research with computational models of language understanding. In this paper, we propose two information-theoretic measures—ambiguity and distinctiveness—derived from a simple model of sentence processing. We test these measures on a set of puns and regular sentences and show that they correlate significantly with human judgments of funniness. Moreover, within a set of puns, the distinctiveness measure distinguishes exceptionally funny puns from mediocre ones. Our work is the first, to our knowledge, to integrate a computational model of general language understanding and humor theory to quantitatively predict humor at a fine-grained level. We present it as an example of a framework for applying models of language processing to understand higher level linguistic and cognitive phenomena.


title: Lower Extremity Exoskeleton: Review and Challenges Surrounding the Technology and its Role in Rehabilitation of Lower Limbs

abstract: With the advancement in the area of robotics, exoskeleton technology has come a long way since its beginnings in the late 60's. Researchers over the world have developed their own exoskeleton prototypes, and some of the well known exoskeleton includes the BLEEX, MIT exoskeleton, HAL, LOPES, ALEX, and many more. Although technologies have advanced since the 60's, challenges still exist in exoskeleton design. This paper reviews different exoskeleton technologies as well as its role in the area of rehabilitation. This purpose in rehabilitation is promising, based on countless of researches which have been carried out.


title: Full Machine Translation for Factoid Question Answering

abstract: In this paper we present an SMT-based approach to Question Answering (QA). QA is the task of extracting exact answers in response to natural language questions. In our approach, the answer is a translation of the question obtained with an SMT system. We use the n-best translations of a given question to find similar sentences in the document collection that contain the real answer. Although it is not the first time that SMT inspires a QA system, it is the first approach that uses a full Machine Translation system for generating answers. Our approach is validated with the datasets of the TREC QA evaluation.


title: Hybrid Indoor Positioning with Wi-Fi and Bluetooth: Architecture and Performance

abstract: Reliable indoor positioning is an important foundation for emerging indoor location based services. Most existing indoor positioning proposals rely on a single wireless technology, e.g., Wi-Fi, Bluetooth, or RFID. A hybrid positioning system combines such technologies and achieves better positioning accuracy by exploiting the different capabilities of the different technologies. In a hybrid system based on Wi-Fi and Bluetooth, the former works as the main infrastructure to enable fingerprint based positioning, while the latter (via hotspot devices) partitions the indoor space as well as a large Wi-Fi radio map. As a result, the Wi-Fi based online position estimation is improved in a divide-and-conquer manner. We study three aspects of such a hybrid indoor positioning system. First, to avoid large positioning errors caused by similar reference positions that are hard to distinguish, we design a deployment algorithm that identifies and separates such positions into different smaller radio maps by deploying Bluetooth hotspots at particular positions. Second, we design methods that improve the partition switching that occurs when a user leaves the detection range of a Bluetooth hotspot. Third, we propose three architectural options for placement of the computation workload. We evaluate all proposals using both simulation and walkthrough experiments in two indoor environments of different sizes. The results show that our proposals are effective and efficient in achieving very good indoor positioning performance.


title: An exploration of affect factors and their role in user technology acceptance: Mediation and causality

abstract: Affect factors have gained researchers' attention in a number of fields. The Information Systems (IS) literature, however, shows some gaps and inconsistencies regarding the role of affect factors in human–computer interaction. Building upon prior research, this study aims at a better understanding of affect factors by clarifying their relationships with each other and with other primary user acceptance factors. Two affect variables that are different in nature were examined: computer playfulness (CP) and perceived enjoyment (PE). We theoretically clarified and methodologically verified their mediating effects and causal relationships with other primary factors influencing user technology acceptance, namely perceived ease of use (PEOU), perceived usefulness (PU), and behavioral intention (BI). Quantitative data were analyzed using R.M. Baron and D. Kenny's (1986) method for mediating effects and P.R. Cohen, A. Carlsson, L. Ballesteros, and R.S. Amant's (1993) path analysis method for causal relationships. These analyses largely supported our hypotheses. Results from this research indicate that a PE → PEOU causal direction is favored, and PEOU partially mediates PE's impacts on PU whereas PE fully mediates CP's impact on PEOU. With the increased interest in various affect factors in user technology acceptance and use, our study sheds light on the role of affect factors from both theoretical and methodological perspectives. Practical implications are discussed as well. © 2008 Wiley Periodicals, Inc.


title: Scaling and Scheduling to Maximize Application Performance within Budget Constraints in Cloud Workflows

abstract: It remains a challenge to provision resources in the cloud such that performance is maximized and financial cost is minimized. A fixed budget can be used to rent a wide variety of resource configurations for varying durations. The two steps - resource acquisition and scheduling/allocation - are dependent on each other and are particularly difficult when considering complex resource usage such as workflows, where task precedence need to be preserved and the budget constraint is assigned for the whole cloud application instead of every single job. The ability to acquire resources dynamically and trivially in the cloud - while being incredibly powerful and useful - exacerbates this particular resource acquisition and scheduling problem. In this paper, we design, implement and evaluate two auto-scaling solutions to minimize job turnaround time within budget constraints for cloud workflows. The scheduling-first algorithm distributes the application-wide budget to each individual job, determines the fastest execution plan and then acquires the cloud resources, while the scaling-first algorithm determines the size and the type of the cloud resources first and then schedules the workflow jobs on the acquired instances. The scaling-first algorithm shows better performance when the budget is low while the scheduling-first algorithm performs better when the budget is high. The two algorithms can reduce the job turnaround time by 9.6% - 45.2% compared to choosing a fixed general machine type. Moreover, they show good tolerance (between -10.2% and 16.7%) to inaccurate parameters (±20% estimation error).


Investigation on road-sign recognition


title: Short paper: detection of GPS spoofing attacks in power grids

abstract: Power companies are deploying a multitude of sensors to monitor the energy grid. Measurements at different locations should be aligned in time to obtain the global state of the grid, and the industry therefore uses GPS as a common clock source. However, these sensors are exposed to GPS time spoofing attacks that cause misaligned aggregated measurements, leading to inaccurate monitoring that affects power stability and line fault contingencies. In this paper, we analyze the resilience of phasor measurement sensors, which record voltages and currents, to GPS spoofing performed by an adversary external to the system. We propose a solution that leverages the characteristics of multiple sensors in the power grid to limit the feasibility of such attacks. In order to increase the robustness of wide-area power grid monitoring, we evaluate mechanisms that allow collaboration among GPS receivers to detect spoofing attacks. We apply multilateration techniques to allow a set of GPS receivers to locate a false GPS signal source. Using simulations, we show that receivers sharing a local clock can locate nearby spoofing adversaries with sufficient confidence.


title: Word Association Norms, Mutual Information, And Lexicography

abstract: The term word association is used in a very particular sense in the psycholinguistic literature. (Generally speaking, subjects respond quicker than normal to the word "nurse" if it follows a highly associated word such as "doctor.") We will extend the term to provide the basis for a statistical description of a variety of interesting linguistic phenomena, ranging from semantic relations of the doctor/nurse type (content word/content word) to lexico-syntactic co-occurrence constraints between verbs and prepositions (content word/function word). This paper will propose a new objective measure based on the information theoretic notion of mutual information, for estimating word association norms from computer readable corpora. (The standard method of obtaining word association norms, testing a few thousand subjects on a few hundred words, is both costly and unreliable.) The proposed measure, the association ratio, estimates word association norms directly from computer readable corpora, making it possible to estimate norms for tens of thousands of words.


title: Building reconstruction using manhattan-world grammars

abstract: We present a passive computer vision method that exploits existing mapping and navigation databases in order to automatically create 3D building models. Our method defines a grammar for representing changes in building geometry that approximately follow the Manhattan-world assumption which states there is a predominance of three mutually orthogonal directions in the scene. By using multiple calibrated aerial images, we extend previous Manhattan-world methods to robustly produce a single, coherent, complete geometric model of a building with partial textures. Our method uses an optimization to discover a 3D building geometry that produces the same set of facade orientation changes observed in the captured images. We have applied our method to several real-world buildings and have analyzed our approach using synthetic buildings.


title: A SURVEY OF CLUSTERING ENSEMBLE ALGORITHMS

abstract: Cluster ensemble has proved to be a good alternative when facing cluster analysis problems. It consists of generating a set of clusterings from the same dataset and combining them into a final clustering. The goal of this combination process is to improve the quality of individual data clusterings. Due to the increasing appearance of new methods, their promising results and the great number of applications, we consider that it is necessary to make a critical analysis of the existing techniques and future projections. This paper presents an overview of clustering ensemble methods that can be very useful for the community of clustering practitioners. The characteristics of several methods are discussed, which may help in the selection of the most appropriate one to solve a problem at hand. We also present a taxonomy of these techniques and illustrate some important applications.


title: Data measurement in research information systems: metrics for the evaluation of data quality

abstract: In recent years, research information systems (RIS) have become an integral part of the university's IT landscape. At the same time, many universities and research institutions are still working on the implementation of such information systems. Research information systems support institutions in the measurement, documentation, evaluation and communication of research activities. Implementing such integrative systems requires that institutions assure the quality of the information on research activities entered into them. Since many information and data sources are interwoven, these different data sources can have a negative impact on data quality in different research information systems. Because the topic is currently of interest to many institutions, the aim of the present paper is firstly to consider how data quality can be investigated in the context of RIS, and then to explain how various dimensions of data quality described in the literature can be measured in research information systems. Finally, a framework as a process flow according to UML activity diagram notation is developed for monitoring and improvement of the quality of these data; this framework can be implemented by technical personnel in universities and research institutions.


title: Analysis and identification of spamming behaviors in Sina Weibo microblog

abstract: Spamming has been a widespread problem for social networks. In recent years there is an increasing interest in the analysis of anti-spamming for microblogs, such as Twitter. In this paper we present a systematic research on the analysis of spamming in Sina Weibo platform, which is currently a dominant microblogging service provider in China. Our research objectives are to understand the specific spamming behaviors in Sina Weibo and find approaches to identify and block spammers in Sina Weibo based on spamming behavior classifiers. To start with the analysis of spamming behaviors we devise several effective methods to collect a large set of spammer samples, including uses of proactive honeypots and crawlers, keywords based searching and buying spammer samples directly from online merchants. We processed the database associated with these spammer samples and interestingly we found three representative spamming behaviors: aggressive advertising, repeated duplicate reposting and aggressive following. We extract various features and compare the behaviors of spammers and legitimate users with regard to these features. It is found that spamming behaviors and normal behaviors have distinct characteristics. Based on these findings we design an automatic online spammer identification system. Through tests with real data it is demonstrated that the system can effectively detect the spamming behaviors and identify spammers in Sina Weibo.


The State of Automated Bridge Play


title: The selection of fusion levels in thoracic idiopathic scoliosis.

abstract: From the material and data reviewed in our study of 405 patients, it appears that postoperative correction of the thoracic spine approximately equals the correction noted on preoperative side-bending roentgenograms. Selective thoracic fusion can be safely performed on a Type-II curve of less than 80 degrees, but care must be taken to use the vertebra that is neutral and stable so that the lower level of the fusion is centered over the sacrum. The lumbar curve spontaneously corrects to balance the thoracic curve when selective thoracic fusion is performed and the lower level of fusion is properly selected. In Type-III, IV, and V thoracic curves the lower level of fusion should be centered over the sacrum to achieve a balanced, stable spine.


Effect of Coconut Oil, Coconut Water and Palm Kernel Oil on Some Biochemical Parameters in Albino Rats.


title: Network Recasting: A Universal Method for Network Architecture Transformation

abstract: This paper proposes network recasting as a general method for network architecture transformation. The primary goal of this method is to accelerate the inference process through the transformation, but there can be many other practical applications. The method is based on block-wise recasting; it recasts each source block in a pre-trained teacher network to a target block in a student network. For the recasting, a target block is trained such that its output activation approximates that of the source block. Such a block-by-block recasting in a sequential manner transforms the network architecture while preserving the accuracy. This method can be used to transform an arbitrary teacher network type to an arbitrary student network type. It can even generate a mixed-architecture network that consists of two or more types of block. The network recasting can generate a network with fewer parameters and/or activations, which reduce the inference time significantly. Naturally, it can be used for network compression by recasting a trained network into a smaller network of the same type. Our experiments show that it outperforms previous compression approaches in terms of actual speedup on a GPU.


title: Extending Neural Generative Conversational Model using External Knowledge Sources

abstract: The use of connectionist approaches in conversational agents has been progressing rapidly due to the availability of large corpora. However current generative dialogue models often lack coherence and are content poor. This work proposes an architecture to incorporate unstructured knowledge sources to enhance the next utterance prediction in chit-chat type of generative dialogue models. We focus on Sequence-to-Sequence (Seq2Seq) conversational agents trained with the Reddit News dataset, and consider incorporating external knowledge from Wikipedia summaries as well as from the NELL knowledge base. Our experiments show faster training time and improved perplexity when leveraging external knowledge.


title: Androids as an Experimental Apparatus: Why Is There an Uncanny Valley and Can We Exploit It?

abstract: Abstract. It seems natural to assume that the more It seems natural to assume that the more closely robots come to resemble people, the more likely they are to elicit the kinds of responses people direct toward each other. However, subtle flaws in appearance and movement only seem eerie in very humanlike robots. This uncanny phenomenon may be symptomatic of entities that elicit a model of a human other but do not measure up to it. If so, a very humanlike robot may provide the best means of finding out what kinds of behavior are perceived as human, since deviations from a human other are more obvious. In pursuing this line of inquiry, it is essential to identify the mechanisms involved in evaluations of human likeness. One hypothesis is that an uncanny robot elicits an innate fear of death and culturally-supported defenses for coping with death’s inevitability. An experiment, which borrows from the methods of terror management research, was performed to test this hypothesis. Across all questions subjects who were exposed to a still image of an uncanny humanlike robot had on average a heightened preference for worldview supporters and a diminished preference for worldview threats relative to the control group.


title: The Social-Emotional Processing Stream: Five Core Constructs and Their Translational Potential for Schizophrenia and Beyond

abstract: Background Cognitive neuroscience approaches to translational research have made great strides toward understanding basic mechanisms of dysfunction and their relation to cognitive deficits, such as thought disorder in schizophrenia. The recent emergence of Social Cognitive and Affective Neuroscience has paved the way for similar progress to be made in explaining the mechanisms underlying the social and emotional dysfunctions (i.e., negative symptoms) of schizophrenia and that characterize virtually all DSM Axis I and II disorders more broadly. Methods This article aims to provide a roadmap for this work by distilling from the emerging literature on the neural bases of social and emotional abilities a set of key constructs that can be used to generate questions about the mechanisms of clinical dysfunction in general and schizophrenia in particular. Results To achieve these aims, the first part of this article sketches a framework of five constructs that comprise a social-emotional processing stream. The second part considers how future basic research might flesh out this framework and translational work might relate it to schizophrenia and other clinical populations. Conclusions Although the review suggests there is more basic research needed for each construct, two in particular—one involving the bottom-up recognition of social and emotional cues, the second involving the use of top-down processes to draw mental state inferences—are most ready for translational work.


title: Short-term load forecasting for the holidays using fuzzy linear regression method

abstract: Summary form only given. Average load forecasting errors for the holidays are much higher than those for weekdays. So far, many studies on the short-term load forecasting have been made to improve the prediction accuracy using various methods such as deterministic, stochastic, artificial neural net (ANN) and neural network-fuzzy methods. In order to reduce the load forecasting error of the 24 hourly loads for the holidays, the concept of fuzzy regression analysis is employed in the short-term load forecasting problem. According to the historical load data, the same type of holiday showed a similar trend of load profile as in previous years. The fuzzy linear regression model is made from the load data of the previous three years and the coefficients of the model are found by solving the mixed linear programming problem. The proposed algorithm shows good accuracy, and the average maximum percentage error is 3.57% in the load forecasting of the holidays for the years of 1996-1997.


title: Decoupled Double Synchronous Reference Frame PLL for Power Converters Control

abstract: This paper deals with a crucial aspect in the control of grid-connected power converters, i.e., the detection of the fundamental-frequency positive-sequence component of the utility voltage under unbalanced and distorted conditions. Specifically, it proposes a positive-sequence detector based on a new decoupled double synchronous reference frame phase-locked loop (DDSRF-PLL), which completely eliminates the detection errors of conventional synchronous reference frame PLL's (SRF-PLL). This is achieved by transforming both positive- and negative-sequence components of the utility voltage into the double SRF, from which a decoupling network is developed in order to cleanly extract and separate the positive- and negative-sequence components. The resultant DDSRF-PLL conducts then to a fast, precise, and robust positive-sequence voltage detection even under unbalanced and distorted grid conditions. The paper presents a detailed description and derivation of the proposed detection method, together with an extensive evaluation using simulation and experimental results from a digital signal processor-based laboratory prototype in order to verify and validate the excellent performance achieved by the DDSRF-PLL


title: Exemplar Component Analysis: A Fast Band Selection Method for Hyperspectral Imagery

abstract: How to find the representative bands is a key issue in band selection for hyperspectral data. Very often, unsupervised band selection is associated with data clustering, and the cluster centers (or exemplars) are considered ideal representatives. However, partitioning the bands into clusters may be very time-consuming and affected by the distribution of the data points. In this letter, we propose a new band selection method, i.e., exemplar component analysis (ECA), aiming at selecting the exemplars of bands. Interestingly, ECA does not involve actual clustering. Instead, it prioritizes the bands according to their exemplar score, which is an easy-to-compute indicator defined in this letter measuring the possibility of bands to be exemplars. As a result, ECA is of high efficiency and immune to distribution structures of the data. The experiments on real hyperspectral data set demonstrate that ECA is an effective and efficient band selection method.


title: Purposeful selection of variables in logistic regression

abstract: BackgroundThe main problem in many model-building situations is to choose from a large set of covariates those that should be included in the "best" model. A decision to keep a variable in the model might be based on the clinical or statistical significance. There are several variable selection algorithms in existence. Those methods are mechanical and as such carry some limitations. Hosmer and Lemeshow describe a purposeful selection of covariates within which an analyst makes a variable selection decision at each step of the modeling process.MethodsIn this paper we introduce an algorithm which automates that process. We conduct a simulation study to compare the performance of this algorithm with three well documented variable selection procedures in SAS PROC LOGISTIC: FORWARD, BACKWARD, and STEPWISE.ResultsWe show that the advantage of this approach is when the analyst is interested in risk factor modeling and not just prediction. In addition to significant covariates, this variable selection procedure has the capability of retaining important confounding variables, resulting potentially in a slightly richer model. Application of the macro is further illustrated with the Hosmer and Lemeshow Worchester Heart Attack Study (WHAS) data.ConclusionIf an analyst is in need of an algorithm that will help guide the retention of significant covariates as well as confounding ones they should consider this macro as an alternative tool.


title: Domain adaptation for semantic role labeling in the biomedical domain

abstract: Motivation: Semantic role labeling (SRL) is a natural language processing (NLP) task that extracts a shallow meaning representation from free text sentences. Several efforts to create SRL systems for the biomedical domain have been made during the last few years. However, state-of-the-art SRL relies on manually annotated training instances, which are rare and expensive to prepare. In this article, we address SRL for the biomedical domain as a domain adaptation problem to leverage existing SRL resources from the newswire domain. ::: ::: Results: We evaluate the performance of three recently proposed domain adaptation algorithms for SRL. Our results show that by using domain adaptation, the cost of developing an SRL system for the biomedical domain can be reduced significantly. Using domain adaptation, our system can achieve 97% of the performance with as little as 60 annotated target domain abstracts. ::: ::: Availability: Our BioKIT system that performs SRL in the biomedical domain as described in this article is implemented in Python and C and operates under the Linux operating system. BioKIT can be downloaded at http://nlp.comp.nus.edu.sg/software. The domain adaptation software is available for download at http://www.mysmu.edu/faculty/jingjiang/software/DALR.html. The BioProp corpus is available from the Linguistic Data Consortium http://www.ldc.upenn.edu ::: ::: Contact: nght@comp.nus.edu.sg


title: Fearful faces selectively increase corticospinal motor tract excitability: A transcranial magnetic stimulation study

abstract: Fearful facial expressions are danger signals that rapidly trigger a cascade of neurobiological processes defensibly associated with action preparation. However, direct evidence for the activating effects of fearful facial expressions on the motor system is absent. The current transcranial magnetic stimulation (TMS) study investigated whether fearful facial expressions selectively increase corticospinal motor tract (CST) excitability. Focal TMS was applied over the left primary motor cortex during the exposure of fearful, happy, and neutral facial expressions in 12 healthy right-handed volunteers. Changes in CST excitability using the motor evoked potential (MEP) were recorded. Results showed significant selective increases in MEP to fearful facial expressions. These findings provide the first direct evidence for selective increases in CST excitability to threat and contribute to evolutionary views on emotion and action preparedness.


title: Review of trust and machine ethics research: towards a bio-inspired computational model of ethical trust (CMET)

abstract: Recent advances in the fields of robotics, cyborg development, moral psychology, trust, multi agent-based systems and socionics have raised the need for a better understanding of ethics, moral reasoning, judgment and decision-making within the system of man and machines. Here we seek to understand key research questions concerning the interplay of ethical trust at the individual level and the social moral norms at the collective end. We review salient works in the fields of trust and machine ethics research, underscore the importance and the need for a deeper understanding of ethical trust at the individual level and the development of collective social moral norms. Drawing upon the recent findings from neural sciences on mirror-neuron system (MNS) and social cognition, we present a bio-inspired Computational Model of Ethical Trust (CMET) to allow investigations of the interplay of ethical trust and social moral norms.


title: What We Can Learn from Looking at Profanity

abstract: Profanity is a common occurrence in online text. Recent studies found swearing words in over 7% of English tweets and 9% of Yahoo! Buzz messages. However, efforts in recognizing, understanding and dealing with profanity do not share resources, namely, their dataset, which imposes duplication of effort and non-comparable results.


title: Big Data and Business Intelligence: Debunking the Myths

abstract: Big data is one of the most discussed, and possibly least understood, terms in use in business today. Big data is said to offer not only unprecedented levels of business intelligence concerning the habits of consumers and rivals, but also to herald a revolution in the way in which business are organized and run. However, big data is not as straightforward as it might seem, particularly when it comes to the so-called dark data from social media. It is not simply the quantity of data that has changed, it is also the speed and the variety of formats with which it is delivered. This article sets out to look at big data and debunk some of the myths that surround it. It focuses on the role of data from social media in particular and highlights two common myths about big data. The first is that because a data set contains billions of items, traditional methodological issues no longer matter. The second is the belief that big data is both a complete and unbiased source of data upon which to base decisions.


title: Aggregate-query processing in data warehousing environments

abstract: In this paper we introduce generalized projections (G P an extension of duplicateeliminating projections, that capture aggregations, groupbys, duplicate-eliminating projections (distinctand duplicate-preserving projections in a common unified framework. Using G P s we extend well known and simple algorithms for SQL queries that use distinct projections to derive algorithms for queries using aggregations like sum, max, min, count, and avg. We develop powerful query rewrite rules for aggregate queries that unify and extend rewrite rules previously known in the literature. We then illustrate the power of our approach by solving a very practical and important problem in data warehousing: how to answer an aggregate query on base tables using materialized aggregate views (summary


Mining User Reviews to Recommend Popular Dishes ∗ †


title: Efficient Support Vector Classifiers For Named Entity Recognition

abstract: Named Entity (NE) recognition is a task in which proper nouns and numerical information are extracted from documents and are classified into categories such as person, organization, and date. It is a key technology of Information Extraction and Open-Domain Question Answering. First, we show that an NE recognizer based on Support Vector Machines (SVMs) gives better scores than conventional systems. However, off-the-shelf SVM classifiers are too inefficient for this task. Therefore, we present a method that makes the system substantially faster. This approach can also be applied to other similar tasks such as chunking and part-of-speech tagging. We also present an SVM-based feature selection method and an efficient training method.


title: Using category theory to assess the relationship between consciousness and integrated information theory

abstract: One of the most mysterious phenomena in science is the nature of conscious experience. Due to its subjective nature, a reductionist approach is having a hard time in addressing some fundamental questions about consciousness. These questions are squarely and quantitatively tackled by a recently developed theoretical framework, called integrated information theory (IIT) of consciousness. In particular, IIT proposes that a maximally irreducible conceptual structure (MICS) is identical to conscious experience. However, there has been no principled way to assess the claimed identity. Here, we propose to apply a mathematical formalism, category theory, to assess the proposed identity and suggest that it is important to consider if there exists a proper translation between the domain of conscious experience and that of the MICS. If such translation exists, we postulate that questions in one domain can be answered in the other domain; very difficult questions in the domain of consciousness can be resolved in the domain of mathematics. We claim that it is possible to empirically test if such a functor exists, by using a combination of neuroscientific and computational approaches. Our general, principled and empirical framework allows us to assess the relationship between the domain of consciousness and the domain of mathematical structures, including those suggested by IIT.


title: A LONGITUDINAL STUDY OF TRUST AND PERCEIVED USEFULNESS IN CONSUMER ACCEPTANCE OF AN ESERVICE: THE CASE OF ONLINE HEALTH SERVICES

abstract: Despite the proliferation of e-services, they are still characterized by uncertainties. As result, consumer trust beliefs are considered an important determinant of e-service adoption. Past work has not however considered the potentially dynamic nature of these trust beliefs, and how early-stage trust might influence later-stage adoption and use. To address this gap, this study draws on the theory of reasoned action and expectation-confirmation theory to carry out a longitudinal study of trust in eservices. Specifically, we examine how trust interacts with other consumer beliefs, such as perceived usefulness, and how together these beliefs influence consumer intentions and actual behaviours toward e-services at both initial and later stages of use. The empirical context is online health information services. Data collection was carried out at two time periods, approximately 7 weeks apart using a student population. The results show that perceived usefulness and trust are important at both initial and later stages in consumer acceptance of online health services. Consumers’ actual usage experiences modify perceptions of usefulness and influence the confirmation of their initial expectations. These results have implications for our understanding of the dynamic nature of trust and perceived usefulness, and their roles in long term success of e-services.


title: SoundNet: investigating a language composed of environmental sounds

abstract: Auditory displays have been used in both human-machine and computer interfaces. However, the use of non-speech audio in assistive communication for people with language disabilities, or in other applications that employ visual representations, is still under-investigated. In this paper, we introduce SoundNet, a linguistic database that associates natural environmental sounds with words and concepts. A sound labeling study was carried out to verify SoundNet associations and to investigate how well the sounds evoke concepts. A second study was conducted using the verified SoundNet data to explore the power of environmental sounds to convey concepts in sentence contexts, compared with conventional icons and animations. Our results show that sounds can effectively illustrate (especially concrete) concepts and can be applied to assistive interfaces.


title: Towards automatic pulmonary nodule management in lung cancer screening with deep learning

abstract: The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.


title: Computational aspects of a branch and bound algorithm for quadratic zero-one programming

abstract: In this paper we describe computational experience in solving unconstrained quadratic zero-one problems using a branch and bound algorithm. The algorithm incorporates dynamic preprocessing techniques for forcing variables and heuristics to obtain good starting points. Computational results and comparisons with previous studies on several hundred test problems with dimensions up to 200 demonstrate the efficiency of our algorithm.


title: Natasha 2: Faster Non-Convex Optimization Than SGD

abstract: We design a stochastic algorithm to train any smooth neural network to $\varepsilon$-approximate local minima, using $O(\varepsilon^{-3.25})$ backpropagations. The best result was essentially $O(\varepsilon^{-4})$ by SGD. ::: More broadly, it finds $\varepsilon$-approximate local minima of any smooth nonconvex function in rate $O(\varepsilon^{-3.25})$, with only oracle access to stochastic gradients.


Some Future Trends and Implications for Systems and Software Engineering Processes


title: Evolution of Mobile Wireless Communication Networks-1G to 5G as well as Future Prospective of Next Generation Communication Network

abstract: Mobile communication systems revolutionized the way people communicate. Evolution of wireless access technologies is about to reach its fourth ge neration (4G) and the 5G mobile networks will focus on the development of the user terminals where the terminals will have access to different wireless technolog ies at the same time and will combine different flows from different technologies. Looking past, wireless acc ess technologies have followed different evolutionary paths aimed at unified target related to performance and efficiency in high mobile environment. The first ge neration (1G) has fulfilled the basic mobile voice, while the second generation (2G) has introduced capacity and coverage. This is followed by the third generat ion (3G), which has quest for data at higher speeds to open the gates for truly "mobile broadband" experience, which was further realized by the fourth generation (4G).The Fourth generation (4G) provides access to wide range of telecommunication services, including advanced mobile services, supported by mobile and fixed networks, which are increasingly packet based, alon g with a support for low to high mobility applicati ons and wide range of data rates, in accordance with servic e demands in multiuser environment. Fifth generation should be more intelligent technology that intercon nects the entire world. This article provides a hig h level overview of the Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX)-the leading technologies for next-generation mobile broadband.


title: Spectral Ensemble Clustering

abstract: Ensemble clustering, also known as consensus clustering, is emerging as a promising solution for multi-source and/or heterogeneous data clustering. The co-association matrix based method, which redefines the ensemble clustering problem as a classical graph partition problem, is a landmark method in this area. Nevertheless, the relatively high time and space complexity preclude it from real-life large-scale data clustering. We therefore propose SEC, an efficient Spectral Ensemble Clustering method based on co-association matrix. We show that SEC has theoretical equivalence to weighted K-means clustering and results in vastly reduced algorithmic complexity. We then derive the latent consensus function of SEC, which to our best knowledge is among the first to bridge co-association matrix based method to the methods with explicit object functions. The robustness and generalizability of SEC are then investigated to prove the superiority of SEC in theory. We finally extend SEC to meet the challenge rising from incomplete basic partitions, based on which a scheme for big data clustering can be formed. Experimental results on various real-world data sets demonstrate that SEC is an effective and efficient competitor to some state-of-the-art ensemble clustering methods and is also suitable for big data clustering.


title: Music and emotions: from enchantment to entrainment.

abstract: Producing and perceiving music engage a wide range of sensorimotor, cognitive, and emotional processes. Emotions are a central feature of the enjoyment of music, with a large variety of affective states consistently reported by people while listening to music. However, besides joy or sadness, music often elicits feelings of wonder, nostalgia, or tenderness, which do not correspond to emotion categories typically studied in neuroscience and whose neural substrates remain largely unknown. Here we review the similarities and differences in the neural substrates underlying these "complex" music-evoked emotions relative to other more "basic" emotional experiences. We suggest that these emotions emerge through a combination of activation in emotional and motivational brain systems (e.g., including reward pathways) that confer its valence to music, with activation in several other areas outside emotional systems, including motor, attention, or memory-related regions. We then discuss the neural substrates underlying the entrainment of cognitive and motor processes by music and their relation to affective experience. These effects have important implications for the potential therapeutic use of music in neurological or psychiatric diseases, particularly those associated with motor, attention, or affective disturbances.


An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea


title: Quality of life after stroke in Pakistan

abstract: There is very little information about the quality of life (QOL) of stroke survivors in LMIC countries with underdeveloped non communicable health infrastructures, who bear two thirds of the global stroke burden. We used a sequential mix methods approach. First, a quantitative analytical cross-sectional study was conducted on 700 participants, who constituted 350 stroke survivor and their caregiver dyads. QOL of stroke survivor was assessed via Stroke Specific Quality of Life Scale (SSQOLS) whereas QOL of caregivers was assessed through RAND-36. In addition; we assessed complications, psychosocial and functional disability of stroke survivors. Following this quantitative survey, caregivers were qualitatively interviewed to uncover contextually relevant themes that would evade quantitative surveys. Multiple linear regression technique was applied to report adjusted β-coefficients with 95% C.I. The QOL study was conducted from January 2014 till June 2014, in two large private and public centers. At each center, 175 dyads were interviewed to ensure representativeness. Median age of stroke survivors was 59(17) years, 68% were male, 60% reported depression and 70% suffered post-stroke complications. The mean SSQOLS score was 164.18 ± 32.30. In the final model severe functional disability [adjβ -33.77(-52.44, -15.22)], depression [adjβ-23.74(-30.61,-16.82)], hospital admissions [adjβ-5.51(-9.23,-1.92)] and severe neurologic pain [adjβ -12.41(-20.10,-4.77)] negatively impacted QOL of stroke survivors (P < 0.01). For caregivers, mean age was 39.18 ± 13.44 years, 51% were female and 34% reported high stress levels. Complementary qualitative study revealed that primary caregivers were depressed, frustrated, isolated and also disappointed by health services. The QOL of Stroke survivors as reported by SSQOLS score was better than compared to those reported from other LMIC settings. However, Qualitative triangulation revealed that younger caregivers felt isolated, depressed, overwhelmed and were providing care at great personal cost. There is a need to develop cost effective holistic home support interventions to improve lives of the survivor dyad as a unit. NCT02351778 (Registered as Observational Study).


title: Mixing augmented reality and EEG technology to create an unique learning tool for construction process

abstract: The Augmented and Virtual Reality environments are proved to create excellent interactive tools for education. The development of relatively low-cost AR and VR visualization devices permitted to these technologies to form a part of educational process. By combining these immersive technologies with the EEG lecture in real-time it is possible to increase the efficiency of the digital learning tools. In the proposed study, we create an interactive immersive educational tool named “AR Constructor”, which fuse the Augmented Reality and EEG technology for the purpose of further instruction in the field of building constructions.


title: K-Means Clustering in Wireless Sensor Networks

abstract: Abstract —A wireless sensor network (WSN) consists of spatially distributed autonomous sensors to monitor physical or environmental conditions and to cooperatively pass their data through the network to a Base Station. Clustering is a critical task in Wireless Sensor Networks for energy efficiency and network stability. Clustering through Central Processing Unit in wireless sensor networks is well known and in use for a long time. Presently clustering through distributed methods is being developed for dealing with the issues like network lifetime and energy. In our work, we implemented both centralized and distributed k-means clustering algorithm in network simulator. k-means is a prototype based algorithm that alternates between two major steps, assigning observations to clusters and computing cluster centers until a stopping criterion is satised. Simulation results are obtained and compared which show that distributed clustering is efficient than centralized clustering. Keywords- wireless sensor network; clustering; ns-2; k-means; network stability


Partition based Hough Transformation for Real Time Road Lane Detection


title: Efficient Object Localization and Pose Estimation with 3D Wireframe Models

abstract: We propose a new and efficient method for 3D object localization and fine-grained 3D pose estimation from a single 2D image. Our approach follows the classical paradigm of matching a 3D model to the 2D observations. Our first contribution is a 3D object model composed of a set of 3D edge primitives learned from 2D object blueprints, which can be viewed as a 3D generalization of HOG features. This model is used to define a matching cost obtained by applying a rigid-body transformation to the 3D object model, projecting it onto the image plane, and matching the projected model to HOG features extracted from the input image. Our second contribution is a very efficient branch-and-bound algorithm for finding the 3D pose that maximizes the matching score. For this, 3D integral images of quantized HOGs are employed to evaluate in constant time the maximum attainable matching scores of individual model primitives. We applied our method to three different datasets of cars and achieved promising results with testing times as low as less than half a second.


title: Ockham's razor and its improper use

abstract: "Ockham's razor" is a methodical principle, due to the medieval philosopher William of Ockham, who mainly opposed an unjustified creation of new terms in philosophy. Since this principle and its later versions are frequently quoted in discussions about anomalies, it will be studied here in some detail. After a short look on the historical roots, the principal modern formulations are summarized. It will be shown that a demand for "simplicity" cannot be generally maintained. Rather, striving for simplicity can conflict with other essentials of scientific method. Ockham's principle - no matter whether in its original or in a modified version - cannot be helpful in a rational decision between competing explanations for the same empirical facts. An incorrect use of Ockham's razor only leads to a perpetuation and corroboration of existing prejudice, and this principle should not be used to easily get rid of unwelcome data or concepts.


title: A Block-Based Division Reversible Data Hiding Method in Encrypted Images

abstract: Due to the increased digital media on the Internet, data security and privacy protection issue have attracted the attention of data communication. Data hiding has become a topic of considerable importance. Nowadays, a new challenge consists of reversible data hiding in the encrypted image because of the correlations of local pixels that are destroyed in an encrypted image; it is difficult to embed secret messages in encrypted images using the difference of neighboring pixels. In this paper, the proposed method uses a block-based division mask and a new encrypted method based on the logistic map and an additive homomorphism to embed data in an encrypted image by histogram shifting technique. Our experimental results show that the proposed method achieves a higher payload than other works and is more immune to attack upon the cryptosystem.


title: Trichoderma asperellum isolated from salinity soil using rice straw waste as biocontrol agent for cowpea plant pathogens

abstract: This study discusses isolation and identification new fungus isolate from salinity soil for controlling soil borne diseases. Among sixteen fungal, a potent isolate coded SRBP_ZSHSG1 was isolated from Sugar beet rhizospher samples collected from Al-Hosainia localities- El-Sharkia- Egypt. Traditional methods consistent with phylogenetic analysis of 18S rRNA sequences showed SRBP_ZSHSG1 has 100% similarity with Trichoderma strains and the most closest is Trichoderma asperellum. Thus, it proposed name Trichoderma asperellum SRBP_ZSHSG1 (ID: KP336489). Results proved SRBP_ZSHSG1 followed by T. roseum and Chaetomium globosum were highly inhibitors to the tested pathogens. These results were confirmed by field experiments. SRBP_ZSHSG1 was able to grow on rice straw (biostraw) and produce most active compounds. The biostaw extract was the most effective bioagent and recorded highest reduction in pathogen numbers. GC/MS analysis of ethyl acetate extract revealed the presence of 9 compounds. These compounds were determined 4 volatile alcohols (1-4) and fatty acid esters (5-9).


title: Aesthetic Measures for Assessing Graphic Screens

abstract: An important aspect of screen design is aesthetic evaluation of screen layouts. However, no one knows how to measure aesthetic value. This article proposes a theoretical approach to capturing the essence of the artists' insight. We introduce several aesthetic measures for graphic screens. The results of our informal study demonstrate close relationships between artistic vision and the proposed measures.


title: A Stereovision-Based Lane Detector for Marked and Non-Marked Urban Roads

abstract: This paper presents a lane detection system that combines stereovision-specific techniques with grayscale image processing for maximizing the robustness and applicability against the difficult conditions found on the urban roads, marked or non marked. The lane marking features are extracting using a fast and robust dark-light-dark transition detector that's aware of the perspective effect. The clothoid lane model is matched to the extracted features using line segment fitting for two distance intervals, under special constraints that ensure correctness. Freeform lane border detection, independent of the geometry constraints, driven by lane marking features only, is used to solve the situations not suited for clothoid representation. Intensive validation techniques are used for tracking initialization, and for monitoring of the noise level on the road, in order to avoid false positives. The results of each detection method are fused together in a Kalman filter based framework.


title: Influence Maximization for Complementary Goods: Why Parties Fail to Cooperate?

abstract: We consider the problem where companies provide different types of products and want to promote their products through viral marketing simultaneously. Most previous works assume products are purely competitive. Different from them, our work considers that each product has a pairwise relationship which can be from strongly competitive to strongly complementary to each other's product. The problem is to maximize the spread size with the presence of different opponents with different relationships on the network. We propose Interacting Influence Maximization (IIM) game to model such problems by extending the model of the Competitive Influence Maximization (CIM) game studied by previous works, which considers purely competitive relationship. As for the theoretical approach, we prove that the Nash equilibrium of highly complementary products of different companies may still be very inefficient due to the selfishness of companies. We do so by introducing a well-known concept in game theory, called Price of Stability (PoS) of the extensive-form game. We prove that in any k selfish players symmetric complementary IIM game, the overall spread of the products can be reduced to as less as 1/k of the optimal spread. Since companies may fail to cooperate with one another, we propose different competitive objective functions that companies may consider and deal with separately. We propose a scalable strategy for maximizing influence differences, called TOPBOSS that is guaranteed to beat the first player in a single-round two-player second-move game. In the experiment, we first propose a learning method to learn the ILT model, which we propose for IIM game, from both synthetic and real data to validate the effectiveness of ILT. We then exhibit that the performance of several heuristic strategies in the traditional influence maximization problem can be improved by acquiring the knowledge of the existence of competitive/complementary products in the network. Finally, we compare the TOPBOSS with different heuristic algorithms in real data and demonstrate the merits of TOPBOSS.


title: Energy Harvesting Circuit on a One-Sided Directional Flexible Antenna

abstract: The present letter describes the design of an energy harvesting circuit on a one-sided directional flexible planar antenna. The circuit is composed of a flexible antenna with an impedance matching circuit, a resonant circuit, and a booster circuit for converting and boosting radio frequency power into a dc voltage. The proposed one-sided directional flexible antenna has a bottom floating metal layer that enables one-sided radiation and easy connection of the booster circuit to the metal layer. The simulated output dc voltage is 2.89 V for an input of 100 mV and a 50 Ω power source at 900 MHz, and power efficiency is 58.7% for 1.0 × 107 Ω load resistance.


title: A real-time method for depth enhanced visual odometry

abstract: Visual odometry can be augmented by depth information such as provided by RGB-D cameras, or from lidars associated with cameras. However, such depth information can be limited by the sensors, leaving large areas in the visual images where depth is unavailable. Here, we propose a method to utilize the depth, even if sparsely available, in recovery of camera motion. In addition, the method utilizes depth by structure from motion using the previously estimated motion, and salient visual features for which depth is unavailable. Therefore, the method is able to extend RGB-D visual odometry to large scale, open environments where depth often cannot be sufficiently acquired. The core of our method is a bundle adjustment step that refines the motion estimates in parallel by processing a sequence of images, in a batch optimization. We have evaluated our method in three sensor setups, one using an RGB-D camera, and two using combinations of a camera and a 3D lidar. Our method is rated #4 on the KITTI odometry benchmark irrespective of sensing modality--compared to stereo visual odometry methods which retrieve depth by triangulation. The resulting average position error is 1.14 % of the distance traveled.


title: Open/Closed Eye Analysis for Drowsiness Detection

abstract: Drowsiness detection is vital in preventing traffic accidents. Eye state analysis - detecting whether the eye is open or closed - is critical step for drowsiness detection. In this paper, we propose an easy algorithm for pupil center and iris boundary localization and a new algorithm for eye state analysis, which we incorporate into a four step system for drowsiness detection: face detection, eye detection, eye state analysis, and drowsy decision. This new system requires no training data at any step or special cameras. Our eye detection algorithm uses Eye Map, thus achieving excellent pupil center and iris boundary localization results on the IMM database. Our novel eye state analysis algorithm detects eye state using the saturation (S) channel of the HSV color space. We analyze our eye state analysis algorithm using five video sequences and show superior results compared to the common technique based on distance between eyelids.


title: Low cost smart security camera with night vision capability using Raspberry Pi and OpenCV

abstract: In order to further maintain peace and provide security to people now a day, Closed-circuit television (CCTV) surveillance system is being utilized. This study focused on the design and implementation of a low cost smart security camera with night vision capability using Raspberry Pi (RPI) and OpenCV. The system was designed to be used inside a warehouse facility. It has human detection and smoke detection capability that can provide precaution to potential crimes and potential fire. The credit card size Raspberry Pi (RPI) with Open Source Computer Vision (OpenCV) software handles the image processing, control algorithms for the alarms and sends captured pictures to user's email via Wi-Fi. As part of its alarm system, it will play the recorded sounds: “intruder” or “smoke detected” when there is a detection. The system uses ordinary webcam but its IR filter was removed in order to have night vision capability.


title: Query-Aware Locality-Sensitive Hashing for Approximate Nearest Neighbor Search

abstract: Locality-Sensitive Hashing (LSH) and its variants are the well-known indexing schemes for the c-Approximate Nearest Neighbor (c-ANN) search problem in high-dimensional Euclidean space. Traditionally, LSH functions are constructed in a query-oblivious manner in the sense that buckets are partitioned before any query arrives. However, objects closer to a query may be partitioned into different buckets, which is undesirable. Due to the use of query-oblivious bucket partition, the state-of-the-art LSH schemes for external memory, namely C2LSH and LSB-Forest, only work with approximation ratio of integer c ≥ 2. ::: ::: In this paper, we introduce a novel concept of query-aware bucket partition which uses a given query as the "anchor" for bucket partition. Accordingly, a query-aware LSH function is a random projection coupled with query-aware bucket partition, which removes random shift required by traditional query-oblivious LSH functions. Notably, query-aware bucket partition can be easily implemented so that query performance is guaranteed. We propose a novel query-aware LSH scheme named QALSH for c-ANN search over external memory. Our theoretical studies show that QALSH enjoys a guarantee on query quality. The use of query-aware LSH function enables QALSH to work with any approximation ratio c > 1. Extensive experiments show that QALSH outperforms C2LSH and LSB-Forest, especially in high-dimensional space. Specifically, by using a ratio c < 2, QALSH can achieve much better query quality.


title: The METIS 5G Architecture: A Summary of METIS Work on 5G Architectures

abstract: During the last two years, the METIS project ("Mobile and wireless communications Enablers for the Twenty-twenty Information Society") has been conducting research on 5G-enabling technology components. This paper provides a summary of METIS work on 5G architectures. The architecture description is presented from different viewpoints. First, a functional architecture is presented that may lay a foundation for development of first novel 5G network functions. It is based on functional decomposition of most relevant 5G technology components provided by METIS. The logical orchestration a control architecture depicts the realization of flexibility, scalability and service orientation needed to fulfil diverse 5G requirements. Finally, a third viewpoint reveals deployment aspects and function placement options for 5G.


title: Biomechanical Analysis of Cuboid Osteotomy Lateral Column Lengthening for Stage II B Adult-Acquired Flatfoot Deformity: A Cadaveric Study

abstract: Purpose. To investigate the effect of cuboid osteotomy lateral column lengthening (LCL) for the correction of stage II B adult-acquired flatfoot deformity in cadaver. Methods. Six cadaver specimens were loaded to 350 N. Flatfoot models were established and each was evaluated radiographically and pedobarographically in the following conditions: (1) intact foot, (2) flatfoot, and (3) cuboid osteotomy LCL (2, 3, 4, and 5 mm). Results. Compared with the flatfoot model, the LCLs showed significant correction of talonavicular coverage on anteroposterior radiographs and talus-first metatarsal angle on both anteroposterior and lateral radiographs (). Compared with the intact foot, the above angles of the LCLs showed no significant difference except the 2 mm LCL. In terms of forefoot pressure, medial pressure of the 2 mm LCL () and lateral pressure of the 3, 4, and 5 mm LCLs showed statistical differences (), but lateral pressure of the 3 mm LCL was not more than the intact foot as compared to the 4 and 5 mm LCLs, which was less than medial pressure. Conclusion. Cuboid osteotomy LCL procedure avoids damage to subtalar joint and has a good effect on correction of stage II B adult-acquired flatfoot deformity with a 3 mm lengthening in cadavers.


title: Penile injection with silicone: case report and review of the literature.

abstract: ABSTRACT Introduction Liquid injectable silicone (LIS) has been used for soft tissue augmentation in excess of 50 years. Until recently, all literature on penile augmentation with LIS consisted of case reports or small cases series, most involving surgical intervention to correct the complications of LIS. New formulations of LIS and new methodologies for injection have renewed interest in this procedure. Aim We reported a case of penile augmentation with LIS and reviewed the pertinent literature. Methods Comprehensive literature review was performed using PubMed. We performed additional searches based on references from relevant review articles. Results Injection of medical grade silicone for soft tissue augmentation has a role in carefully controlled study settings. Historically, the use of LIS for penile augmentation has had poor outcomes and required surgical intervention to correct complications resulting from LIS. Conclusions We currently discourage the use of LIS for penile augmentation until carefully designed and evaluated trials have been completed. Silberstein J, Downs T, and Goldstein I. Penile injection with silicone: Case report and review of the literature. J Sex Med 2008;5:2231–2237.


title: Automated Game Testing with ICARUS: Intelligent Completion of Adventure Riddles via Unsupervised Solving

abstract: With ICARUS, we introduce a framework for autonomous video game playing, testing, and bug reporting. We report on the design rationale, the practical implementation, and its use in game development industry projects. The underlying solving mechanic is based on discrete reinforcement learning in a dualistic fashion, encompassing volatile short-term memory as well as persistent long-term memory that spans across distinct game iterations. In combination with heuristics that reduce the search space and the possibility to employ pre-defined situation-dependent action choices, the system manages to traverse complete playthrough iterations in roughly the same amount of time that a professional game tester requires for a speedrun. The ICARUS project was developed at Daedalic Entertainment. The software can be used to generically run all adventure games built with the popular Visionaire Engine and is currently used for evaluating daily builds, for large-scale hardware compatibility and performance tests, as well as for semi-supervised quality assurance playthroughs. The supplementary video depicts real-time solving with active control and observation via a web control panel.


title: A Compact Two Via Slot-Type Electromagnetic Bandgap Structure

abstract: A novel two via slot-type electromagnetic bandgap (TVS-EBG) structure is presented in this letter. Design and analysis of the TVS-EBG structure are done by using the $LC$ analytical model. In the central located via EBG (CLV-EBG) and the edge located via EBG (ELV-EBG) structure, current flowing through via gives the inductor ( $L$ ) and the gap between the adjacent cells gives the capacitance ( $C$ ). From the $LC$ analytical model, in order to design compact EBG structure, the product of $LC$ should be high. TVS-EBG structure increases the value of $C$ and $L$ per unit EBG cell by making a slot on the square patch of the cell and two via between square patch of the cell and ground plane. TVS-EBG structure simulated using Ansoft HFSS and tested. Simulated and experimental results prove that, compared with CLV-EBG, complementary split ring resonator-based EBG, and ELV-EBG, a size reduction of 35.29%, 17.20%, and 13.48% has been achieved, respectively.


title: Sweat it out? The effects of physical exercise on cognition and behavior in children and adults with ADHD: a systematic literature review

abstract: As attention–deficit/hyperactivity disorder (ADHD) is one of the most frequently diagnosed developmental disorders in childhood, effective yet safe treatment options are highly important. Recent research introduced physical exercise as a potential treatment option, particularly for children with ADHD. The aim of this review was to systematically analyze potential acute and chronic effects of cardio and non-cardio exercise on a broad range of functions in children with ADHD and to explore this in adults as well. Literature on physical exercise in patients with ADHD was systematically reviewed based on categorizations for exercise type (cardio versus non-cardio), effect type (acute versus chronic), and outcome measure (cognitive, behavioral/socio-emotional, and physical/(neuro)physiological). Furthermore, the methodological quality of the reviewed papers was addressed. Cardio exercise seems acutely beneficial regarding various executive functions (e.g., impulsivity), response time and several physical measures. Beneficial chronic effects of cardio exercise were found on various functions as well, including executive functions, attention and behavior. The acute and chronic effects of non-cardio exercise remain more questionable but seem predominantly positive too. Research provides evidence that physical exercise represents a promising alternative or additional treatment option for patients with ADHD. Acute and chronic beneficial effects of especially cardio exercise were reported with regard to several cognitive, behavioral, and socio-emotional functions. Although physical exercise may therefore represent an effective treatment option that could be combined with other treatment approaches of ADHD, more well-controlled studies on this topic, in both children and adults, are needed.


title: Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

abstract: We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schrödinger equation is mapped onto a nonlinear statistical regression problem of reduced complexity. Regression models are trained on and compared to atomization energies computed with hybrid density-functional theory. Cross validation over more than seven thousand organic molecules yields a mean absolute error of ∼10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.


title: A 365nW −61.5 dBm sensitivity, 1.875 cm2 2.4 GHz wake-up receiver with rectifier-antenna co-design for passive gain

abstract: A 2.4 GHz 365nW wake-up receiver (WuRX) with RF envelope detection using rectifier-antenna co-design for passive voltage gain and RF filtering is presented. The RF frequency-tunable WuRX uses a programmable 32-bit OOK wake-up signature, achieving sensitivity of −61.5dBm for 2.5 kb/s without any off-chip matching components between IC and antenna. RF filtering in the high-Q rectifier-antenna interface results in 10−3 BER even with interferer-to-carrier ratio of 19.1 dB for CW blocker at 3MHz offset. The 65-nm CMOS WuRX IC occupies 1.1mm2, while the WuRX (including antenna) occupies 1.875cm2.


title: RETRACTED ARTICLE: Flickr image quality evaluation by deeply fusing heterogeneous visual cues

abstract: Flickr is a photo and video hosting site with over 2 million groups. There are more than 35 million new photos uploading every day. But at present, there are no tools to organize these huge numbers of users’ aesthetic tendency. Although Flickr allows users to add different groups manually, they are difficult to maintain updates when new users are added or deleted. In this paper, we put forward a series of Flickr users system that each loop contains similar users aesthetic tendency. We observed: (1) an aesthetic model of thought should be flexible, because different visual features represent different data sets. And (2) significant differences are existing in the number of photos from different Flickr users. So in our work, a new probabilistic topic model is proposed to describe the aesthetic interest of each Flickr user’s potential spatial distribution. After that, an affinity graph is described by aesthetic interests of Flickr users. Obviously, intensive users of Flickr are similar in taste. Thus, these users are divided into different Flickr bounds efficient dense graph discover. It is proposed that the Flickr bound discovery is fully automatic. Experiments show that our proposed method is accurate for 60000 Flickr user community.


title: Analysis and Comparison of SEPIC, Landsman and Zeta Converters for PV Fed Induction Motor Drive Applications

abstract: Conventional Buck, Boost and Buck-Boost converters are not suitable for low and medium voltage Induction Motor (IM) drive applications. Moreover, these DC-DC converters not suitable for power factor correction, constant output voltage and to reduce harmonic distortion. In order to avoid drawbacks of basic DC-DC converters, three advanced DC-DC converters are studied in this article, namely, SEPIC, Zeta and Landsman converters. These converters work as Buck-Boost converters based on load requirement. The P&O MPPT technique is used to track the fast change of solar irradiances by the iterative process. Furthermore, it is used for the soft starting of squirrel cage induction motor. These converters output given to three-phase Voltage Source Inverter (VSI) to run the induction motor drive. The three converters operation is compared for IM drive applications by using MATAB Simulink software.