title: Structural Relationship among the Rice Glutelin Polypeptides

abstract: When the glutelin protein fraction of rice (Oryza sativa L.) seeds was fractionated by sodium dodecyl sulfate polyacrylamide gel electrophoresis, three size classes of proteins, 51 kilodaltons (kD), 34 to 37 kD, and 21 to 22 kD, as well as a contaminating prolamine polypeptide of 14 kD were detected. Antibodies were raised against these proteins and employed in studies to determine whether a precursor-product relationship existed among the glutelin components. Antibodies of the 34 to 37 kD and 21 to 22 kD polypeptides strongly reacted with the 51 kD protein, and conversely, anti-51 kD protein cross reacted with both of the putative subunits. Immunoprecipitation of in vitro translated products resulted in the synthesis of only the precursor form, indicating that the alpha and beta subunits are proteolytic products of the 51 kD precursor protein. The poly(A)(+) RNA directed in vitro translated product was about 2000 daltons larger than both the authentic glutelin precursor and the in vitro translated product from polysome run-off synthesis. Western blot analysis of the 34 to 37 kD and 21 to 22 kD polypeptides partially digested with Staphylococcus aureus V8 protease revealed distinct patterns indicating that these proteins are structurally unrelated. As observed for the glutelins, the rice prolamines are also synthesized as a precursor of 16 kD, 2000 daltons larger than the mature polypeptide. Addition of dog pancreatic microsomal membranes to a wheat germ protein translation system resulted in the processing of the prolamine preprotein but not the preproglutelin to the mature form.


title: Automatic mashup generation from multiple-camera concert recordings

abstract: A large number of videos are captured and shared by the audience from musical concerts. However, such recordings are typically perceived as boring mainly because of their limited view, poor visual quality and incomplete coverage. It is our objective to enrich the viewing experience of these recordings by exploiting the abundance of content from multiple sources. In this paper, we propose a novel \Virtual Director system that automatically combines the most desirable segments from different recordings resulting in a single video stream, called mashup. We start by eliciting requirements from focus groups, interviewing professional video editors and consulting film grammar literature. We design a formal model for automatic mashup generation based on maximizing the degree of fulfillment of the requirements. Various audio-visual content analysis techniques are used to determine how well the requirements are satisfied by a recording. To validate the system, we compare our mashups with two other mashups: manually created by a professional video editor and machine generated by random segment selection. The mashups are evaluated in terms of visual quality, content diversity and pleasantness by 40 subjects. The results show that our mashups and the manual mashups are perceived as comparable, while both of them are significantly higher than the random mashups in all three terms.


title: Virtual network embedding through topology-aware node ranking

abstract: Virtualizing and sharing networked resources have become a growing trend that reshapes the computing and networking architectures. Embedding multiple virtual networks (VNs) on a shared substrate is a challenging problem on cloud computing platforms and large-scale sliceable network testbeds. In this paper we apply the Markov Random Walk (RW) model to rank a network node based on its resource and topological attributes. This novel topology-aware node ranking measure reflects the relative importance of the node. Using node ranking we devise two VN embedding algorithms. The first algorithm maps virtual nodes to substrate nodes according to their ranks, then embeds the virtual links between the mapped nodes by finding shortest paths with unsplittable paths and solving the multi-commodity flow problem with splittable paths. The second algorithm is a backtracking VN embedding algorithm based on breadth-first search, which embeds the virtual nodes and links during the same stage using node ranks. Extensive simulation experiments show that the topology-aware node rank is a better resource measure and the proposed RW-based algorithms increase the long-term average revenue and acceptance ratio compared to the existing embedding algorithms.


title: Can neural networks be easily interpreted in software cost estimation?

abstract: Software development effort estimation with the aid of neural networks has generally been viewed with skepticism by a majority of the software cost estimation community. Although, neural networks have shown their strengths in solving complex problems, their shortcoming of being 'black boxes' models has prevented them from being accepted as a common practice for cost estimation. In this paper, we study the interpretation of cost estimation models based on a backpropagation three layer perceptron network. Our proposed idea comprises mainly of the use of a method that maps this neural network to a fuzzy rule based system. Consequently, if the obtained fuzzy rules are easily interpreted, the neural network will also be easy to interpret. Our case study is based on the COCOMO'81 dataset.


title: Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities

abstract: There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals' mood and wellbeing. In this paper, we investigate the effectiveness of neural network models for predicting users' level of stress by using the location information collected by smartphones. We characterize the mobility patterns of individuals using the GPS metrics presented in the literature and employ these metrics as input to the network. We evaluate our approach on the open-source StudentLife dataset. Moreover, we discuss the challenges and trade-offs involved in building machine learning models for digital mental health and highlight potential future work in this direction.


title: Sea ice remote sensing using AMSR‐E 89‐GHz channels

abstract: [1] Recent progress in sea ice concentration remote sensing by satellite microwave radiometers has been stimulated by two developments: First, the new sensor Advanced Microwave Scanning Radiometer-EOS (AMSR-E) offers spatial resolutions of approximately 6 × 4 km at 89 GHz, nearly 3 times the resolution of the standard sensor SSM/I at 85 GHz (15 × 13 km). Second, a new algorithm enables estimation of sea ice concentration from the channels near 90 GHz, despite the enhanced atmospheric influence in these channels. This allows full exploitation of their horizontal resolution, which is up to 4 times finer than that of the channels near 19 and 37 GHz, the frequencies used by the most widespread algorithms for sea ice retrieval, the NASA-Team and Bootstrap algorithms. The ASI algorithm used combines a model for retrieving the sea ice concentration from SSM/I 85-GHz data proposed by Svendsen et al. (1987) with an ocean mask derived from the 18-, 23-, and 37-GHz AMSR-E data using weather filters. During two ship campaigns, the correlation of ASI, NASA-Team 2, and Bootstrap algorithms ice concentrations with bridge observations were 0.80, 0.79, and 0.81, respectively. Systematic differences over the complete AMSR-E period (2002–2006) between ASI and NASA-Team 2 are below −2 ± 8.8%, and between ASI and Bootstrap are 1.7 ± 10.8%. Among the geophysical implications of the ASI algorithm are: (1) Its higher spatial resolution allows better estimation of crucial variables in numerical atmospheric and ocean models, for example, the heat flux between ocean and atmosphere, especially near coastlines and in polynyas. (2) It provides an additional time series of ice area and extent for climate studies.


title: The Negative Attitudes towards Robots Scale and Reactions to Robot Behaviour in a Live Human-Robot Interaction Study

abstract: This paper describes the use of the Negative Attitudes Towards Robots Scale (NARS) to explain participants' evaluations of robot behaviour styles in a Human-Robot Interaction (HRI) study. Twentyeight participants interacted with a robot in two experimental conditions in which the robot’s behaviour was varied. Reliability analysis and a PCA was performed on the NARS items, creating three new subscales. Correlations between the subscales and other evaluations of the robot's behaviour found meaningful results, supporting the use of the NARS in English speaking samples.


title: Automatic Texts Summarization: Current State of the Art

abstract: To facilitate the task of reading and searching information, it became necessary to find a way to reduce the size of documents without affecting the content. The solution is in Automatic text summarization system, it allows, from an input text to produce another smaller and more condensed without losing relevant data and meaning conveyed by the original text. The research works carried out on this area have experienced lately strong progress especially in English language. However, researches in Arabic text summarization are very few and are still in their beginning. In this paper we expose a literature review of recent techniques and works on automatic text summarization field research, and then we focus our discussion on some works concerning automatic text summarization in some languages. We will discuss also some of the main problems that affect the quality of automatic text summarization systems.


MOS-AK : Open Compact Modeling Forum Invited IWCM Paper


title: Memory and information processing in neuromorphic systems

abstract: A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.


Keyframe-Based Visual-Inertial SLAM Using Nonlinear Optimization


title: Journal of Child Psychology and Psychiatry 45:7 (2004), pp 1308–1316 10.1111/j.1469-7610.2004.00328.x Online aggressor/targets, aggressors, and targets: a comparison of associated youth characteristics

abstract: Background: While most youth report positive experiences and activities online, little is known about experiences of Internet victimization and associated correlates of youth, specifically in regards to Internet harassment. Methods: The Youth Internet Safety Survey is a cross-sectional, nationally representative telephone survey of young regular Internet users in the United States. Interviews were conducted between the fall of 1999 and the spring of 2000 and examined characteristics of Internet harassment, unwanted exposure to sexual material, and sexual solicitation that had occurred on the Internet in the previous year. One thousand, five hundred and one regular Internet users between the ages of 10 and 17 years were interviewed, along with one parent or guardian. To assess the characteristics surrounding Internet harassment, four groups of youth were compared: 1) targets of aggression (having been threatened or embarrassed by someone; or feeling worried or threatened by someone’s actions); 2) online aggressors (making rude or nasty comments; or harassing or embarrassing someone with whom the youth was mad at); 3) aggressor/targets (youth who report both being an aggressor as well as a target of Internet harassment); and 4) non-harassment involved youth (being neither a target nor an aggressor online). Results: Of the 19% of young regular Internet users involved in online aggression, 3% were aggressor/targets, 4% reported being targets only, and 12% reported being online aggressors only. Youth aggressor/targets reported characteristics similar to conventional bully/victim youth, including many commonalities with aggressor-only youth, and significant psychosocial challenge. Conclusions: Youth aggressor/targets are intense users of the Internet who view themselves as capable web users. Beyond this, however, these youth report significant psychosocial challenge, including depressive symptomatology, problem behavior, and targeting of traditional bullying. Implications for intervention are discussed. Keywords: Internet, bully, adolescence, computers, depression, child development. Abbreviations: YISS: Youth Internet Safety Survey; AOR: Adjusted Odds Ratio.


title: A Survey of Prevention/Mitigation against Memory Corruption Attacks

abstract: Memory corruption vulnerability is prevalent in software that are written using languages that lack memory safety features, e.g., C and C++. This has become a serious problem because the number of the attacks that exploit this vulnerability has increased. More specifically, this vulnerability allows control-flow-hijacking, a memory corruption attack that involves a well-known dangerous program stack. Several countermeasures have been proposed both in academia and the information technology industry to thwart such attacks. Some of these countermeasures have been implemented and used in practice. However, memory corruption attacks continue to be a serious problem because even these countermeasures are simply bypassed by new attacks. In this paper, we survey and classify protection and mitigation technologies that are especially pervasive in operating systems and compilers. This study aims to organize the pervasive countermeasures against these attacks. We present the existing countermeasures to address the current serious problem and propose modifications to these countermeasures that can be implemented in the future.


Deep learning approaches to problems in speech recognition, computational chemistry, and natural language text processing


title: FeTa: A DCA Pruning Algorithm with Generalization Error Guarantees

abstract: Recent DNN pruning algorithms have succeeded in reducing the number of parameters in fully connected layers, often with little or no drop in classification accuracy. However, most of the existing pruning schemes either have to be applied during training or require a costly retraining procedure after pruning to regain classification accuracy. We start by proposing a cheap pruning algorithm for fully connected DNN layers based on difference of convex functions (DC) optimisation, that requires little or no retraining. We then provide a theoretical analysis for the growth in the Generalization Error (GE) of a DNN for the case of bounded perturbations to the hidden layers, of which weight pruning is a special case. Our pruning method is orders of magnitude faster than competing approaches, while our theoretical analysis sheds light to previously observed problems in DNN pruning. Experiments on commnon feedforward neural networks validate our results.


title: A taxonomy of architectural patterns for self-adaptive systems

abstract: Autonomic systems are able to adapt themselves to unpredicted and unexpected situations. Such adaptation capabilities can reside in individual components as well as in ensembles of components. In particular, a variety of different architectural patterns can be conceived to support self-adaptation at the level both of components and of ensembles. In this paper, we propose a classification of such self-adaptation patterns -- for both the component level and the system level -- by means of a taxonomy organized around the locus in which the feedback loops promoting adaptation reside. We show that the proposed classification covers most self-adaptation patterns, and enables deriving further ones by applying a simple set of composition mechanisms. Three examples of existing patterns of the taxonomy are detailed in the paper to show the applicability of the approach. As discussed in the paper, the advantage of the proposed classification is twofold: it enables identifying the (possibly common) properties of the existing self-adaptation patterns; and, consequently, it can help developers in choosing the most appropriate self-adaptation patterns for the development of autonomic systems.


title: DFPS: Distributed FP-growth algorithm based on Spark

abstract: Frequent Itemset Mining (FIM) is the most important and time-consuming step of association rules mining. With the increment of data scale, many efficient single-machine algorithms of FIM, such as FP-growth and Apriori, cannot accomplish the computing tasks within reasonable time. As a result of the limitation of single-machine methods, researchers presented some distributed algorithms based on MapReduce and Spark, such as PFP and YAFIM. Nevertheless, the heavy disk I/O cost at each MapReduce operation makes PFP not efficient enough. YAFIM needs to generate candidate frequent itemsets in each iterative step. It makes YAFIM time-consuming. And if the scale of data is large enough, YAFIM algorithm will not work due to the limitation of memory since the candidate frequent itemsets need to be stored in the memory. And the size of candidate itemsets is very large especially facing the massive data. In this work, we propose a distributed FP-growth algorithm based on Spark, we call it DFPS. DFPS partitions computing tasks in such a way that each computing node builds the conditional FP-tree and adopts a pattern fragment growth method to mine the frequent itemsets independently. DFPS doesn't need to pass messages between nodes during mining frequent itemsets. Our performance study shows that DFPS algorithm is more excellent than YAFIM, especially when the length of transactions is long, the number of items is large and the data is massive. And DFPS has an excellent scalability. The experimental results show that DFPS is more than 10 times faster than YAFIM for T10I4D100K dataset and Pumsb_star dataset.


title: Weakly-Supervised Acquisition of Labeled Class Instances using Graph Random Walks

abstract: We present a graph-based semi-supervised label propagation algorithm for acquiring open-domain labeled classes and their instances from a combination of unstructured and structured text sources. This acquisition method significantly improves coverage compared to a previous set of labeled classes and instances derived from free text, while achieving comparable precision.


title: Epidemiology of falls in residential aged care: analysis of more than 70,000 falls from residents of bavarian nursing homes.

abstract: OBJECTIVE ::: Falls and fall-related injuries are leading problems in residential aged care facilities. The objective of this study was to provide descriptive data about falls in nursing homes. ::: ::: ::: DESIGN/SETTING/PARTICIPANTS ::: Prospective recording of all falls over 1 year covering all residents from 528 nursing homes in Bavaria, Germany. ::: ::: ::: MEASUREMENTS ::: Falls were reported on a standardized form that included a facility identification code, date, time of the day, sex, age, degree of care need, location of the fall, and activity leading to the fall. Data detailing homes' bed capacities and occupancy levels were used to estimate total person-years under exposure and to calculate fall rates. All analyses were stratified by residents' degree of care need. ::: ::: ::: RESULTS ::: More than 70,000 falls were recorded during 42,843 person-years. The fall rate was higher in men than in women (2.18 and 1.49 falls per person-year, respectively). Fall risk differed by degree of care need with lower fall risks both in the least and highest care categories. About 75% of all falls occurred in the residents' rooms or in the bathrooms and only 22% were reported within the common areas. Transfers and walking were responsible for 41% and 36% of all falls respectively. Fall risk varied during the day. Most falls were observed between 10 am and midday and between 2 pm and 8 pm. ::: ::: ::: CONCLUSION ::: The differing fall risk patterns in specific subgroups may help to target preventive measures.


title: Large margin multi-task metric learning

abstract: Multi-task learning (MTL) improves the prediction performance on multiple, different but related, learning problems through shared parameters or representations. One of the most prominent multi-task learning algorithms is an extension to support vector machines (svm) by Evgeniou et al. [15]. Although very elegant, multi-task svm is inherently restricted by the fact that support vector machines require each class to be addressed explicitly with its own weight vector which, in a multi-task setting, requires the different learning tasks to share the same set of classes. This paper proposes an alternative formulation for multi-task learning by extending the recently published large margin nearest neighbor (1mnn) algorithm to the MTL paradigm. Instead of relying on separating hyperplanes, its decision function is based on the nearest neighbor rule which inherently extends to many classes and becomes a natural fit for multi-task learning. We evaluate the resulting multi-task 1mnn on real-world insurance data and speech classification problems and show that it consistently outperforms single-task kNN under several metrics and state-of-the-art MTL classifiers.


title: Double-oracle Algorithm for Computing an Exact Nash Equilibrium in Zero-sum Extensive-form Games

abstract: We investigate an iterative algorithm for computing an exact Nash equilibrium in two-player zero-sum extensive-form games with imperfect information. The approach uses the sequence-form representation of extensive-form games and the double-oracle algorithmic framework. The main idea is to restrict the game by allowing the players to play only some of the sequences of available actions, then iteratively solve this restricted game, and exploit fast best-response algorithms to add additional sequences to the restricted game for the next iteration. In this paper we (1) extend the sequence-form double-oracle method to be applicable on non-deterministic extensive-form games, (2) present more efficient methods for maintaining valid restricted game and computing best-response sequences, and finally we (3) provide theoretical guarantees of the convergence of the algorithm to a Nash equilibrium. We experimentally evaluate our algorithm on two types of games: a search game on a graph and simplified variants of Poker. The results show significant running-time improvements compared to the previous variant of the double-oracle algorithm, and demonstrate the ability to find an exact solution of much larger games compared to solving full linear program for the complete game.


title: A Framework for End-to-End Evaluation of 5G mmWave Cellular Networks in ns-3

abstract: The growing demand for ubiquitous mobile data services along with the scarcity of spectrum in the sub-6 GHz bands has given rise to the recent interest in developing wireless systems that can exploit the large amount of spectrum available in the millimeter wave (mmWave) frequency range. Due to its potential for multi-gigabit and ultra-low latency links, mmWave technology is expected to play a central role in 5th Generation (5G) cellular networks. Overcoming the poor radio propagation and sensitivity to blockages at higher frequencies presents major challenges, which is why much of the current research is focused at the physical layer. However, innovations will be required at all layers of the protocol stack to effectively utilize the large air link capacity and provide the end-to-end performance required by future networks. Discrete-event network simulation will be an invaluable tool for researchers to evaluate novel 5G protocols and systems from an end-to-end perspective. In this work, we present the first-of-its-kind, open-source framework for modeling mmWave cellular networks in the ns-3 simulator. Channel models are provided along with a configurable physical and MAC-layer implementation, which can be interfaced with the higher-layer protocols and core network model from the ns-3 LTE module to simulate end-to-end connectivity. The framework is demonstrated through several example simulations showing the performance of our custo mmmWave stack.


title: Unforgettable film music: The role of emotion in episodic long-term memory for music

abstract: BackgroundSpecific pieces of music can elicit strong emotions in listeners and, possibly in connection with these emotions, can be remembered even years later. However, episodic memory for emotional music compared with less emotional music has not yet been examined. We investigated whether emotional music is remembered better than less emotional music. Also, we examined the influence of musical structure on memory performance.ResultsRecognition of 40 musical excerpts was investigated as a function of arousal, valence, and emotional intensity ratings of the music. In the first session the participants judged valence and arousal of the musical pieces. One week later, participants listened to the 40 old and 40 new musical excerpts randomly interspersed and were asked to make an old/new decision as well as to indicate arousal and valence of the pieces. Musical pieces that were rated as very positive were recognized significantly better.ConclusionMusical excerpts rated as very positive are remembered better. Valence seems to be an important modulator of episodic long-term memory for music. Evidently, strong emotions related to the musical experience facilitate memory formation and retrieval.


title: The integration of the internal and external milieu in the insula during dynamic emotional experiences

abstract: Whilst external events trigger emotional responses, interoception (the perception of internal physiological states) is fundamental to core emotional experience. By combining high resolution functional neuroimaging with concurrent physiological recordings, we investigated the neural mechanisms of interoceptive integration during free listening to an emotionally salient audio film. We found that cardiac activity, a key interoceptive signal, was robustly synchronised across participants and centrally represented in the posterior insula. Effective connectivity analysis revealed that the anterior insula, specifically tuned to the emotionally salient moments of the audio stream, serves as an integration hub of interoceptive processing: interoceptive states represented in the posterior insula are integrated with exteroceptive representations by the anterior insula to highlight these emotionally salient moments. Our study for the first time demonstrates the insular hierarchy for interoceptive processing during natural emotional experience. These findings provide an ecologically-valid framework for elucidating the neural underpinnings of emotional deficits in neuropsychiatric disorders.


title: Tap 'n' shake: gesture-based smartwatch-smartphone communications system

abstract: Smartwatches have recently seen a surge in popularity, and the new technology presents a number of interesting opportunities and challenges, many of which have not been adequately dealt with by existing applications. Current smartwatch messaging systems fail to adequately address the problem of smartwatches requiring two-handed interactions. This paper presents Tap 'n' Shake, a novel gesture-based messaging system for Android smartwatches and smartphones addressing the problem of two-handed interactions by utilising various motion-gestures within the applications. The results of a user evaluation carried out with sixteen subjects demonstrated the usefulness and usability of using gestures over two-handed interactions for smartwatches. Additionally, the study provides insight into the types of gestures that subjects preferred to use for various actions in a smartwatch-smartphone messaging system.


title: Multi-view point clouds registration and stitching based on SIFT feature

abstract: In order to solve multi-view point clouds registration in large non-feature marked scenes, a new registration and stitching method was proposed based on 2D SIFT(Scale-invariant feature transform) features. Firstly, we used texture mapping method to generate 2D effective texture image, and then extracted and match SIFT features, obtained accurate key points and registration relationship between the effective texture images. Secondly, we reflected the SIFT key points and registration relationship to the 3D point clouds data, obtained key points and registration relationship of multi-view point clouds, we can achieve multi-view point clouds stitching. Our algorithm used texture mapping method to generate 2D effective texture image, it can eliminate interference of the holes and ineffective points, and can eliminate unnecessary mistake matching. Our algorithm used correct extracted matching point pairs to stitch, avoiding stepwise iterated of ICP algorithm, so our algorithm is simple to calculate, and it's matching precision and matching efficiency are improved to some extent. We carried experiments on two-view point clouds in two large indoor; the experiment results verified the validity of our algorithm.


title: PRODIGY: an integrated architecture for planning and learning

abstract: Artificial intelligence has progressed to the point where multiple cognitive capabilities are being integrated into computational architectures, such as SOAR, PRODIGY, THEO, and ICARUS. This paper reports on the PRODIGY architecture, describing its planning and problem solving capabilities and touching upon its multiple learning methods. Learning in PRODIGY occurs at all decision points and integration in PRODIGY is at the knowledge level; the learning and reasoning modules produce mutually interpretable knowledge structures. Issues in architectural design are discussed, providing a context to examine the underlying tenets of the PRODIGY architecture.


abstract: The field of multiple criteria decision analysis (MCDA) - also sometimes termed multiple criteria decision aid, or multiple criteria decision making (MCDM) - has developed rapidly over the past quarter century and in the process a number of divergent schools of thought have emerged. Multiple Criteria Decision Analysis: An Integrated Approach provides a comprehensive yet widely accessible overview of the main streams of thought within MCDA. Two principal aims are: To provide sufficient awareness of the underlying philosophies and theories, understanding of the practical detail of the methods, and insight into practice to enable researchers, students and industry practitioners to implement MCDA methods in an informed manner; To develop an integrated view of MCDA, incorporating both integration of different schools of thought within MCDA and integration of MCDA with broader management theory, science and practice, thereby informing the development of theory and practice across these areas. It is felt that this two-fold emphasis gives a book which will be of value to the following three groups: Practicing decision analysts or graduate students in MCDA for whom this book should serve as a state-of-the-art review, especially as regards techniques outside of their own specialization; Operational researchers or graduate students in OR/MS who wish to extend their knowledge into the tools of MCDA; Managers or management students who need to understand what MCDA can offer them. Review: The book...is an excellent overview of the different multiple criteria approaches developed in divergent schools of thought which emerged in the last three decades. This book is not only a theoretical document, but also provides good coverage of practical issues. It can be recommended to a broad audience, ranging from those in academic institutions to practitioners, as well as those who are interested in finding information on multiple criteria approaches, methods and techniques. It is an excellent book in the main area of Operations Research and Decision Analysis, with a special focus on Multiple Criteria and is suitable for undergraduate and graduate students.' JosA(c) R. Figueira, DIMACS Center, Rutgers University.


title: A Conceptual Framework for Assessing Motivation and Self-Regulated Learning in College Students

abstract: A conceptual framework for assessing student motivation and self-regulated learning in the college classroom is presented. The framework is based on a self-regulatory (SRL) perspective on student motivation and learning in contrast to a student approaches to learning (SAL) perspective. The differences between SRL and SAL approaches are discussed, as are the implications of the SRL conceptual framework for developing instruments to assess college student motivation and learning. The conceptual framework may be useful in guiding future research on college student motivation and learning.


title: Key elements to enable millimeter wave communications for 5G wireless systems

abstract: Current cellular spectrum at below 3 GHz bands is experiencing severe shortage and cannot keep up with the dramatic proliferation of mobile traffic in the near future, requiring the search for innovative solutions to enable the 5G era. mmWave communications, with a possible gigabit-per-second data rate, have attracted great attention as a candidate for 5G broadband cellular communication networks. However, a complete characterization of mmWave links for 5G wireless networks still remains elusive and there are many challenges and research areas that need to be addressed. In this work we discuss several key elements to enable mmWave communications in 5G: · Channel characteristics regarding mmWave signal attenuation due to free space propagation, atmospheric gaseous and rain are explained. · The hybrid (digital plus analog) beamforming architecture in mmWave system is discussed. · The blockage effect in mmWave communications due to penetration loss and possible approaches are presented. · The application of mmWave transmission with narrow beams in non-orthogonal device-todevice communication is proposed. · mmWave transmission in the booster cell of heterogeneous anchor-booster networks. · mmWave transmission for small cell backhaul is further discussed.


title: Cross-Graph Learning of Multi-Relational Associations

abstract: Cross-graph Relational Learning (CGRL) refers to the problem of predicting the strengths or labels of multi-relational tuples of heterogeneous object types, through the joint inference over multiple graphs which specify the internal connections among each type of objects. CGRL is an open challenge in machine learning due to the daunting number of all possible tuples to deal with when the numbers of nodes in multiple graphs are large, and because the labeled training instances are extremely sparse as typical. Existing methods such as tensor factorization or tensor-kernel machines do not work well because of the lack of convex formulation for the optimization of CGRL models, the poor scalability of the algorithms in handling combinatorial numbers of tuples, and/or the non-transductive nature of the learning methods which limits their ability to leverage unlabeled data in training. This paper proposes a novel framework which formulates CGRL as a convex optimization problem, enables transductive learning using both labeled and unlabeled tuples, and offers a scalable algorithm that guarantees the optimal solution and enjoys a linear time complexity with respect to the sizes of input graphs. In our experiments with a subset of DBLP publication records and an Enzyme multi-source dataset, the proposed method successfully scaled to the large cross-graph inference problem, and outperformed other representative approaches significantly.


title: Dimensional differences for evaluating the quality of footwear fit

abstract: Very few standards exist for fitting products to people. Footwear is a noteworthy example. This study is an attempt to evaluate the quality of footwear fit using two-dimensional foot outlines. Twenty Hong Kong Chinese students participated in an experiment that involved three pairs of dress shoes and one pair of athletic shoes. The participants’ feet were scanned using a commercial laser scanner, and each participant wore and rated the fit of each region of each shoe. The shoe lasts were also scanned and were used to match the foot scans with the last scans. The ANOVA showed significant (p 5 0.05) differences among the four pairs of shoes for the overall, fore-foot and rear-foot fit ratings. There were no significant differences among shoes for mid-foot fit rating. These perceived differences were further analysed after matching the 2D outlines of both last and feet. The pointwise dimensional difference between foot and shoe outlines were computed and analysed after normalizing with foot perimeter. The dimensional difference (DD) plots along the foot perimeter showed that fore-foot fit was strongly correlated (R 2 4 0.8) with two of the minimums in the DD-plot while mid-foot fit was strongly correlated (R 2 4 0.9) with the dimensional difference around the arch region and a point on the lateral side of the foot. The DD-plots allow the designer to determine the critical locations that may affect footwear fit in addition to quantifying the nature of misfit so that design changes to shape and material may be possible.


title: Amino acid sequence homologies between rabbit, rat, and human serum retinol-binding proteins.

abstract: The main transporting protein for vitamin A in rabbit serum, the retinol-binding protein (RBP), was isolated and its amino acid sequence determined. Rabbit RBP was found to be highly homologous to human RBP, whose amino acid sequence was elucidated earlier, and to rat RBP. The rat RBP sequence was obtained by combining information deduced from the nucleotide sequences of two overlapping cDNA clones with the NH2-terminal sequence of the isolated protein determined by automated Edman degradation. The identity between the three proteins is approximately 90%. The high degree of homology between RBP molecules from different species is probably explained by the fact that RBP participates in at least three types of molecular interactions: in the binding of prealbumin, in the interaction with retinol, and in the recognition of a specific cell surface receptor. All these interactions should lead to a conservation of RBP structure. The amino acid differences between rabbit, rat, and human RBP are discussed in light of the recent elucidation of the three-dimensional structure of human RBP. Hybridization of a probe isolated from a rat RBP cDNA clone to restriction enzyme-digested genomic DNA from rat and mouse suggests that RBP is encoded by a single gene.


title: Dynamic data flow testing of object oriented systems

abstract: Data flow testing has recently attracted new interest in the context of testing object oriented systems, since data flow information is well suited to capture relations among the object states, and can thus provide useful information for testing method interactions. Unfortunately, classic data flow testing, which is based on static analysis of the source code, fails to identify many important data flow relations due to the dynamic nature of object oriented systems. In this paper, we propose a new technique to generate test cases for object oriented software. The technique exploits useful inter-procedural data flow information extracted dynamically from execution traces for object oriented systems. The technique is designed to enhance an initial test suite with test cases that exercise complex state based method interactions. The experimental results indicate that dynamic data flow testing can indeed generate test cases that exercise relevant behaviors otherwise missed by both the original test suite and by test suites that satisfy classic data flow criteria.


title: A Capacitor-Free CMOS Low-Dropout Regulator

abstract: A 2.5V capacitor-free CMOS low dropout regulator for powering analog circuits is presented. Stable operation is achieved with or without a large load capacitor by replacing the bulky external output capacitor with an operational amplifier-based active circuit. The regulator provides a 100mA output current and consumes 80muA regardless of the load current.


Strategic Surge Pricing and Forecast Communication on On-Demand Service Platforms


title: Gen Y customer loyalty in online shopping: An integrated model of trust, user experience and branding

abstract: Generational theory posits that generational cohorts develop similar attitudes and beliefs. Gen Y, Millennials, will become the largest customer segment for hotels worldwide. This group likes to travel and prefers to spend money on experiences rather than materialistic items. Their responses to online marketing is expected to be different as they process website information five times faster than older generations and are the most emotional and least loyal customers compared to all other generations. The aim of this research is to develop and test a comprehensive model that explains how Gen Y develops loyalty to a hotel booking website. To test the research model, an online survey was distributed to a systematic random sample of 2500 Gen Yers. Findings highlight that trust is the most important antecedent of e-loyalty in online shopping for Gen Y customers. Brand equity is also a key precursor of e-loyalty. Finally, a positive online experience (flow), is also a significant precursor of e-loyalty for this cohort. Gen Y has emerged as an enormous force with growing spending power.Their responses to online shopping is different from other cohorts.Trust is the most important antecedent of e-loyalty for Gen Y customers.Brand equity is also a key precursor of e-loyalty.Flow is also a significant precursor of e-loyalty for this cohort.


title: Compressive sensing: From theory to applications, a survey

abstract: Compressive sensing (CS) is a novel sampling paradigm that samples signals in a much more efficient way than the established Nyquist sampling theorem. CS has recently gained a lot of attention due to its exploitation of signal sparsity. Sparsity, an inherent characteristic of many natural signals, enables the signal to be stored in few samples and subsequently be recovered accurately, courtesy of CS. This article gives a brief background on the origins of this idea, reviews the basic mathematical foundation of the theory and then goes on to highlight different areas of its application with a major emphasis on communications and network domain. Finally, the survey concludes by identifying new areas of research where CS could be beneficial.


title: A Multimodal Database for Affect Recognition and Implicit Tagging

abstract: MAHNOB-HCI is a multimodal database recorded in response to affective stimuli with the goal of emotion recognition and implicit tagging research. A multimodal setup was arranged for synchronized recording of face videos, audio signals, eye gaze data, and peripheral/central nervous system physiological signals. Twenty-seven participants from both genders and different cultural backgrounds participated in two experiments. In the first experiment, they watched 20 emotional videos and self-reported their felt emotions using arousal, valence, dominance, and predictability as well as emotional keywords. In the second experiment, short videos and images were shown once without any tag and then with correct or incorrect tags. Agreement or disagreement with the displayed tags was assessed by the participants. The recorded videos and bodily responses were segmented and stored in a database. The database is made available to the academic community via a web-based system. The collected data were analyzed and single modality and modality fusion results for both emotion recognition and implicit tagging experiments are reported. These results show the potential uses of the recorded modalities and the significance of the emotion elicitation protocol.


title: Representation and Classification of Text Documents: A Brief Review

abstract: Text classification is one of the important research issues in the field of text mining, where the documents are classified with supervised knowledge. In literature we can find many text representation schemes and classifiers/learning algorithms used to classify text documents to the predefined categories. In this paper, we present various text representation schemes and compare different classifiers used to classify text documents to the predefined classes. The existing methods are compared and contrasted based on qualitative parameters viz., criteria used for classification, algorithms adopted and classification time complexities. General Terms Pattern Recognition, Text Mining, Algorithms


title: Blocking Adult Images Based on Statistical Skin Detection

abstract: This work is aimed at the detection of adult images that appear in Internet. Skin detection is of the paramount importance in the detection of adult images. We build a maximum entropy model for this task. This model, called the First Order Model in this paper, is subject to constraints on the color gradients of neighboring pixels. Parameter estimation as well as optimization cannot be tackled without approximations. With Bethe tree approximation, parameter estimation is eradicated and the Belief Propagation algorithm permits to obtain exact and fast solution for skin probabilities at pixel locations. We show by the Receiver Operating Characteristics (ROC) curves that our skin detection improves the performance in the previous work in the context of skin pixel detecton rate and false positive rate. The output of skin detection is a grayscale skin map with the gray level indicating the belief of skin. We then calculate 9 simple features from this map which form a feature vector. We use the fit ellipses to catch the characteristics of skin distribution. Two fit ellipses are used for each skin map---the fit ellipse of all skin regions and the fit ellipse of the largest skin region. They are called respectively Global Fit Ellipse and Local Fit Ellipse in this paper. A multi-layer perceptron classifier is trained for these features. Plenty of experimental results are presented including photographs and a ROC curve calculated over a test set of 5,084 photographs, which show stimulating performance for such simple features.


title: Practical techniques for searches on encrypted data

abstract: It is desirable to store data on data storage servers such as mail servers and file servers in encrypted form to reduce security and privacy risks. But this usually implies that one has to sacrifice functionality for security. For example, if a client wishes to retrieve only documents containing certain words, it was not previously known how to let the data storage server perform the search and answer the query, without loss of data confidentiality. We describe our cryptographic schemes for the problem of searching on encrypted data and provide proofs of security for the resulting crypto systems. Our techniques have a number of crucial advantages. They are provably secure: they provide provable secrecy for encryption, in the sense that the untrusted server cannot learn anything about the plaintext when only given the ciphertext; they provide query isolation for searches, meaning that the untrusted server cannot learn anything more about the plaintext than the search result; they provide controlled searching, so that the untrusted server cannot search for an arbitrary word without the user's authorization; they also support hidden queries, so that the user may ask the untrusted server to search for a secret word without revealing the word to the server. The algorithms presented are simple, fast (for a document of length n, the encryption and search algorithms only need O(n) stream cipher and block cipher operations), and introduce almost no space and communication overhead, and hence are practical to use today.


title: Precision diagnosis: a view of the clinical decision support systems (CDSS) landscape through the lens of critical care

abstract: Improving diagnosis and treatment depends on clinical monitoring and computing. Clinical decision support systems (CDSS) have been in existence for over 50 years. While the literature points to positive impacts on quality and patient safety, outcomes, and the avoidance of medical errors, technical and regulatory challenges continue to retard their rate of integration into clinical care processes and thus delay the refinement of diagnoses towards personalized care. We conducted a systematic review of pertinent articles in the MEDLINE, US Department of Health and Human Services, Agency for Health Research and Quality, and US Food and Drug Administration databases, using a Boolean approach to combine terms germane to the discussion (clinical decision support, tools, systems, critical care, trauma, outcome, cost savings, NSQIP, APACHE, SOFA, ICU, and diagnostics). References were selected on the basis of both temporal and thematic relevance, and subsequently aggregated around four distinct themes: the uses of CDSS in the critical and surgical care settings, clinical insertion challenges, utilization leading to cost-savings, and regulatory concerns. Precision diagnosis is the accurate and timely explanation of each patient’s health problem and further requires communication of that explanation to patients and surrogate decision-makers. Both accuracy and timeliness are essential to critical care, yet computed decision support systems (CDSS) are scarce. The limitation arises from the technical complexity associated with integrating and filtering large data sets from diverse sources. Provider mistrust and resistance coupled with the absence of clear guidance from regulatory bodies further retard acceptance of CDSS. While challenges to develop and deploy CDSS are substantial, the clinical, quality, and economic impacts warrant the effort, especially in disciplines requiring complex decision-making, such as critical and surgical care. Improving diagnosis in health care requires accumulation, validation and transformation of data into actionable information. The aggregate of those processes—CDSS—is currently primitive. Despite technical and regulatory challenges, the apparent clinical and economic utilities of CDSS must lead to greater engagement. These tools play the key role in realizing the vision of a more ‘personalized medicine’, one characterized by individualized precision diagnosis rather than population-based risk-stratification.


title: Parametric motion graphs

abstract: In this paper, we present an example-based motion synthesis technique that generates continuous streams of high-fidelity, controllable motion for interactive applications, such as video games. Our method uses a new data structure called a parametric motion graph to describe valid ways of generating linear blend transitions between motion clips dynamically generated through parametric synthesis in realtime. Our system specifically uses blending-based parametric synthesis to accurately generate any motion clip from an entire space of motions by blending together examples from that space. The key to our technique is using sampling methods to identify and represent good transitions between these spaces of motion parameterized by a continuously valued parameter. This approach allows parametric motion graphs to be constructed with little user effort. Because parametric motion graphs organize all motions of a particular type, such as reaching to different locations on a shelf, using a single, parameterized graph node, they are highly structured, facilitating fast decision-making for interactive character control. We have successfully created interactive characters that perform sequences of requested actions, such as cartwheeling or punching.


title: Face Recognition System Using SVM Classifier and Feature Extraction by PCA and LDA Combination

abstract: Feature representation and classification are two key steps for face recognition. A novel method for face recognition was presented based on combination of PCA (Principal Component Analysis), LDA (Linear Discriminate Analysis) and SVM (Support Vector Machine). PCA and LDA combination was used for feature extraction and SVM were used for classification. The normalization had been done to eliminate redundant information interference previous to feature extraction. The experiments were implemented on ORL face database with the approach. Compared with PCA and Nearest Neighbor Classifier (NCC) combination method, PCA, LDA and NCC combination method, our approach improved face recognition rate.


title: Railway Revenue Management: Overview and Models

abstract: The railway industry offers similar revenue management opportunities to those found in the airline industry. The railway industry caters for the delivery and management of cargo as well as the transport of passengers. Unlike the airline industry, the railway industry has seen relatively little attention to revenue management problems. We provide an overview of the published literature for both passenger and freight rail revenue management. We include a summary of the some the available models and include some possible extensions. From the existing literature and talks with industry, it is clear that that there is room to exploit revenue management techniques in the railway industry, an industry that has revenues of $60 billion in the US and promises huge growth in Europe in the forthcoming years.


title: FORSIGS: Forensic Signature Analysis of the Hard Drive for Multimedia File Fingerprints

abstract: Computer forensics is emerging as an important tool in the fight against crime. Increasingly, computers are being used to facilitate new criminal activity, or used in the commission of existing crimes. The networked world has seen increases in, and the volume of, information that may be shared amongst hosts. This has given rise to major concerns over paedophile activity, and in particular the spread of multimedia files amongst this community. This paper presents a novel scheme for the automated analysis of storage media for digital pictures or files of interest using forensic signatures. The scheme first identifies potential multimedia files of interest and then compares the data to file signatures to ascertain whether a malicious file is resident on the computer. A case study of the forsigs application presented within this paper demonstrates the applicability of the approach for identification and retrieval of malicious multimedia files.


title: Data Assimilation Using an Ensemble Kalman Filter Technique

abstract: The possibility of performing data assimilation using the flow-dependent statistics calculated from an ensemble of short-range forecasts (a technique referred to as ensemble Kalman filtering) is examined in an idealized environment. Using a three-level, quasigeostrophic, T21 model and simulated observations, experiments are performed in a perfect-model context. By using forward interpolation operators from the model state to the observations, the ensemble Kalman filter is able to utilize nonconventional observations. In order to maintain a representative spread between the ensemble members and avoid a problem of inbreeding, a pair of ensemble Kalman filters is configured so that the assimilation of data using one ensemble of shortrange forecasts as background fields employs the weights calculated from the other ensemble of short-range forecasts. This configuration is found to work well: the spread between the ensemble members resembles the difference between the ensemble mean and the true state, except in the case of the smallest ensembles. A series of 30-day data assimilation cycles is performed using ensembles of different sizes. The results indicate that (i) as the size of the ensembles increases, correlations are estimated more accurately and the root-meansquare analysis error decreases, as expected, and (ii) ensembles having on the order of 100 members are sufficient to accurately describe local anisotropic, baroclinic correlation structures. Due to the difficulty of accurately estimating the small correlations associated with remote observations, a cutoff radius beyond which observations are not used, is implemented. It is found that (a) for a given ensemble size there is an optimal value of this cutoff radius, and (b) the optimal cutoff radius increases as the ensemble size increases.


title: A Survey on Rainfall Prediction using Artificial Neural Network

abstract: ABSTRACT Rainfall prediction is one of the most important and challenging task in the modern world. In general, climate and rainfall are highly non-linear and complicated phenomena, which require advanced computer modeling and simulation for their accurate prediction. An Artificial Neural Network (ANN) can be used to predict the behavior of such nonlinear systems. ANN has been successfully used by most of the researchers in this field for the last twenty-five years. This paper provides a survey of available literature of some methodologies employed by different researchers to utilize ANN for rainfall prediction. The survey also reports that rainfall prediction using ANN technique is more suitable than traditional statistical and numerical methods . General Terms Rainfall, Artificial Neural Network, Prediction. Keywords Rainfall, Neural Network, BPN, RBF, SVM, SOM, ANN. 1. INTRODUCTION Rainfall brings the most important role in the matter of human life in all kinds of weather happenings. The effect of rainfall for human civilization is very colossal. Rainfall is natural climatic phenomena whose prediction is challenging and demanding. Accurate information on rainfall is essential for the planning and management of water resources and also crucial for reservoir operation and flooding prevention. Additionally, rainfall has a strong influence on traffic, sewer systems and other human activities in the urban areas. Nevertheless, rainfall is one of the most complex and difficult elements of the hydrology cycle to understand and to model due to the complexity of the atmospheric processes that generate rainfall and the tremendous range of variation over a wide range of scales both in space and time. Thus, accurate rainfall prediction is one of the greatest challenges in operational hydrology, despite many advances in weather forecasting in recent decades. Rainfall means crops; and crop means life. Rainfall prediction is closely related to agriculture sector, which contributes significantly to the economy of the nation. On a worldwide scale, large numbers of attempts have been made by different researchers to predict rainfall accurately using various techniques. But due to the nonlinear nature of rainfall, prediction accuracy obtained by these techniques is still below the satisfactory level. Artificial neural network algorithm becomes an attractive inductive approach in rainfall prediction owing to their highly nonlinearity, flexibility and data driven learning in building models without any prior knowledge about catchment behavior and flow processes. Artificial neural networks have been successfully used in these days in various aspects of science and engineering because of its ability to model both linear and non-linear systems without the need to make assumptions as are implicit in most traditional statistical approaches. ANN has been used as an effective model over the simple linear regression model. This paper provides a literature survey on rainfall prediction using different neural networks used by different researchers. The paper also discusses the concept of some neural network architectures briefly which will be helpful to the new researchers in this field. The objective of this survey is to make the prediction of rainfall more accurate in the recent future. The paper has been constructed with the sections. Section II discussed the concept of neural network. Differentmethodologies used by researchers to predict rainfall has been discussed in section III. Section IV discusses the literature survey of rainfall prediction all over the world. At last a conclusion is discussed in the section V.


title: Software Process Improvement in Multimodel Environments

abstract: Software is increasing in size and complexity. As a direct implication, organizations find it more difficult to achieve the desired product quality. Process improvement models are used to improve product quality. In recent years the combination of different improvement models is increasing. The main challenge in these environments is the interoperability of chosen models. An incorrect integration approach diminishes efficiency and effectiveness on applying these models. The general objective of this thesis is to identify principles and process characteristics for designing a system of processes at the architectural level. As a result of this research work, an improved understanding of improvement models interoperability is expected by identifying the technical and structural relationships between processes that will help architecting a system of processes.


title: The Closer the Better: Similarity of Publication Pairs at Different Co-Citation Levels

abstract: We investigate the similarities of pairs of articles which are co-cited at the different co- citation levels of the journal, article, section, paragraph, sentence and bracket. Our results indicate that textual similarity, intellectual overlap (shared references), author overlap (shared authors), proximity in publication time all rise monotonically as the co-citation level gets lower (from journal to bracket). While the main gain in similarity happens when moving from journal to article co-citation, all level changes entail an increase in similarity, especially section to paragraph and paragraph to sentence/bracket levels. We compare results from four journals over the years 2010-2015: Cell, the European Journal of Operational Research, Physics Letters B and Research Policy, with consistent general outcomes and some interesting differences. Our findings motivate the use of granular co-citation information as defined by meaningful units of text, with implications for, among others, the elaboration of maps of science and the retrieval of scholarly literature.


title: Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds

abstract: With multiple crowd gatherings of millions of people every year in events ranging from pilgrimages to protests, concerts to marathons, and festivals to funerals; visual crowd analysis is emerging as a new frontier in computer vision. In particular, counting in highly dense crowds is a challenging problem with far-reaching applicability in crowd safety and management, as well as gauging political significance of protests and demonstrations. In this paper, we propose a novel approach that simultaneously solves the problems of counting, density map estimation and localization of people in a given dense crowd image. Our formulation is based on an important observation that the three problems are inherently related to each other making the loss function for optimizing a deep CNN decomposable. Since localization requires high-quality images and annotations, we introduce UCF-QNRF dataset that overcomes the shortcomings of previous datasets, and contains 1.25 million humans manually marked with dot annotations. Finally, we present evaluation measures and comparison with recent deep CNN networks, including those developed specifically for crowd counting. Our approach significantly outperforms state-of-the-art on the new dataset, which is the most challenging dataset with the largest number of crowd annotations in the most diverse set of scenes.


title: Global Correspondence Optimization for Non-Rigid Registration of Depth Scans

abstract: We present a registration algorithm for pairs of deforming and partial range scans that addresses the challenges of non-rigid registration within a single non-linear optimization. Our algorithm simultaneously solves for correspondences between points on source and target scans, confidence weights that measure the reliability of each correspondence and identify non-overlapping areas, and a warping field that brings the source scan into alignment with the target geometry. The optimization maximizes the region of overlap and the spatial coherence of the deformation while minimizing registration error. All optimization parameters are chosen automatically; hand-tuning is not necessary. Our method is not restricted to part-in-whole matching, but addresses the general problem of partial matching, and requires no explicit prior correspondences or feature points. We evaluate the performance and robustness of our method using scan data acquired by a structured light scanner and compare our method with existing non-rigid registration algorithms.


title: Dermoscopy of pigmented skin lesions* (Part II)

abstract: Dermoscopy is a non-invasive technique combining digital photography and light microscopy for in vivo observation and diagnosis of pigmented skin lesions. For dermoscopic analysis, pigmented skin lesions are covered with liquid (mineral oil, alcohol, or water) and examined under magnification ranging from x 6 to x 100, in some cases using a dermatoscope connected to a digital imaging system. The improved visualization of surface and subsurface structures obtained with this technique allows the recognition of morphological structures within the lesions that would not be detected otherwise. These morphological structures can be classified on the basis of both global features, allowing a quick, albeit only preliminary categorization of a given pigmented skin lesion, and local features, representing the letters of the dermoscopic alphabet. Classification of melanoma-specific dermoscopic criteria - namely, atypical pigment network, irregular dots/globules, irregular streaks, blue-whitish veil and regression structures, to name the most important ones - forms the basis of diagnostic algorithms designed to aid the clinician in assessing whether or not a melanocytic lesion is melanoma. A certain number of lesions defy both clinical and dermoscopic diagnosis, and in those cases the ultimate standard for diagnosis is histopathology, especially when performed by well-trained and competent dermatopathologists.


title: Hierarchical Reinforcement Learning

abstract: A response generating system can be seen as a mapping from a set of external states (inputs) to a set of actions (outputs). This mapping can be done in principally different ways. One method is to divide the state space into a set of discrete states and store the optimal response for each state. This is denominated a memory mapping system. Another method is to approximate continuous functions from the input space to the output space. I denominate this method projective mapping, although the function does not have to be linear. The latter method is the most common one in feed forward neural networks, where an input vector is projected on a “weight vector”.


title: Comparison of decomposition rates between autopsied and non-autopsied human remains.

abstract: Penetrating trauma has been cited as a significant factor in the rate of decomposition. Therefore, penetrating trauma may have an effect on estimations of time-since-death in medicolegal investigations and on research examining decomposition rates and processes when autopsied human bodies are used. The goal of this study was to determine if there are differences in the rate of decomposition between autopsied and non-autopsied human remains in the same environment. The purpose is to shed light on how large incisions, such as those from a thorocoabdominal autopsy, effect time-since-death estimations and research on the rate of decomposition that use both autopsied and non-autopsied human remains. In this study, 59 non-autopsied and 24 autopsied bodies were studied. The number of accumulated degree days required to reach each decomposition stage was then compared between autopsied and non-autopsied remains. Additionally, both types of bodies were examined for seasonal differences in decomposition rates. As temperature affects the rate of decomposition, this study also compared the internal body temperatures of autopsied and non-autopsied remains to see if differences between the two may be leading to differential decomposition. For this portion of this study, eight non-autopsied and five autopsied bodies were investigated. Internal temperature was collected once a day for two weeks. The results showed that differences in the decomposition rate between autopsied and non-autopsied remains was not statistically significant, though the average ADD needed to reach each stage of decomposition was slightly lower for autopsied bodies than non-autopsied bodies. There was also no significant difference between autopsied and non-autopsied bodies in the rate of decomposition by season or in internal temperature. Therefore, this study suggests that it is unnecessary to separate autopsied and non-autopsied remains when studying gross stages of human decomposition in Central Texas and that penetrating trauma may not be a significant factor in the overall rate of decomposition.


title: GreenMiner: a hardware based mining software repositories software energy consumption framework

abstract: Green Mining is a field of MSR that studies software energy consumption and relies on software performance data. Unfortunately there is a severe lack of publicly available software power use performance data. This means that green mining researchers must generate this data themselves by writing tests, building multiple revisions of a product, and then running these tests multiple times (10+) for each software revision while measuring power use. Then, they must aggregate these measurements to estimate the energy consumed by the tests for each software revision. This is time consuming and is made more difficult by the constraints of mobile devices and their OSes. In this paper we propose, implement, and demonstrate Green Miner: the first dedicated hardware mining software repositories testbed. The Green Miner physically measures the energy consumption of mobile devices (Android phones) and automates the testing of applications, and the reporting of measurements back to developers and researchers. The Green Miner has already produced valuable results for commercial Android application developers, and has been shown to replicate other power studies' results.


title: Nested Sequential Monte Carlo Methods

abstract: We propose nested sequential Monte Carlo (NSMC), a methodology to sample from sequences of probability distributions, even where the random variables are high-dimensional. NSMC generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm. Furthermore, NSMC can in itself be used to produce such properly weighted samples. Consequently, one NSMC sampler can be used to construct an efficient high-dimensional proposal distribution for another NSMC sampler, and this nesting of the algorithm can be done to an arbitrary degree. This allows us to consider complex and high-dimensional models using SMC. We show results that motivate the efficacy of our approach on several filtering problems with dimensions in the order of 100 to 1 000.


title: Structured sparsity-inducing norms through submodular functions

abstract: Sparse methods for supervised learning aim at finding good linear predictors from as few variables as possible, i.e., with small cardinality of their supports. This combinatorial selection problem is often turned into a convex optimization problem by replacing the cardinality function by its convex envelope (tightest convex lower bound), in this case the l1-norm. In this paper, we investigate more general set-functions than the cardinality, that may incorporate prior knowledge or structural constraints which are common in many applications: namely, we show that for nondecreasing submodular set-functions, the corresponding convex envelope can be obtained from its Lovasz extension, a common tool in submodular analysis. This defines a family of polyhedral norms, for which we provide generic algorithmic tools (subgradients and proximal operators) and theoretical results (conditions for support recovery or high-dimensional inference). By selecting specific submodular functions, we can give a new interpretation to known norms, such as those based on rank-statistics or grouped norms with potentially overlapping groups; we also define new norms, in particular ones that can be used as non-factorial priors for supervised learning.


title: Cultural adaptation and validation of the Health Literacy Questionnaire (HLQ): robust nine-dimension Danish language confirmatory factor model

abstract: Health literacy is an important construct in population health and healthcare requiring rigorous measurement. The Health Literacy Questionnaire (HLQ), with nine scales, measures a broad perception of health literacy. This study aimed to adapt the HLQ to the Danish setting, and to examine the factor structure, homogeneity, reliability and discriminant validity. The HLQ was adapted using forward-backward translation, consensus conference and cognitive interviews (n = 15). Psychometric properties were examined based on data collected by face-to-face interview (n = 481). Tests included difficulty level, composite scale reliability and confirmatory factor analysis (CFA). Cognitive testing revealed that only minor re-wording was required. The easiest scale to respond to positively was 'Social support for health', and the hardest were 'Navigating the healthcare system' and 'Appraisal of health information'. CFA of the individual scales showed acceptably high loadings (range 0.49-0.93). CFA fit statistics after including correlated residuals were good for seven scales, acceptable for one. Composite reliability and Cronbach's α were >0.8 for all but one scale. A nine-factor CFA model was fitted to items with no cross-loadings or correlated residuals allowed. Given this restricted model, the fit was satisfactory. The HLQ appears robust for its intended application of assessing health literacy in a range of settings. Further work is required to demonstrate sensitivity to measure changes.


title: The role of gut microbiota in the gut-brain axis: current challenges and perspectives

abstract: Brain and the gastrointestinal (GI) tract are intimately connected to form a bidirectional neurohumoral communication system. The communication between gut and brain, knows as the gut-brain axis, is so well established that the functional status of gut is always related to the condition of brain. The researches on the gut-brain axis were traditionally focused on the psychological status affecting the function of the GI tract. However, recent evidences showed that gut microbiota communicates with the brain via the gut-brain axis to modulate brain development and behavioral phenotypes. These recent findings on the new role of gut microbiota in the gut-brain axis implicate that gut microbiota could associate with brain functions as well as neurological diseases via the gut-brain axis. To elucidate the role of gut microbiota in the gut-brain axis, precise identification of the composition of microbes constituting gut microbiota is an essential step. However, identification of microbes constituting gut microbiota has been the main technological challenge currently due to massive amount of intestinal microbes and the difficulties in culture of gut microbes. Current methods for identification of microbes constituting gut microbiota are dependent on omics analysis methods by using advanced high tech equipment. Here, we review the association of gut microbiota with the gut-brain axis, including the pros and cons of the current high throughput methods for identification of microbes constituting gut microbiota to elucidate the role of gut microbiota in the gut-brain axis.


title: Cryogenic 2–13 GHz Eleven Feed for Reflector Antennas in Future Wideband Radio Telescopes

abstract: The system design of a cryogenic 2-13 GHz feed is considered with emphasis on its application in future wideband radio telescope systems. The feed is based on the so-called Eleven antenna and the design requires careful integration of various sub-designs in order to realize cryogenic operation. The various sub-designs include the electrical design of the Eleven antenna, design of the critical center puck, alternative solutions for integrating the Eleven antenna with low-noise amplifiers (LNAs), mechanical and cryogenic design and tests, and system noise temperature estimation and measurements. A great deal of simulated and measured results are presented throughout this paper, including the electrical, mechanical and cryogenic performance, and an assessment of the system noise temperature. The objective of this work is to present a good feed candidate that is well-suited for VLBI2010 and SKA radio telescopes. Further developments needed to completely fulfill the requirements for these future wideband radio telescopes are also discussed.


title: GIFT: Towards Scalable 3D Shape Retrieval

abstract: Projective analysis is an important solution in three-dimensional (3D) shape retrieval, since human visual perceptions of 3D shapes rely on various 2D observations from different viewpoints. Although multiple informative and discriminative views are utilized, most projection-based retrieval systems suffer from heavy computational cost, and thus cannot satisfy the basic requirement of scalability for search engines. In the past three years, shape retrieval contest (SHREC) pays much attention to the scalability of 3D shape retrieval algorithms, and organizes several large scale tracks accordingly [1] – [3] . However, the experimental results indicate that conventional algorithms cannot be directly applied to large datasets. In this paper, we present a real-time 3D shape search engine based on the projective images of 3D shapes. The real-time property of our search engine results from the following aspects: 1) efficient projection and view feature extraction using GPU acceleration; 2) the first inverted file, called F-IF, is utilized to speed up the procedure of multiview matching; and 3) the second inverted file, which captures a local distribution of 3D shapes in the feature manifold, is adopted for efficient context-based reranking. As a result, for each query the retrieval task can be finished within one second despite the necessary cost of IO overhead. We name the proposed 3D shape search engine, which combines G PU acceleration and i nverted f ile ( t wice), as GIFT . Besides its high efficiency, GIFT also outperforms state-of-the-art methods significantly in retrieval accuracy on various shape benchmarks (ModelNet40 dataset, ModelNet10 dataset, PSB dataset, McGill dataset) and competitions (SHREC14LSGTB, ShapeNet Core55, WM-SHREC07).


title: www.informatik2011.de Implementing IT Strategy – Laying a Foundation

abstract: The failure to implement corporate strategies in general and IT strategies in particular can be the reason for lost opportunities, duplicated efforts, incompatible systems, and wasted resources and thus lead to a competitive disadvantage. Empirical works in both domains, business and IT, show a number of problems that exist in practice. Although, being a major concern of business and IT executives, little research and a lack of methodological guidance to support the execution of decisions and actions exists. Since it is important and reasonable to design a methodological support driven by present problems, this article deduces features to compare approaches to IT strategy implementation found in literature. By combining empirical research and method engineering, the approach is in line with the guidelines of design science. On this foundation, we take the first step towards designing the process model of a comprehensive method to implement IT strategy.


title: Beam search algorithms for multilabel learning

abstract: Multilabel learning is a machine learning task that is important for applications, but challenging. A recent method for multilabel learning called probabilistic classifier chains (PCCs) has several appealing properties. However, PCCs suffer from the computational issue that inference (i.e., predicting the label of an example) requires time exponential in the number of tags. Also, PCC accuracy is sensitive to the ordering of the tags while training. In this paper, we show how to use the classical technique of beam search to solve both these problems. Specifically, we show how to apply beam search to make inference tractable, and how to integrate beam search with training to determine a suitable tag ordering. Experimental results on a range of datasets show that the proposed improvements yield a state-of-the-art method for multilabel learning.


PRE-TRAINED DEEP NEURAL NETWORK USING SPARSE AUTOENCODERS AND SCATTERING WAVELET TRANSFORM FOR MUSICAL GENRE RECOGNITION


Use of Hybrid Systems to model the RobotiQ Adaptive Gripper


title: Self-organizing tool for smart design with predictive customer needs and wants to realize Industry 4.0

abstract: Following the first three industrial revolutions, Industry 4.0 (I4) aims at realizing mass customization at a mass production cost. Currently, however, there is a lack of smart analytics tools for achieving such a goal. This paper investigates this issues and then develops a predictive analytics framework integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a self-organizing map (SOM) is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The selection of patterns from big data with SOM helps with clustering and with the selection of optimal attributes. A car customization case study shows that the SOM is able to assign new clusters when growing knowledge of customer needs and wants. The self-organizing tool offers a number of features suitable to smart design that is required in realizing Industry 4.0.


title: A reflection on agile requirements engineering: solutions brought and challenges posed

abstract: The software development industry has rapidly accepted agile methods. Empirical studies suggest that due to their flexible and emergent nature, agile methods brought solutions to several chronic problems of traditional software development methods. One among the many is the acceptance of requirements changes at later stages of development. However, knowledge about the solutions that agile brought to requirements engineering (RE) is fragmented. Also, little is known about whether the agile philosophy, while introducing solutions to well-known RE problems from the past, has unintentionally opened new challenges. This paper offers a reflection on this matter. Based on the results of our recently published systematic review on agile RE, we reflect on the differences of 'traditional' and agile RE and the practices adopted by the latter, on the solutions and challenges of agile RE, and on some implications that agile RE might have posed for research and practice.


title: A case study on user experience (UX) evaluation of mobile augmented reality prototypes

abstract: Mobile Augmented Reality (MAR) blends the real world with digital objects especially in ubiquitous devices such as smartphones. The MAR applications provide an intelligent interface for users. In this, valuable digital information is advertised in physical spaces. However, the success of these applications is tied directly to the degree of user acceptance. This makes understanding the needs and expectations of the MAR's potential users of paramount importance for designing and building the proper application. The objective of the paper is to expose an important gap in the development of novel applications in the virtual world. Previous research has shown that it is essential to study and understand the needs and expectations of the potential users of the upcoming application or system. Studying user needs and expectations before offering the developed application ensures a minimum level of acceptance and, of course, success. This paper presents a detailed study comprising of a user- experience (UX) evaluation of different prototypes through the use of three different UX evaluation methods. This kind of evaluation allows new developments to offer systems, which do not fail. The main contributions of this study are that it: 1) solicits expectations when consumers use MAR applications, 2) assesses the UX over different prototypes using three different metrics, 3) provides methodological insights on UX evaluation experiments and, 4) is useful for anyone who wants to develop handheld applications after understanding user expectations and how his experience should progress. The results of the study show that users value concreteness, realizability, personalization, novelty, intuitiveness and the usefulness of presented information. Paying attention to these factors can help develop more acceptable MAR applications and lead to more novel future designs.


title: Quarter-Mode Substrate Integrated Waveguide and Its Application to Antennas Design

abstract: A quarter-mode substrate integrated waveguide (QMSIW) which is the quadrant sector of a square waveguide resonator is proposed and investigated in this paper. The QMSIW is realized by bisecting the half-mode substrate integrated waveguide (HMSIW) into two parts along the fictitious quasi-magnetic wall when it operates with TE101 and TE202 modes as the way bisecting the substrate integrated waveguide (SIW) to HMSIW. The QMSIW can almost preserve the field distribution of original SIW and leaky wave is achieved from the dielectric aperture of the QMSIW. When the feeding port is placed at one corner of the QMSIW, a linearly polarized radiation is obtained when the QMSIW resonates in TE101QM mode, and when the QMSIW resonates in TE202QM mode, a circularly polarized (CP) wave is achieved. An antenna is designed, fabricated, and measured based on the proposed QMSIW. The measurement results match with the simulation results very well.


title: Secretory form of rheumatoid arthritis–associated autoantibodies in serum are mainly of the IgM isotype, suggesting a continuous reactivation of autoantibody responses at mucosal surfaces

abstract: Several lines of evidence obtained in recent years indicate a role of mucosal surfaces in the development of autoimmune responses associated with rheumatoid arthritis (RA). Therefore, more attention is going to the influence of the microbiome in RA development. Secretory antibodies are typically produced at mucosal surfaces and transported through epithelial cells for secretion at the luminal site. Importantly, secretory antibodies comprise both IgM and IgA, and both isotypes can harbour a J-chain that binds to the polymeric Ig receptor (pIgR).1 Following transport through epithelial cells, the antibodies are enzymatically released at the luminal site by cleavage of the pIgR leaving a fragment of the receptor, the secretory component (SC) bound to the immunoglobulin. SC-containing antibodies can however also be detected systemically in the circulation, although the mechanism by which these SC-containing antibodies arise in serum is still unclear.2 Interestingly, antigen-specific secretory antibodies are present in serum after mucosal immunisation,3 indicating that the presence of secretory antibodies may provide information about their origin: the mucosa. Well-known autoantibodies in RA are rheumatoid factor (RF), ACPA (against citrullinated proteins) and anti-CarP (against carbamylated proteins).4 Secretory RF (SC-RF) and secretory ACPA (SC-ACPA) have been detected in RA sera,5 6 indicating a mucosal origin. Although it is often postulated that SC-containing autoantibodies are of the IgA isotype, this is unknown as both IgM and IgA can harbour SC. Nonetheless, it is important to define the isotype of SC-containing autoantibodies as this provides relevant information on the type of B cell (re)activated at …


title: Design of hemi-spherical conformal array with a wide-angle scanning

abstract: A hemi-spherical conformal array with wide-angle scanning is designed in this paper. After the selection of active region, the elements which are not contributed to the array are switched off. The dual polarized patch antennas are employed as radiation elements, then the required polarization in the main beam region is realized by designing the weighted individual feeding ports of the patch antennas before the pattern synthesis is performed. The modified differential evolution (MDE) algorithm, which has a better performance in synthesis of the conformal array, is then employed to optimize the pattern with low side lobe level (SLL). During this progress, the modified Bernstein polynomial is used to reduce the number of optimization variables, which can greatly accelerate the optimization progress. Finally, a hemi-spherically covered conformal array with 143 radiation elements is designed and the produced cross-polarization is very low in the main beam region and the SLL is less than −20dB.


title: Joint face alignment and segmentation via deep multi-task learning

abstract: Face alignment and segmentation are challenging problems which have been extensively studied in the field of multimedia. These two tasks are closely related and their learning processes are supposed to benefit each other. Hence, we present a joint multi-task learning algorithm for both face alignment and segmentation using deep convolutional neural network (CNN). The proposed multi-task learning approach allows CNN model to simultaneously share visual knowledge between different tasks. With a carefully designed refinement residual module, the cross-layer features are fused in a collaborative manner. To the best of our knowledge, this is the first time that face alignment and segmentation are learned together via deep multi-task learning. Our experiments show that learning these two related tasks simultaneously builds a synergy between them, improves the performance of each individual task, and rivals recent approaches. Furthermore, we demonstrate the effectiveness of our model in two practical applications: virtual makeup and face swap.


title: An Investigation of Heart Disease Prediction Techniques

abstract: Heart disease indicates the type of condition which leads to heart malfunction. Not all people with coronary artery disease have chest pain as a symptom. There are other factors such as R-Blood Pressure, S-Cholesterol, F-Blood Sugar, R-ECG, Thalach, Ex-Ang, Number of major Vessels blocked, Thallium Scan which also results in heart disease. At an initial stage the prediction of heart disease can save human lives. Data mining techniques such as Naive Bayes, Decision Tree, K-nearest neighbor, etc., are used in the Heart Disease prediction based on the parameters / factors. The objective of this research is to investigate about the various factors and its significance in identifying the heart disease. Further it also explores about the existing techniques and models used for prediction. This work concludes with the key challenges which are not yet explored.


title: Contextualized Non-local Neural Networks for Sequence Learning

abstract: Recently, a large number of neural mechanisms and models have been proposed for sequence learning, of which selfattention, as exemplified by the Transformer model, and graph neural networks (GNNs) have attracted much attention. In this paper, we propose an approach that combines and draws on the complementary strengths of these two methods. Specifically, we propose contextualized non-local neural networks (CN3), which can both dynamically construct a task-specific structure of a sentence and leverage rich local dependencies within a particular neighbourhood.Experimental results on ten NLP tasks in text classification, semantic matching, and sequence labelling show that our proposed model outperforms competitive baselines and discovers task-specific dependency structures, thus providing better interpretability to users.


title: Blind navigation support system based on Microsoft Kinect

abstract: This paper presents a system which extends the use of the traditional white cane by the blind for navigation purposes in indoor environments. Depth data of the scene in front of the user is acquired using the Microsoft Kinect sensor which is then mapped into a pattern representation. Using neural networks, the proposed system uses this information to extract relevant features from the scene, enabling the detection of possible obstacles along the way. The results show that the neural network is able to correctly classify the type of pattern presented as input.


title: Tracking Emerges by Colorizing Videos

abstract: We use large amounts of unlabeled video to learn models for visual tracking without manual human supervision. We leverage the natural temporal coherency of color to create a model that learns to colorize gray-scale videos by copying colors from a reference frame. Quantitative and qualitative experiments suggest that this task causes the model to automatically learn to track visual regions. Although the model is trained without any ground-truth labels, our method learns to track well enough to outperform the latest methods based on optical flow. Moreover, our results suggest that failures to track are correlated with failures to colorize, indicating that advancing video colorization may further improve self-supervised visual tracking.


title: UP-Hist Tree: An Efficient Data Structure for Mining High Utility Patterns from Transaction Databases

abstract: High-utility itemset mining is an emerging research area in the field of Data Mining. Several algorithms were proposed to find high-utility itemsets from transaction databases and use a data structure called UP-tree for their working. However, algorithms based on UP-tree generate a lot of candidates due to limited information availability in UP-tree for computing utility value estimates of itemsets. In this paper, we present a data structure named UP-Hist tree which maintains a histogram of item quantities with each node of the tree. The histogram allows computation of better utility estimates for effective pruning of the search space. Extensive experiments on real as well as synthetic datasets show that our algorithm based on UP-Hist tree outperforms the state of the art pattern-growth based algorithms in terms of the total number of candidate high utility itemsets generated that needs to be verified.


title: Series-Connected IGBTs Using Active Voltage Control Technique

abstract: With series insulated-gate bipolar transistor (IGBT) operation, well-matched gate drives will not ensure balanced dynamic voltage sharing between the switching devices. Rather, it is IGBT parasitic capacitances, mainly gate-to-collector capacitance Cgc, that dominate transient voltage sharing. As Cgc is collector voltage dependant and is significantly larger during the initial turn-off transition, it dominates IGBT dynamic voltage sharing. This paper presents an active control technique for series-connected IGBTs that allows their dynamic voltage transition dVce/dt to adaptively vary. Both switch ON and OFF transitions are controlled to follow a predefined dVce/dt. Switching losses associated with this technique are minimized by the adaptive dv/dt control technique incorporated into the design. A detailed description of the control circuits is presented in this paper. Experimental results with up to three series devices in a single-ended dc chopper circuit, operating at various low voltage and current levels, are used to illustrate the performance of the proposed technique.


title: A Survey on Silicon PUFs and Recent Advances in Ring Oscillator PUFs

abstract: Silicon physical unclonable function (PUF) is a popular hardware security primitive that exploits the intrinsic variation of IC manufacturing process to generate chip-unique information for various security related applications. For example, the PUF information can be used as a chip identifier, a secret key, the seed for a random number generator, or the response to a given challenge. Due to the unpredictability and irreplicability of IC manufacturing variation, silicon PUF has emerged as a promising hardware security primitive and gained a lot of attention over the past few years. In this article, we first give a survey on the current state-of-the-art of silicon PUFs, then analyze known attacks to PUFs and the countermeasures. After that we discuss PUF-based applications, highlight some recent research advances in ring oscillator PUFs, and conclude with some challenges and opportunities in PUF research and applications.