title: Machine Intelligence Techniques for Next-Generation Context-Aware Wireless Networks

abstract: The next generation wireless networks (i.e. 5G and beyond), which would be extremely dynamic and complex due to the ultra-dense deployment of heterogeneous networks (HetNets), poses many critical challenges for network planning, operation, management and troubleshooting. At the same time, generation and consumption of wireless data are becoming increasingly distributed with ongoing paradigm shift from people-centric to machine-oriented communications, making the operation of future wireless networks even more complex. In mitigating the complexity of future network operation, new approaches of intelligently utilizing distributed computational resources with improved context-awareness becomes extremely important. In this regard, the emerging fog (edge) computing architecture aiming to distribute computing, storage, control, communication, and networking functions closer to end users, have a great potential for enabling efficient operation of future wireless networks. These promising architectures make the adoption of artificial intelligence (AI) principles which incorporate learning, reasoning and decision-making mechanism, as natural choices for designing a tightly integrated network. Towards this end, this article provides a comprehensive survey on the utilization of AI integrating machine learning, data analytics and natural language processing (NLP) techniques for enhancing the efficiency of wireless network operation. In particular, we provide comprehensive discussion on the utilization of these techniques for efficient data acquisition, knowledge discovery, network planning, operation and management of the next generation wireless networks. A brief case study utilizing the AI techniques for this network has also been provided.


title: A case for an interleaving constrained shared-memory multi-processor

abstract: Shared-memory multi-threaded programming is inherently more difficult than single-threaded programming. The main source of complexity is that, the threads of an application can interleave in so many different ways. To ensure correctness, a programmer has to test all possible thread interleavings, which, however, is impractical. Many rare thread interleavings remain untested in production systems, and they are the root cause for a majority of concurrency bugs. We propose a shared-memory multi-processor design that avoids untested interleavings to improve the correctness of a multi-threaded program. Since untested interleavings tend to occur infrequently at runtime, the performance cost of avoiding them is not high. We propose to encode the set of tested correct interleavings in a program's binary executable using Predecessor Set (PSet) constraints. These constraints are efficiently enforced at runtime using processor support, which ensures that the runtime follows a tested interleaving. We analyze several bugs in open source applications such as MySQL, Apache, Mozilla, etc., and show that, by enforcing PSet constraints, we can avoid not only data races and atomicity violations, but also other forms of concurrency bugs.


title: A Novel Feature-based Bayesian Model for Query Focused Multi-document Summarization

abstract: Supervised learning methods and LDA based topic model have been successfully applied in the field of multi-document summarization. In this paper, we propose a novel supervised approach that can incorporate rich sentence features into Bayesian topic models in a principled way, thus taking advantages of both topic model and feature based supervised learning methods. Experimental results on DUC2007, TAC2008 and TAC2009 demonstrate the effectiveness of our approach.


title: Economic Possibilities for Our Children: Artificial Intelligence and the Future of Work, Education, and Leisure

abstract: Many experts believe that in the coming decades, artificial intelligence will change, and perhaps significantly reduce, the demand for human labor in the economy, but there remains much uncertainty about the accuracy of this claim and what to do about it. This paper identifies several ways in which the artificial intelligence community can help society to anticipate and shape such outcomes in a socially beneficial direction. First, different technical aspirations for the field of AI may be associated with different social outcomes, increasing the stakes of decisions made in the AI community. Second, the extent of researchers' efforts to apply AI to different social and economic domains will influence the distribution of cognition between humans and machines in those domains. Third, the AI community can play a key role in initiating a more nuanced and inclusive public discussion of the social and economic possibilities afforded by AI technologies. To pave the way for such dialogue, we suggest a line of research aimed at better understanding the nature, pace, and drivers of progress in AI in order to more effectively anticipate and shape AI's role in society.


title: Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints

abstract: This paper studies the link between resting-state functional connectivity (FC), measured by the correlations of the fMRI BOLD time courses, and structural connectivity (SC), estimated through fiber tractography. Instead of a static analysis based on the correlation between SC and the FC averaged over the entire fMRI time series, we propose a dynamic analysis, based on the time evolution of the correlation between SC and a suitably windowed FC. Assessing the statistical significance of the time series against random phase permutations, our data show a pronounced peak of significance for time window widths around 20-30 TR (40-60 sec). Using the appropriate window width, we show that FC patterns oscillate between phases of high modularity, primarily shaped by anatomy, and phases of low modularity, primarily shaped by inter-network connectivity. Building upon recent results in dynamic FC, this emphasizes the potential role of SC as a transitory architecture between different highly connected resting state FC patterns. Finally, we show that networks implied in consciousness-related processes, such as the default mode network (DMN), contribute more to these brain-level fluctuations compared to other networks, such as the motor or somatosensory networks. This suggests that the fluctuations between FC and SC are capturing mind-wandering effects.


title: A Novel Hypergraph-Based Genetic Algorithm (HGGA) Built on Unimodular and Anti-homomorphism Properties for DNA Sequencing by Hybridization

abstract: The sequencing by hybridization (SBH) of determining the order in which nucleotides should occur on a DNA string is still under discussion for enhancements on computational intelligence although the next generation of DNA sequencing has come into existence. In the last decade, many works related to graph theory-based DNA sequencing have been carried out in the literature. This paper proposes a method for SBH by integrating hypergraph with genetic algorithm (HGGA) for designing a novel analytic technique to obtain DNA sequence from its spectrum. The paper represents elements of the spectrum and its relation as hypergraph and applies the unimodular property to ensure the compatibility of relations between l-mers. The hypergraph representation and unimodular property are bound with the genetic algorithm that has been customized with a novel selection and crossover operator reducing the computational complexity with accelerated convergence. Subsequently, upon determining the primary strand, an anti-homomorphism is invoked to find the reverse complement of the sequence. The proposed algorithm is implemented in the GenBank BioServer datasets, and the results are found to prove the efficiency of the algorithm. The HGGA is a non-classical algorithm with significant advantages and computationally attractive complexity reductions ranging to \(O(n^{2} )\) with improved accuracy that makes it prominent for applications other than DNA sequencing like image processing, task scheduling and big data processing.


Probabilistic encryption


title: A systematic procedure to study the influence of occupant behavior on building energy consumption

abstract: Efforts have been devoted to the identification of the impacts of occupant behavior on building energy consumption. Various factors influence building energy consumption at the same time, leading to the lack of precision when identifying the individual effects of occupant behavior. This paper reports the development of a new methodology for examining the influences of occupant behavior on building energy consumption; the method is based on a basic data mining technique (cluster analysis). To deal with data inconsistencies, min–max normalization is performed as a data preprocessing step before clustering. Grey relational grades, a measure of relevancy between two factors, are used as weighted coefficients of different attributes in cluster analysis. To demonstrate the applicability of the proposed method, the method was applied to a set of residential buildings’ measurement data. The results show that the method facilitates the evaluation of building energy-saving potential by improving the behavior of building occupants, and provides multifaceted insights into building energy end-use patterns associated with the occupant behavior. The results obtained could help prioritize efforts at modification of occupant behavior in order to reduce building energy consumption, and help improve modeling of occupant behavior in numerical simulation.


title: Employee Absenteeism, Organizational Commitment, and Job Satisfaction: Another Look

abstract: Abstract Previous research suggested weak relations between work attitudes and indirect measures of employee absenteeism (e.g., frequency and time-lost; Hackett, 1989). In the present study, absences were regarded as voluntary or involuntary based on the reasons provided by the employees or supervisors. It was hypothesized that voluntary as opposed to involuntary absenteeism can be predicted by organizational commitment, job satisfaction, and their interactive effect. Intention to quit was expected to predict voluntary absenteeism as well. Subjects were 140 clerks in an Israeli municipality (average age was 41). Moderated multiple regression analyses of attitudes and objective (personnel records) or subjective (self-reported) absence data yielded support for the first hypothesis. Nonetheless, the intention to quit was not significantly related to either type of absence. Theoretical and methodological considerations were discussed and implications for continued research were outlined.


title: Learning to Teach Reinforcement Learning Agents

abstract: In this article, we study the transfer learning model of action advice under a budget. We focus on reinforcement learning teachers providing action advice to heterogeneous students playing the game of Pac-Man under a limited advice budget. First, we examine several critical factors affecting advice quality in this setting, such as the average performance of the teacher, its variance and the importance of reward discounting in advising. The experiments show that the best performers are not always the best teachers and reveal the non-trivial importance of the coefficient of variation (CV) as a statistic for choosing policies that generate advice. The CV statistic relates variance to the corresponding mean. Second, the article studies policy learning for distributing advice under a budget. Whereas most methods in the relevant literature rely on heuristics for advice distribution, we formulate the problem as a learning one and propose a novel reinforcement learning algorithm capable of learning when to advise or not. The proposed algorithm is able to advise even when it does not have knowledge of the student’s intended action and needs significantly less training time compared to previous learning approaches. Finally, in this article, we argue that learning to advise under a budget is an instance of a more generic learning problem: Constrained Exploitation Reinforcement Learning.


title: NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks

abstract: Massive and dynamic networks arise in many practical applications such as social media, security and public health. Given an evolutionary network, it is crucial to detect structural anomalies, such as vertices and edges whose "behaviors'' deviate from underlying majority of the network, in a real-time fashion. Recently, network embedding has proven a powerful tool in learning the low-dimensional representations of vertices in networks that can capture and preserve the network structure. However, most existing network embedding approaches are designed for static networks, and thus may not be perfectly suited for a dynamic environment in which the network representation has to be constantly updated. In this paper, we propose a novel approach, NetWalk, for anomaly detection in dynamic networks by learning network representations which can be updated dynamically as the network evolves. We first encode the vertices of the dynamic network to vector representations by clique embedding, which jointly minimizes the pairwise distance of vertex representations of each walk derived from the dynamic networks, and the deep autoencoder reconstruction error serving as a global regularization. The vector representations can be computed with constant space requirements using reservoir sampling. On the basis of the learned low-dimensional vertex representations, a clustering-based technique is employed to incrementally and dynamically detect network anomalies. Compared with existing approaches, NetWalk has several advantages: 1) the network embedding can be updated dynamically, 2) streaming network nodes and edges can be encoded efficiently with constant memory space usage, 3). flexible to be applied on different types of networks, and 4) network anomalies can be detected in real-time. Extensive experiments on four real datasets demonstrate the effectiveness of NetWalk.


title: Multiple Object Detection by a Deformable Part-Based Model and an R-CNN

abstract: Multiple object detection is a key challenge in object detection. Feature extraction and occlusion handling are two key elements in multiple object detection. However, existing methods do not perform well in these aspects during detecting multiple objects. A region-based convolutional network (R-CNN) has achieved a great success in region-based feature extraction, and the part filters in a deformable part-based model (DPM) are very suitable for detecting occluded objects. In this letter, we present a framework that integrates the R-CNN and the DPM for detecting multiple objects. In addition, we propose a new filter based on the dense subgraph discovery algorithm for refining the candidate proposals generated by the DPM. Through combining these two models, we can detect each single object with high accuracy among all objects in an image, especially in the situation that objects are stayed close together. Different from the traditional methods, our framework has the capability to detect multiple objects belonging to various classes rather than only one typical class such as a person or a car. Experimental results on the PASCAL VOC dataset show that our framework achieves better performance compared to using only the R-CNN or the DPM alone in multiple object detection.


title: Attachment Patterns and Complex Trauma in a Sample of Adults Diagnosed with Gender Dysphoria

abstract: The current study investigated attachment representations and complex trauma in a sample of gender dysphoric adults. Although it has been proven that the psychological wellbeing of gender diverse persons is largely mediated by family acceptance and support, research on their relationships with parental figures is scarce. A total of 95 adults took part in the study. The attachment distribution was as follows: 27% secure, 27% insecure and 46% disorganised. Regarding early traumas, 56% experienced four or more traumatic forms. Further, gender dysphoric adults showed significantly higher levels of attachment disorganisation and polyvictimisation, relative to controls. Comparisons of subgroups, defined by natal gender, showed that trans women, compared to control males, had more involving and physically and psychologically abusive fathers, and were more often separated from their mothers; trans men, relative to female controls, had more involving mothers and were more frequently separated from and neglected by their fathers. The research has several implications for treatment, clinical health psychology, family support and education.


title: SLAM-based Pseudo-GNSS/INS localization system for indoor LiDAR mobile mapping systems

abstract: The emergence of Mobile Mapping Systems (MMS) has set a marked paradigm in the photogrammetric and mapping community that has not only facilitated comprehensive 3D mapping of different environments but has also paved way for new aspects of applied research in this direction. Out of the many essential blocks that make these MMS a viable tool for mapping, the positioning and orientation module is considered to be a crucial yet an expensive component. The integration of such a module with mapping sensors has allowed for the extensive implementation of such systems to provide high-quality maps. However, while such systems do not lack in system robustness and performance in general, the deployment of these systems is restricted to applications and environments where a consistent availability of GNSS signals is assured. Extending these MMS to GNSS-denied areas, such as indoor environments, is therefore quite challenging and necessitates the development of an alternative module that can act as a viable substitute to GNSS/INS for system operation without having to resort to an exhaustive modification of the same to function in GNSS-denied locations. In this research, such a case has been considered for the implementation of an indoor MMS using an Unmanned Ground Vehicle (UGV) and a 3D laser scanner for the task of generating high density maps of GNSS-denied indoor areas. To mitigate the absence of GNSS data, this paper proposes a Pseudo-GNSS/INS module integrated framework which utilizes probabilistic Simultaneous Localization and Mapping (SLAM) techniques to estimate the platform pose and heading from 3D laser scanner data. This proposed framework has been implemented based on three major notions: (i) using geometric methods for sparse point cloud extraction to carry out real-time SLAM, (ii) generating position data and geo-referencing signals from these real-time SLAM pose estimates, and (iii) carrying out the entire operation through use of a single 3D mapping sensor. The final geo-referenced point cloud can then be generated through post-processing by the Iterative Closest Projected Point (ICPP) registration technique which also diminishes the effect of sensor measurement noise. The implementation, performance and results of the proposed MMS framework for an indoor mapping system have been presented in this paper that demonstrate the ability of this Pseudo-GNSS/INS framework to operate flexibly in GNSS-denied areas.


title: Bio-sensing textile based patch with integrated optical detection system for sweat monitoring

abstract: Sensors, which can be integrated into clothing and used to measure biochemical changes in body fluids, such as sweat, constitute a major advancement in the area of wearable sensors. Initial applications for such technology exist in personal health and sports performance monitoring. However, sample collection is a complicated matter as analysis must be done in real-time in order to obtain a useful examination of its composition. This work outlines the development of a textile-based fluid handling platform which uses a passive pump to gather sweat and move it through a pre-defined channel for analysis. The system is tested both in vitro and in vivo. In addition, a pH sensor, which depends on the use of a pH sensitive dye and paired emitter-detector LEDs to measure colour changes, has been developed. In vitro and on-body trials have shown that the sensor has the potential to record real-time variations in sweat during exercise.


title: A Smart Health Monitoring Chair for Nonintrusive Measurement of Biological Signals

abstract: We developed nonintrusive methods for simultaneous electrocardiogram, photoplethysmogram, and ballistocardiogram measurements that do not require direct contact between instruments and bare skin. These methods were applied to the design of a diagnostic chair for unconstrained heart rate and blood pressure monitoring purposes. Our methods were operationalized through capacitively coupled electrodes installed in the chair back that include high-input impedance amplifiers, and conductive textiles installed in the seat for capacitive driven-right-leg circuit configuration that is capable of recording electrocardiogram information through clothing. Photoplethysmograms were measured through clothing using seat mounted sensors with specially designed amplifier circuits that vary in light intensity according to clothing type. Ballistocardiograms were recorded using a film type transducer material, polyvinylidenefluoride (PVDF), which was installed beneath the seat cover. By simultaneously measuring signals, beat-to-beat heart rates could be monitored even when electrocardiograms were not recorded due to movement artifacts. Beat-to-beat blood pressure was also monitored using unconstrained measurements of pulse arrival time and other physiological parameters, and our experimental results indicated that the estimated blood pressure tended to coincide with actual blood pressure measurements. This study demonstrates the feasibility of our method and device for biological signal monitoring through clothing for unconstrained long-term daily health monitoring that does not require user awareness and is not limited by physical activity.


title: Low-Resource Speech-to-Text Translation

abstract: Speech-to-text translation has many potential applications for low-resource languages, but the typical approach of cascading speech recognition with machine translation is often impossible, since the transcripts needed to train a speech recognizer are usually not available for low-resource languages. Recent work has found that neural encoder-decoder models can learn to directly translate foreign speech in high-resource scenarios, without the need for intermediate transcription. We investigate whether this approach also works in settings where both data and computation are limited. To make the approach efficient, we make several architectural changes, including a change from character-level to word-level decoding. We find that this choice yields crucial speed improvements that allow us to train with fewer computational resources, yet still performs well on frequent words. We explore models trained on between 20 and 160 hours of data, and find that although models trained on less data have considerably lower BLEU scores, they can still predict words with relatively high precision and recall---around 50% for a model trained on 50 hours of data, versus around 60% for the full 160 hour model. Thus, they may still be useful for some low-resource scenarios.


title: Integrated Schottky diodes in BCD5 technology for high frequency soft switched power converters

abstract: In this paper, the development of integrated Schottky diodes in the BCD5 technology is described. The structure adopted is analyzed and model equations are carried out to evaluate diode characteristics. The diodes were applied to the integration of a zero voltage transition (ZVT) bridge power converter switched at high frequency for voice coil motor driving, where fast and recovery-free diodes are required to implement the auxiliary branches. The experimental results validating diode behaviour are reported.


title: Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-To-Image Translation

abstract: The applicability of computer vision to real paintings and artworks has been rarely investigated, even though a vast heritage would greatly benefit from techniques which can understand and process data from the artistic domain. This is partially due to the small amount of annotated artistic data, which is not even comparable to that of natural images captured by cameras. In this paper, we propose a semantic-aware architecture which can translate artworks to photo-realistic visualizations, thus reducing the gap between visual features of artistic and realistic data. Our architecture can generate natural images by retrieving and learning details from real photos through a similarity matching strategy which leverages a weakly-supervised semantic understanding of the scene. Experimental results show that the proposed technique leads to increased realism and to a reduction in domain shift, which improves the performance of pre-trained architectures for classification, detection, and segmentation. Code is publicly available at: https://github.com/aimagelab/art2real.


Learning distributed representations of concepts.


title: Overview and analysis of methodologies for building ontologies

abstract: The use of methodologies in software and knowledge engineering is very extensive due to their important advantages. In the case of the development of ontologies, until now, several methodological proposals have been presented for building ontologies. Some of these methodologies are designed for building ontologies from scratch or reusing other ontologies without modifying them, concretely, the following cases can be mentioned: the Cyc methodology, the approach proposed by Uschold and King, Gruninger and Fox's methodology, the KACTUS methodology, METHONTOLOGY and the SENSUS methodology. There is even a proposal for re-engineering ontologies, and several proposals for collaborative construction of ontologies.In this article, we describe the methodologies and check their degree of maturity, contrasting them with respect to the IEEE standard for software development. Before this, we justify to what extent this standard can be used. A conclusion to this study is that there is no completely mature methodological proposal for building ontologies, since there are some important activities and techniques that are missing in all these methodologies. However, all the methodologies do not have the same degree of maturity. In fact, METHONTOLOGY is a very mature methodology. The other conclusion of this article is that, although work to unify proposals can be interesting, maybe several approaches should coexist.


title: Illustration2Vec: a semantic vector representation of illustrations

abstract: Referring to existing illustrations helps novice drawers to realize their ideas. To find such helpful references from a large image collection, we first build a semantic vector representation of illustrations by training convolutional neural networks. As the proposed vector space correctly reflects the semantic meanings of illustrations, users can efficiently search for references with similar attributes. Besides the search with a single query, a semantic morphing algorithm that searches the intermediate illustrations that gradually connect two queries is proposed. Several experiments were conducted to demonstrate the effectiveness of our methods.


An innovation - diffusion view of implementation of enterprise resource planning (ERP) systems and development of a research model


title: Algorithms for K-Terminal Reliability Problems with Node Failures

abstract: Consider a distributed processing system with a set K of sites that can either cooperate in computing a function or hold resources required by other sites. The system is implemented using a communication network with unreliable nodes. Two simplied reliability problems then arise. In the rst problem, we are interested in computing the probability that every operational pair of sites in K can communicate with each other. This problem is known to be #P-complete. In the second problem, the sites in K are service centers. Our reliability measure is the probability that every operational site in the network is connected to at least one operational service center. In this paper, we dene the class of t-polygon graphs, t 3, as the intersection graphs of straight line chords in a convex t-gon. Hence, any t-polygon graph is a circle graph. We show that both problems admit polynomial time solutions when the underlying graph of the network is restricted to a t-polygon graph, for a x ed t.


title: Exploring instructors’ technology readiness, attitudes and behavioral intentions towards e-learning technologies in Egypt and United Arab Emirates

abstract: This paper explores the association between technology readiness, (a meta-construct consisting of optimism, innovativeness, discomfort, and insecurity), attitude, and behavioral intention towards e-learning technologies adoption within an education institution context. The empirical study data is collected at two private universities located in Egypt and UAE. The research explores the role of instructors’ technology readiness level, in shaping their attitudes, preference to human interaction and ultimately behavioral intentions towards adopting e-learning technologies. Analysis of the data (Mann-Whitney U non-parametric test) shows no significant differences between instructors at the two universities in terms of technology readiness, attitudes, behavioral intentions, and preference to human interaction. The exploratory results provide evidence for the relationship between instructors’ technology, attitude, and behavioral intentions to adopt e-learning technologies. The study finds that preference to human interaction is equally important in Egypt and UAE with a strong potential to affect instructor’s behavioral intentions for adopting e-learning technologies. The research results provide initial insights to education managers on the nature and mechanisms of the relationship among the research variables, which would improve the ability of educational institutions to introduce and adopt e-learning technologies. An additional contribution is the validity and reliability tests for Technology Readiness (TR) scale, which shows its viability as a meaningful measurement instrument for use in an educational setting.


title: Building semantic kernels for text classification using wikipedia

abstract: Document classification presents difficult challenges due to the sparsity and the high dimensionality of text data, and to the complex semantics of the natural language. The traditional document representation is a word-based vector (Bag of Words, or BOW), where each dimension is associated with a term of the dictionary containing all the words that appear in the corpus. Although simple and commonly used, this representation has several limitations. It is essential to embed semantic information and conceptual patterns in order to enhance the prediction capabilities of classification algorithms. In this paper, we overcome the shortages of the BOW approach by embedding background knowledge derived from Wikipedia into a semantic kernel, which is then used to enrich the representation of documents. Our empirical evaluation with real data sets demonstrates that our approach successfully achieves improved classification accuracy with respect to the BOW technique, and to other recently developed methods.


title: Short-Text Clustering using Statistical Semantics

abstract: Short documents are typically represented by very sparse vectors, in the space of terms. In this case, traditional techniques for calculating text similarity results in measures which are very close to zero, since documents even the very similar ones have a very few or mostly no terms in common. In order to alleviate this limitation, the representation of short-text segments should be enriched by incorporating information about correlation between terms. In other words, if two short segments do not have any common words, but terms from the first segment appear frequently with terms from the second segment in other documents, this means that these segments are semantically related, and their similarity measure should be high. Towards achieving this goal, we employ a method for enhancing document clustering using statistical semantics. However, the problem of high computation time arises when calculating correlation between all terms. In this work, we propose the selection of a few terms, and using these terms with the Nystr\"om method to approximate the term-term correlation matrix. The selection of the terms for the Nystr\"om method is performed by randomly sampling terms with probabilities proportional to the lengths of their vectors in the document space. This allows more important terms to have more influence on the approximation of the term-term correlation matrix and accordingly achieves better accuracy.


title: Return on marketing: Using customer equity to focus marketing strategy

abstract: The authors present a unified strategic framework that enables competing marketing strategy options to be traded off on the basis of projected financial return, which is operationalized as the change in a firm’s customer equity relative to the incremental expenditure necessary to produce the change. The change in the firm’s customer equity is the change in its current and future customers’ lifetime values, summed across all customers in the industry. Each customer’s lifetime value results from the frequency of category purchases, average quantity of purchase, and brand-switching patterns combined with the firm’s contribution margin. The brand-switching matrix can be estimated from either longitudinal panel data or cross-sectional survey data, using a logit choice model. Firms can analyze drivers that have the greatest impact, compare the drivers’ performance with that of competitors’ drivers, and project return on investment from improvements in the drivers. To demonstrate how the approach can be implemented in a specific corporate setting and to show the methods used to test and validate the model, the authors illustrate a detailed application of the approach by using data from the airline industry. Their framework enables what-if evaluation of marketing return on investment, which can include such criteria as return on quality, return on advertising, return on loyalty programs, and even return on corporate citizenship, given a particular shift in customer perceptions. This enables the firm to focus marketing efforts on strategic initiatives that generate the greatest return.


title: The Genomes of Three Uneven Siblings: Footprints of the Lifestyles of Three Trichoderma Species

abstract: The genus Trichoderma contains fungi with high relevance for humans, with applications in enzyme production for plant cell wall degradation and use in biocontrol. Here, we provide a broad, comprehensive overview of the genomic content of these species for "hot topic" research aspects, including CAZymes, transport, transcription factors, and development, along with a detailed analysis and annotation of less-studied topics, such as signal transduction, genome integrity, chromatin, photobiology, or lipid, sulfur, and nitrogen metabolism in T. reesei, T. atroviride, and T. virens, and we open up new perspectives to those topics discussed previously. In total, we covered more than 2,000 of the predicted 9,000 to 11,000 genes of each Trichoderma species discussed, which is >20% of the respective gene content. Additionally, we considered available transcriptome data for the annotated genes. Highlights of our analyses include overall carbohydrate cleavage preferences due to the different genomic contents and regulation of the respective genes. We found light regulation of many sulfur metabolic genes. Additionally, a new Golgi 1,2-mannosidase likely involved in N-linked glycosylation was detected, as were indications for the ability of Trichoderma spp. to generate hybrid galactose-containing N-linked glycans. The genomic inventory of effector proteins revealed numerous compounds unique to Trichoderma, and these warrant further investigation. We found interesting expansions in the Trichoderma genus in several signaling pathways, such as G-protein-coupled receptors, RAS GTPases, and casein kinases. A particularly interesting feature absolutely unique to T. atroviride is the duplication of the alternative sulfur amino acid synthesis pathway.


title: Evaluating question answering over linked data

abstract: The availability of large amounts of open, distributed, and structured semantic data on the web has no precedent in the history of computer science. In recent years, there have been important advances in semantic search and question answering over RDF data. In particular, natural language interfaces to online semantic data have the advantage that they can exploit the expressive power of Semantic Web data models and query languages, while at the same time hiding their complexity from the user. However, despite the increasing interest in this area, there are no evaluations so far that systematically evaluate this kind of systems, in contrast to traditional question answering and search interfaces to document spaces. To address this gap, we have set up a series of evaluation challenges for question answering over linked data. The main goal of the challenge was to get insight into the strengths, capabilities, and current shortcomings of question answering systems as interfaces to query linked data sources, as well as benchmarking how these interaction paradigms can deal with the fact that the amount of RDF data available on the web is very large and heterogeneous with respect to the vocabularies and schemas used. Here, we report on the results from the first and second of such evaluation campaigns. We also discuss how the second evaluation addressed some of the issues and limitations which arose from the first one, as well as the open issues to be addressed in future competitions.


title: Driver Identification: a Time Series Classification Approach

abstract: In the last years, researchers and auto vehicle developers are focusing their attention on the topic of driver identification encouraged by an increasing number of always more sophisticated vehicle sensors able to extract information about the driver. Indeed, driver identification can be very useful to customize and improve driver experience, increase the safety on the road and reduce the global environmental problems. In this work, we propose and explore a set of features (extracted from a car monitoring system) identifying the driver basing on his/her driving behavior. The proposed behavioral features are exploited using a time series classification approach and a multi-layer perceptron (MLP) network is used to evaluate the ability of the proposed features to identify the vehicle driver. The proposed features are tested on a real data set composed of totally 66 observations (each observation consists of a given person driving a given car on a predefined path). The obtained results show that the proposed features have effective driver identification ability.


title: Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences

abstract: Statistical models of the human body surface are generally learned from thousands of high-quality 3D scans in predefined poses to cover the wide variety of human body shapes and articulations. Acquisition of such data requires expensive equipment, calibration procedures, and is limited to cooperative subjects who can understand and follow instructions, such as adults. We present a method for learning a statistical 3D Skinned Multi-Infant Linear body model (SMIL) from incomplete, low-quality RGB-D sequences of freely moving infants. Quantitative experiments show that SMIL faithfully represents the RGB-D data and properly factorizes the shape and pose of the infants. To demonstrate the applicability of SMIL, we fit the model to RGB-D sequences of freely moving infants and show, with a case study, that our method captures enough motion detail for General Movements Assessment (GMA), a method used in clinical practice for early detection of neurodevelopmental disorders in infants. SMIL provides a new tool for analyzing infant shape and movement and is a step towards an automated system for GMA.


Visibility Analysis for 3 D Urban Environments Research development and practical application


title: Assessing It/Business Alignment

abstract: Abstract Alignment is the perennial business chart-topper on top-ten lists of IT issues. What follows is a methodology developed by the author for assessing a company's alignment. Modeled after the Capability Maturity Model ® developed by Carnegie Mellon's Software Engineering Institute, but focused on a more strategic set of business practices, this tool has been successfully tested at more than 50 Global 2000 companies and is currently the subject of a benchmarking study sponsored by the Society for Information Management and the Conference Board. the primary objective of the assessment is to identify specific recommendations for improving the alignment of IT and the business.


title: Automatic Sentiment Detection in Naturalistic Audio

abstract: Audio sentiment analysis using automatic speech recognition is an emerging research area where opinion or sentiment exhibited by a speaker is detected from natural audio. It is relatively underexplored when compared to text based sentiment detection. Extracting speaker sentiment from natural audio sources is a challenging problem. Generic methods for sentiment extraction generally use transcripts from a speech recognition system, and process the transcript using text-based sentiment classifiers. In this study, we show that this baseline system is suboptimal for audio sentiment extraction. Alternatively, new architecture using keyword spotting (KWS) is proposed for sentiment detection. In the new architecture, a text-based sentiment classifier is utilized to automatically determine the most useful and discriminative sentiment-bearing keyword terms, which are then used as a term list for KWS. In order to obtain a compact yet discriminative sentiment term list, iterative feature optimization for maximum entropy sentiment model is proposed to reduce model complexity while maintaining effective classification accuracy. A new hybrid ME-KWS joint scoring methodology is developed to model both text and audio based parameters in a single integrated formulation. For evaluation, two new databases are developed for audio based sentiment detection, namely, YouTube sentiment database and another newly developed corpus called UT-Opinion Opinion audio archive. These databases contain naturalistic opinionated audio collected in real-world conditions. The proposed solution is evaluated on audio obtained from videos in youtube.com and UT-Opinion corpus. Our experimental results show that the proposed KWS based system significantly outperforms the traditional ASR architecture in detecting sentiment for challenging practical tasks.


Unsupervised Models for Spatial, Temporal and Relational Systems


title: The ProtégéVOWL Plugin: Ontology Visualization for Everyone

abstract: The Visual Notation for OWL Ontologies (VOWL) provides a visual language for the representation of ontologies. In contrast to related work, VOWL aims at an intuitive and interactive visualization that is also understandable to users less familiar with ontologies. This paper presents ProtegeVOWL, a first implementation of VOWL realized as a plugin for the ontology editor Protege. It accesses the internal ontology representation provided by the OWL API and defines graphical mappings according to the VOWL specification. The information visualization toolkit Prefuse is used to render the visual elements and to combine them into a force-directed graph layout. Results from a preliminary user study indicate that ProtegeVOWL does indeed provide a comparatively intuitive and usable ontology visualization.


Character Recognition Using Convolutional Neural Networks


title: Infrastructure and Ethnographic Practice

abstract: By bringing together science studies, information science and ethnographic fieldwork in interdisciplinary research the author argues for the relevance of ethnographic practices when studying information systems as infrastructures of communication. Ethnographic fieldwork focuses attention on fringes and materialities of infrastructures and renders the researcher able to read the invisible layers of control and access, to understand the changes in the social orderings that are brought about by information technology. Numerous examples and personal accounts of studies of infrastructures with ethnographic tools show how paying analytical attention to mundane aspects of information infrastructures helps to understand the consequences of the imbrication of infrastructure and human organization.


title: Flexible residential smart grid simulation framework

abstract: Different scheduling and coordination algorithms controlling household appliances' operations can potentially lead to energy consumption reduction and/or load balancing in conjunction with different electricity pricing methods used in smart grid programs. In order to easily implement different algorithms and evaluate their efficiency against other ideas, a flexible simulation framework is desirable in both research and business fields. However, such a platform is currently lacking or underdeveloped. As a result, this paper proposes a simulation framework to focus on demand side residential energy consumption coordination in response to different pricing methods. This simulation framework, equipped with an appliance consumption library using realistic values, aims to closely represent the average usage of different types of appliances. The simulation results from a prototype program built from the proposed framework yield closely matching values compared to surveyed real life consumption records. Several sample coordination algorithms, pricing schemes, and communication scenarios are also implemented to illustrate the use of the simulation framework.


title: Family Routines and Rituals: A Context for Occupational Therapy Interventions

abstract: PURPOSE. This paper explores the importance of family daily routines and rituals for the family’s functioning and sense of identity. METHODS. The findings of this paper are derived from an analysis of the morning routines of 40 families with children with disabilities in the United States and Canada. The participants lived in urban and rural areas. Forty of the 49 participants were mothers and the majority of the families were of European descent. Between one and four interviews were conducted with each participant. Topics included the family’s story, daily routines, and particular occupations. Data on the morning routines of the families were analyzed for order and affective and symbolic meaning using a narrative approach. FINDINGS. The findings are presented as narratives of morning activities in five families. These narratives are examples for rituals, routines, and the absence of a routine. Rituals are discussed in terms of their affective and symbolic qualities, routines are discussed in terms of the order they give to family life, whereas the lack of family routine is discussed in terms of lack of order in the family. CONCLUSIONS. Family routines and rituals are organizational and meaning systems that may affect family’s ability to adapt them.


title: Learning Online Smooth Predictors for Realtime Camera Planning Using Recurrent Decision Trees

abstract: We study the problem of online prediction for realtime camera planning, where the goal is to predict smooth trajectories that correctly track and frame objects of interest (e.g., players in a basketball game). The conventional approach for training predictors does not directly consider temporal consistency, and often produces undesirable jitter. Although post-hoc smoothing (e.g., via a Kalman filter) can mitigate this issue to some degree, it is not ideal due to overly stringent modeling assumptions (e.g., Gaussian noise). We propose a recurrent decision tree framework that can directly incorporate temporal consistency into a data-driven predictor, as well as a learning algorithm that can efficiently learn such temporally smooth models. Our approach does not require any post-processing, making online smooth predictions much easier to generate when the noise model is unknown. We apply our approach to sports broadcasting: given noisy player detections, we learn where the camera should look based on human demonstrations. Our experiments exhibit significant improvements over conventional baselines and showcase the practicality of our approach.


title: Hybrid Genetic-fuzzy approach to Autonomous Mobile Robot

abstract: An Autonomous Mobile Robot (AMR) is a machine able to extract information from its environment and use knowledge about its world to move safely in a meaningful and purposeful manner. Robot Navigation and Obstacle Avoidance are from the most important problems in mobile robots, especially in unknown environments. It must be able to interact with other objects safely. Several techniques such as Fuzzy logic, Reinforcement learning, Neural Networks and Genetic Algorithms, have applied to AMR in order to improve their performance. During the past several years Hybrid Genetic-fuzzy method has emerged as one of the most active and fruitful areas for research in the application of intelligent system design. The objective of this work is to provide a Hybrid method by which an improved set of rules governing the actions and behavior of a simple navigating and obstacle avoiding AMR. Genes are in the form of distances and angles labels. The chromosomes are represented as a rule written in a Boolean algebraic form. The method used to enhance the performance employs a simulation model designed by using Visual Basic software.


title: Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures

abstract: This paper addresses the problem of evaluating learning systems in safety critical domains such as autonomous driving, where failures can have catastrophic consequences. We focus on two problems: searching for scenarios when learned agents fail and assessing their probability of failure. The standard method for agent evaluation in reinforcement learning, Vanilla Monte Carlo, can miss failures entirely, leading to the deployment of unsafe agents. We demonstrate this is an issue for current agents, where even matching the compute used for training is sometimes insufficient for evaluation. To address this shortcoming, we draw upon the rare event probability estimation literature and propose an adversarial evaluation approach. Our approach focuses evaluation on adversarially chosen situations, while still providing unbiased estimates of failure probabilities. The key difficulty is in identifying these adversarial situations -- since failures are rare there is little signal to drive optimization. To solve this we propose a continuation approach that learns failure modes in related but less robust agents. Our approach also allows reuse of data already collected for training the agent. We demonstrate the efficacy of adversarial evaluation on two standard domains: humanoid control and simulated driving. Experimental results show that our methods can find catastrophic failures and estimate failures rates of agents multiple orders of magnitude faster than standard evaluation schemes, in minutes to hours rather than days.


title: Security Services Using Blockchains: A State of the Art Survey

abstract: This paper surveys blockchain-based approaches for several security services. These services include authentication, confidentiality, privacy and access control list, data and resource provenance, and integrity assurance. All these services are critical for the current distributed applications, especially due to the large amount of data being processed over the networks and the use of cloud computing. Authentication ensures that the user is who he/she claims to be. Confidentiality guarantees that data cannot be read by unauthorized users. Privacy provides the users the ability to control who can access their data. Provenance allows an efficient tracking of the data and resources along with their ownership and utilization over the network. Integrity helps in verifying that the data has not been modified or altered. These services are currently managed by centralized controllers, for example, a certificate authority. Therefore, the services are prone to attacks on the centralized controller. On the other hand, blockchain is a secured and distributed ledger that can help resolve many of the problems with centralization. The objectives of this paper are to give insights on the use of security services for current applications, to highlight the state of the art techniques that are currently used to provide these services, to describe their challenges, and to discuss how the blockchain technology can resolve these challenges. Further, several blockchain-based approaches providing such security services are compared thoroughly. Challenges associated with using blockchain-based security services are also discussed to spur further research in this area.


A Case Study of an Online Assignment Submission System at UOM


Self-Regulated Learning and the 21 st Century Competencies


title: Agglomerative clustering using the concept of mutual nearest neighbourhood

abstract: A method for determining the mutual nearest neighbours (MNN) and mutual neighbourhood value (mnv) of a sample point, using the conventional nearest neighbours, is suggested. A nonparametric, hierarchical, agglomerative clustering algorithm is developed using the above concepts. The algorithm is simple, deterministic, noniterative, requires low storage and is able to discern spherical and nonspherical clusters. The method is applicable to a wide class of data of arbitrary shape, large size and high dimensionality. The algorithm can discern mutually homogenous clusters. Strong or weak patterns can be discerned by properly choosing the neighbourhood width.


title: Quantum algorithm for solving linear systems of equations

abstract: Solving linear systems of equations is a common problem that arises both on its own and as a subroutine in more complex problems: given a matrix A and a vector b(-->), find a vector x(-->) such that Ax(-->) = b(-->). We consider the case where one does not need to know the solution x(-->) itself, but rather an approximation of the expectation value of some operator associated with x(-->), e.g., x(-->)(dagger) Mx(-->) for some matrix M. In this case, when A is sparse, N x N and has condition number kappa, the fastest known classical algorithms can find x(-->) and estimate x(-->)(dagger) Mx(-->) in time scaling roughly as N square root(kappa). Here, we exhibit a quantum algorithm for estimating x(-->)(dagger) Mx(-->) whose runtime is a polynomial of log(N) and kappa. Indeed, for small values of kappa [i.e., poly log(N)], we prove (using some common complexity-theoretic assumptions) that any classical algorithm for this problem generically requires exponentially more time than our quantum algorithm.


EVALUATION OF POTENTIAL ANTIMICROBIAL ACTIVITY OF L EVOFLOXACIN LOADED SOLID LIPID NANOPARTICLES


title: On calculating losses in current carrying conductors in an external alternating magnetic field

abstract: A common approach in calculating ac resistance is to add together losses due to a magnetically isolated conductor and the losses due to eddy currents induced in the same conductor by an externally excited magnetic field in the absence of primary conductor current. This method is employed in the design of large current carrying devices such as multiphase cables and reactive elements. The present analysis shows that this "superposition" method for calculating ac resistance is an approximation which is valid only under restrictive conditions. This is illustrated by calculating the total joulean losses in a single layer coil in Cartesian coordinates and identifying the contributions due to the primary coil current and eddy currents induced by the external field.


title: Data Series Management: Fulfilling the Need for Big Sequence Analytics

abstract: Massive data sequence collections exist in virtually every scientific and social domain, and have to be analyzed to extract useful knowledge. However, no existing data management solution (such as relational databases, column stores, array databases, and time series management systems) can offer native support for sequences and the corresponding operators necessary for complex analytics. We argue for the need to study the theory and foundations for sequence management of big data sequences, and to build corresponding systems that will enable scalable management and analysis of very large sequence collections. To this effect, we need to develop novel techniques to efficiently support a wide range of sequence queries and mining operations, while leveraging modern hardware. The overall goal is to allow analysts across domains to tap in the goldmine of the massive and ever-growing sequence collections they (already) have.


title: Improving Local Search for Fuzzy Scheduling Problems

abstract: The integration of fuzzy set theory and fuzzy logic into scheduling is a rather new aspect with growing importance for manufacturing applications, resulting in various unsolved aspects. In the current paper, we investigate an improved local search technique for fuzzy scheduling problems with fitness plateaus, using a multi criteria formulation of the problem. We especially address the problem of changing job priorities over time as studied at the Sherwood Press Ltd, a Nottingham based printing company, who is a collaborator on the project.


title: Wireless Charging of a Metal-Body Device

abstract: Conventional tightly coupled inductive charging has been popular in the mobile device market for years. However, tightly coupled solutions have been unable to efficiently charge a metal encased device due to substantial heat generation. In this paper, we demonstrate that loosely coupled inductive charging technology is a viable solution to charge a metal encased device. Ample power can be delivered through the metal case with negligible case heating. Our prototype is built by machining a lump of aluminum which is attached to a commercial smart phone. Resonator coupling efficiency is measured to be 90% with a certified AirFuel Alliance transmitter. Measured power to a load is 8.2 W, which is suitable for mobile devices.


title: Multi-resolution texture analysis for fingerprint based age-group estimation

abstract: In this paper the possibility of using digital fingerprints to estimate age-groups of human being, particularly children is investigated. To our knowledge, age-group estimation in humans, using digital fingerprints have not been addressed formally. Age-group estimation can be applied in many areas like on-line child protection, access control and customized internet services etc. Motivated by the fact that human digital fingerprint vary in texture as the person ages, a multi-resolution texture approach for automatic age-group estimation has been presented in this paper. Three standard classifiers were used to judge the accuracy of the proposed method. In the process of this research study, a novel method for digital fingerprint reference point generation was developed, which provides reference point for very poor quality images also. The proposed reference point generation method is compared with core-point method using FG-NET DB1 dataset. Experimental results proves that a digital fingerprint can be used to identify age-groups, particularly children. A classification accuracy of 80 percent was achieved for children below the age of 14 by using the aforesaid method.


title: Detecting People Using Mutually Consistent Poselet Activations ⋆

abstract: Bourdev and Malik (ICCV 09) introduced a new notion of parts, poselets, constructed to be tightly clustered both in the configuration space of keypoints, as well as in the appearance space of image patches. In this paper we develop a new algorithm for detecting people using poselets. Unlike that work which used 3D annotations of keypoints, we use only 2D annotations which are much easier for naive human annotators. The main algorithmic contribution is in how we use the pattern of poselet activations. Individual poselet activations are noisy, but considering the spatial context of each can provide vital disambiguating information, just as object detection can be improved by considering the detection scores of nearby objects in the scene. This can be done by training a two-layer feed-forward network with weights set using a max margin technique. The refined poselet activations are then clustered into mutually consistent hypotheses where consistency is based on empirically determined spatial keypoint distributions. Finally, bounding boxes are predicted for each person hypothesis and shape masks are aligned to edges in the image to provide a segmentation. To the best of our knowledge, the resulting system is the current best performer on the task of people detection and segmentation with an average precision of 47.8% and 40.5% respectively on PASCAL VOC 2009.


title: Analysis of DDoS-capable IoT malwares

abstract: The Internet of Things (IoT) revolution promises to make our lives easier by providing cheap and always connected smart embedded devices, which can interact on the Internet and create added values for human needs. But all that glitters is not gold. Indeed, the other side of the coin is that, from a security perspective, this IoT revolution represents a potential disaster. This plethora of IoT devices that flooded the market were very badly protected, thus an easy prey for several families of malwares that can enslave and incorporate them in very large botnets. This, eventually, brought back to the top Distributed Denial of Service (DDoS) attacks, making them more powerful and easier to achieve than ever. This paper aims at provide an up-to-date picture of DDoS attacks in the specific subject of the IoT, studying how these attacks work and considering the most common families in the IoT context, in terms of their nature and evolution through the years. It also explores the additional offensive capabilities that this arsenal of IoT malwares has available, to mine the security of Internet users and systems. We think that this up-to-date picture will be a valuable reference to the scientific community in order to take a first crucial step to tackle this urgent security issue.


title: Developing an Assessment Method of Active Aging: University of Jyvaskyla Active Aging Scale

abstract: Objective: To develop an assessment method of active aging for research on older people. Method: A multiphase process that included drafting by an expert panel, a pilot study for item analysis and ...


title: SIFT, SURF & Seasons: Appearance-based Long-term Localization in Outdoor Environments

abstract: In this paper, we address the problem of outdoor, appearance-based topological localization, particularly over long periods of time where seasonal changes alter the appearance of the environment. We investigate a straightforward method that relies on local image features to compare single-image pairs. We first look into which of the dominating image feature algorithms, SIFT or the more recent SURF, that is most suitable for this task. We then fine-tune our localization algorithm in terms of accuracy, and also introduce the epipolar constraint to further improve the result. The final localization algorithm is applied on multiple data sets, each consisting of a large number of panoramic images, which have been acquired over a period of nine months with large seasonal changes. The final localization rate in the single-image matching, cross-seasonal case is between 80% to 95%.


title: Influence of Hyperparameters on Random Forest Accuracy

abstract: In this paper we present our work on the Random Forest (RF) family of classification methods. Our goal is to go one step further in the understanding of RF mechanisms by studying the parametrization of the reference algorithm Forest-RI. In this algorithm, a randomization principle is used during the tree induction process, that randomly selects K features at each node, among which the best split is chosen. The strength of randomization in the tree induction is thus led by the hyperparameter K which plays an important role for building accurate RF classifiers. We have decided to focus our experimental study on this hyperparameter and on its influence on classification accuracy. For that purpose, we have evaluated the Forest-RI algorithm on several machine learning problems and with different settings of K in order to understand the way it acts on RF performance. We show that default values of K traditionally used in the literature are globally near-optimal, except for some cases for which they are all significatively sub-optimal. Thus additional experiments have been led on those datasets, that highlight the crucial role played by feature relevancy in finding the optimal setting of K .


title: MiNiaturized Quarter-Mode Substrate Integrated Waveguide (QMSIW) antenna using Sierpinski fractal geometry

abstract: A planar Sierpinski fractal on a Quarter Mode Substrate Integrated Waveguide (QMSIW) antenna is proposed in this paper. The designed antenna has an overall dimension of 35 × 40 mm2. The QMSIW is obtained by bisecting the Half Mode Substrate Integrated Waveguide (HMSIW) along the fictitious quasi-magnetic wall while maintaining similar performance characteristics. Two stages of Sierpinski fractals have been introduced and their effects in the frequency reduction have been investigated. Fractal structures increase the effective current path leading to miniaturization of antennas. Rectangular slots have been cut on the ground plane to perturb the electric field for further miniaturization. The QMSIW antenna with second stage of Sierpinski fractal has been designed, fabricated and measured. The measured and simulated results match closely.


title: Semantic Component Retrieval in Software Engineering

abstract: In the early days of programming the concept of subroutines, and through this software reuse, was invented to spare limited hardware resources. Since then software systems have become increasingly complex and developing them would not have been possible without reusable software elements such as standard libraries and frameworks. Furthermore, other approaches commonly subsumed under the umbrella of software reuse such as product lines and design patterns have become very successful in recent years. However, there are still no software component markets available that would make buying software components as simple as buying parts in a do-it-yourself hardware store and millions of software fragments are still lying un(re)used in configuration management repositories all over the world. The literature primarily blames this on the immense effort required so far to set up and maintain searchable component repositories and the weak mechanisms available for retrieving components from them, resulting in a severe usability problem. In order to address these issues within this thesis, we developed a proactive component reuse recommendation system, naturally integrated into test-first development approaches, which is able to propose semantically appropriate, reusable components according to the specification a developer is just working on. We have implemented an appropriate system as a plugin for the well-known Eclipse IDE and demonstrated its usefulness by carrying out a case study from a popular agile development book. Furthermore, we present a precision analysis for our approach and examples of how components can be retrieved based on a simplified semantics description in terms of standard test cases.


title: Interaction between NTF2 and xFxFG-containing nucleoporins is required to mediate nuclear import of RanGDP 1 1Edited by I. B. Holland

abstract: Nuclear transport factor 2 (NTF2) is a small, homodimeric protein that binds to both RanGDP and xFxFG repeat-containing nucleoporins, such as yeast Nsp1p and vertebrate p62. NTF2 is required for efficient nuclear protein import and has been shown to mediate the nuclear import of RanGDP. We have used the crystal structures of rat NTF2 and its complex with RanGDP to design a mutant, W7A-NTF2, in which the affinity for xFxFG-repeat nucleoporins is reduced while wild-type binding to RanGDP is retained. The 2.5 A resolution crystal structure of W7A-NTF2 is virtually superimposable upon the wild-type protein structure, indicating that the mutation had not introduced a more general conformational change. Therefore, our data suggest that the exposed side-chain of residue 7 is crucial to the interaction between NTF2 and xFxFG repeat-containing nucleoporins. Consistent with its reduced affinity for xFxFG nucleoporins, fluorescently labelled W7A-NTF2 binds less strongly to the nuclear envelope of permeabilized cultured cells than wild-type NTF2 and, when microinjected into Xenopus oocytes, colloidal gold coated with W7A-NTF2 binds less strongly to the central channel of nuclear pore complexes than wild-type NTF2-coated gold. Significantly, W7A-NTF2 only weakly stimulated the nuclear import of fluorescein-labelled RanGDP, providing direct evidence that an interaction between NTF2 and xFxFG repeat-containing nucleoporins is required to mediate the nuclear import of RanGDP.


title: Simple robust road lane detection algorithm

abstract: Lane detection plays an important role in intelligent vehicle systems. Therefore, this paper presents a robust road lane marker detection algorithm to detect the left and right lane markers. The algorithm consists of optimization of Canny edge detection and Hough Transform. The system captures images from a front viewing vision sensor placed facing the road behind the windscreen as input. Then a series of image processing is applied to generate the road model. Canny edge detection performs features recognition then followed by Hough Transform lane generation. The algorithm detects visible left and right lane markers on the road based on real-time video processing.


A Standard Model of the Mind: Toward a Common Computational Framework across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics


title: Stronger Baselines for Trustable Results in Neural Machine Translation

abstract: Interest in neural machine translation has grown rapidly as its effectiveness has been demonstrated across language and data scenarios. New research regularly introduces architectural and algorithmic improvements that lead to significant gains over "vanilla" NMT implementations. However, these new techniques are rarely evaluated in the context of previously published techniques, specifically those that are widely used in state-of-theart production and shared-task systems. As a result, it is often difficult to determine whether improvements from research will carry over to systems deployed for real-world use. In this work, we recommend three specific methods that are relatively easy to implement and result in much stronger experimental systems. Beyond reporting significantly higher BLEU scores, we conduct an in-depth analysis of where improvements originate and what inherent weaknesses of basic NMT models are being addressed. We then compare the relative gains afforded by several other techniques proposed in the literature when starting with vanilla systems versus our stronger baselines, showing that experimental conclusions may change depending on the baseline chosen. This indicates that choosing a strong baseline is crucial for reporting reliable experimental results.


title: Robust Fault Tolerant Control of Networked Control System Based on LMI Method

abstract: For the networked control system(NCS),the uncertainty of network-induced delays is converted to the uncertainty of the parameter matrix,and the states are transformed to the augmented states to eliminate the effect of the asynchronism.Based on Lyapunove stability theory and Linear Matrix Inequality(LMI),the sufficient conditions for closed-loop of networked control system possessing asymptotically stable against sensor or actuator failure are given,and the corresponding design method of robust fault-tolerant controller is presented via solving several linear matrix inequalities.Finally,numerical examples are given to demonstrate the effectiveness and feasibility of the proposed approach.


ACM'S computing classification system reflects changing times


title: Efficacy of intra-articular hypertonic dextrose prolotherapy versus normal saline for knee osteoarthritis: a protocol for a triple-blinded randomized controlled trial

abstract: Knee Osteoarthritis (KOA) is a very common condition with prevalence rising with age. It is a major contributor to global disability and has a large socioeconomic burden worldwide. Conservative therapies have marginal effectiveness, and surgery is reserved for severe symptomatic KOA. Dextrose Prolotherapy (DPT) is an evidence-based injection-based therapy for chronic musculoskeletal conditions including KOA. The standard “whole joint” injection method includes intra-articular injection and multiple extra-articular injections at soft tissue bony attachments. The procedure is painful and requires intensive procedural training often unavailable in conventional medical education, which potentially limits access. Intra-articular injection offers the possibility of a less painful, more accessible treatment. The aim of this project is to assess the clinical efficacy of intra-articular injection of DPT versus normal saline (NS) for KOA. Seventy-six participants with KOA will be recruited from the community. We will conduct a single center, parallel group, superiority randomized controlled trial comparing DPT and NS injections, with blinding of physician, participants, outcome assessors and statisticians. Each group will receive injections at week 0, 4, 8 and 16. The primary outcome will be the Western Ontario McMaster University Osteoarthritis Index pain scale (WOMAC), and secondary outcomes include WOMAC composite score, the WOMAC function and stiffness subscale, the Visual Analogue Score of pain, objective physical function tests (the 30 s chair stand, 40- m fast paced walk test, the Timed up and go test) and the EuroQol-5D (EQ-5D). All outcomes will be evaluated at baseline, and 16, 26 and 52 weeks. All analyses will be conducted on an intention-to-treat basis using linear mixed regression models. This paper presents the rationale, design, method and operational aspects of the trial. The findings will determine whether IA DPT, an inexpensive and simple injection, is a safe and effective non-surgical option for KOA. The results can be translated directly to clinical practice, with potentially substantial impact to patient care. The trial ( ::: ChiCTR-IPC-15006617 ::: ) is registered under Chinese Clinical Trials Registry on 17th June 2015.


title: A conceptual scheme for cyber-physical systems based energy management in building structures

abstract: Concerns about energy efficiency and carbon footprints are becoming more significant in the power industry and public policy, as evidenced by the concerted shift to renewable energy sources and energy conservation paradigms. One of the largest consumers of energy in the US is the residential and commercial buildings sector. This paper explores a conceptual framework of a cyber-physical system (CPS) for energy management in such structures, viz. a Cyber-enabled Efficient Building Energy Management System (CEBEMS), aimed at attaining some level of energy self-sufficiency.


title: The second educational revolution: rethinking education in the age of technology

abstract: This paper drew upon a recent book (Rethinking Education in the Age of Technology) to summarize a number of prospects and challenges arising from the appropriation of digital technology into learning and educational practice. Tensions between traditional models of schooling and the affordances of digital media were noted, while the promise of these technologies for shaping a new system of education was reviewed. It was argued that new technology brings radical opportunities but also significant challenges. The urgency of seeking a coherent model for the future of education in a technological age was stressed.


title: Halophiles – Taxonomy, Diversity, Physiology and Applications

abstract: Halophiles are salt-loving organisms inhabiting environments with the capacity to balance the osmotic pressure of the environment and resist the denaturing effects of salts such as salt induced co-aggregation of proteins. They are diverse group of organisms that thrive extreme saline environments. Depending upon their requirement for salt concentration, they are classified as halotolerant, moderately halophile and extreme halophile. Halophiles have been routinely isolated from marine salterns and hypersaline lakes with 3.5–4.5 M (20–30 g% NaCl). Extremely halophilic archaea were characterized from saline environment in different parts of Turkey, “Solar de Atacama”, Chile, two salt lakes in Xin-Jiang, China. The family Halobacteriaceae contains 96 species classified in 27 genera. Mechanism of salt tolerance and halophily has been studied to some extent. The organisms devise protection against high concentration of Na+. They are reported to produce certain biochemicals like glycine betaine which acts as compatible solute for high concentration of NaCl. The cell envelope and outer membrane shows modification in composition based on ionic strength of outer environment. Extremophiles are being looked upon as a treasure of novel biomolecules, biomaterials and metabolites. Haloarchaeal enzymes are unusually stable and adapted to extreme environments thus are suitable candidates for applications in industrial processes that are performed under harsh conditions of high ionic strength. Bacteriorhodopsin, a photochemical produced by halophiles has a potential in conversion of light energy into chemical energy and electricity. Halophiles have wide range of biotechnological potential in industry e.g. biosurfactant production, biopolymers in oil recovery, proteases and amylases in detergent industry, poly-beta hydroxyalkanoate as biodegradable plastic, exopolysaccharide and bioremediation of contaminated hypersaline brines etc.


title: Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics

abstract: De-noising and extraction of the weak signature are crucial to fault prognostics in which case features are often very weak and masked by noise. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. In this paper, the performance of wavelet decomposition-based de-noising and wavelet filter-based de-noising methods are compared based on signals from mechanical defects. The comparison result reveals that wavelet filter is more suitable and reliable to detect a weak signature of mechanical impulse-like defect signals, whereas the wavelet decomposition de-noising method can achieve satisfactory results on smooth signal detection. In order to select optimal parameters for the wavelet filter, a two-step optimization process is proposed. Minimal Shannon entropy is used to optimize the Morlet wavelet shape factor. A periodicity detection method based on singular value decomposition (SVD) is used to choose the appropriate scale for the wavelet transform. The signal de-noising results from both simulated signals and experimental data are presented and both support the proposed method.


Purification of human alpha-2-macroglobulin by chromatography on Cibacron Blue Sepharose.


title: Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance

abstract: We introduce a new methodology that combines deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance (MR) data. This combination is relevant for segmentation problems, where the visual object of interest presents large shape and appearance variations, but the annotated training set is small, which is the case for various medical image analysis applications, including the one considered in this paper. In particular, level set methods are based on shape and appearance terms that use small training sets, but present limitations for modelling the visual object variations. Deep learning methods can model such variations using relatively small amounts of annotated training, but they often need to be regularised to produce good generalisation. Therefore, the combination of these methods brings together the advantages of both approaches, producing a methodology that needs small training sets and produces accurate segmentation results. We test our methodology on the MICCAI 2009 left ventricle segmentation challenge database (containing 15 sequences for training, 15 for validation and 15 for testing), where our approach achieves the most accurate results in the semi-automated problem and state-of-the-art results for the fully automated challenge.


title: Comparison of Insect Gut Cellulase and Xylanase Activity Across Different Insect Species with Distinct Food Sources

abstract: Insect guts represent unique natural biocatalyst systems for biocatalyst discovery and biomass deconstruction mechanism studies. In order to guide the further research for enzyme discovery and biodiversity analysis, we carried out comprehensive xylanase and cellulase activity assays for the gut contents of three insect species representing different orders and food sources. The three insect species are grasshopper (Acrididae sp.), woodborer (Cerambycidae spp.), and silkworm (Bombyx mori) to represent the wood-consuming, grass-consuming, and leaf-consuming insects from Orthoptera, Coleoptera, and Lepidoptera orders, respectively. Generally speaking, the enzyme activity assays have shown that the cellulase and xylanase activities for grasshopper and woodborer guts are significantly higher than those of silkworm under various conditions. In addition, both pH and temperature have a significant impact on the enzyme activities in the gut contents. For the grasshopper gut, the means of xylanase and cellulase activities at pH 7 were 3,397 and 404 μM mg−1 min−1, which are significantly higher than the activities at pH 4 and 10 (P 0.99). The newly developed model serves well to compare the characteristics of the enzyme mixtures among different insect species, which can be applied to other studies of natural biocatalyst systems for the future. Overall, the data indicated that grasshopper and woodborer guts are valuable resources for discovering the novel biocatalysts for various biorefinery applications.


title: The Cost of Children: Differences between the Genders

abstract: In this paper we estimate the opportunity cost of children. The underlying theoretical model is represented by a household production model. In the empirical analysis, we consider three different cohorts for men and women born between 1955 and 1970. For the women in the two oldest cohorts, the opportunity cost of two children is estimated to 28-29 per cent of full income, which in monetary terms is close to the estimated income difference between women employed in the public and private sector. The opportunity cost of fatherhood is generally positive, but only significantly positive for men born between 1960 and 1965.


title: Demonstration of quantum error correction and universal gate set on a binomial bosonic logical qubit

abstract: Logical qubit encoding and quantum error correction (QEC) protocols have been experimentally demonstrated in various physical systems with multiple physical qubits, generally without reaching the break-even point, at which the lifetime of the quantum information exceeds that of the single best physical qubit within the logical qubit. Logical operations are challenging, owing to the necessary non-local operations at the physical level, making bosonic logical qubits that rely on higher Fock states of a single oscillator attractive, given their hardware efficiency. QEC that reaches the break-even point and single logical-qubit operations have been demonstrated using the bosonic cat code. Here, we experimentally demonstrate repetitive QEC approaching the break-even point of a single logical qubit encoded in a hybrid system consisting of a superconducting circuit and a bosonic cavity using a binomial bosonic code. This is achieved while simultaneously maintaining full control of the single logical qubit, including encoding, decoding and a high-fidelity universal quantum gate set with 97% average process fidelity. The corrected logical qubit has a lifetime 2.8 times longer than that of its uncorrected counterpart. We also perform a Ramsey experiment on the corrected logical qubit, reporting coherence twice as long as for the uncorrected case.


title: Characterizing and enhancing global memory data coalescing on GPUs

abstract: Effective parallel programming for GPUs requires careful attention to several factors, including ensuring coalesced access of data from global memory. There is a need for tools that can provide feedback to users about statements in a GPU kernel where non-coalesced data access occurs, and assistance in fixing the problem. In this paper, we address both these needs. We develop a two-stage framework where dynamic analysis is first used to detect and characterize uncoalesced accesses in arbitrary PTX programs. Transformations to optimize global memory access by introducing coalesced access are then implemented, using feedback from the dynamic analysis or using a model-driven approach. Experimental results demonstrate the use of the tools on a number of benchmarks from the Rodinia and Polybench suites.


title: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and<0.5MB model size

abstract: Recent research on deep neural networks has focused primarily on improving accuracy. For a given accuracy level, it is typically possible to identify multiple DNN architectures that achieve that accuracy level. With equivalent accuracy, smaller DNN architectures offer at least three advantages: (1) Smaller DNNs require less communication across servers during distributed training. (2) Smaller DNNs require less bandwidth to export a new model from the cloud to an autonomous car. (3) Smaller DNNs are more feasible to deploy on FPGAs and other hardware with limited memory. To provide all of these advantages, we propose a small DNN architecture called SqueezeNet. SqueezeNet achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters. Additionally, with model compression techniques we are able to compress SqueezeNet to less than 0.5MB (510x smaller than AlexNet).


title: Missing Not at Random Models for Latent Growth Curve Analyses

abstract: The past decade has seen a noticeable shift in missing data handling techniques that assume a missing at random (MAR) mechanism, where the propensity for missing data on an outcome is related to other analysis variables. Although MAR is often reasonable, there are situations where this assumption is unlikely to hold, leading to biased parameter estimates. One such example is a longitudinal study of substance use where participants with the highest frequency of use also have the highest likelihood of attrition, even after controlling for other correlates of missingness. There is a large body of literature on missing not at random (MNAR) analysis models for longitudinal data, particularly in the field of biostatistics. Because these methods allow for a relationship between the outcome variable and the propensity for missing data, they require a weaker assumption about the missing data mechanism. This article describes 2 classic MNAR modeling approaches for longitudinal data: the selection model and the pattern mixture model. To date, these models have been slow to migrate to the social sciences, in part because they required complicated custom computer programs. These models are now quite easy to estimate in popular structural equation modeling programs, particularly Mplus. The purpose of this article is to describe these MNAR modeling frameworks and to illustrate their application on a real data set. Despite their potential advantages, MNAR-based analyses are not without problems and also rely on untestable assumptions. This article offers practical advice for implementing and choosing among different longitudinal models.


title: Modeling the IT value paradox

abstract: Assessing the value of IT investments has stumped managers and academics for years. If viewed as a commodity input, however, IT should be measured in the context in which the investment takes place.


title: VR-VIBE: A Virtual Environment for Co-operative Information Retrieval

abstract: We present a virtual reality application called VR-VIBE which is intended to support the co-operative browsing and filtering of large document stores. VR-VIBE extends a visualisation approach proposed in a previous two dimensional system called VIBE into three dimensions, allowing more information to be visualised at one time and supporting more powerful styles of interaction, The essence of VR-VIBE is that multiple users can explore the results of applying several simultaneous queries to a corpus of documents. By arranging the queries into a spatial framework, the system shows the relative attraction of each document to each query by its spatial position and also shows the absolute relevance of each document to all of the queries. Users may then navigate the space, select individual documents, control the display according to a dynamic relevance threshold and dynamically drag the queries to new positions to see the effect on the document space. Co-operative browsing is supported by directly embodying users and providing them with the ability to interact over live audio connections and to attach brief textual annotations to individual documents. Finally, we conclude with some initial observations gleaned from our experience of constructing VR-VIBE and using it in the laboratory setting.


Appropriating the Postmodern: Copyright and the Challenge of the New:


title: Balancing uplink and downlink delay of VoIP traffic in WLANs using Adaptive Priority Control (APC)

abstract: In IEEE 802.11 wireless networks, the downlink delay rises as the number of VoIP nodes increases while the uplink delay remains small due to the same chance of media access between nodes and the Access Point (AP). This degrades the capacity and QoS of VoIP significantly. Therefore, we introduce Adaptive Priority Control (APC) to balance the downlink and uplink delay of VoIP traffic at the MAC layer, by giving to the AP a higher transmission priority, which is adaptively decided according to the uplink and downlink traffic volume. And, we verify through theoretical analysis that APC is an optimal method to balance the uplink and downlink delay.


title: Ulnar-sided wrist pain : Diagnosis and treatment

abstract: Ulnar-sided wrist pain has often been equated with low back pain because of its insidious onset, vague and chronic nature, intermittent symptoms, and frustration that it induces in patients. Chronic ulnar-sided wrist pain may be accompanied by a history of workers' compensation claims and unrelenting and irresolvable pain, and it may occur in patients with dfficult personalities. Despite these issues, many patients with ulnar-sided wrist pain have pathologic lesions that may be amenable to surgical treatment.


title: A Neural Framework for Generalized Topic Models

abstract: Topic models for text corpora comprise a popular family of methods that have inspired many extensions to encode properties such as sparsity, interactions with covariates, and the gradual evolution of topics. In this paper, we combine certain motivating ideas behind variations on topic models with modern techniques for variational inference to produce a flexible framework for topic modeling that allows for rapid exploration of different models. We first discuss how our framework relates to existing models, and then demonstrate that it achieves strong performance, with the introduction of sparsity controlling the trade off between perplexity and topic coherence.


title: One time key Issuing for Verification and Detecting Caller ID Spoofing Attacks

abstract: Caller ID has been used to tell the recipient who is calling before answering the call. In fact, nowadays using just Caller ID is not enough to proof the real caller since there are several ways to manipulate the caller identity. There are number of solutions to proof the caller e.g. using Time base, SMS base, or hardware. Even using DSA and CA, it can lead to data leak or inconsistent verification processing. The One-time password practices can mitigate the risk of Man-in-the-middle attacks because SSL has vulnerability assessment that can lead to MITM or man in the middle attack. The attacker can intercept SSL verification process between Server and client for sniffing then spoofing. It would be better if we can find a solution that does not rely on CA, Third party and/or external hardware. In this paper, we propose the solution with self-controlled security and one-time key issue to avoid data leak. The one-time key issuance is a good solution for verification and detecting caller ID Spoofing attacker through this methodology since it does not rely on third-party CA and store certification anywhere. This solution provides the best of key management as the one-time secret key is used. Results from our test lab show effectively verification rates and good performance where resource and power consumption are not impacted.


title: A Pretrained DenseNet Encoder for Brain Tumor Segmentation

abstract: This article presents a convolutional neural network for the automatic segmentation of brain tumors in multimodal 3D MR images based on a U-net architecture.We evaluate the use of a densely connected convolutional network encoder (DenseNet) which was pretrained on the ImageNet data set. We detail two network architectures that can take into account multiple 3D images as inputs. This work aims to identify if a generic pretrained network can be used for very specific medical applications where the target data differ both in the number of spatial dimensions as well as in the number of inputs channels. Moreover in order to regularize this transfer learning task we only train the decoder part of the U-net architecture. We evaluate the effectiveness of the proposed approach on the BRATS 2018 segmentation challenge where we obtained dice scores of 0.79, 0.90, 0.85 and 95/% Hausdorff distance of 2.9mm, 3.95mm, and 6.48mm for enhanced tumor core, whole tumor and tumor core respectively on the validation set. This scores degrades to 0.77, 0.88, 0.78 and 95 /% Hausdorff distance of 3.6mm, 5.72mm, and 5.83mm on the testing set.


title: A 2.3-mW, 5-Gb/s Low-Power Decision-Feedback Equalizer Receiver Front-End and its Two-Step, Minimum Bit-Error-Rate Adaptation Algorithm

abstract: This paper presents a low-power decision-feedback equalizer (DFE) receiver front-end and a two-step minimum bit-error-rate (BER) adaptation algorithm. A high energy efficiency of 0.46 mW/Gbps is made possible by the combination of a direct-feedback finite-impulse-response (FIR) DFE, an infinite-impulse-response (IIR) DFE, and a clock-and-data recovery (CDR) circuit with adjustable timing offsets. Based on this architecture, the power-hungry stages used in prior DFE receivers such as the continuous-time linear equalizer (CTLE), the current-mode summing circuit for a multitap DFE, and the fast selection logic for a loop-unrolling DFE can all be removed. A two-step adaptation algorithm that finds the equalizer coefficients minimizing the BER is described. First, an extra data sampler with adjustable voltage and timing offsets measures the single-bit response (SBR) of the channel and coarsely tunes the initial coefficient values in the foreground. Next, the same circuit measures the eye-opening and bit-error rates and fine tunes the coefficients in background using a stochastic hill-climbing algorithm. A prototype DFE receiver fabricated in a 65-nm LP/RF CMOS dissipates 2.3 mW and demonstrates measured eye-opening values of 174 mV pp and 0.66 UIpp while operating at 5 Gb/s with a -15-dB loss channel.


title: Wide-Bandwidth and High-Linearity Envelope- Tracking Front-End Module for LTE-A Carrier Aggregation Applications

abstract: The envelope-tracking front-end module (ETFEM) consisting of an envelope-tracking supply modulator (ETSM), a dual-power-mode power amplifier (PA), and a single-pole-double-throw (SPDT) switch is proposed for the Long-Term Evolution-Advanced (LTE-A) intraband carrier aggregation (CA) signal. The linear stage of ETSM is implemented by introducing the dual-path crossover current-reuse mechanism to obtain the measured efficiency 79.6%, 76.4%, 74%, and 72.8% for 7.4-, 9.6-, 11.2-, and 12.3-dB peak-to-average-power ratio (PAPR) 16-QAM LTE-A CA signals with $1 \times 20$ , $2 \times 20$ , $3 \times 20$ , and $4 \times 20$ MHz bandwidths, respectively. A SiGe BiCMOS dual-power-mode PA with two output power modes is realized and incorporated with ETSM to improve the adjacent channel leakage ratio (ACLR) by 11.7, 7.9, and 4.7 dB and the error vector magnitude (EVM) by 7.1%, 6.1%, and 4.1% at 26.8-, 24.3-, and 21.7-dBm output powers for the LTE-A 16-QAM signals with $1 \times 20$ , $2 \times 20$ , and $3 \times 20$ MHz bandwidths at 1.95 GHz, respectively. The SPDT switch integrated with ETSM provides 0.3-dB power loss reduction, 1.1-dB ACLR improvement, and 0.7% EVM enhancement at 12-dBm average input power. The ETFEM operated at 12.5-dB PAPR 64-QAM LTE-A CA signal with $4 \times 20$ MHz bandwidth demonstrates that the improvements of 1.3-dB ACLR and 1% EVM at 13.1-dBm output power are achieved for the wide-bandwidth and high-linearity FEM applications.