title: Efficient Implementation of the Fuzzy c-Means Clustering Algorithms

abstract: This paper reports the results of a numerical comparison of two versions of the fuzzy c-means (FCM) clustering algorithms. In particular, we propose and exemplify an approximate fuzzy c-means (AFCM) implementation based upon replacing the necessary ``exact'' variates in the FCM equation with integer-valued or real-valued estimates. This approximation enables AFCM to exploit a lookup table approach for computing Euclidean distances and for exponentiation. The net effect of the proposed implementation is that CPU time during each iteration is reduced to approximately one sixth of the time required for a literal implementation of the algorithm, while apparently preserving the overall quality of terminal clusters produced. The two implementations are tested numerically on a nine-band digital image, and a pseudocode subroutine is given for the convenience of applications-oriented readers. Our results suggest that AFCM may be used to accelerate FCM processing whenever the feature space is comprised of tuples having a finite number of integer-valued coordinates.


An efficient k'-means clustering algorithm


Critical review of cybersecurity protection procedures and practice in water distribution systems


title: Busy with pain: disorganization in subjective time in experimental pain.

abstract: The cold pressor test was used to investigate perception of time and change of mood in subjects experiencing pain. Using a within-group design, 15 subjects were tested in a pain vs no-pain condition. Subjects were requested to fill in the mood adjective checklist (MACL) concerning the mood factors of activity, calmness and pleasantness, and were interviewed about their perceptions before and immediately after each condition. The results indicated that subjects in the pain condition estimated retrospective time passage as significantly shorter, indicating a disorganization of temporal orientation (the relative dominance of past, present and future in a person's thought). In addition, results indicated that many of the subjects in the pain condition, although underestimating time, experienced time as long-lasting. The main finding concerning mood was that the activity level was increased, and calmness and pleasantness were significantly reduced in the pain condition. The main conclusion is that pain per se changes the perception of duration of time intervals and temporal orientation within the same individual.


title: A Manually Annotated Chinese Corpus for Non-task-oriented Dialogue Systems

abstract: This paper presents a large-scale corpus for non-task-oriented dialogue response selection, which contains over 27K distinct prompts more than 82K responses collected from social media. To annotate this corpus, we define a 5-grade rating scheme: bad, mediocre, acceptable, good, and excellent, according to the relevance, coherence, informativeness, interestingness, and the potential to move a conversation forward. To test the validity and usefulness of the produced corpus, we compare various unsupervised and supervised models for response selection. Experimental results confirm that the proposed corpus is helpful in training response selection models.


title: Data classification with deep learning using Tensorflow

abstract: Deep learning is a subfield of machine learning which uses artificial neural networks that is inspired by the structure and function of the human brain. Despite being a very new approach, it has become very popular recently. Deep learning has achieved much higher success in many applications where machine learning has been successful at certain rates. In particular It is preferred in the classification of big data sets because it can provide fast and efficient results. In this study, we used Tensorflow, one of the most popular deep learning libraries to classify MNIST dataset, which is frequently used in data analysis studies. Using Tensorflow, which is an open source artificial intelligence library developed by Google, we have studied and compared the effects of multiple activation functions on classification results. The functions used are Rectified Linear Unit (ReLu), Hyperbolic Tangent (tanH), Exponential Linear Unit (eLu), sigmoid, softplus and softsign. In this Study, Convolutional Neural Network (CNN) and SoftMax classifier are used as deep learning artificial neural network. The results show that the most accurate classification rate is obtained using the ReLu activation function.


title: Route repetition and route retracing: effects of cognitive aging

abstract: Retracing a recently traveled route is a frequent navigation task when learning novel routes or exploring unfamiliar environments. In the present study we utilized virtual environments technology to investigate age-related differences in repeating and retracing a learned route. In the training phase of the experiment participants were guided along a route consisting of multiple intersections each featuring one unique landmark. In the subsequent test phase, they were guided along short sections of the route and asked to indicate overall travel direction (repetition or retracing), the direction required to continue along the route, and the next landmark they would encounter. Results demonstrate age-related deficits in all three tasks. More specifically, in contrast to younger participants, the older participants had greater problems during route retracing than during route repetition. While route repetition can be solved with egocentric response or route strategies, successfully retracing a route requires allocentric processing. The age-related deficits in route retracing are discussed in the context of impaired allocentric processing and shift from allocentric to egocentric navigation strategies as a consequence of age-related hippocampal degeneration.


title: Compositional dependence of the bowing parameter for the direct and indirect band gaps in Ge1−ySny alloys

abstract: Photoluminescence spectroscopy has been used to determine the direct gap E0 of Ge1−ySny alloys over a broad compositional range from pure Ge to Sn concentrations exceeding 10%. A fit of the compositional dependence of E0 using a standard quadratic expression is not fully satisfactory, revealing that the bowing parameter (quadratic coefficient) b0 is compositionally dependent. Excellent agreement with the data is obtained with b0(y) = (2.66 ± 0.09) eV − (5.4 ± 1.1)y eV. A theoretical model of the bowing is presented, which explains the strong compositional dependence of the bowing parameter and suggest a similar behavior for the indirect gap. Combining the model predictions with experimental data for samples with y ≤ 0.06, it is proposed that the bowing parameter for the indirect gap is bind(y) = (1.11 ± 0.07) eV − (0.78 ± 0.05)y eV. The compositional dependence of the bowing parameters shifts the crossover concentration from indirect to direct gap behavior to yc = 0.087, significantly higher than the valu...


title: Complete Odometry Estimation of a Vehicle Using Single Automotive Radar and a Gyroscope

abstract: In this paper, we propose an algorithm for complete odometry of a vehicle on a horizontal plane., that is., estimation of linear velocity vector (forward and sideslip speeds) and angular speed of a vehicle. The vehicle is equipped with an automotive Radar sensor and a vertical gyro. The Radar sensor provides radial speed and azimuth angle of number of objects in the environment. We first derive the kinematic constraints imposed on the vehicle motion and stationary points in the environment. Using the constraints we classify the points detected by the Radar to stationary and non-stationary points. It is known that using data from a single Radar., the abovementioned constraints are singular. Previous works have thus proposed the use of more than one Radar sensor., or they have neglected the sideslip speed. In our work, we then use the Radar data of the stationary objects and a gyro data to solve an optimization algorithm to calculate vehicle odometry. Experimentation has been performed with a non-road vehicle driven on a straight path and on a circular path. We report our findings and show efficacy of the algorithm in comparison to the state of art [8] as well as wheel odometry and a complete navigation solution (including GNSS) as the reference path.


3D Pose Estimation of Bin Picking Object using Deep Learning and 3D Matching


title: Zero-shot Transfer Learning for Semantic Parsing

abstract: While neural networks have shown impressive performance on large datasets, applying these models to tasks where little data is available remains a challenging problem. ::: In this paper we propose to use feature transfer in a zero-shot experimental setting on the task of semantic parsing. ::: We first introduce a new method for learning the shared space between multiple domains based on the prediction of the domain label for each example. ::: Our experiments support the superiority of this method in a zero-shot experimental setting in terms of accuracy metrics compared to state-of-the-art techniques. ::: In the second part of this paper we study the impact of individual domains and examples on semantic parsing performance. ::: We use influence functions to this aim and investigate the sensitivity of domain-label classification loss on each example. ::: Our findings reveal that cross-domain adversarial attacks identify useful examples for training even from the domains the least similar to the target domain. Augmenting our training data with these influential examples further boosts our accuracy at both the token and the sequence level.


title: Dermoscopy in differentiating palmar syphiloderm from palmar papular psoriasis

abstract: Palmar syphiloderm is one of the most common presentations of secondary syphilis and its recognition is of utmost importance in order to promptly identify such a disease and initiate appropriate workup/management. However, the differential diagnosis with palmar papular psoriasis often poses some difficulties, with consequent possible diagnostic errors/delays and prescription of improper therapies. In this report, we underline the role of dermoscopy as a supportive tool to facilitate the non-invasive recognition of palmar syphiloderm and its distinction from palmar papular psoriasis.


title: Sliding mode control strategy for three-phase DVR employing twelve-switch voltage source converter

abstract: In this study, a new method is proposed for the suppression of voltage amplitude fluctuations on the load terminals. Moreover, the voltage harmonics on the load terminals are effectively reduced below the defined limits in the IEEE-519 standard. In the studied topology, a twelve-switch three-phase voltage source converter is used for effective control on the zero-sequence components due to the unbalanced voltages or single phase voltage sags. In order to obtain a fast dynamic response, sliding mode control strategy is used to control the system. The existence condition of the sliding mode is presented. The performance of the closed-loop system is verified with cases of voltage sag and utility voltage harmonics. The overall system is explained in detail and verified by using MATLAB/Simulink.


title: Anomalous Sound Detection Using Deep Audio Representation and a BLSTM Network for Audio Surveillance of Roads

abstract: Surveillance systems based on image analysis can automatically detect road accidents to ensure a quick intervention by rescue teams. However, in some situations, the visual information is insufficiently reliable, whereas the use of a sound detector can greatly improve the overall reliability of the surveillance system. In this paper, we focus on detecting two classes of anomalous sounds for audio surveillance on roads, i.e., tire skidding and car crash, whose occurrences are an evidently acoustic indication of road accidents or disruptions. In the proposed method, we extract a feature of deep audio representation (DAR) and then use a classifier of a bidirectional long short-term memory network to determine the class of the sound to which each test audio segment belongs. We propose a framework based on multiple-stage deep autoencoder network (DAN) to extract the DAR, which fuses complementary information from several input features and thus can be more discriminative and robust than those input features. In the experiments, we discuss the influences of the parameter settings of the DAN’s hidden layers on the performance of DAR and compare the DAR with other features. Furthermore, the proposed method is compared to the state-of-the-art methods. In evaluating the data with various signal-to-noise ratios, the results show that the DAR outperforms other features, and the proposed method is superior to the state-of-the-art methods for detecting anomalous sounds on roads.


title: Ion sensitive field effect transistor for applications in bioelectronic sensors: A research review

abstract: During recent decades increasing interest has been shown in the development of bioelectronic sensors based on ion sensitive field effect transistors (ISFETs). Many ISFET-based pH sensors have been commercialized and attempts have also been made to commercialize ISFET based bioelectronic sensors for applications in the fields of medical, environmental, food safety, military and biotechnology areas. The growing interest for development of these sensors is due to the fact that they are manufactured by means of semiconductor technology which has already entered into multifunctional “More than Moore” regime that is increasingly multidisciplinary in nature. Technology involved has, therefore, innovative potential that may result in the appearance of new sensor and device technologies in future. The basic theoretical principles of ISFET usage in bioanalytical practice, the operation principle of ISFET, its modeling and a brief introduction of ISFET technology are considered in this review.


title: A Wireless Sensor Network-Based Ubiquitous Paprika Growth Management System

abstract: Wireless Sensor Network (WSN) technology can facilitate advances in productivity, safety and human quality of life through its applications in various industries. In particular, the application of WSN technology to the agricultural area, which is labor-intensive compared to other industries, and in addition is typically lacking in IT technology applications, adds value and can increase the agricultural productivity. This study attempts to establish a ubiquitous agricultural environment and improve the productivity of farms that grow paprika by suggesting a ‘Ubiquitous Paprika Greenhouse Management System’ using WSN technology. The proposed system can collect and monitor information related to the growth environment of crops outside and inside paprika greenhouses by installing WSN sensors and monitoring images captured by CCTV cameras. In addition, the system provides a paprika greenhouse environment control facility for manual and automatic control from a distance, improves the convenience and productivity of users, and facilitates an optimized environment to grow paprika based on the growth environment data acquired by operating the system.


title: 3D attention: measurement of visual saliency using eye tracking glasses

abstract: Understanding and estimating human attention in different interactive scenarios is an important part of human computer interaction. With the advent of wearable eye-tracking glasses and Google glasses, monitoring of human visual attention will soon become ubiquitous. The presented work describes the precise estimation of human gaze fixations with respect to its environment, without the need of artificial landmarks in the field of view, and being capable of providing attention mapping onto 3D information. It enables full 3D recovery of the human view frustum and the gaze pointer in a previously acquired 3D model of the environment in real time. The key contribution is that our methodology enables mapping of fixations directly into an automatically computed 3d model. This innovative methodology will open new opportunities for human attention studies during interaction with its environment, bringing new potential into automated processing for human factors technologies.


title: A precise analysis of Cuckoo hashing

abstract: Cuckoo hashing was introduced by Pagh and Rodler in 2001. Its main feature is that it provides constant worst-case search time. The aim of this article is to present a precise average case analysis of Cuckoo hashing. In particular, we determine the probability that Cuckoo hashing produces no conflicts and give an upper bound for the construction time, that is linear in the size of the table. The analysis rests on a generating function approach to the so called Cuckoo Graph, a random bipartite graph, and an application of a double saddle point method to obtain asymptotic expansions. Furthermore, we provide some results concerning the structure of these kinds of random graphs. Our results extend the analysis of Devroye and Morin [2003]. Additionally, we provide numerical results confirming the mathematical analysis.


Detection and Localization of Video Copy-Move Forgery in Temporal and Spatial Domain


title: A Case for NOW (Networks of Workstations)

abstract: In this paper, we argue that because of recent technology advances, networks of workstations (NOWs) are poised to become the primary computing infrastructure for science and engineering, from low end interactive computing to demanding sequential and parallel applications. We identify three opportunities for NOWs that will benefit end- users: dramatically improving virtual memory and file system performance by using the aggregate DRAM of a NOW as a giant cache for disk; achieving cheap, highly available, and scalable file storage by using redundant arrays of workstation disks, using the LAN as the I/O backplane; and finally, multiple CPUs for parallel computing. We describe the technical challenges in exploiting these opportunities - namely, efficient communication hardware and software, global coordination of multiple workstation operating systems, and enterprise-scale network file systems. We are currently building a 100-node NOW prototype to demonstrate that practical solutions exist to these technical challenges.


title: A game of refactoring: Studying the impact of gamification in software refactoring

abstract: Software refactoring is an essential skill for developers. It improves the quality of the software and reduces maintenance costs in the long run. In this paper, we investigate the impact that gamification can have on the refactoring process and on the usability of the existing refactoring tools. For this reason we created CodeArena, a gamification system that tracks and rewards refactorings during development. We used CodeArena in an exploratory study which involved 12 students of computer science. Then, we collected the feedback provided by these students via a survey. Although the gamification had less effect than expected, our findings can be useful to practitioners interested in promoting refactoring tools via gamification.


title: Understanding and leveraging social networks for crowdfunding: opportunities and challenges

abstract: Crowdfunding provides a new way for creatives to share their work and acquire resources from their social network to influence what new ideas are realized. Yet, we understand very little about this growing phenomenon. Grounded in existing work on social network analysis, we interview 58 crowdfunding project creators to investigate how crowdfunders use their social network to reach their campaign goals. We identified three main challenges, which include understanding network capabilities, activating network connections, and expanding network reach. From our findings, we develop initial design implications for support tools to help crowdfunding project creators better understand and leverage their social network.


title: DivQ: diversification for keyword search over structured databases

abstract: Keyword queries over structured databases are notoriously ambiguous. No single interpretation of a keyword query can satisfy all users, and multiple interpretations may yield overlapping results. This paper proposes a scheme to balance the relevance and novelty of keyword search results over structured databases. Firstly, we present a probabilistic model which effectively ranks the possible interpretations of a keyword query over structured data. Then, we introduce a scheme to diversify the search results by re-ranking query interpretations, taking into account redundancy of query results. Finally, we propose α-nDCG-W and WS-recall, an adaptation of α-nDCG and S-recall metrics, taking into account graded relevance of subtopics. Our evaluation on two real-world datasets demonstrates that search results obtained using the proposed diversification algorithms better characterize possible answers available in the database than the results of the initial relevance ranking.


Facelift: Current Concepts, Techniques, and Principles


title: Relation Extraction from the Web Using Distant Supervision

abstract: Extracting information from Web pages requires the ability to work at Web scale in terms of the number of documents, the number of domains and domain complexity. Recent approaches have used existing knowledge bases to learn to extract information with promising results. In this paper we propose the use of distant supervision for relation extraction from the Web. Distant supervision is a method which uses background information from the Linking Open Data cloud to automatically label sentences with relations to create training data for relation classifiers. Although the method is promising, existing approaches are still not suitable for Web extraction as they suffer from three main issues: data sparsity, noise and lexical ambiguity. Our approach reduces the impact of data sparsity by making entity recognition tools more robust across domains, as well as extracting relations across sentence boundaries. We reduce the noise caused by lexical ambiguity by employing statistical methods to strategically select training data. Our experiments show that using a more robust entity recognition approach and expanding the scope of relation extraction results in about 8 times the number of extractions, and that strategically selecting training data can result in an error reduction of about 30%.


title: Learning methods for generic object recognition with invariance to pose and lighting

abstract: We assess the applicability of several popular learning methods for the problem of recognizing generic visual categories with invariance to pose, lighting, and surrounding clutter. A large dataset comprising stereo image pairs of 50 uniform-colored toys under 36 azimuths, 9 elevations, and 6 lighting conditions was collected (for a total of 194,400 individual images). The objects were 10 instances of 5 generic categories: four-legged animals, human figures, airplanes, trucks, and cars. Five instances of each category were used for training, and the other five for testing. Low-resolution grayscale images of the objects with various amounts of variability and surrounding clutter were used for training and testing. Nearest neighbor methods, support vector machines, and convolutional networks, operating on raw pixels or on PCA-derived features were tested. Test error rates for unseen object instances placed on uniform backgrounds were around 13% for SVM and 7% for convolutional nets. On a segmentation/recognition task with highly cluttered images, SVM proved impractical, while convolutional nets yielded 16/7% error. A real-time version of the system was implemented that can detect and classify objects in natural scenes at around 10 frames per second.


title: A Robust Fingerprint Indexing Scheme Using Minutia Neighborhood Structure and Low-Order Delaunay Triangles

abstract: Fingerprint indexing is a key technique in automatic fingerprint identification systems (AFIS). However, handling fingerprint distortion is still a problem. This paper concentrates on a more accurate fingerprint indexing algorithm that efficiently retrieves the top N possible matching candidates from a huge database. To this end, we design a novel feature based on minutia neighborhood structure (we call this minutia detail and it contains richer minutia information) and a more stable triangulation algorithm (low-order Delaunay triangles, consisting of order 0 and 1 Delaunay triangles), which are both insensitive to fingerprint distortion. The indexing features include minutia detail and attributes of low-order Delaunay triangle (its handedness, angles, maximum edge, and related angles between orientation field and edges). Experiments on databases FVC2002 and FVC2004 show that the proposed algorithm considerably narrows down the search space in fingerprint databases and is stable for various fingerprints. We also compared it with other indexing approaches, and the results show our algorithm has better performance, especially on fingerprints with distortion.


title: Multilevel Models for Categorical Data using SAS ® PROC GLIMMIX: The Basics

abstract: Multilevel models (MLMs) are frequently used in social and health sciences where data are typically hierarchical in nature. However, the commonly used hierarchical linear models (HLMs) are only appropriate when the outcome of interest is normally distributed; when dealing with outcomes that are not normally distributed (i.e., binary, categorical, ordinal), a transformation and an appropriate error distribution for the response variable needs to be incorporated into the model and therefore, hierarchical generalized linear models (HGLMs) need to be used. This paper provides an


title: Using the random forest classifier to assess and predict student learning of Software Engineering Teamwork

abstract: The overall goal of our Software Engineering Teamwork Assessment and Prediction (SETAP) project is to develop effective machine-learning-based methods for assessment and early prediction of student learning effectiveness in software engineering teamwork. Specifically, we use the Random Forest (RF) machine learning (ML) method to predict the effectiveness of software engineering teamwork learning based on data collected during student team project development. These data include over 100 objective and quantitative Team Activity Measures (TAM) obtained from monitoring and measuring activities of student teams during the creation of their final class project in our joint software engineering classes which ran concurrently at San Francisco State University (SFSU), Fulda University (Fulda) and Florida Atlantic University (FAU). In this paper we provide the first RF analysis results done at SFSU on our full data set covering four years of our joint SE classes. These data include 74 student teams with over 380 students, totaling over 30000 discrete data points. These data are grouped into 11 time intervals, each measuring important phases of project development during the class (e.g. early requirement gathering and design, development, testing and delivery). We briefly elaborate on the methods of data collection and describe the data itself. We then show prediction results of the RF analysis applied to this full data set. Results show that we are able to detect student teams who are bound to fail or need attention in early class time with good (about 70%) accuracy. Moreover, the variable importance analysis shows that the features (TAM measures) with high predictive power make intuitive sense, such as late delivery/late responses, time used to help each other, and surprisingly statistics on commit messages to the code repository, etc. In summary, we believe we demonstrate the viability of using ML on objective and quantitative team activity measures to predict student learning of software engineering teamwork, and point to easy-to-measure factors that can be used to guide educators and software engineering managers to implement early intervention for teams bound to fail. Details about the project and the complete ML training database are downloadable from the project web site.


title: Re-thinking the power transmission model for sub-Saharan Africa

abstract: A vision for a United States of Africa has been advocated since the dawn of independence by, among others, pioneers like Kwame Nkrumah, Gamal Abdel Nasser and Julius Nyerere. More recently the idea gained new momentum, through such initiatives as the New Partnership for African Development (NEPAD) and the African Union (AU) to create a single market of Africa's 750 million people that is competitive within itself and within the global economy. This would be achieved through a deliberate, systematic and concerted effort to integrate, upgrade and modernize infrastructure that would offer the required catalyst for economic growth. However, the prioritization by African policy makers of a grand plan to link up the entire African continent's electric power grid networks would appear to be incompatible with 21st century thinking. The specifications for a centrally managed power grid were made by Nicola Tesla in 1883 and have served the power industry for over 125 years but are now obsolete in the era of digital micro-electronics and smart grid concepts. This paper examines some issues that surround the evolution of sub-Sahara African regional power pools and highlights the anomalies and perhaps wrong timing around the conceptualization and prioritization of grand inter-state power grids. The authors then propose an alternative model that conforms to a new and more sustainable paradigm in electricity supply economics. The proposals are however not meant to provide a panacea but it is hoped that the article will ignite a debate that will lead to a lasting solution.


title: BitShred: feature hashing malware for scalable triage and semantic analysis

abstract: The sheer volume of new malware found each day is growing at an exponential pace. This growth has created a need for automatic malware triage techniques that determine what malware is similar, what malware is unique, and why. In this paper, we present BitShred, a system for large-scale malware similarity analysis and clustering, and for automatically uncovering semantic inter- and intra-family relationships within clusters. The key idea behind BitShred is using feature hashing to dramatically reduce the high-dimensional feature spaces that are common in malware analysis. Feature hashing also allows us to mine correlated features between malware families and samples using co-clustering techniques. Our evaluation shows that BitShred speeds up typical malware triage tasks by up to 2,365x and uses up to 82x less memory on a single CPU, all with comparable accuracy to previous approaches. We also develop a parallelized version of BitShred, and demonstrate scalability within the Hadoop framework.


title: Behavioral Treatment of Sleep Problems in a Child with a Visual Impairment.

abstract: In this study, treatment focused on parenting practices for a 4 1 ⁄ 2 -year-old girl with a visual impairment caused by Leber's congenital amaurosis and problems initiating and maintaining sleep. The sleep problem was effectively treated with a graduated extinction procedure. Sleep problems are frequently found in children and adults with visual impairments (that is, blindness or low vision). In their longitudinal study, Jan, Freeman, and Scott (1977) found a prevalence of 20% for prob- lems falling asleep in 85 blind children and only 5.9% in a control group of 85 sighted children. More recently, Troster, Bram- bring, and Van der Burg (1996) found high prevalence rates of difficulties falling asleep or staying asleep in children from birth to age 6 with visual impairments Of the 67 sighted children, 152 children with low vision, and 113 children who were blind, with and without additional impair- ments, they found that sleep problems oc- curred in 48.2% of the blind children, 43.7% of the children with low vision who had additional impairments, 63.2% of the blind children with additional impairments, 23.5% of the children with low vision only, and 21% of the sighted children. Mindell and De Marco (1997) also found more sleep problems in 28 blind children than in 22 sighted children aged 4 - 36 months. The blind children went to bed later at night, were more awake at night, and slept for an average of an hour less than did the sighted children. The parents of the blind children responded differently to sleep disturbances than did the parents of the sighted children. At nighttime waking, they tended to talk to their children more, to read to their children, and to stay with their children longer than did the parents of the sighted children. Leger, Prevot et al. (1999) also found shorter sleep duration in 77 blind children than in 79 sighted children aged 3- 18 years. Significantly more blind chil- dren (17.4%) than sighted children (2.6%) slept less than 7 hours per night. Sleep problems were more prevalent in the blind children than in the sighted children. Of the children who were blind, 13.4% suffered from daytime sleepiness compared to only 1.3% of the sighted children. Also sleep- walking and grinding one's teeth, which fall into the category of parasomnias, that is, disturbances that intrude into the sleep pro- cess, were more prevalent among the chil- dren who were blind.


title: Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN

abstract: Network modeling is a critical component for building self-driving Software-Defined Networks, particularly to find optimal routing schemes that meet the goals set by administrators. However, existing modeling techniques do not meet the requirements to provide accurate estimations of relevant performance metrics such as delay and jitter. In this paper we propose a novel Graph Neural Network (GNN) model able to understand the complex relationship between topology, routing and input traffic to produce accurate estimates of the per-source/destination pair mean delay and jitter. GNN are tailored to learn and model information structured as graphs and as a result, our model is able to generalize over arbitrary topologies, routing schemes and variable traffic intensity. In the paper we show that our model provides accurate estimates of delay and jitter (worst case R2 = 0.86) when testing against topologies, routing and traffic not seen during training. In addition, we present the potential of the model for network operation by presenting several use-cases that show its effective use in per-source/destination pair delay/jitter routing optimization and its generalization capabilities by reasoning in topologies and routing schemes not seen during training.


title: The State of Public Infrastructure-as-a-Service Cloud Security

abstract: The public Infrastructure-as-a-Service (IaaS) cloud industry has reached a critical mass in the past few years, with many cloud service providers fielding competing services. Despite the competition, we find some of the security mechanisms offered by the services to be similar, indicating that the cloud industry has established a number of “best-practices,” while other security mechanisms vary widely, indicating that there is also still room for innovation and experimentation. We investigate these differences and possible underlying reasons for it. We also contrast the security mechanisms offered by public IaaS cloud offerings and with security mechanisms proposed by academia over the same period. Finally, we speculate on how industry and academia might work together to solve the pressing security problems in public IaaS clouds going forward.


title: An Experimental Study of LSTM Encoder-Decoder Model for Text Simplification

abstract: Text simplification (TS) aims to reduce the lexical and structural complexity of a text, while still retaining the semantic meaning. Current automatic TS techniques are limited to either lexical-level applications or manually defining a large amount of rules. Since deep neural networks are powerful models that have achieved excellent performance over many difficult tasks, in this paper, we propose to use the Long Short-Term Memory (LSTM) Encoder-Decoder model for sentence level TS, which makes minimal assumptions about word sequence. We conduct preliminary experiments to find that the model is able to learn operation rules such as reversing, sorting and replacing from sequence pairs, which shows that the model may potentially discover and apply rules such as modifying sentence structure, substituting words, and removing words for TS.


title: Microgrid power electronic converters: State of the art and future challenges

abstract: This paper presents a review of the state of the art of power electric converters used in microgrids. The paper focuses primarily on grid connected converters. Different topologies and control and modulation strategies for these specific converters are critically reviewed. Moreover, future challenges in respect of these converters are identified along with their potential solutions.


title: A toolset for navigation in virtual environments

abstract: Maintaining knowledge of current position and orienta- tion is frequently a problem for people in virtual environ- ments. In this paper we present a toolset of techniques based on principles of navigation derived from real world analogs. We include a discussion of human and avian navigation behaviors and show how knowledge about them were used to design our tools. We also sum- marize an informal study we performed to determine how our tools influenced the subjects' navigation behav- ior. We conclude that principles extracted from real world navigation aids such as maps can be seen to apply in virtual environments. Our basic approach to this research begins with a charac- terization of the problem domain in terms of its key char- acteristics. This means we must develop a classification of virtual environments, navigational tasks, and orienta- tion. We also want to consider human abilities, both in- nate and artificially enhanced. Based on our best understanding, we build tools to aid users in the naviga- tional tasks we have identified. Empirical studies of the tools are then used to evaluate their effectiveness. In many cases, we hope to begin to understand why certain tools are more effective than others and to feed this knowledge back into our theoretical framework. Even in the physical world, the natural navigational abil- ities of humans and other animals are not completely un- derstood. When the world is a virtual one, the problem is exacerbated by the degradation of sensory cues resulting from poor resolution, device latencies, and other short- comings of current technologies. As virtual spaces be- come larger, more abstract, and more dynamic, the cues and stimuli associated with the physical world may be lacking altogether. This paper focuses specifically on navigation and the ef- fects differing tools and environmental cues have on the way in which people perform a simple set of generic ex- ploration and searching tasks. We will begin with a brief summary of a classification of virtual worlds intended to set the context for the rest of the paper. We will then summarize some hypotheses concerning navigational ca- pabilities of humans and birds. Finally, we will describe a toolset of possible techniques we built based on these hypotheses and summarize an informal empirical study designed to determine how the tools themselves affect their users' behavior.


title: Secure Authentication and Four-Way Handshake Scheme for Protected Individual Communication in Public Wi-Fi Networks

abstract: This paper proposes a secure key exchange scheme for Wi-Fi protected access II pre-shared key (WPA2-PSK)-based public Wi-Fi networks. The existing public Wi-Fi networks have several vulnerabilities, which are caused by eavesdropping stations in the same network. The main problem is that all stations in the same network have the same pre-shared key after the association. The attackers can derive an encryption key by eavesdropping on the four-way handshake procedure. Thus, we apply an elliptic curve public key cryptography concept to the proposed scheme to keep the key safe. In the proposed scheme, only an access point (AP) has its public key and private key pair. The proposed scheme solves the problem by exchanging a secondary key that each user determines or generated in the station during the authentication procedure. In the proposed scheme, the secondary key is encrypted by a station before it is transmitted to the AP. The AP can only decrypt the encrypted authentication message using its private key. By using the secondary key, each user can generate a unique pre-shared key and other following keys, which are derived from the four-way handshake procedure. Therefore, the exchange of the secondary key can defend against attacks from the malicious station in the same network. The safety of the proposed scheme is analyzed by several attack scenarios defined in this paper. Consequently, the proposed scheme provides more security level, 192 bits or 256 bits, compared with the conventional WPA2-PSK-based public Wi-Fi networks.


title: Competing with giants. Survival strategies for local companies in emerging markets.

abstract: The arrival of a multinational corporation often looks like a death sentence to local companies in an emerging market. After all, how can they compete in the face of the vast financial and technological resources, the seasoned management, and the powerful brands of, say, a Compaq or a Johnson & Johnson? But local companies often have more options than they might think, say the authors. Those options vary, depending on the strength of globalization pressures in an industry and the nature of a company's competitive assets. In the worst case, when globalization pressures are strong and a company has no competitive assets that it can transfer to other countries, it needs to retreat to a locally oriented link within the value chain. But if globalization pressures are weak, the company may be able to defend its market share by leveraging the advantages it enjoys in its home market. Many companies in emerging markets have assets that can work well in other countries. Those that operate in industries where the pressures to globalize are weak may be able to extend their success to a limited number of other markets that are similar to their home base. And those operating in global markets may be able to contend head-on with multinational rivals. By better understanding the relationship between their company's assets and the industry they operate in, executives from emerging markets can gain a clearer picture of the options they really have when multinationals come to stay.


title: Random walk with restart: fast solutions and applications

abstract: How closely related are two nodes in a graph? How to compute this score quickly, on huge, disk-resident, real graphs? Random walk with restart (RWR) provides a good relevance score between two nodes in a weighted graph, and it has been successfully used in numerous settings, like automatic captioning of images, generalizations to the “connection subgraphs”, personalized PageRank, and many more. However, the straightforward implementations of RWR do not scale for large graphs, requiring either quadratic space and cubic pre-computation time, or slow response time on queries. We propose fast solutions to this problem. The heart of our approach is to exploit two important properties shared by many real graphs: (a) linear correlations and (b) block-wise, community-like structure. We exploit the linearity by using low-rank matrix approximation, and the community structure by graph partitioning, followed by the Sherman–Morrison lemma for matrix inversion. Experimental results on the Corel image and the DBLP dabasets demonstrate that our proposed methods achieve significant savings over the straightforward implementations: they can save several orders of magnitude in pre-computation and storage cost, and they achieve up to 150 × speed up with 90%+ quality preservation.


title: Webshell Detection Based on Random Forest–Gradient Boosting Decision Tree Algorithm

abstract: Webshell is a type of web backdoor which can be used by hackers to control web servers remotely. It is true that webshell becomes increasingly hard to detect because of the use of more and more hiding technologies, such decoding and encrypting. However, webshell still can be detected with high accuracy by virtue of machine learning algorithms. In this paper, we proposed a PHP webshell detecting model, the RF-GBDT (Random Forest–Gradient Boosting Decision Tree) model, which is the combination of random forest classifier and GBDT classifier. Besides, we not only used the common statistical features of PHP source files, such as information entropy, index of coincidence and so forth, but also extracted opcode sequence features from PHP source files, including TF-IDF vector and hash vector. Based on the RF-GBDT model and those effective features, the RF-GBDT PHP webshell prediction model shows an excellent performance, achieving the accuracy of 99.169% with false positive rate of 0.682%, shadowing several popular webshell detectors.


title: Positive-tone photodefinable polyimide for low temperature cure

abstract: Photodefinable Polyimides (PI) and polybenz-oxazoles (PBO) which have been widely used for various electronic applications such as buffer coating, interlayer dielectric and protection layer usually need high temperature cure condition over 300 °C to complete the cyclization and achieve good film properties. In addition, PI and PBO are also utilized recently for re-distribution layer of wafer level package. In this application, lower temperature curability is strongly required in order to prevent the thermal damage of the semi-conductor device and the other packaging material. Then, to meet this requirement, we focused on pre-cyclized polyimide with phenolic hydroxyl groups since this polymer showed the good solubility to aqueous TMAH and there was no need to apply high temperature cure condition. As a result of our study, the positive-tone photodefinable material could be obtained by using DNQ and combination of epoxy cross-linker enabled to enhance the chemical and PCT resistance of the cured film made even at 170 °C. Furthermore, the adhesion to copper was improved probably due to secondary hydroxyl groups which were generated from reacted epoxide groups. In this report, we introduce our concept of novel photodefinable positive-tone polyimide for low temperature cure.


title: An Advanced Moving Object Detection Algorithm for Automatic Traffic Monitoring In Real-World Limited Bandwidth Networks

abstract: Machine-controlled motion detection technology is Associate in nursing integral element of intelligent transportation systems, and is especially essential for management of traffic and maintenance of traffic police investigation systems. Traffic police investigation systems mistreatment video communication over real-world networks with restricted information measure typically encounter difficulties attributable to network congestion and/or unstable information measure. This is often particularly problematic in wireless video communication. This has necessitated the event of a rate management theme that alters the bit-rate to match the procurable network information measure, thereby manufacturing variable bit-rate video streams. However, complete and correct detection of moving objects beneath variable bit-rate video streams could be a terribly tough task. During this paper, we tend to propose Associate in nursing approach for motion detection that utilizes Associate in nursing analysis - primarily based radial basis perform network as its principal element. This approach is applicable not solely in high bit-rate video streams, however in low bit- rate video streams, as well. The planned approach consists of a varied background generation stage and a moving object detection stage. Throughout the assorted background generation stage, the lower-dimensional Eigen-patterns and also the adjustive background model are established in variable bit -rate video streams by mistreatment the planned approach so as to accommodate the properties of variable bit-rate video streams. Throughout the moving object detection stage, moving objects are extracted via the planned approach in each low bit -rate and high bit- rate video streams; detection results are then generated through the output worth of the planned approach. The detection results created through our approach indicate it to be extremely effective in variable bit-rate video streams over real-world restricted information measure networks. Additionally, the planned methodology will be simply achieved for period of time application. Quantitative and qualitative evaluations demonstrate that it offers blessings over different state- of-the -art ways. For example, and accuracy rates created via the planned approach were up to eighty six.38% and 89.88% beyond those created via different compared ways.


title: General classes of Bayesian lower bounds for outage error probability and MSE

abstract: In this paper, new classes of lower bounds on the outage error probability and on the minimum mean-square-error (MSE) in Bayesian parameter estimation are proposed. The outage error probability and the MSE are important criteria in parameter estimation. However, computation of these terms is usually not tractable. The proposed outage error probability class of lower bounds is derived using reverse Holder inequality. This class is utilized to derive a new class of Bayesian MSE bounds. It is shown that the tightest bound from the proposed class is achieved by the generalized maximum a-posteriori probability (MAP) estimation. In addition, for unimodal symmetric conditional probability density functions, the tightest MSE bound in this class coincides with the minimum MSE (MMSE) obtained by the conditional expectation estimator. It is proved that the tightest MSE bound in this class is always tighter than the Ziv-Zakai lower bounds.


title: Physics-Based SPICE-Compatible Compact Model for Simulating Hybrid MTJ/CMOS Circuits

abstract: A simulation framework that can comprehend the impact of material changes from the device level to the system level design can be of great value, especially to evaluate the impact of emerging devices on various applications. To that effect, we developed a SPICE-based hybrid magnetic tunnel junction (MTJ)/CMOS simulator, which can be used to explore new opportunities in large scale system design. In the proposed simulation framework, MTJ modeling is based on Landau-Lifshitz-Gilbert (LLG) equation incorporating both spin-torque and external magnetic field(s). LLG, along with heat diffusion equation, thermal variations, and electron transport, is implemented using SPICE-in-built voltage-dependent current sources and capacitors. The proposed simulation framework is flexible because the physical device parameters such as MgO thickness, ferromagnet material anisotropy (Ku), and device dimensions are user-defined parameters. Furthermore, we benchmarked this model with experiments in terms of switching current density (JC), switching time (TSWITCH), and tunneling magnetoresistance. Finally, we used the simulation framework to study different MTJ structures, such as in-plane magnet anisotropy and perpendicular magnet anisotropy, the impact of parametric process variations and temperature on the yield of spin transfer torque magnetoresistive random access memories, magnetic flip-flops, and spin-torque oscillators.


title: Reshaping 3D facial scans for facial appearance modeling and 3D facial expression analysis

abstract: 3D face scans have been widely used for face modeling and face analysis. Due to the fact that face scans provide variable point clouds across frames, they may not capture complete facial data or miss point-to-point correspondences across various facial scans, thus causing difficulties to use such data for analysis. This paper presents an efficient approach to represent facial shapes from face scans through the reconstruction of face models based on regional information and a generic model. A hybrid approach using two vertex mapping algorithms, displacement mapping and point-to-surface mapping, and a regional blending algorithm are proposed to reconstruct the facial surface detail. The resulting models can represent individual facial shapes consistently and adaptively, establishing the facial point correspondence across individual models. The accuracy of the generated models is evaluated quantitatively. The applicability of the models is validated through the application for 3D facial expression recognition based on the databases of static 3DFE and dynamic 4DFE. A comparison with the state of the art has also been reported.


title: Validating the distinction between computer addiction and engagement: online game playing and personality

abstract: This article considers validatory evidence for the previously made distinction between (pathological) computing-related addictions and (non-pathological) high engagement in computing activities, and an associated distinction between core and peripheral criteria for diagnosing computing-related addictions. Using data provided by 388 players of a massively multiplayer online role playing game (MMORPG) via an online questionnaire, psychometric measures of engagement and addiction to the MMORPG taking into account the distinction between core and peripheral addiction criteria are shown to be differentially related to personality factors (extraversion, emotional stability, agreeableness, negative valence, and attractiveness). Addiction scale scores are shown to increase as negativity on all five personality characteristics increases, with these characteristics predicting 20% of the variance in addiction scores, but the same pattern is shown to occur for only one characteristic (negative valence) for the engagement scale, with personality characteristics predicting only around 2% of the variance in engagement scores. It is concluded that there is reasonable support for the distinctions between addiction and engagement and between core and peripheral criteria. Implications are discussed.


title: Career success: The effects of human capital, person‐environment fit and organizational support

abstract: Purpose ::: ::: ::: ::: – The purpose of this paper is to review relevant literatures on career success and develop a theoretical framework and testable propositions concerning how human capital, person‐environment fit and organizational support relate to career success. Whilst acknowledging the substantial literature that has accumulated regarding the various antecedents and operationalizations involved in employees' career success, there is little research as how person‐environment fit and career success are related. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: – Literature outlining approaches to career success is summarized and research at the intersection of person‐environment fit and organizational support/career success are reviewed. This is followed by a set of propositions based upon the antecedents of career success. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: – It is suggested that person‐environment fit and organizational support are important antecedents of career success. Knowledge of career changes and these antecedents help individuals and organizations manage career success. ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: – Future research should examine empirically the linkages suggested by the paper along with other relationships asserted or implied by person‐environment fit and career success literature as mentioned in the paper. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: – Both employers and employees may benefit from integrating different types of fit into the psychological contract because each fit will impact aspects of career success. Therefore, organizations need to select and develop employees that can easily adjust and fit into careers that are compatible with their work environments. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: – This paper contributes to the literature by being one of the first to examine the effects of different types of person‐environment fit on career success.


title: Sorting of tropomyosin isoforms in synchronised NIH 3T3 fibroblasts: Evidence for distinct microfilament populations

abstract: The nonmuscle actin cytoskeleton consists of multiple networks of actin microfilaments. Many of these filament systems are bound by the actin-binding protein tropomyosin (Tm). We investigated whether Tm isoforms could be cell cycle regulated during G0 and G1 phases of the cell cycle in synchronised NIH 3T3 fibroblasts. Using Tm isoform-specific antibodies, we investigated protein expression levels of specific Tms in G0 and G1 phases and whether co-expressed isoforms could be sorted into different compartments. Protein levels of Tms 1, 2, 5a, 6, from the α Tm(fast) and β-Tm genes increased approximately 2-fold during mid-late G1. Tm 3 levels did not change appreciably during G1 progression. In contrast, Tm 5NM gene isoform levels (Tm 5NM-1-11) increased 2-fold at 5 h into G1 and this increase was maintained for the following 3 h. However, Tm 5NM-1 and -2 levels decreased by a factor of three during this time. Comparison of the staining of the antibodies CG3 (detects all Tm 5NM gene products), WS5/9d (detects only two Tms from the Tm 5NM gene, Tm 5NM-1 and -2) and α(f)9d (detects specific Tms from the α Tm(fast) and β-Tm genes) antibodies revealed 3 spatially distinct microfilament systems. Tm isoforms detected by α(f)9d were dramatically sorted from isoforms from the Tm 5NM gene detected by CG3. Tm 5NM-1 and Tm 5NM-2 were not incorporated into stress fibres, unlike other Tm 5NM isoforms, and marked a discrete, punctate, and highly polarised compartment in NIH 3T3 fibroblasts. All microfilament systems, excluding that detected by the WS5/9d antibody, were observed to coalign into parallel stress fibres at 8 h into G1. However, Tms detected by the CG3 and α(f)9d antibodies were incorporated into filaments at different times indicating distinct temporal control mechanisms. Microfilaments in NIH 3T3 cells containing Tm 5NM isoforms were more resistant to cytochalasin D-mediated actin depolymerisation than filaments containing isoforms from the α Tm(fast) and β-Tm genes. This suggests that Tm 5NM isoforms may be in different microfilaments to α Tm(fast) and β-Tm isoforms even when present in the same stress fibre. Staining of primary mouse fibroblasts showed identical Tm sorting patterns to those seen in cultured NIH 3T3 cells. Furthermore, we demonstrate that sorting of Tms is not restricted to cultured cells and can be observed in human columnar epithelial cells in vivo. We conclude that the expression and localisation of Tm isoforms are differentially regulated in G0 and G1 phase of the cell cycle. Tms mark multiple microfilament compartments with restricted tropomyosin composition. The creation of distinct microfilament compartments by differential sorting of Tm isoforms is observable in primary fibroblasts, cultured 3T3 cells and epithelial cells in vivo.


title: A note on maximizing a submodular set function subject to a knapsack constraint

abstract: In this paper, we obtain an (1-e^-^1)-approximation algorithm for maximizing a nondecreasing submodular set function subject to a knapsack constraint. This algorithm requires O(n^5) function value computations.


title: Outdoor Navigation of a Mobile Robot Using Differential GPS and Curb Detection

abstract: This paper demonstrates a reliable navigation of a mobile robot in outdoor environment. We fuse differential GPS and odometry data using the framework of extended Kalman filter to localize a mobile robot. And also, we propose an algorithm to detect curbs through the laser range finder. An important feature of road environment is the existence of curbs. The mobile robot builds the map of the curbs of roads and the map is used for tracking and localization. The navigation system for the mobile robot consists of a mobile robot and a control station. The mobile robot sends the image data from a camera to the control station. The control station receives and displays the image data and the teleoperator commands the mobile robot based on the image data. Since the image data does not contain enough data for reliable navigation, a hybrid strategy for reliable mobile robot in outdoor environment is suggested. When the mobile robot is faced with unexpected obstacles or the situation that, if it follows the command, it can happen to collide, it sends a warning message to the teleoperator and changes the mode from teleoperated to autonomous to avoid the obstacles by itself. After avoiding the obstacles or the collision situation, the mode of the mobile robot is returned to teleoperated mode. We have been able to confirm that the appropriate change of navigation mode can help the teleoperator perform reliable navigation in outdoor environment through experiments in the road.


Machine Learning Based Classification of Colorectal Cancer Tumour Tissue in Whole-Slide Images


title: Extension of Zoutendijk Method for Solving Constrained Multiobjective Optimization Problems

abstract: Abstract This paper extends the use of Zoutendijk method for constrained multiobjective optimization problems. This extension is a nonparametric direction-based algorithm. More precisely, considering all objective functions and binding constraints, this algorithm proposes a convex quadratic subproblem for generating a convenient improving feasible direction. Then, by using some elementary computation, the step length corresponding to the current direction is obtained. Some useful theoretical results corresponding to the proposed method are demonstrated. Using some of these theoretical results and under some mild conditions, the convergence of the proposed method is proved. The Zoutendijk multiobjective optimization (ZMO) method is not a population-based method. However, in order to find an approximation of the nondominated frontier, we need to have an appropriate population of initial feasible solutions. To achieve this aim, in this paper a cutting plane-like procedure which can generate an appropriate population of feasible solutions over the feasible set is proposed. Finally, in order to show its superiority, the proposed method is implemented for some well-known test problems. By employing some performance assessment criteria, the obtained numerical results are compared with the NSGA II method for all test problems. To have a more convenient comparison, the results are depicted in some performance profiles. Moreover, the obtained nondominated frontiers of these methods are compared for some test problems. The numerical results confirm the high performance of the ZMO method.


title: AnalyzeThis: unobtrusive mental health monitoring by voice

abstract: Mental illness is one of the most undertreated health problems worldwide. Previous work has shown that there are remarkably strong cues to mental illness in short samples of the voice. These cues are evident in severe forms of illness, but it would be most valuable to make earlier diagnoses from a richer feature set. Furthermore there is an abstraction gap between these voice cues and the diagnostic cues used by practitioners. We believe that by closing this gap, we can build more effective early diagnostic systems for mental illness. In order to develop improved monitoring, we need to translate the high-level cues used by practitioners into features that can be analyzed using signal processing and machine learning techniques. In this paper we describe the elicitation process that we used to tap the practitioners' knowledge. We borrow from both AI (expert systems) and HCI (contextual inquiry) fields in order to perform this knowledge transfer. The paper highlights an unusual and promising role for HCI - the analysis of interaction data for health diagnosis.


title: SensePath: Understanding the Sensemaking Process Through Analytic Provenance

abstract: Sensemaking is described as the process of comprehension, finding meaning and gaining insight from information, producing new knowledge and informing further action. Understanding the sensemaking process allows building effective visual analytics tools to make sense of large and complex datasets. Currently, it is often a manual and time-consuming undertaking to comprehend this: researchers collect observation data, transcribe screen capture videos and think-aloud recordings, identify recurring patterns, and eventually abstract the sensemaking process into a general model. In this paper, we propose a general approach to facilitate such a qualitative analysis process, and introduce a prototype, SensePath, to demonstrate the application of this approach with a focus on browser-based online sensemaking. The approach is based on a study of a number of qualitative research sessions including observations of users performing sensemaking tasks and post hoc analyses to uncover their sensemaking processes. Based on the study results and a follow-up participatory design session with HCI researchers, we decided to focus on the transcription and coding stages of thematic analysis. SensePath automatically captures user's sensemaking actions, i.e., analytic provenance, and provides multi-linked views to support their further analysis. A number of other requirements elicited from the design session are also implemented in SensePath, such as easy integration with existing qualitative analysis workflow and non-intrusive for participants. The tool was used by an experienced HCI researcher to analyze two sensemaking sessions. The researcher found the tool intuitive and considerably reduced analysis time, allowing better understanding of the sensemaking process.


title: Energy Efficient Data Collection via Supervised In-Network Classification of Sensor Data

abstract: In wireless sensor networks, data collection (or gathering) is the task of transmitting rounds of measurements of physical phenomena from the sensor nodes to a sink node. We study how to increase the efficiency of data collection via supervised in-network classification of rounds of measurements. We assume that the end users of the data are interested only in rounds characterized by certain patterns. Hence the wireless sensor network uses classification to select the rounds of measurements that are transmitted to the base station. The energy consumption is potentially reduced by avoiding the transmission of rounds of measurements that are not of interest to the end users. In-network classification requires distributed feature extraction and transmission. Such tasks can be less or more energy expensive than the transmission of measurements without classification. We provide analytical results and simulations on real data to show requirements and key trade-offs for the design of in-network data classification systems that can improve the collection efficiency. Besides, we study the impact of spatial subsampling of the sensor data (a way to further decrease energy consumption) on the classification performance.


title: Hierarchical stochastic neighbor embedding

abstract: In recent years, dimensionality-reduction techniques have been developed and are widely used for hypothesis generation in Exploratory Data Analysis. However, these techniques are confronted with overcoming the trade-off between computation time and the quality of the provided dimensionality reduction. In this work, we address this limitation, by introducing Hierarchical Stochastic Neighbor Embedding (Hierarchical-SNE). Using a hierarchical representation of the data, we incorporate the well-known mantra of Overview-First, Details-On-Demand in non-linear dimensionality reduction. First, the analysis shows an embedding, that reveals only the dominant structures in the data (Overview). Then, by selecting structures that are visible in the overview, the user can filter the data and drill down in the hierarchy. While the user descends into the hierarchy, detailed visualizations of the high-dimensional structures will lead to new insights. In this paper, we explain how Hierarchical-SNE scales to the analysis of big datasets. In addition, we show its application potential in the visualization of Deep-Learning architectures and the analysis of hyperspectral images.


title: Survey of Inter-Satellite Communication for Small Satellite Systems: Physical Layer to Network Layer View

abstract: Small satellite systems enable whole new class of missions for navigation, communications, remote sensing and scientific research for both civilian and military purposes. As individual spacecraft are limited by the size, mass and power constraints, mass-produced small satellites in large constellations or clusters could be useful in many science missions such as gravity mapping, tracking of forest fires, finding water resources, etc. Constellation of satellites provide improved spatial and temporal resolution of the target. Small satellite constellations contribute innovative applications by replacing a single asset with several very capable spacecraft which opens the door to new applications. With increasing levels of autonomy, there will be a need for remote communication networks to enable communication between spacecraft. These space based networks will need to configure and maintain dynamic routes, manage intermediate nodes, and reconfigure themselves to achieve mission objectives. Hence, inter-satellite communication is a key aspect when satellites fly in formation. In this paper, we present the various researches being conducted in the small satellite community for implementing inter-satellite communications based on the Open System Interconnection (OSI) model. This paper also reviews the various design parameters applicable to the first three layers of the OSI model, i.e., physical, data link and network layer. Based on the survey, we also present a comprehensive list of design parameters useful for achieving inter-satellite communications for multiple small satellite missions. Specific topics include proposed solutions for some of the challenges faced by small satellite systems, enabling operations using a network of small satellites, and some examples of small satellite missions involving formation flying aspects.


title: External force estimation using joint torque sensors and its application to impedance control of a robot manipulator

abstract: This paper proposes an algorithm to estimate external forces exerted on the end-effector of a robot manipulator using information from joint torque sensors (JTS). The algorithm is the combination of Time Delay Estimation (TDE) and input estimation technique where the external force is considered as an unknown input to the robot manipulator. Based on TDE's idea, the estimator which does not require an accurate dynamics model of the robot manipulator is developed. The simultaneous input and state estimation (SISE) is used to reduce not only nonlinear uncertainties of the robot dynamics but also the noise of measurements. The performance of the proposed estimation algorithm is evaluated through an application to impedance control of a four degree-of-freedom manipulator.


title: Synthesis of graphene-based nanosheets via chemical reduction of

abstract: Reduction of a colloidal suspension of exfoliated graphene oxide sheets in water with hydrazine hydrate results in their aggregation and subsequent formation of a high-surface-area carbon material which consists of thin graphene-based sheets. The reduced material was characterized by elemental analysis, thermo-gravimetric analysis, scanning electron microscopy, X-ray photoelectron spectroscopy, NMR spectroscopy, Raman spectroscopy, and by electrical conductivity measurements.


title: Mechanism and control of a 4WD robotic platform for omnidirectional wheelchairs

abstract: This paper presents mechanism and control of a Four-Wheel-Drive (4WD) robotic platform for wheelchairs. The 4WD mechanism equips four wheels, two omni-wheels in front and two normal tires in rear. The normal wheel and the omni-wheel, mounted on the same side of the base, are interconnected by a synchro-drive transmission to rotate in unison with a drive motor. To control chair orientation on the 4WD platform, the third motor is installed on the platform. The chair with the proposed omnidirectional 4WD system is capable of moving in any direction which is so-called holonomic and omnidirectional mobility.


title: Pre-processing Techniques for On-Line Capsule Inspection Based on Machine Vision

abstract: Machine vision has provided great convenience for manufacturing quality inspection. To facilitate on-line capsule inspection base on machine vision, in this paper, a capsule image acquisition system and pre-processing techniques are proposed. Capsule has a cylindrical surface, let it pure rolling on a flat surface, then image it several times during its rolling can make its surface fully visible. As the defect contrasting with capsule is various in different parts, it is needed to segment the capsule image into parts in which the gray values are homogeneous. A method synthesized both local threshold and projection can perform this segmentation quite well. Experiments bear out that this algorithm has good compatibility and the processing time can satisfy on-line inspection.


title: Focal-plane-arrays and CMOS readout techniques of infrared imaging systems

abstract: A discussion of CMOS readout technologies for infrared (IR) imaging systems is presented. First, the description of various types of IR detector materials and structures is given. The advances of detector fabrication technology and microelectronics process technology have led to the development of large format array of IR imaging detectors. For such large IR FPAs which is the critical component of the advanced infrared imaging system, general requirement and specifications are described. To support a good interface between the FPA and downstream signal processing stage, both conventional and CMOS readout techniques are presented and discussed. Finally, future development directions including the smart focal plane concept are also introduced.


title: Performance Evaluation of Deep Learning Tools in Docker Containers

abstract: With the success of deep learning techniques in a broad range of application domains, many deep learning software frameworks have been developed and are being updated frequently to adapt to new hardware features and software libraries, which bring a big challenge for end users and system administrators. To address this problem, container techniques are widely used to simplify the deployment and management of deep learning software. However, it remains unknown whether container techniques bring any performance penalty to deep learning applications. The purpose of this work is to systematically evaluate the impact of docker container on the performance of deep learning applications. We first benchmark the performance of system components (IO, CPU and GPU) in a docker container and the host system and compare the results to see if there's any difference. According to our results, we find that computational intensive jobs, either running on CPU or GPU, have small overhead indicating docker containers can be applied to deep learning programs. Then we evaluate the performance of some popular deep learning tools deployed in a docker container and the host system. It turns out that the docker container will not cause noticeable drawbacks while running those deep learning tools. So encapsulating deep learning tool in a container is a feasible solution.


title: What to Do with the Singularity Paradox?

abstract: The paper begins with an introduction of the Singularity Paradox, an observation that: “Superintelligent machines are feared to be too dumb to possess commonsense”. Ideas from leading researchers in the fields of philosophy, mathematics, economics, computer science and robotics regarding the ways to address said paradox are reviewed and evaluated. Suggestions are made regarding the best way to handle the Singularity Paradox.


title: A simple parallel cartesian tree algorithm and its application to parallel suffix tree construction

abstract: We present a simple linear work and space, and polylogarithmic time parallel algorithm for generating multiway Cartesian trees. We show that bottom-up traversals of the multiway Cartesian tree on the interleaved suffix array and longest common prefix array of a string can be used to answer certain string queries. By adding downward pointers in the tree (e.g. using a hash table), we can also generate suffix trees from suffix arrays on arbitrary alphabets in the same bounds. In conjunction with parallel suffix array algorithms, such as the skew algorithm, this gives a rather simple linear work parallel, O(n e) time (0 We also present experimental results comparing the performance of the algorithm to existing sequential implementations and a second parallel algorithm that we implement. We present comparisons for the Cartesian tree algorithm on its own and for constructing a suffix tree. The results show that on a variety of strings our algorithm is competitive with the sequential version on a single processor and achieves good speedup on multiple processors. We present experiments for three applications that require only the Cartesian tree, and also for searching using the suffix tree.


title: A Reinforcement Learning Approach for Host-Based Intrusion Detection Using Sequences of System Calls

abstract: Intrusion detection has emerged as an important technique for network security. Due to the complex and dynamic properties of intrusion behaviors, machine learning and data mining methods have been widely employed to optimize the performance of intrusion detection systems (IDSs). However, the results of existing work still need to be improved both in accuracy and in computational efficiency. In this paper, a novel reinforcement learning approach is presented for host-based intrusion detection using sequences of system calls. A Markov reward process model is introduced for modeling the behaviors of system call sequences and the intrusion detection problem is converted to predicting the value functions of the Markov reward process. A temporal different learning algorithm using linear basis functions is used for value function prediction so that abnormal temporal behaviors of host processes can be predicted accurately and efficiently. The proposed method has advantages over previous algorithms in that the temporal property of system call data is well captured in a natural and simple way and better intrusion detection performance can be achieved. Experimental results on the MIT system call data illustrate that compared with previous work, the proposed method has better detection accuracy with low training costs.


title: A Novel Endmember Bundle Extraction and Clustering Approach for Capturing Spectral Variability Within Endmember Classes

abstract: Spectral variability, unrelated to the purity of endmembers, can change the geometry of the dataspace and affect conventional methods used to identify endmembers. Several methods have been developed to identify and extract endmember bundles representing the spectral variability within each endmember class. These methods, however, operate on the geometry of the dataspace. In addition, they commonly use $k$ -means clustering that requires a priori the number of endmember classes present in a scene and may fail to group endmember spectra representing spectral variability within each class. This paper introduces a novel approach, spectral curve-based endmember extraction (SCEE), which allows for the extraction and clustering of multiple spectra representing spectral variability within endmember classes. The significant differences between SCEE and conventional methods are: i) SCEE is based on the shape of a spectral curve, not the geometry of the data simplex; and ii) SCEE extracts multiple endmember bundle candidates representing a particular class, without a priori knowledge of the number of endmember classes in a scene. Once multiple endmember bundle candidates are identified, they are automatically grouped by sequential pairwise clustering in order to determine the final number of endmember classes. The performance of SCEE is compared with that of other state-of-the-art endmember bundle extraction methods using simulated data and hyperspectral imagery of a mine pit and Cuprite. Results showed that multiple endmember bundles identified by SCEE gave better matches with spectral variability of reference spectra than those by other methods and were better able to encompass the range of variability within each class.


title: Market Models for Federated Clouds

abstract: Multi-cloud systems have enabled resource and service providers to co-exist in a market where the relationship between clients and services depends on the nature of an application and can be subject to a variety of different Quality of Service (QoS) constraints. Deciding whether a cloud provider should host (or finds it profitable to host) a service in the long-term would be influenced by parameters such as the service price, the QoS guarantees required by customers, the deployment cost (taking into account both cost of resource provisioning and operational expenditure, e.g. energy costs) and the constraints over which these guarantees should be met. In a federated cloud system users can combine specialist capabilities offered by a limited number of providers, at particular cost bands—such as availability of specialist co-processors and software libraries. In addition, federation also enables applications to be scaled on-demand and restricts lock in to the capabilities of a particular provider. We devise a market model to support federated clouds and investigate its efficiency in two real application scenarios:(i) energy optimisation in built environments and (ii) cancer image processing both requiring significant computational resources to execute simulations. We describe and evaluate the establishment of such an application based federation and identify a cost-decision based mechanism to determine when tasks should be outsourced to external sites in the federation. The following contributions are provided: (i) understanding the criteria for accessing sites within a federated cloud dynamically, taking into account factors such as performance, cost, user perceived value, and specific application requirements; (ii) developing and deploying a cost based federated cloud framework for supporting real applications over three federated sites at Cardiff (UK), Rutgers and Indiana (USA), (iii) a performance analysis of the application scenarios to determine how task submission could be supported across these three sites, subject to particular revenue targets.


title: SATIRE: a software architecture for smart AtTIRE

abstract: Personal instrumentation and monitoring services that collect and archive the physical activities of a user have recently been introduced for various medical, personal, safety, and entertainment purposes. A general software architecture is needed to support different categories of such monitoring services. This paper presents a software architecture, implementation, and preliminary evaluation of SATIRE, a wearable personal monitoring service transparently embedded in user garments. SATIRE records the owner's activity and location for subsequent automated uploading and archiving. The personal archive can later be searched for particular events to answer questions regarding past and present user activity, location, and behavior patterns. A short feasibility and usage study of a prototype based on MicaZ motes provides a proof of concept for the SATIRE architecture.


title: Robust and Inexpensive Six-Axis Force–Torque Sensors Using MEMS Barometers

abstract: Current commercial force–torque sensors are sensitive and accurate, but are also typically expensive and fragile. These features limit their use in cost-sensitive applications and unstructured environments such as people's homes. This paper presents a new design for an inexpensive and robust force–torque sensor that uses microelectromechanical system barometer transducers. The new design results in a six-axis force–torque sensor with an $R^2$ greater than 0.90 for F $_{x}$ and F $_{y}$ , and an $R^2$ greater than 0.98 for F $_{z}$ , M $_{x}$ , M $_{y}$ , and M $_{z}$ during compound loading. Furthermore, this sensor can be assembled in two days with off-the-shelf components for less than 20 USD.


title: DepSys: Dependency Aware Integration of Cyber-Physical Systems for Smart Homes

abstract: As sensor and actuator networks mature, they become a core utility of smart homes like electricity and water and enable the running of many CPS applications. Like other Cyber-Physical Systems (CPSs), when a number of applications share physical world entities, it raises many systems of systems interdependency problems. Such problems arise in the cyber part mainly because each application has assumptions on the physical world entities without knowing how other applications work. In this work, we propose DepSys, a utility sensing and actuation infrastructure for smart homes that provides comprehensive strategies to specify, detect, and resolve conflicts in a home setting. Based on real home data, we demonstrate the severity of conflicts when multiple CPSs are integrated and the significant ability of detecting and resolving such conflicts using DepSys.


title: Open source software for personal information managers and personal knowledge management

abstract: Technology is common in the domain of knowledge distribution, but it rarely enhances the process of knowledge use. Distribution delivers knowledge to the potential user's desktop but cannot dictate what he or she does with it thereafter. It would be interesting to envision technologies that help to manage personal knowledge as it applies to decisions and actions. The viewpoints about knowledge vary from individual, community, society, personnel development or national development. Personal Knowledge Management (PKM) integrates Personal Information Management (PIM), focused on individual skills, with Knowledge Management (KM). KM Software is a subset of Enterprise content management software and which contains a range of software that specialises in the way information is collected, stored and/or accessed. This article focuses on KM skills, PKM and PIM Open Sources Software, Social Personal Management and also highlights the Comparison of knowledge base management software and its use.


title: A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

abstract: Endowing machines with sensing capabilities similar to those of humans is a prevalent quest in engineering and computer science. In the pursuit of making computers sense their surroundings, a huge effort has been conducted to allow machines and computers to acquire, process, analyze and understand their environment in a human-like way. Focusing on the sense of hearing, the ability of computers to sense their acoustic environment as humans do goes by the name of machine hearing. To achieve this ambitious aim, the representation of the audio signal is of paramount importance. In this paper, we present an up-to-date review of the most relevant audio feature extraction techniques developed to analyze the most usual audio signals: speech, music and environmental sounds. Besides revisiting classic approaches for completeness, we include the latest advances in the field based on new domains of analysis together with novel bio-inspired proposals. These approaches are described following a taxonomy that organizes them according to their physical or perceptual basis, being subsequently divided depending on the domain of computation (time, frequency, wavelet, image-based, cepstral, or other domains). The description of the approaches is accompanied with recent examples of their application to machine hearing related problems.


title: Enhancing Community Interactions with Data-Driven Chatbots-The DBpedia Chatbot

abstract: In this demo, we introduce the DBpedia chatbot, a knowledge-graph-driven chatbot designed to optimize community interaction. The bot was designed for integration into community software to facilitate the answering of recurrent questions. Four main challenges were addressed when building the chatbot, namely (1) understanding user queries, (2) fetching relevant information based on the queries, (3) tailoring the responses based on the standards of each output platform (i.e. Web, Slack, Facebook) as well as (4) developing subsequent user interactions with the DBpedia chatbot. With this demo, we will showcase our solutions to these four challenges.


title: Membrane budding and scission by the ESCRT machinery: it's all in the neck

abstract: The endosomal sorting complexes required for transport (ESCRTs) catalyse one of the most unusual membrane remodelling events in cell biology. ESCRT-I and ESCRT-II direct membrane budding away from the cytosol by stabilizing bud necks without coating the buds and without being consumed in the buds. ESCRT-III cleaves the bud necks from their cytosolic faces. ESCRT-III-mediated membrane neck cleavage is crucial for many processes, including the biogenesis of multivesicular bodies, viral budding, cytokinesis and, probably, autophagy. Recent studies of ultrastructures induced by ESCRT-III overexpression in cells and the in vitro reconstitution of the budding and scission reactions have led to breakthroughs in understanding these remarkable membrane reactions.


title: Rectenna Design and Signal Optimization for Electromagnetic Energy Harvesting and Wireless Power Transfer

abstract: This work addresses two key topics in the field of energy harvesting and wireless power transfer. The first is the optimum signal design for improved RF-DC conversion efficiency in rectifier circuits by using time varying envelope signals. The second is the design of rectifiers that present reduced sensitivity to input power and output load variations by introducing resistance compression network (RCN) structures. key words: rectenna, energy harvesting, wireless power transfer, resistance compression network, rectifier


title: Network design considerations for distributed control systems

abstract: This paper discusses the impact of network architecture on control performance in a class of distributed control systems called networked control systems (NCSs) and provides design considerations related to control quality of performance as well as network quality of service. The integrated network-control system changes the characteristics of time delays between application devices. This study first identifies several key components of the time delay through an analysis of network protocols and control dynamics. The analysis of network and control parameters is used to determine an acceptable working range of sampling periods in an NCS. A network-control simulator and an experimental networked a machine tool have been developed to help validate and demonstrate the performance analysis results and identify the special performance characteristics in an NCS. These performance characteristics are useful guidelines for choosing the network and control parameters when designing an NCS.


title: Design, modeling, estimation and control for aerial grasping and manipulation

abstract: This paper addresses mechanics, design, estimation and control for aerial grasping. We present the design of several light-weight, low-complexity grippers that allow quadrotors to grasp and perch on branches or beams and pick up and transport payloads. We then show how the robot can use rigid body dynamic models and sensing to verify a grasp, to estimate the the inertial parameters of the grasped object, and to adapt the controller and improve performance during flight. We present experimental results with different grippers and different payloads and show the robot's ability to estimate the mass, the location of the center of mass and the moments of inertia to improve tracking performance.


title: Testing Incremental Difficulty Design in Platformer Games

abstract: Designing difficulty levels in platformer games is a challenge for game designers. It is important because design decisions that affect difficulty also directly affect player experience. Consequently, design strategies for balancing game difficulty are discussed by both academics and game designers. In this paper, we study how manipulating the following design decisions, commonly found in platformers, moderates difficulty: Scroll Speed, Target Size, Jump Task Complexity, and Perspective. Results for Scroll Speed and Target Size indicate that errors increase as speed increases and platform size decreases. However, results for jump task complexity demonstrate a separation of errors from task complexity. Specifically, while double-jump tasks are harder than single-jump tasks, triple-jump tasks appear to be as difficult as double-jump tasks. Additionally, the study demonstrates how changes in perspective affect the errors made by players in gameplay. The study results are applicable both to automatic level generation and dynamic difficulty adjustment in platformer games.


title: Automated extraction of product comparison matrices from informal product descriptions

abstract: We propose a procedure for extracting comparison matrices from informal product descriptions.We evaluate our proposal against numerous categories of products mined from BestBuy.Matrices exhibit numerous comparable information and can supplement or even refine technical descriptions.A user study shows that our automated approach retrieves a significant portion of correct information.Users can compute, control, edit and refine matrices in a Web environment called MatrixMiner. Domain analysts, product managers, or customers aim to capture the important features and differences among a set of related products. A case-by-case reviewing of each product description is a laborious and time-consuming task that fails to deliver a condense view of a family of product.In this article, we investigate the use of automated techniques for synthesizing a product comparison matrix (PCM) from a set of product descriptions written in natural language. We describe a tool-supported process, based on term recognition, information extraction, clustering, and similarities, capable of identifying and organizing features and values in a PCM - despite the informality and absence of structure in the textual descriptions of products.We evaluate our proposal against numerous categories of products mined from BestBuy. Our empirical results show that the synthesized PCMs exhibit numerous quantitative, comparable information that can potentially complement or even refine technical descriptions of products. The user study shows that our automatic approach is capable of extracting a significant portion of correct features and correct values. This approach has been implemented in MatrixMiner a web environment with an interactive support for automatically synthesizing PCMs from informal product descriptions. MatrixMiner also maintains traceability with the original descriptions and the technical specifications for further refinement or maintenance by users.


title: A textile integrated long-term ECG monitor with capacitively coupled electrodes

abstract: Capacitive EGC sensors are a promising approach for future ECG measurement systems as they do not rely on a moist and direct skin contact. This is especially desirable for long-term monitoring, because capacitively coupled electrodes (CCE) do not cause skin irritations to the patient. In this paper, we present a textile integrated CCE-based ECG monitoring system for long-term application. The system comprises of a convenient garment, active capacitive electrodes as well as a pluggable low-power acquisition unit with Bluetooth telemetry. For this system special electrodes have been developed whose capacitive areas exhibit textile properties. They are completely integrated into the garment and equipped with a textile layer that transports humidity away from the wearer. Additionally, a large breathable reference electrode is used. In the first setup it acts as a conductive grounding when the garment is worn on the skin, and in the second setting it is capacitively coupled, when wearing a shirt beneath. ECG measurements were taken in both settings and qualitative results with the subjects sitting and walking are presented. Particularly, it is pointed out that textile-integrated CCEs in conjunction with a conductive reference electrode is a feasible solution for long-term ECG monitoring in terms of both signal quality and ergonomics.


title: A rose by any other name…? The OECD’S proposed revised definition of intangibles

abstract: Recent political events and a rewrite of the OECD TP Guidelines� chapter on intangibles are causing a considerable compliance challenge for multinationals. Tax authorities are running out of ways to increase tax revenues. Without venturing into the politically undesirable territory of tax rate increases, intangibles are one of the very few areas left to explore.