title: A Fuzzy Approach for Mining Quantitative Association Rules

abstract: During the last ten years, data mining, also known as knowledge discovery in databases, has established its position as a prominent and important research area. Mining association rules is one of the important research problems in data mining. Many algorithms have been proposed to find association rules in databases with binary attributes. In this paper, we deal with the problem of mining association rules in databases containing both quantitative and categorical attributes. An example of such an association might be ``10% of married people between age 50 and 70 have at least 2 cars''''. We introduce a new definition of quantitative association rules based on fuzzy set theory. Using the fuzzy set concept, the discovered rules are more understandable to a human. Moreover, fuzzy sets handle numerical values better than existing methods because fuzzy sets soften the effect of sharp boundaries. The above example could be rephrased eg. 10% of married old people have several cars. In this paper we present a new algorithm for mining fuzzy quantitative association rules. The algorithm uses new definitions for interesting mea- sures. Experimental results show the efficiency of the algorithm for large databases.


title: Effect of Training Leading to Repetition Failure on Muscular Strength: A Systematic Review and Meta-Analysis

abstract: It remains unclear whether repetitions leading to failure (failure training) or not leading to failure (non-failure training) lead to superior muscular strength gains during resistance exercise. Failure training may provide the stimulus needed to enhance muscular strength development. However, it is argued that non-failure training leads to similar increases in muscular strength without the need for high levels of discomfort and physical effort, which are associated with failure training. We conducted a systematic review and meta-analysis to examine the effect of failure versus non-failure training on muscular strength. Five electronic databases were searched using terms related to failure and non-failure training. Studies were deemed eligible for inclusion if they met the following criteria: (1) randomised and non-randomised studies; (2) resistance training intervention where repetitions were performed to failure; (3) a non-failure comparison group; (4) resistance training interventions with a total of ≥3 exercise sessions; and (5) muscular strength assessment pre- and post-training. Random-effects meta-analyses were performed to pool the results of the included studies and generate a weighted mean effect size (ES). Eight studies were included in the meta-analysis (combined studies). Training volume was controlled in four studies (volume controlled), while the remaining four studies did not control for training volume (volume uncontrolled). Non-failure training resulted in a 0.6–1.3 % greater strength increase than failure training. A small pooled effect favouring non-failure training was found (ES = 0.34; p = 0.02). Significant small pooled effects on muscular strength were also found for non-failure versus failure training with compound exercises (ES = 0.37–0.38; p = 0.03) and trained participants (ES = 0.37; p = 0.049). A slightly larger pooled effect favouring non-failure training was observed when volume-uncontrolled studies were included (ES = 0.41; p = 0.047). No significant effect was found for the volume-controlled studies, although there was a trend favouring non-failure training. The methodological quality of the included studies in the review was found to be moderate. Exercise compliance was high for the studies where this was reported (n = 5), although limited information on adverse events was provided. Overall, the results suggest that despite statistically significant effects on muscular strength being found for non-failure compared with failure training, the small percentage of improvement shown for non-failure training is unlikely to be meaningful. Therefore, it appears that similar increases in muscular strength can be achieved with failure and non-failure training. Furthermore, it seems unnecessary to perform failure training to maximise muscular strength; however, if incorporated into a programme, training to failure should be performed sparingly to limit the risks of injuries and overtraining.


title: A Sensorimotor Circuit in Mouse Cortex for Visual Flow Predictions

abstract: Summary The cortex is organized as a hierarchical processing structure. Feedback from higher levels of the hierarchy, known as top-down signals, have been shown to be involved in attentional and contextual modulation of sensory responses. Here we argue that top-down input to the primary visual cortex (V1) from A24b and the adjacent secondary motor cortex (M2) signals a prediction of visual flow based on motor output. A24b/M2 sends a dense and topographically organized projection to V1 that targets most neurons in layer 2/3. By imaging the activity of A24b/M2 axons in V1 of mice learning to navigate a 2D virtual environment, we found that their activity was strongly correlated with locomotion and resulting visual flow feedback in an experience-dependent manner. When mice were trained to navigate a left-right inverted virtual environment, correlations of neural activity with behavior reversed to match visual flow. These findings are consistent with a predictive coding interpretation of visual processing.


title: FLUX: A Logic Programming Method for Reasoning Agents

abstract: FLUX is a programming method for the design of agents that reason logically about their actions and sensor information in the presence of incomplete knowledge. The core of FLUX is a system of Constraint Handling Rules, which enables agents to maintain an internal model of their environment by which they control their own behavior. The general action representation formalism of the fluent calculus provides the formal semantics for the constraint solver. FLUX exhibits excellent computational behavior due to both a carefully restricted expressiveness and the inference paradigm of progression.


title: The Corpus DIMEx100: transcription and evaluation

abstract: In this paper the transcription and evaluation of the corpus DIMEx100 for Mexican Spanish is presented. First we describe the corpus and explain the linguistic and computational motivation for its design and collection process; then, the phonetic antecedents and the alphabet adopted for the transcription task are presented; the corpus has been transcribed at three different granularity levels, which are also specified in detail. The corpus statistics for each transcription level are also presented. A set of phonetic rules describing phonetic context observed empirically in spontaneous conversation is also validated with the transcription. The corpus has been used for the construction of acoustic models and a phonetic dictionary for the construction of a speech recognition system. Initial performance results suggest that the data can be used to train good quality acoustic models.


Helicopter Vibration Reduction with Pendulum Absorbers


title: Deep Reinforcement Learning for Building HVAC Control

abstract: Buildings account for nearly 40% of the total energy consumption in the United States, about half of which is used by the HVAC (heating, ventilation, and air conditioning) system. Intelligent scheduling of building HVAC systems has the potential to significantly reduce the energy cost. However, the traditional rule-based and model-based strategies are often inefficient in practice, due to the complexity in building thermal dynamics and heterogeneous environment disturbances. In this work, we develop a data-driven approach that leverages the deep reinforcement learning (DRL) technique, to intelligently learn the effective strategy for operating the building HVAC systems. We evaluate the performance of our DRL algorithm through simulations using the widely-adopted EnergyPlus tool. Experiments demonstrate that our DRL-based algorithm is more effective in energy cost reduction compared with the traditional rule-based approach, while maintaining the room temperature within desired range.


title: Sleep disorders and depression: brief review of the literature, case report, and nonpharmacologic interventions for depression

abstract: Sleep disorders are so frequently associated with depression that, in the absence of sleep complaints, a diagnosis of depression should be made with caution. Insomnia, in particular, may occur in 60%–80% of depressed patients. Depressive symptoms are important risk factors for insomnia, and depression is considered an important comorbid condition in patients with chronic insomnia of any etiology. In addition, some drugs commonly prescribed for the treatment of depression may worsen insomnia and impair full recovery from the illness. The aim of this paper is to review briefly and discuss the following topics: common sleep disturbances during depression (in particular pavor nocturnus, nightmares, hypersomnia, and insomnia); circadian sleep disturbances; and treatment of depression by manipulation of the sleep-wake rhythm (chronotherapy, light therapy, cycles of sleep, and manipulation of the sleep-wake rhythm itself). Finally, we present a case report of a 65-year-old Caucasian woman suffering from insomnia associated with depression who was successfully treated with sleep deprivation.


Medical Sentiment Analysis using Social Media: Towards building a Patient Assisted System


title: An economically viable solution to geofencing for mass-market applications

abstract: Geofencing services deliver location-relevant information to mobile subscribers, who enter a geographic “fence” or boundary around the information's demarcation area. With Geographic Positioning System (GPS) capabilities on many phones, such services facilitate a variety of mass-market applications ranging from mobile proximity marketing to proximity-based dating. Applications delivering such services operate on top of a geofencing engine that manages location tracking of subscribers and returns triggers when fence-crossing events occur. We discuss the critical aspects of geofencing and the challenges faced when deploying such services to the mass market. We present an economically viable geofencing solution that scales to large populations and supports a high number of fences per subscriber. Through distributed processing supported by an appropriate client-server protocol, this solution optimizes the air interface usage and mobile battery power. The technical viability of our solution is supported by actual traffic data from mobile proximity marketing and a social networking service.


title: Informatics Related Branch's Curriculum and Role of Project Management☆

abstract: The most important goal of the software industry is to produce successful product. During the process of production several times the product fails due to lack of proper management. This paper is exploring the role of software engineering courses in computer engineering related branches and then reasons why software developers lack project management in proper software management trainings. Our findings reflect that in majority of computer related branches like computer science, computer engineering, information system engineering there is no place for software project management course. Our findings are based on a survey of course curriculums of computer engineering, computer science and information system engineering courses taught in Turkish universities.


title: Synthesis of Control Protocols for Autonomous Systems

abstract: This article provides a review of control protocol synthesis techniques that incorporate methodologies from formal methods and control theory to provide correctness guarantee for different types of autonomous systems, including those with discrete and continuous state space. The correctness of the system is defined with respect to a given specification expressed as a formula in linear temporal logic to precisely describe the desired properties of the system. The formalism presented in this article admits nondeterminism, allowing uncertainties in the system to be captured. A particular emphasis is on alleviating some of the difficulties, e.g., heterogeneity in the underlying dynamics and computational complexity, that naturally arise in the construction of control protocols for autonomous systems.


title: Internet of vehicles: From intelligent grid to autonomous cars and vehicular clouds

abstract: Traditionally, the vehicle has been the extension of the man's ambulatory system, docile to the driver's commands. Recent advances in communications, controls and embedded systems have changed this model, paving the way to the Intelligent Vehicle Grid. The car is now a formidable sensor platform, absorbing information from the environment (and from other cars) and feeding it to drivers and infrastructure to assist in safe navigation, pollution control and traffic management. The next step in this evolution is just around the corner: the Internet of Autonomous Vehicles. Pioneered by the Google car, the Internet of Vehicles will be a distributed transport fabric capable to make its own decisions about driving customers to their destinations. Like other important instantiations of the Internet of Things (e.g., the smart building), the Internet of Vehicles will have communications, storage, intelligence, and learning capabilities to anticipate the customers' intentions. The concept that will help transition to the Internet of Vehicles is the Vehicular Cloud, the equivalent of Internet cloud for vehicles, providing all the services required by the autonomous vehicles. In this article, we discuss the evolution from Intelligent Vehicle Grid to Autonomous, Internet-connected Vehicles, and Vehicular Cloud.


Actors in a smalltalk multiprocessor- a case for limited parallelism


title: Smart Governance: A Cross-Case Analysis of Smart City Initiatives

abstract: City governments around the world have embarked on smart-city initiatives and projects with the aim of creating a future-ready urban environment, in which critical infrastructure components and online services are intelligent, efficient, and highly interconnected. Reshaping administrative structures and processes across multiple local government agencies and departments appears as an important prerequisite to success in these initiatives. Moreover, the model of governance in such multi-agency initiatives is key to achieving desired outcomes and sustainability. Such "smart" governance is viewed as foundational to creating smart cities. This study investigates and documents the governance models that emerged in four recent smart city initiatives (Seattle/USA, eCityGov Alliance/USA, Munich/Germany, and Turin/Italy). The findings indicate that governance models vary from one case to another depending on a number of factors. Furthermore, despite notable similarities decision-making processes differed across the cases, and each case exhibits specific methods of conflict resolution. However, stakeholder involvement in governance was found fundamental across all cases.


The Critical Success Factors Study for e-Government Implementation


Hand Gesture Based Wheelchair Movement Control for Disabled Person Using MEMS


title: A lightweight active service migration framework for computational offloading in mobile cloud computing

abstract: Cloud computing enables access to the widespread services and resources in cloud datacenters for mitigating resource limitations in low-potential client devices. Computational cloud is an attractive platform for computational offloading due to the attributes of scalability and availability of resources. Therefore, mobile cloud computing (MCC) leverages the application processing services of computational clouds for enabling computational-intensive and ubiquitous mobile applications on smart mobile devices (SMDs). Computational offloading frameworks focus on offloading intensive mobile applications at different granularity levels which involve resource-intensive mechanism of application profiling and partitioning at runtime. As a result, the energy consumption cost (ECC) and turnaround time of the application is increased. This paper proposes an active service migration (ASM) framework for computational offloading to cloud datacenters, which employs lightweight procedure for the deployment of runtime distributed platform. The proposed framework employs coarse granularity level and simple developmental and deployment procedures for computational offloading in MCC. ASM is evaluated by benchmarking prototype application on the Android devices in the real MCC environment. It is found that the turnaround time of the application reduces up to 45 % and ECC of the application reduces up to 33 % in ASM-based computational offloading as compared to traditional offloading techniques which shows the lightweight nature of the proposed framework for computational offloading.


Network Flow Forecasting by a Novel Twin Support Vector Regression Algorithm


title: Wideband Dual-Polarized Magnetically Coupled Patch Antenna Array With High Port Isolation

abstract: A dual-polarized magnetically coupled patch antenna array with high isolation is presented in this paper. The proposed ${{1}} \times {{2}}$ antenna array is composed of two square patches printed on the horizontal substrate, two pairs of feeding structures printed on the two vertical substrates, a pair of metal cavities and a novel artificial periodic structures (APS) composed of metal strips etched vertically on the ground plane. Each feeding structure is orthogonally mounted and excites the square patch magnetically. The feeding structure comprises a metal loop or quasi-loop printed on one side of the vertical substrate and an open-ended transmission line printed on the other side. The metal cavities and APS can further improve the port isolation between array elements. The simulation and measurement results of single antenna element demonstrate its extremely high port isolation (better than 45 dB over 20% impendence bandwidth for ${\text{VSWR}} 1.5). For the array, numerical and experimental results show that the port isolation with same polarization can be improved to over 45 dB across the frequency band of 2.5–2.7 GHz. The proposed antenna array achieves wide bandwidth with very high isolation, high gain, and high radiation efficiency.


title: CoAP for content-centric networks

abstract: We analyze the design space for implementing the Constrained Application Protocol (CoAP) within Content-Centric Networks (CCN), identifying several CoAP specific scenarios and seeing how they map to CCNs. We present an evaluation, recommendations for implementations and extensions, and directions for future work. Our key result is that while several protocol features and flows map naturally, some communication patterns are more difficult to capture without modifications or additions to the CCN model. In addition to many conceptual and also non-performance related advantages, our experimental evaluation demonstrates that it is feasible and useful to implement CoAP over CCN, with the performance similar to that of CoAP over UDP over IP, and in several cases outperforming it significantly.


title: PII: S0954-898X(97)80751-8 Metric-space analysis of spike trains: theory, algorithms and application

abstract: We present the mathematical basis of a new approach to the analysis of temporal coding. The foundation of the approach is the construction of several families of novel distances (metrics) between neuronal impulse trains. In contrast to most previous approaches to the analysis of temporal coding, the present approach does not attempt to embed impulse trains in a vector space, and does not assume a Euclidean notion of distance. Rather, the proposed metrics formalize physiologically-based hypotheses for what aspects of the firing pattern might be stimulus-dependent, and make essential use of the point process nature of neural discharges. We show that these families of metrics endow the space of impulse trains with related but inequivalent topological structures. We show how these metrics can be used to determine whether a set of observed responses have stimulus-dependent temporal structure without a vector-space embedding. We show how multidimensional scaling can be used to assess the similarity of these metrics to Euclidean distances. For two of these families of metrics (one based on spike times and one based on spike intervals), we present highly efficient computational algorithms for calculating the distances. We illustrate these ideas by application to artificial datasets and to recordings from auditory and visual cortex.


title: Online opinion leaders and their influence on purchase intentions

abstract: Opinion leader plays an important role in promoting about product or brand related information to other consumer counterparts. Subsequently online platforms have become an avenue for opinion leaders to play their role on the social media. Blogs for example has become an integral part of consumers' reference point during the decision making process. This study examines the role of online opinion leaders in influencing consumer purchase intentions.


title: iHAT: Interactive hierarchical aggregation table

abstract: In the search for single-nucleotide polymorphisms (SNPs), genome wide association studies have become an important technique for the identification of associations between genotype and phenotype of a diverse set of sequence-based data. In this work, we present a methodology for the visual assessment of SNPs using interactive hierarchical aggregation techniques combined with methods known from traditional sequence browsers and cluster heatmaps. Our prototype tool iHAT supports the visualization of multiple sequence alignments, associated metadata, and hierarchical clusterings. Moreover, data-type dependent colormaps and aggregation strategies as well as different filtering options support the user in finding correlations between sequences and metadata. Similar to other visualizations such as parallel coordinates or heatmaps, iHAT is aimed at exploiting the human pattern-recognition ability for spotting patterns that might indicate correlation or anticorrelation. Together with its interactive features and a database backend for fast data retrieval, we consider iHAT as a prototype for a visual analytics system for genome-wide association studies.


title: A framework for benchmarking entity-annotation systems

abstract: In this paper we design and implement a benchmarking framework for fair and exhaustive comparison of entity-annotation systems. The framework is based upon the definition of a set of problems related to the entity-annotation task, a set of measures to evaluate systems performance, and a systematic comparative evaluation involving all publicly available datasets, containing texts of various types such as news, tweets and Web pages. Our framework is easily-extensible with novel entity annotators, datasets and evaluation measures for comparing systems, and it has been released to the public as open source. We use this framework to perform the first extensive comparison among all available entity annotators over all available datasets, and draw many interesting conclusions upon their efficiency and effectiveness. We also draw conclusions between academic versus commercial annotators.


title: From taxi GPS traces to social and community dynamics: A survey

abstract: Vehicles equipped with GPS localizers are an important sensory device for examining people’s movements and activities. Taxis equipped with GPS localizers serve the transportation needs of a large number of people driven by diverse needs; their traces can tell us where passengers were picked up and dropped off, which route was taken, and what steps the driver took to find a new passenger. In this article, we provide an exhaustive survey of the work on mining these traces. We first provide a formalization of the data sets, along with an overview of different mechanisms for preprocessing the data. We then classify the existing work into three main categories: social dynamics, traffic dynamics and operational dynamics. Social dynamics refers to the study of the collective behaviour of a city’s population, based on their observed movements; Traffic dynamics studies the resulting flow of the movement through the road network; Operational dynamics refers to the study and analysis of taxi driver’s modus operandi. We discuss the different problems currently being researched, the various approaches proposed, and suggest new avenues of research. Finally, we present a historical overview of the research work in this field and discuss which areas hold most promise for future research.


title: Deep Generative Models with Learnable Knowledge Constraints

abstract: The broad set of deep generative models (DGMs) has achieved remarkable advances. However, it is often difficult to incorporate rich structured domain knowledge with the end-to-end DGMs. Posterior regularization (PR) offers a principled framework to impose structured constraints on probabilistic models, but has limited applicability to the diverse DGMs that can lack a Bayesian formulation or even explicit density evaluation. PR also requires constraints to be fully specified a priori, which is impractical or suboptimal for complex knowledge with learnable uncertain parts. In this paper, we establish mathematical correspondence between PR and reinforcement learning (RL), and, based on the connection, expand PR to learn constraints as the extrinsic reward in RL. The resulting algorithm is model-agnostic to apply to any DGMs, and is flexible to adapt arbitrary constraints with the model jointly. Experiments on human image generation and templated sentence generation show models with learned knowledge constraints by our algorithm greatly improve over base generative models.


title: Cryptographic hash functions : cryptanalysis, design and applications

abstract: Cryptographic hash functions are an important tool in cryptography to achieve certain security goals such as authenticity, digital signatures, digital time stamping, and entity authentication. They are also strongly related to other important cryptographic tools such as block ciphers and pseudorandom functions. The standard and widely used hash functions such as MD5 and SHA-1 follow the design principle of Merkle-Damgard iterated hash function construction which was presented independently by Ivan Damgard and Ralph Merkle at Crypto'89. It has been established that neither these hash functions nor the Merkle-Damgard construction itself meet certain security requirements. This thesis aims to study the attacks on this popular construction and propose schemes that offer more resistance against these attacks as well as investigating alternative approaches to the Merkle-Damgard style of designing hash functions. This thesis aims at analysing the security of the standard hash function Cellular Authentication and Voice Encryption Algorithm (CAVE) used for authentication and key-derivation in the second generation (2G) North American IS-41 mobile phone system. In addition, this thesis studies the analysis issues of message authentication codes (MACs) designed using hash functions. With the aim to propose some efficient and secure MAC schemes based on hash functions. ::: ::: ::: ::: This thesis works on three aspects of hash functions: design, cryptanalysis and applications with the following significant contributions: ::: ::: ::: ::: * Proposes a family of variants to the Damgard-Merkle construction called 3CG for better protection against specific and generic attacks. Analysis of the linear variant of 3CG called 3C is presented including its resistance to some of the known attacks on hash functions. ::: ::: ::: ::: * Improves the known cryptanalytical techniques to attack 3C and some other similar designs including a linear variant of GOST, a Russian standard hash function. ::: ::: ::: ::: * Proposes a completely novel approach called Iterated Halving, alternative to the standard block iterated hash function construction. ::: ::: ::: ::: * Analyses provably secure HMAC and NMAC message authentication codes (MACs) based on weaker assumptions than stated in their proofs of security. Proposes an efficient variant for NMAC called NMAC-1 to authenticate short messages. Proposes a variant for NMAC called M-NMAC which offers better protection against the complete key-recovery attacks than NMAC. As well it is shown that M-NMAC with hash functions also resists side-channel attacks against which HMAC and NMAC are vulnerable. Proposes a new MAC scheme called O-NMAC based on hash functions using just one secret key. ::: ::: ::: ::: * Improves the open cryptanalysis of the CAVE algorithm. ::: ::: ::: ::: * Analyses the security and legal implications of the latest collision attacks on the widely used MD5 and SHA-1 hash functions.


title: Exploring user's emotional relationships with IT products: a structural equation model

abstract: Decades of ergonomics research change the consumers' criteria by which they value and choose an IT product. Compared with the emphasis placed on new functions, reliability, and good after-service in past, emotional response to products has become an important user requirement to improve the customer acceptance in the market. This study develops a model and validates two specific antecedent variables-computer experience and capability-which are hypothesized to be fundamental determinants of users' emotional response to IT products usage. Adding four more variables-such as attitude, belief, end user satisfaction and impact-test the parsimony of the model further. The research data were derived from a questionnaire survey circulated to 618 people in the worldwide and LISREL, a technique for modeling structural equation systems, was used to analyze the responses.


Performance Comparison of Naïve Bayes and J 48 Classification Algorithms


title: Maximizing Cloud Providers Revenues via Energy Aware Allocation Policies

abstract: Cloud providers, like Amazon, offer their data centers' computational and storage capacities for lease to paying customers. High electricity consumption, associated with running a data center, not only reflects on its carbon footprint, but also increases the costs of running the data center itself. This paper addresses the problem of maximizing the revenues of Cloud providers by trimming down their electricity costs. As a solution allocation policies which are based on the dynamic powering servers on and off are introduced and evaluated. The policies aim at satisfying the conflicting goals of maximizing the users' experience while minimizing the amount of consumed electricity. The results of numerical experiments and simulations are described, showing that the proposed scheme performs well under different traffic conditions.


title: Self-organizing tree models for image synthesis

abstract: We present a method for generating realistic models of temperate-climate trees and shrubs. This method is based on the biological hypothesis that the form of a developing tree emerges from a self-organizing process dominated by the competition of buds and branches for light or space, and regulated by internal signaling mechanisms. Simulations of this process robustly generate a wide range of realistic trees and bushes. The generated forms can be controlled with a variety of interactive techniques, including procedural brushes, sketching, and editing operations such as pruning and bending of branches. We illustrate the usefulness and versatility of the proposed method with diverse tree models, forest scenes, animations of tree development, and examples of combined interactive-procedural tree modeling.


title: A learning based approach to control synthesis of Markov decision processes for linear temporal logic specifications

abstract: We propose to synthesize a control policy for a Markov decision process (MDP) such that the resulting traces of the MDP satisfy a linear temporal logic (LTL) property. We construct a product MDP that incorporates a deterministic Rabin automaton generated from the desired LTL property. The reward function of the product MDP is defined from the acceptance condition of the Rabin automaton. This construction allows us to apply techniques from learning theory to the problem of synthesis for LTL specifications even when the transition probabilities are not known a priori. We prove that our method is guaranteed to find a controller that satisfies the LTL property with probability one if such a policy exists, and we suggest empirically with a case study in traffic control that our method produces reasonable control strategies even when the LTL property cannot be satisfied with probability one.


title: A review of automatic selection methods for machine learning algorithms and hyper-parameter values

abstract: Machine learning studies automatic algorithms that improve themselves through experience. It is widely used for analyzing and extracting value from large biomedical data sets, or “big biomedical data,” advancing biomedical research, and improving healthcare. Before a machine learning model is trained, the user of a machine learning software tool typically must manually select a machine learning algorithm and set one or more model parameters termed hyper-parameters. The algorithm and hyper-parameter values used can greatly impact the resulting model’s performance, but their selection requires special expertise as well as many labor-intensive manual iterations. To make machine learning accessible to layman users with limited computing expertise, computer science researchers have proposed various automatic selection methods for algorithms and/or hyper-parameter values for a given supervised machine learning problem. This paper reviews these methods, identifies several of their limitations in the big biomedical data environment, and provides preliminary thoughts on how to address these limitations. These findings establish a foundation for future research on automatically selecting algorithms and hyper-parameter values for analyzing big biomedical data.


Helical rim advancement flaps for reconstruction.


title: Validated Assessment Scales for the Lower Face

abstract: BackgroundAge-related upper face changes such as wrinkles, lines, volume loss, and anatomic alterations may affect quality of life and psychological well-being. The development of globally accepted tools to assess these changes objectively is an essential contribution to aesthetic research and routi


title: The structure and properties of gluten: an elastic protein from wheat grain

abstract: The wheat gluten proteins correspond to the major storage proteins that are deposited in the starchy endosperm cells of the developing grain. These form a continuous proteinaceous matrix in the cells of the mature dry grain and are brought together to form a continuous viscoelastic network when flour is mixed with water to form dough. These viscoelastic properties underpin the utilization of wheat to give bread and other processed foods. One group of gluten proteins, the HMM subunits of glutenin, is particularly important in conferring high levels of elasticity (i.e. dough strength). These proteins are present in HMM polymers that are stabilized by disulphide bonds and are considered to form the 'elastic backbone' of gluten. However, the glutamine-rich repetitive sequences that comprise the central parts of the HMM subunits also form extensive arrays of interchain hydrogen bonds that may contribute to the elastic properties via a 'loop and train' mechanism. Genetic engineering can be used to manipulate the amount and composition of the HMM subunits, leading to either increased dough strength or to more drastic changes in gluten structure and properties.


title: Efficacy of a microsavings intervention in increasing income and reducing economic dependence upon sex work among women in Mongolia

abstract: Diversifying options for income generation and employment may enhance health outcomes for women engaged in sex work. This randomized trial tested the efficacy of a microsavings intervention on reductions in economic dependence upon sex work and changes in income among women in Mongolia. Women who attended microfinance plus HIV prevention sessions reported a significantly lower percentage of income from sex work, increased odds of reporting no income from sex work, and increased odds that sex work was not their main source of income compared to women who received HIV prevention sessions alone. No significant changes in total income were observed in the treatment condition.


title: A 200Hz small range image sensor using a multi-spot laser projector

abstract: In this paper, a high-speed range image sensor using a multi-spot laser projector is constructed. Several highspeed range image sensors have been developed recently. Their sampling rate is around the video rate (30 Hz or so) and a faster sensor is required. The proposed sensor has achieved 200 Hz measurement. It consists of a commercially available laser projector and a high-speed CCD camera. The number of pixels is 361 and the measurement range is 800-2000 mm. Although the acquired range image is sparse, the proposed sensor is thought to be adequate for several applications such as robot vision because of its high-speed imaging and compactness. Some characteristics such as measurement errors are discussed, and the effectiveness of the proposed sensor is verified by experiments.


title: What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction

abstract: E-learning is emerging as the new paradigm of modern education. Worldwide, the e-learning market has a growth rate of 35.6%, but failures exist. Little is known about why many users stop their online learning after their initial experience. Previous research done under different task environments has suggested a variety of factors affecting user satisfaction with e-Learning. This study developed an integrated model with six dimensions: learners, instructors, courses, technology, design, and environment. A survey was conducted to investigate the critical factors affecting learners' satisfaction in e-Learning. The results revealed that learner computer anxiety, instructor attitude toward e-Learning, e-Learning course flexibility, e-Learning course quality, perceived usefulness, perceived ease of use, and diversity in assessments are the critical factors affecting learners' perceived satisfaction. The results show institutions how to improve learner satisfaction and further strengthen their e-Learning implementation.


title: Methods for isolation, purification and structural elucidation of bioactive secondary metabolites from marine invertebrates

abstract: In the past few decades, marine natural products bioprospecting has yielded a considerable number of drug candidates. Two marine natural products have recently been admitted as new drugs: Prialt (also known as ziconotide) as a potent analgesic for severe chronic pain and Yondelis (known also as trabectedin or E-743) as antitumor agent for the treatment of advanced soft tissue sarcoma. In this protocol, methods for bioactivity-guided isolation, purification and identification of secondary metabolites from marine invertebrates such as sponges, tunicates, soft corals and crinoids are discussed. To achieve this goal, solvent extraction of usually freeze-dried sample of marine organisms is performed. Next, the extract obtained is fractionated by liquid-liquid partitioning followed by various chromatographic separation techniques including thin layer chromatography, vacuum liquid chromatography, column chromatography (CC) and preparative high-performance reversed-phase liquid chromatography. Isolation of bioactive secondary metabolites is usually monitored by bioactivity assays, e.g., antioxidant (2,2-diphenyl-1-picryl hydrazyl) and cytotoxicity (microculture tetrazolium) activities that ultimately yield the active principles. Special care should be taken when performing isolation procedures adapted to the physical and chemical characteristics of the compounds isolated, particularly their lipo- or hydrophilic characters. Examples of isolation of compounds of different polarities from extracts of various marine invertebrates will be presented in this protocol. Structure elucidation is achieved using recent spectroscopic techniques, especially 2D NMR and mass spectrometry analysis.


title: Combing Haar and MBLBP features for face detection using multi-exit asymmetric boosting

abstract: This paper proposes a visual face detection framework that enables fast image processing while achieving high detection rates. The proposed framework combines both multi block local binary pattern (MBLBP) features and Haar- like features using multi-exit asymmetric boosting for robust face detection. In this framework, the integral image is utilized to facilitate rapid extraction for both the MBLBP features and the Haar-like features. Experimental results showed that combing MBLBP and Haar-like features can achieve better detection rate than each of them can do individually.


title: Dynamic Bayesian Network for Probabilistic Modeling of Tunnel Excavation Processes

abstract: This article introduces a dynamic Bayesian network (DBN) model for probabilistic assessment of tunnel construction performance. The article facilitates the quantification of uncertainties in the construction process and of the risk from extraordinary events that cause severe delays and damages. Stochastic dependencies resulting from the influence of human factors and other external factors are addressed in the model. An efficient algorithm for evaluating the DBN model is presented, which is a modification of the so-called Frontier algorithm. The proposed model and algorithm are applied to an illustrative case study, the excavation of a road tunnel by means of the New Austrian Tunneling Method.


title: Sparse Representation on Graphs by Tight Wavelet Frames and Applications

abstract: In this paper, we introduce a new (constructive) characterization of tight wavelet frames on non-flat domains in both continuum setting, i.e. on manifolds, and discrete setting, i.e. on graphs; discuss how fast tight wavelet frame transforms can be computed and how they can be effectively used to process graph data. We start with defining the quasi-affine systems on a given manifold $\cM$ that is formed by generalized dilations and shifts of a finite collection of wavelet functions $\Psi:=\{\psi_j: 1\le j\le r\}\subset L_2(\R)$. We further require that $\psi_j$ is generated by some refinable function $\phi$ with mask $a_j$. We present the condition needed for the masks $\{a_j: 0\le j\le r\}$ so that the associated quasi-affine system generated by $\Psi$ is a tight frame for $L_2(\cM)$. Then, we discuss how the transition from the continuum (manifolds) to the discrete setting (graphs) can be naturally done. In order for the proposed discrete tight wavelet frame transforms to be useful in applications, we show how the transforms can be computed efficiently and accurately by proposing the fast tight wavelet frame transforms for graph data (WFTG). Finally, we consider two specific applications of the proposed WFTG: graph data denoising and semi-supervised clustering. Utilizing the sparse representation provided by the WFTG, we propose $\ell_1$-norm based optimization models on graphs for denoising and semi-supervised clustering. On one hand, our numerical results show significant advantage of the WFTG over the spectral graph wavelet transform (SGWT) by [1] for both applications. On the other hand, numerical experiments on two real data sets show that the proposed semi-supervised clustering model using the WFTG is overall competitive with the state-of-the-art methods developed in the literature of high-dimensional data classification, and is superior to some of these methods.


title: A linear, single-stage, nanosecond pulse generator for delivering intense electric fields to biological loads

abstract: A compact pulse generator capable of producing high voltage pulses with halfmaximum widths as short as 2.5 ns and amplitudes as high as 5 kV has been developed to enable current and future in vivo and in vitro research into the effects of ultra-short, intense electric fields on biological matter. This pulse generator is small, simple, and free of saturable magnetic cores, which frequently introduce amplitude jitter and an undesirable correlation between amplitude and pulse width. In place of a non-linear pulse-forming network is a single-stage resonant network that drives a bank of junction recovery diodes. The diodes function as an opening switch that commutes current from an inductor to a resistive load. The use of air-core inductors in the resonant network results in a stable output pulse with an amplitude that scales linearly with input voltage and a pulse width that is independent of amplitude. The ability to scale the output amplitude independently of the pulse width simplifies the setup for experiments that require pulses with different electric field strengths but the same rise time and duration. Jurkat T lymphoblast cells exposed to 2.5 ns fields produced by this pulse generator showed an increasing degree of electropermeabilization with increasing pulse dosage and electric field intensity.


title: A programmable SDN+NFV-based architecture for UAV telemetry monitoring

abstract: The explosive growth in the worldwide use of Unmanned Aerial Vehicles (UAVs) has raised a critical concern with respect to the adequate management of their ad hoc network configuration as required by their mobility management process. As UAVs migrate among ground control stations, associated network services, routing and operational control must also rapidly migrate to ensure a seamless transition. In this paper, we present a novel, lightweight and modular architecture which supports high mobility and situational-awareness through the application of Software Defined Networking (SDN) and Network Function Virtualization (NFV) principles on top of the UAV infrastructure. By combining SDN+NFV programmability we can achieve a robust migration of UAV-related network services, such as network monitoring and anomaly detection as well as smooth UAV migration that confronts high mobility requirements. The proposed container-based monitoring and anomaly detection Network Functions (NFs) as employed within our architecture can be tuned to specific UAV types providing operators better insight during live, high-mobility deployments. We evaluate our architecture against telemetry from over 80 flights from a scientific research UAV infrastructure showing our ability to tune and detect emerging challenges.


title: Efficient Task Scheduling Multi-Objective Particle Swarm Optimization in Cloud Computing

abstract: Task scheduling in data centers is a complex task due to their evolution in size, complexity, and performance. At the same time, customers’ requirements have become more sophisticated in terms of execution time and throughput. Against this background, this work presents a new model of resource allocation that optimizes task scheduling using a multi-objective optimization (MOO) and particle swarm optimization (PSO) algorithm. In more detail, we develop a novel multi-objective PSO (MOPSO) algorithm, based on a new ranking strategy. The main insight of this algorithm is that the tasks are scheduled to the virtual machines to minimize waiting time and maximize system throughput. The algorithm leads to a reduction in execution time of 20%, a reduction the waiting time of 30%, and shows improvements of up to 40% in throughput compared to the current state of the art.


title: Minimal inhibitory concentration (MIC) determination of disinfectant and/or sterilizing agents

abstract: Due to the growing number of outbreaks of infection in hospital and nurseries, it becomes essential to set up a sanitation program that indicates that the appropriate chemical agent was chosen for application in the most effective way. Validating the effectiveness of decontamination and disinfection is an important and often challenging task. In order to study and compare the behavior of selected microorganisms, they were submitted to minimal inhibitory concentration (MIC). The MIC intervals, which reduced bacteria populations over 6 log10, were: 59 to 156 mg/L of quaternary ammonium compounds (QACs); 63 to 10000 mg/L of chlorhexidine; 1375 to 3250 mg/L of glutaraldehyde; 39 to 246 mg/L of formaldehyde; 43750 to 87500 mg/L of ethanol; 1250 to 6250 mg/L of iodine in polyvinyl-pyrolidone complexes, 150 to 4491 mg/L of chlorine-releasing-agents (CRAs) and 469 to 2500 mg/L of hydrogen peroxide. Chlorhexidine showed non inhibitory activity over germinating spores. A. calcoaceticus showed resistance to the majority of the agents tested, followed by E. cloacae and S. marcescens.


Acral Lentiginous Melanoma, Rare Form of Marjolin?s Ulcer: A Case and Literature Review


title: 3D Mapping for high-fidelity unmanned ground vehicle lidar simulation

abstract: High-fidelity simulation is a key enabling technology for the widespread deployment of large unmanned ground vehicles (UGVs). However, current approaches for lidar simulation leave much to be desired, particularly for scenes with vegetation. We introduce a novel 3D mapping technique that learns high-fidelity models for geo-specific lidar simulation directly from pose tagged lidar data. We introduce a novel stochastic, volumetric model that captures and can reproduce the statistical interactions of lidar with terrain. We show how to automatically learn the model directly from 3D mapping data collected by a UGV in the target environment. We extend our approach using terrain-classification techniques to develop a hybrid surface–volumetric model that combines the efficiency of surface modeling for areas that are well approximated by large surfaces (e.g. roads, bare earth) with our volumetric approach for more complex areas (e.g. bushes, trees) without sacrificing overall fidelity. We quantitatively compare the performance of our approach against more conventional methods on large outdoor datasets from urban and off-road environments. Our results show significant performance gains using our volumetric and hybrid approaches over the state-of-the-art, laying the ground work for truly high-fidelity simulation engines for UGVs.


Universal Design 2016: Learning from the Past, Designing for the Future


Applied Statistics and Probability for Engineers


title: Supplication and appeasement in conflict and negotiation: The interpersonal effects of disappointment, worry, guilt, and regret

abstract: This study examined the social effects of emotions related to supplication and appeasement in conflict ::: and negotiation. In a computer-simulated negotiation, participants in Experiment 1 were confronted with ::: a disappointed or worried opponent (supplication), with a guilty or regretful opponent (appeasement), or ::: with a nonemotional opponent (control). Compared with controls, participants conceded more when the ::: other experienced supplication emotions and conceded less when the other experienced appeasement ::: emotions (especially guilt). Experiment 2 replicated the effects of disappointment and guilt and showed ::: that they are moderated by the perceiver’s dispositional trust: Negotiators high in trust conceded more to ::: a disappointed counterpart than to a happy one, but those with low trust were unaffected. In Experiment ::: 3, trust was manipulated through information about the other’s personality (cooperative vs. competitive), ::: and a similar moderation was obtained.


Cloud Service Reliability: Modeling and Analysis


title: Squeeze-and-Excitation Networks

abstract: Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local receptive fields. In order to boost the representational power of a network, much existing work has shown the benefits of enhancing spatial encoding. In this work, we focus on channels and propose a novel architectural unit, which we term the"Squeeze-and-Excitation"(SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We demonstrate that by stacking these blocks together, we can construct SENet architectures that generalise extremely well across challenging datasets. Crucially, we find that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at slight computational cost. SENets formed the foundation of our ILSVRC 2017 classification submission which won first place and significantly reduced the top-5 error to 2.251%, achieving a 25% relative improvement over the winning entry of 2016.


Anti-pattern Detection as a Knowledge Utilization


title: Real-Time Procedural Generation of Personalized Facade and Interior Appearances Based on Semantics

abstract: This article presents a computational model for procedural generation of customized facade and interior styles of buildings for use in three-dimensional virtual environments of games and simulations. The model makes use of two types of input information: geometric and semantic. The geometric information is related to the two-dimensional floor plan of a building, with its parts and dimensions, as well as positions for doors and windows. The semantic information enables the creation of architectural styles, enabling variations for materials and textures for the facade and inner parts, as well as for the shapes and dimensions of doors and windows. Changing one or more input parameters modifies the final appearance of the result. The proposed computational model can be used to generate large virtual environments, as it allows mixing different floor plans and architectural styles to achieve visual diversity. The main characteristics of the work are the realtime procedural generation of 3D buildings, the customization of facades and building interiors, as well as the use of semantic to assign meaning to the different elements of the house.


title: Reliable localized on-line learning in non-stationary environments

abstract: On-line learning allows to adapt to changing nonstationary environments. But typically with on-line learning a hypothesis of the data relation is adapted based on a stream of single local training examples, continuously changing the global input-output relation. Hence with these single examples the whole hypothesis is revised incrementally, which might be harmful to the overall predictive quality of the learned model. Nevertheless, for a reliable adaptation, the learned model must yield good predictions in every step. Therefor, the IRMA approach to online learning enables an adaptation that reliably incorporates a new example with a stringent local, but minimal global influence on the input-output relation. The main contribution of this paper is twofold. First, it presents an extension of IRMA regarding the setup of the stiffness, i.e. its hyper-parameter. Second, the IRMA approach is investigated for the first time on a non-trivial realworld application in a non-stationary environment. It is compared with state of the art algorithms on predicting future electric loads in a power grid where a continuous adaptation is necessary to adapt to season and weather conditions. The results show that the performance is increased significantly by IRMA.


title: Per-packet load-balanced, low-latency routing for clos-based data center networks

abstract: Clos-based networks including Fat-tree and VL2 are being built in data centers, but existing per-flow based routing causes low network utilization and long latency tail. In this paper, by studying the structural properties of Fat-tree and VL2, we propose a per-packet round-robin based routing algorithm called Digit-Reversal Bouncing (DRB). DRB achieves perfect packet interleaving. Our analysis and simulations show that, compared with random-based load-balancing algorithms, DRB results in smaller and bounded queues even when traffic load approaches 100%, and it uses smaller re-sequencing buffer for absorbing out-of-order packet arrivals. Our implementation demonstrates that our design can be readily implemented with commodity switches. Experiments on our testbed, a Fat-tree with 54 servers, confirm our analysis and simulations, and further show that our design handles network failures in 1-2 seconds and has the desirable graceful performance degradation property.


title: Metamorphic worm that carries its own morphing engine

abstract: Metamorphic malware changes its internal structure across generations, but its functionality remains unchanged. Well-designed metamorphic malware will evade signature detection. Recent research has revealed techniques based on hidden Markov models (HMMs) for detecting many types of metamorphic malware, as well as techniques for evading such detection. A worm is a type of malware that actively spreads across a network to other host systems. In this project we design and implement a prototype metamorphic worm that carries its own morphing engine. This is challenging, since the morphing engine itself must be morphed across replications, which imposes restrictions on the structure of the worm. Our design employs previously developed techniques to evade detection. We provide test results to confirm that this worm effectively evades signature and HMM-based detection, and we consider possible detection strategies. This worm provides a concrete example that should prove useful for additional metamorphic detection research.


title: A body joint improves vertical to horizontal transitions of a wall-climbing robot

abstract: Several recently-designed robots are able to scale steep surfaces using animal-inspired strategies for foot attachment and leg kinematics. These designs could be valuable for reaching high vantage points or for overcoming large obstacles. However, most of these robots cannot transition between intersecting surfaces. For example, our previous Climbing Mini-WhegsTM robot cannot make a 90deg transition from a vertical wall up onto a flat horizontal surface. It is known that cockroaches bend their body to accomplish such transitions. This concept has been simplified to a single-axis body joint which allows ground-walking robots to cross uneven terrain. In this work, we examine the effect of a body joint on wall-climbing vehicles using both a kinematic simulation and two prototype Climbing Mini-WhegsTM robots. The simulation accurately predicts that the better design has the body joint axle closer to the center of the robot than to the front wheel- legs for orthogonal exterior transitions for a wide range of initial conditions. In the future, the methods and principles demonstrated here could be used to improve the design of climbing robots for other environments.


title: The role of personality, age, and gender in tweeting about mental illness

abstract: Mental illnesses, such as depression and post traumatic stress disorder (PTSD), are highly underdiagnosed globally. Populations sharing similar demographics and personality traits are known to be more at risk than others. In this study, we characterise the language use of users disclosing their mental illness on Twitter. Language-derived personality and demographic estimates show surprisingly strong performance in distinguishing users that tweet a diagnosis of depression or PTSD from random controls, reaching an area under the receiveroperating characteristic curve ‐ AUC ‐ of around .8 in all our binary classification tasks. In fact, when distinguishing users disclosing depression from those disclosing PTSD, the single feature of estimated age shows nearly as strong performance (AUC = .806) as using thousands of topics (AUC = .819) or tens of thousands of n-grams (AUC = .812). We also find that differential language analyses, controlled for demographics, recover many symptoms associated with the mental illnesses in the clinical literature.


title: A Survey on the Security of Hypervisors in Cloud Computing

abstract: This survey paper focuses on the security of hyper visors in the cloud. Topics covered in this paper include attacks that allow a malicious virtual machine (VM) to compromise the hyper visor, as well as techniques used by malicious VMs to steal more than their allocated share of physical resources, and ways to bypass the isolation between the VMs by using side-channels to steal data. Also discussed are the security requirements and architectures for hyper visors to successfully defend against such attacks.


title: Online Persuasion in Facebook and Mixi: A Cross-Cultural Comparison

abstract: Social networking sites persuade millions of users each day to adopt specific behaviors. To understand this phenomenon in the context of persuasive technology, we analyzed how persuasion takes place in leading social networking sites from two different countries: Facebook in the U.S. and Mixi in Japan. We compared the two services on four persuasion goals: creating profile pages, inviting friends, responding to content by friends, and returning to the site often. Our analysis reveals the differences and similarities in how Facebook and Mixi are designed to influence users toward the achievement of these four goals. In general, Facebook's persuasive design is more assertive and mechanistic, while Mixi's approach, by comparison, is subtle and indirect. These persuasion styles seem to map generally to cultural differences between the U.S. and Japan.


title: Document Dissimilarity within and across Languages: a Benchmarking Study

abstract: Quantifying the similarity or dissimilarity between documents is an important task in authorship attribution, information retrieval, plagiarism detection, text mining, and many other areas of linguistic computing. Numerous similarity indices have been devised and used, but relatively little attention has been paid to calibrating such indices against externally imposed standards, mainly because of the difficulty of establishing agreed reference levels of inter-text similarity. The present article introduces a multi-register corpus gathered for this purpose, in which each text has been located in a similarity space based on ratings by human readers. This provides a resource for testing similarity measures derived from computational text-processing against reference levels derived from human judgement, i.e. external to the texts themselves. We describe the results of a benchmarking study in five different languages in which some widely used meas- ures perform comparatively poorly. In particular, several alternative correlational measures (Pearson r, Spearman rho, tetrachoric correlation) consistently outper- form cosine similarity on our data. A method of using what we call 'anchor texts' to extend this method from monolingual inter-text similarity-scoring to inter-text similarity-scoring across languages is also proposed and tested.


title: Is resting membrane potential a possible indicator of viability of muscle bundles used in the in vitro caffeine contracture test?

abstract: In 22 patients susceptible to and 34 patients not susceptible to malignant hyperthermia, we examined which muscle conditions may influence the degree of sensitivity of skeletal muscle to the in vitro caffeine contracture test: predrug resting membrane potential, predrug twitch tension, and maximum contracture induced by 32 mM caffeine in two caffeine tests performed respectively at 30 and 75 min after biopsy. No differences in the measured variables were observed between the first and the second caffeine tests in the 34 patients susceptible to malignant hyperthermia. The first caffeine test was found to be positive in all of the 22 patients susceptible to malignant hyperthermia. However, in eight patients, the second caffeine test was negative and the muscle fibers were found to be significantly depolarized. Resting membrane potential was -73.4 +/- 7.9 mV before the first caffeine test and -65.8 +/- 8.8 mV before the second test. We suggest that when time-induced partial depolarization of malignant hyperthermia-susceptible fibers occurs, fibers may become less sensitive to caffeine.


title: Reliable and intuitive teleoperation of LineScout: a mobile robot for live transmission line maintenance

abstract: Power line inspection and maintenance is a slowly but surely emerging field for robotics. This paper describes the control scheme implemented in LineScout technology, one of the first teleoperated obstacle crossing systems that has progressed to the stage of actually performing very-high-voltage power line jobs. Following a brief overview of the hardware and software architecture, key challenges associated with the objectives of achieving reliability, robustness and ease of operation are presented. The coordinated control through visual feedback of all motors needed for obstacle crossing calls for a coherent strategy, an effective graphical user interface and rules to ensure safe, predictable operation. Other features such as automatic weight balancing are introduced to lighten the workload and let the operator concentrate on inspecting power line components. Open architecture was considered for progressive improvements. The features required to succeed in making power line robots fully autonomous are also discussed.


title: The importance of the label hierarchy in hierarchical multi-label classification

abstract: We address the task of hierarchical multi-label classification (HMC). HMC is a task of structured output prediction where the classes are organized into a hierarchy and an instance may belong to multiple classes. In many problems, such as gene function prediction or prediction of ecological community structure, classes inherently follow these constraints. The potential for application of HMC was recognized by many researchers and several such methods were proposed and demonstrated to achieve good predictive performances in the past. However, there is no clear understanding when is favorable to consider such relationships (hierarchical and multi-label) among classes, and when this presents unnecessary burden for classification methods. To this end, we perform a detailed comparative study over 8 datasets that have HMC properties. We investigate two important influences in HMC: the multiple labels per example and the information about the hierarchy. More specifically, we consider four machine learning tasks: multi-label classification, hierarchical multi-label classification, single-label classification and hierarchical single-label classification. To construct the predictive models, we use predictive clustering trees (a generalized form of decision trees), which are able to tackle each of the modelling tasks listed. Moreover, we investigate whether the influence of the hierarchy and the multiple labels carries over for ensemble models. For each of the tasks, we construct a single tree and two ensembles (random forest and bagging). The results reveal that the hierarchy and the multiple labels do help to obtain a better single tree model, while this is not preserved for the ensemble models.


title: A Checklist of the Aspidogastrea (Platyhelminthes: Trematoda) of the World

abstract: A checklist of records of aspidogastrean trematodes (Aspidogastrea) is provided on the basis of a comprehensive survey of the literature since 1826, when the first aspidogastrean species was reported, until December 2014. We list 61 species representing 13 genera within 4 families and 2 orders of aspidogastreans associated with 298 species of invertebrate and vertebrate hosts. The majority of records include bivalves (44% of the total number of host-parasite associations), whereas records from bony fishes represent 32% of host-parasite associations. The aspidogastreans have worldwide distribution, with the highest number of records in the Neartic Region for freshwater hosts and the North Atlantic Ocean for marine ones. The checklist includes a parasite-host list with data on host habitat, site of infection and distribution area of parasites, and a host-parasite list. A limited number of molecular studies on aspidogastreans does not allow us to unravel phylogenetic relationships within the Aspidogastrea.


title: Synthetic gecko foot-hair micro/nanostructures as dry adhesives

abstract: This paper proposes techniques to fabricate synthetic gecko foot-hairs as dry adhesives for future wall-climbing and surgical robots, and models for understanding the synthetic hair design issues. Two nanomolding fabrication techniques are proposed: the first method uses nanoprobe indented flat wax surface and the second one uses a nano-pore membrane as a template. These templates are molded with silicone rubber, polyimide, etc. type of polymers under vacuum. Next, design parameters such as length, diameter, stiffness, density, and orientation of hairs are determined for non-matting and rough surface adaptability. Preliminary micro/nano-hair prototypes showed adhesion close to the predicted values for natural specimens (around 100 nN each).


title: Comparison of group recommendation algorithms

abstract: In recent years recommender systems have become the common tool to handle the information overload problem of educational and informative web sites, content delivery systems, and online shops. Although most recommender systems make suggestions for individual users, in many circumstances the selected items (e.g., movies) are not intended for personal usage but rather for consumption in groups. This paper investigates how effective group recommendations for movies can be generated by combining the group members' preferences (as expressed by ratings) or by combining the group members' recommendations. These two grouping strategies, which convert traditional recommendation algorithms into group recommendation algorithms, are combined with five commonly used recommendation algorithms to calculate group recommendations for different group compositions. The group recommendations are not only assessed in terms of accuracy, but also in terms of other qualitative aspects that are important for users such as diversity, coverage, and serendipity. In addition, the paper discusses the influence of the size and composition of the group on the quality of the recommendations. The results show that the grouping strategy which produces the most accurate results depends on the algorithm that is used for generating individual recommendations. Therefore, the paper proposes a combination of grouping strategies which outperforms each individual strategy in terms of accuracy. Besides, the results show that the accuracy of the group recommendations increases as the similarity between members of the group increases. Also the diversity, coverage, and serendipity of the group recommendations are to a large extent dependent on the used grouping strategy and recommendation algorithm. Consequently for (commercial) group recommender systems, the grouping strategy and algorithm have to be chosen carefully in order to optimize the desired quality metrics of the group recommendations. The conclusions of this paper can be used as guidelines for this selection process.


title: A constrained variational principle for direct estimation and smoothing of the diffusion tensor field from complex DWI

abstract: In this paper, we present a novel constrained variational principle for simultaneous smoothing and estimation of the diffusion tensor field from complex valued diffusion-weighted images (DWI). The constrained variational principle involves the minimization of a regularization term of L/sup p/ norms, subject to a nonlinear inequality constraint on the data. The data term we employ is the original Stejskal-Tanner equation instead of the linearized version usually employed in literature. The complex valued nonlinear form leads to a more accurate (when compared to the linearized version) estimate of the tensor field. The inequality constraint requires that the nonlinear least squares data term be bounded from above by a known tolerance factor. Finally, in order to accommodate the positive definite constraint on the diffusion tensor, it is expressed in terms of Cholesky factors and estimated. The constrained variational principle is solved using the augmented Lagrangian technique in conjunction with the limited memory quasi-Newton method. Experiments with complex-valued synthetic and real data are shown to depict the performance of our tensor field estimation and smoothing algorithm.


title: Surface reconstruction and path planning for industrial inspection with a climbing robot

abstract: Inspection and maintenance tasks in the power industry often require tools to be moved smoothly in direct contact or close proximity over surfaces of curved tube-like structures. This paper addresses the problem of navigating robotic devices, namely a wheeled climbing robot carrying an inspection tool, on surfaces as they typically appear in steam chests of power plants. The surfaces are modeled by triangle meshes. Several state-of-the-art surface reconstruction methods are evaluated, and meshes are constructed for 3D point clouds taken with different laser scanners and for tube structures varying in shape, dimension and surface property. Each mesh represents an embedded graph, and thus directly enables graph search methods to plan paths of triangle strips over the surface. Discrete path planning and continuous robot control are combined to a hybrid system, which steers the robot smoothly along the triangle strip to the target regions on the surface. The navigation approach applies to robotic surface inspection, and is verified in simulation experiments by moving a robot on triangle meshes reconstructed from real data.


title: Intelligent Text Extraction from PDF Documents

abstract: In recent years, PDF has become the de-facto standard for the exchange of print-oriented documents on the Web. This includes many business documents such as financial reports, newsletters and patent applications, and there are many commercial applications that require data to be extracted from these documents and processed by computer systems. A number of products currently exist on the market that navigate, extract and transform data from HTML pages; a process known as wrapping. One such methodology is Lixto, a product of research at our institute. However, none of these products are currently able to work with PDF files. We are investigating this possibility as part of the NEX-TWRAP project. This paper describes our work in progress, and details some of the low-level page segmentation techniques that we have investigated


title: Architectural patterns revisited – a pattern language

abstract: Architectural patterns are a key concept in the field of software architecture: they offer well-established solutions to architectural problems, help to document the architectural design decisions, facilitate communication between stakeholders through a common vocabulary, and describe the quality attributes of a software system as forces. Regrettably, finding and applying the appropriate architectural patterns in practice still remains largely ad-hoc and unsystematic. This is due to the lack of consensus in the community with respect to the “philosophy” and granularity of architectural patterns, as well as the lack of a coherent pattern language. In this paper we attempt to establish common ground in the architectural patterns community by proposing a pattern language that acts as a superset of the existing architectural pattern collections and categorizations. This language is particularly focused on establishing the relationships between the patterns and performs a categorization based on the concept of “architectural views”.


title: A Comparison of Serratus Anterior Muscle Activation During a Wall Slide Exercise and Other Traditional Exercises

abstract: STUDY DESIGN ::: Single-group repeated-measures design. ::: ::: ::: OBJECTIVES ::: To investigate the ability of the wall slide exercise to activate the serratus anterior muscle (SA) at and above 90 degrees of humeral elevation. ::: ::: ::: BACKGROUND ::: Strengthening of the SA is a critical component of rehabilitation for patients with shoulder impingement syndromes. Traditional SA exercises have included scapular protraction exercises such as the push-up plus. These exercises promote activation of the SA near 90 degrees of humeral elevation, but not in positions above 90 degrees where patients typically experience pain. ::: ::: ::: METHODS AND MEASURES ::: Twenty healthy subjects were studied performing 3 exercises: (1) wall slide, (2) plus phase of a wall push-up plus, and (3) scapular plane shoulder elevation. Three-dimensional position of the thorax, scapula, and humerus and muscle activity from the SA, upper and lower trapezius, and latissimus dorsi were recorded. The magnitudes of activation for each muscle at 90 degrees, 120 degrees, and 140 degrees of humeral elevation were quantified from EMG records. Repeated-measures analyses of variance were used to determine the degree to which the different exercises activated the SA at the 3 humeral positions. ::: ::: ::: RESULTS ::: The intensity of SA activity was not significantly different between the 3 exercises at 90 degrees of humeral elevation (P = .40). For the wall slide and scapular plane shoulder elevation exercises, SA activity increased with increasing humeral elevation angle (P = .001), with no significant differences between the 2 exercises (P = .36). ::: ::: ::: CONCLUSION ::: The wall slide is an effective exercise to activate the SA muscle at and above 90 degrees of shoulder elevation. During this exercise, SA activation is not significantly different from SA activation during the push-up plus and scapular plane shoulder elevation, 2 exercises previously validated in the literature.


title: The Link Prediction Problem in Bipartite Networks

abstract: We define and study the link prediction problem in bipartite networks, specializing general link prediction algorithms to the bipartite case. In a graph, a link prediction function of two vertices denotes the similarity or proximity of the vertices. Common link prediction functions for general graphs are defined using paths of length two between two nodes. Since in a bipartite graph adjacency vertices can only be connected by paths of odd lengths, these functions do not apply to bipartite graphs. Instead, a certain class of graph kernels (spectral transformation kernels) can be generalized to bipartite graphs when the positive-semidefinite kernel constraint is relaxed. This generalization is realized by the odd component of the underlying spectral transformation. This construction leads to several new link prediction pseudokernels such as the matrix hyperbolic sine, which we examine for rating graphs, authorship graphs, folksonomies, document-feature networks and other types of bipartite networks.


title: Automatic Parking Space Detection and Tracking for Underground and Indoor Environments

abstract: Even though many public parking lots are located underground and indoors, most existing automatic parking space detection and tracking methods have difficulty handling such scenarios due to severe illumination and complex obstacle conditions. To overcome this problem, this paper proposes a method that detects and tracks parking spaces in underground and indoor environments by fusing sensors already mounted on mass produced vehicles. The proposed detection method finds parking spaces based on a high-level fusion of two complementary approaches: parking slot marking-based and free space-based. Parking slots are detected by estimating parallel line pairs and free spaces are detected by recognizing the positions of parked vehicles as well as pillars. The proposed tracking method enhances the previous method by considering pillar information. Since pillars degrade parking slot tracking performance, this method estimates a pillar region and utilizes it to remove false edges and to estimate the amount of occlusion.


title: 1 Topic-Based Sentiment Analysis for the Social Web: The role of Mood and Issue-Related Words 1

abstract: General sentiment analysis for the social web has become increasingly useful for shedding light on the role of emotion in online communication and offline events in both academic research and data journalism. Nevertheless, existing general‐purpose social web sentiment analysis algorithms may not be optimal for texts focussed around specific topics. This article introduces 2 new methods, mood setting and lexicon extension, to improve the accuracy of topic‐specific lexical sentiment strength detection for the social web. Mood setting allows the topic mood to determine the default polarity for ostensibly neutral expressive text. Topic‐specific lexicon extension involves adding topic‐specific words to the default general sentiment lexicon. Experiments with 8 data sets show that both methods can improve sentiment analysis performance in corpora and are recommended when the topic focus is tightest.


title: A Theory of Universal Artificial Intelligence based on Algorithmic Complexity

abstract: Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental prior probability distribution is known. Solomono’s theory of universal induction formally solves the problem of sequence prediction for unknown prior distribution. We combine both ideas and get a parameterless theory of universal Artificial Intelligence. We give strong arguments that the resulting AI model is the most intelligent unbiased agent possible. We outline for a number of problem classes, including sequence prediction, strategic games, function minimization, reinforcement and supervised learning, how the AI model can formally solve them. The major drawback of the AI model is that it is uncomputable. To overcome this problem, we construct a modified algorithm AI tl , which is still eectively more intelligent than any other time t and space l bounded agent. The computation time of AI tl is of the order t·2 l . Other discussed topics are formal definitions of intelligence order relations, the horizon problem and relations of the AI theory to other AI approaches.


title: Comparison of IoT Constrained Devices Operating Systems: A Survey

abstract: As it refers to the ever-growing network of physical objects that feature an IP address for internet connectivity, the Internet of Things (IoT) is becoming very challenging for research community and domain. Operating systems for IoT devices play a vital role in this regard. These low-end devices can not use traditional operating system (OS) such as Windows or Linux due to stringent resource constraints. A lot of efforts have been made to design operating systems for these concerned devices. The main contribution of the paper is the comparative study of the most recent operating systems for low-end IoT devices. The comparison will focus on the major OS keys as architecture, scheduling, real-time capabilities, programming model, memory footprint, network connectivity, hardware support and energy efficiency. We highlight these OS keys values at the selected OSs: FreeRTOS, Mbed, Contiki, TinyOS and RIOT.


title: 600V LPT-CSTBT™ on advanced thin wafer technology

abstract: Electrical characteristics of the fabricated 600V class CSTBT™ with a Light Punch Through (LPT) structure on an advanced thin wafer technology are presented for the first time. The electrical characteristics of LPT-CSTBT are superior to the conventional Punch Through type (PT) one, especially in low current density regions because of the inherent lower built-in potential. Furthermore, we also have evaluated the effects of the mechanical stress on the device characteristics after soldering, utilizing a novel evaluation method with a very small size sub-chip layout. The results validate the proposed tool is useful to examine the influence of the mechanical stress on the electrical characteristics.


title: Design of a wideband bandpass filter using microstrip parallel-coupled dual-mode ring resonator

abstract: In this paper, a micostrip parallel-coupled dual-mode ring resonator is analyzed based on its equivalent transmission line circuit and the even- and odd-mode method. Closed-form formulas relating the transmission zeros and 3dB fractional bandwidth of the resonator to the circuit parameters are derived, and are verified effective by using a circuit simulator and an electromagnetic simulator. A bandpass filter is designed by using this type of dual-mode resonator, and it shows a wide passband with a 3dB fractional bandwidth of 30%, low reflection loss in the passband, and very sharp passband skirt property.


title: A Wide Dynamic Range CMOS Laser Radar Receiver With a Time-Domain Walk Error Compensation Scheme

abstract: This integrated receiver channel designed for a pulsed time-of-flight (TOF) laser rangefinder consists of a fully differential transimpedance amplifier channel and a timing discriminator. The amplitude-dependent timing walk error is compensated by measuring the width and rise time of the received pulse echo and using this information for calibration. The measured bandwidth, transimpedance and minimum detectable signal (SNR ~10) of the receiver channel are 230 MHz, $100~\text {k}\Omega $ and ${\sim } 1~\mu \text {A}$ respectively. The single-shot precision of the receiver is ~3 cm at an SNR of 13 and the measurement accuracy is ±4 mm with compensation within a dynamic range of ~1:100 000. The receiver circuit was realized in a $0.35~\mu \text {m}$ CMOS process and has a power consumption of 150 mW. The functionality of the receiver channel was verified over a temperature range of -20 °C to +50 °C.


title: Classifying Time Series Using Local Descriptors with Hybrid Sampling

abstract: Time series classification (TSC) arises in many fields and has a wide range of applications. Here, we adopt the bag-of-words (BoW) framework to classify time series. Our algorithm first samples local subsequences from time series at feature-point locations when available. It then builds local descriptors, and models their distribution by Gaussian mixture models (GMM), and at last it computes a Fisher Vector (FV) to encode each time series. The encoded FV representations of time series are readily used by existing classifiers, e.g., SVM, for training and prediction. In our work, we focus on detecting better feature points and crafting better local representations, while using existing techniques to learn codebook and encode time series. Specifically, we develop an efficient and effective peak and valley detection algorithm from real-case time series data. Subsequences are sampled from these peaks and valleys, instead of sampled randomly or uniformly as was done previously. Then, two local descriptors, Histogram of Oriented Gradients (HOG-1D) and Dynamic time warping-Multidimensional scaling (DTW-MDS), are designed to represent sampled subsequences. Both descriptors complement each other, and their fused representation is shown to be more descriptive than individual ones. We test our approach extensively on 43 UCR time series datasets, and obtain significantly improved classification accuracies over existing approaches, including NNDTW and shapelet transform.


title: Pricing Intermodal Freight Transport Services: A Cost-Plus-Pricing Strategy

abstract: This paper investigates transport services pricing problems faced by intermodal freight transport operators with fixed transport capacities in an intermodal freight transport network. We first present an optimal intermodal freight transport planning model to minimize the total transport cost. This model captures modality change phenomena, due time requirements, and the possibility to subcontract transport demands. A cost-plus-pricing strategy is proposed to determine the service price as the sum of the operational cost and the targeted profit margins of transport operators under different transport scenarios, i.e., self-transporting, subcontracting, and a combination of them. For the reference transport demand specified by a customer, a list of service packages with different due times, demand sizes, and the determined service prices will be offered to the customer. Based on the urgency of delivering containers and the prices of different service packages, the customer will make the final selection decisions. A case study is given to illustrate the planning model and our proposed pricing strategy.


GraphDB : Modeling and Querying Graphs in Databases


title: Genome-Wide Polymorphism and Comparative Analyses in the White-Tailed Deer (Odocoileus virginianus): A Model for Conservation Genomics

abstract: The white-tailed deer (Odocoileus virginianus) represents one of the most successful and widely distributed large mammal species within North America, yet very little nucleotide sequence information is available. We utilized massively parallel pyrosequencing of a reduced representation library (RRL) and a random shotgun library (RSL) to generate a complete mitochondrial genome sequence and identify a large number of putative single nucleotide polymorphisms (SNPs) distributed throughout the white-tailed deer nuclear and mitochondrial genomes. A SNP validation study designed to test specific classes of putative SNPs provides evidence for as many as 10,476 genome-wide SNPs in the current dataset. Based on cytogenetic evidence for homology between cow (Bos taurus) and white-tailed deer chromosomes, we demonstrate that a divergent genome may be used for estimating the relative distribution and density of de novo sequence contigs as well as putative SNPs for species without draft genome assemblies. Our approach demonstrates that bioinformatic tools developed for model or agriculturally important species may be leveraged to support next-generation research programs for species of biological, ecological and evolutionary importance. We also provide a functional annotation analysis for the de novo sequence contigs assembled from white-tailed deer pyrosequencing reads, a mitochondrial phylogeny involving 13,722 nucleotide positions for 10 unique species of Cervidae, and a median joining haplotype network as a putative representation of mitochondrial evolution in O. virginianus. The results of this study are expected to provide a detailed template enabling genome-wide sequence-based studies of threatened, endangered or conservationally important non-model organisms.


title: Topical Coherence in LDA-based Models through Induced Segmentation

abstract: This paper presents an LDA-based model that generates topically coherent segments within documents by jointly segmenting documents and assigning topics to their words. The coherence between topics is ensured through a copula, binding the topics associated to the words of a segment. In addition, this model relies on both document and segment specific topic distributions so as to capture fine grained differences in topic assignments. We show that the proposed model naturally encompasses other state-of-the-art LDA-based models designed for similar tasks. Furthermore, our experiments, conducted on six different publicly available datasets, show the effectiveness of our model in terms of perplexity, Normalized Pointwise Mutual Information, which captures the coherence between the generated topics, and the Micro F1 measure for text classification.


Beyond Wise et al.: Neuroleptic-induced “anhedonia” in rats: Pimozide blocks reward quality of food


title: High dimensional data clustering

abstract: Clustering in high-dimensional spaces is a recurrent problem in many domains, for example in object recognition. High-dimensional data usually live in different lowdimensional subspaces hidden in the original space. This paper presents a clustering approach which estimates the specific subspace and the intrinsic dimension of each class. Our approach adapts the Gaussian mixture model framework to high-dimensional data and estimates the parameters which best fit the data. We obtain a robust clustering method called High- Dimensional Data Clustering (HDDC). We apply HDDC to locate objects in natural images in a probabilistic framework. Experiments on a recently proposed database demonstrate the effectiveness of our clustering method for category localization.


title: Find the Conversation Killers: a Predictive Study of Thread-ending Posts

abstract: How to improve the quality of conversations in online communities has attracted considerable attention recently. Having engaged, urbane, and reactive online conversations has a critical effect on the social life of Internet users. In this study, we are particularly interested in identifying a post in a multi-party conversation that is unlikely to be further replied to, which therefore kills that thread of the conversation. For this purpose, we propose a deep learning model called the ConverNet. ConverNet is attractive due to its capability of modeling the internal structure of a long conversation and its appropriate encoding of the contextual information of the conversation, through effective integration of attention mechanisms. Empirical experiments on real-world datasets demonstrate the effectiveness of the proposal model. For the widely concerned topic, our analysis also offers implications for improving the quality and user experience of online conversations.


title: Modeling of a Balancing Robot on a Rolling Pipe

abstract: In this paper, we describe the modeling of a balancing robot. The balancing robot is a biped stepping robot, and aims to balance on a rolling pipe. The motion equations of the balancing robot and rolling pipe are formulated in order to grasp the robot motion and to design a stable robot controller for balancing on the pipe. Particularly, the calculation procedure of the contact forces is proposed, which generates between the robot foot and the pipe surface. Finally, simulation results are shown based on the formulation.


title: Mining Overlapping Communities and Inner Role Assignments through Bayesian Mixed-Membership Models of Networks with Context-Dependent Interactions

abstract: Community discovery and role assignment have been recently integrated into an unsupervised approach for the exploratory analysis of overlapping communities and inner roles in networks. However, the formation of ties in these prototypical research efforts is not truly realistic, since it does not account for a fundamental aspect of link establishment in real-world networks, i.e., the explicative reasons that cause interactions among nodes. Such reasons can be interpreted as generic requirements of nodes, that are met by other nodes and essentially pertain both to the nodes themselves and to their interaction contexts (i.e., the respective communities and roles). ::: ::: In this article, we present two new model-based machine-learning approaches, wherein community discovery and role assignment are seamlessly integrated and simultaneously performed through approximate posterior inference in Bayesian mixed-membership models of directed networks. The devised models account for the explicative reasons governing link establishment in terms of node-specific and contextual latent interaction factors. The former are inherently characteristic of nodes, while the latter are characterizations of nodes in the context of the individual communities and roles. The generative process of both models assigns nodes to communities with respective roles and connects them through directed links, which are probabilistically governed by their node-specific and contextual interaction factors. The difference between the proposed models lies in the exploitation of the contextual interaction factors. More precisely, in one model, the contextual interaction factors have the same impact on link generation. In the other model, the contextual interaction factors are weighted by the extent of involvement of the linked nodes in the respective communities and roles. ::: ::: We develop MCMC algorithms implementing approximate posterior inference and parameter estimation within our models. ::: ::: Finally, we conduct an intensive comparative experimentation, which demonstrates their superiority in community compactness and link prediction on various real-world and synthetic networks.


title: Color Psychology: Effects of Perceiving Color on Psychological Functioning in Humans

abstract: Color is a ubiquitous perceptual stimulus that is often considered in terms of aesthetics. Here we review theoretical and empirical work that looks beyond color aesthetics to the link between color and psychological functioning in humans. We begin by setting a historical context for research in this area, particularly highlighting methodological issues that hampered earlier empirical work. We proceed to overview theoretical and methodological advances during the past decade and conduct a review of emerging empirical findings. Our empirical review focuses especially on color in achievement and affiliation/attraction contexts, but it also covers work on consumer behavior as well as food and beverage evaluation and consumption. The review clearly shows that color can carry important meaning and can have an important impact on people's affect, cognition, and behavior. The literature remains at a nascent stage of development, however, and we note that considerable work on boundary conditions, moderators, and r...


title: Carbon nanotube field emission cathodes fabricated with trivalent chromium conversion coated substrates

abstract: Abstract The properties of carbon nanotube (CNT) field emission cathodes fabricated by a dip coating method with trivalent chromium conversion coated substrates are studied. Two kinds of substrates with different morphologies, one with a rough crackled surface and the other with a smooth surface, were used for making the CNT cathodes, and their I – V curves and emission patterns were evaluated. The results show that, as compared to the smooth substrate surface, the rough surface with self-assembled sub-micro-cracks on the substrate can dramatically enhance the uniformity of the emission pattern and the emission efficiency. The cathode fabricated with the crackled substrate shows good field emission properties such as high brightness, good uniformity, a low turn-on field (0.86 V/μm) and a high current density of 10 mA/cm 2 at 2.5 V/μm.