title: Model-based fault detection and diagnosis: status and applications

abstract: Abstract For the improvement of reliability, safety and efficiency advanced methods of supervision, fault-detection and fault diagnosis become increasingly important for many technical processes. This holds especially for safety related processes like aircraft, trains, automobiles, power plants and chemical plants. The classical approaches are limit or trend checking of some measurable output variables. Because they do not give a deeper insight and usually do not allow a fault diagnosis, model-based methods of fault-detection were developed by using input and output signals and applying dynamic process models. These methods are based, e.g., on parameter estimation, parity equations or state observers. Also signal model approaches were developed. The goal is to generate several symptoms indicating the difference between nominal and faulty status. Based on different symptoms fault diagnosis procedures follow, determining the fault by applying classification or inference methods. This contribution gives a short introduction into the field and shows some applications for an actuator, a passenger car and a combustion engine.


title: Building and animating user-specific volumetric face rigs

abstract: Currently, the two main approaches to realistic facial animation are 1) blendshape models and 2) physics-based simulation. Blendshapes are fast and directly controllable, but it is not easy to incorporate features such as dynamics, collision resolution, or incompressibility of the flesh. Physics-based methods can deliver these effects automatically, but modeling of muscles, bones, and other anatomical features of the face is difficult, and direct control over the resulting shape is lost. We propose a method that combines the benefits of blendshapes with the advantages of physics-based simulation. We acquire 3D scans of a given actor with various facial expressions and compute a set of volumetric blendshapes that are compatible with physics-based simulation, while accurately matching the input scans. Furthermore, our volumetric blendshapes are driven by the same weights as traditional blendshapes, which many users are familiar with. Our final facial rig is capable of delivering physics-based effects such as dynamics and secondary motion, collision response, and volume preservation without the burden of detailed anatomical modeling.


title: MIMO Signal Processing in Offset-QAM Based Filter Bank Multicarrier Systems

abstract: Next-generation communication systems have to comply with very strict requirements for increased flexibility in heterogeneous environments, high spectral efficiency, and agility of carrier aggregation. This fact motivates research in advanced multicarrier modulation (MCM) schemes, such as filter bank-based multicarrier (FBMC) modulation. This paper focuses on the offset quadrature amplitude modulation (OQAM)-based FBMC variant, known as FBMC/OQAM, which presents outstanding spectral efficiency and confinement in a number of channels and applications. Its special nature, however, generates a number of new signal processing challenges that are not present in other MCM schemes, notably, in orthogonal-frequency-division multiplexing (OFDM). In multiple-input multiple-output (MIMO) architectures, which are expected to play a primary role in future communication systems, these challenges are intensified, creating new interesting research problems and calling for new ideas and methods that are adapted to the particularities of the MIMO-FBMC/OQAM system. The goal of this paper is to focus on these signal processing problems and provide a concise yet comprehensive overview of the recent advances in this area. Open problems and associated directions for future research are also discussed.


title: On the Use of Data Mining Tools for Data Preparation in Classification Problems

abstract: The data preparation phase is a critical step in the KDD (Knowledge Discovery in Databases) process. This phase is crucial for a good data mining result because if data is not correctly prepared, all the next phases of the process are compromised. DMPML is a framework that stores preprocessed data for different data mining algorithms in an XML document and retrieves the correct codification by the use of an XSLT document according to the needs of the data mining algorithm. This paper presents a comparison between DMPML and three data mining applications (Weka, Rapid Miner, and KNIME) that implement the directed graph approach, concerning the time spent to create and execute the data preparation tasks for two data mining algorithms. The tests were executed using different types of data sets: numerical, categorical, and mixed. We observed that the scheme used by DMPML can simplify the usage of different data mining algorithms and significantly reduce the time spent creating the data preparation tasks.


title: Hyper-graph matching via reweighted random walks

abstract: Establishing correspondences between two feature sets is a fundamental issue in computer vision, pattern recognition, and machine learning. This problem can be well formulated as graph matching in which nodes represent feature points while edges describe pairwise relations between feature points. Recently, several researches have tried to embed higher-order relations of feature points by hyper-graph matching formulations. In this paper, we generalize the previous hyper-graph matching formulations to cover relations of features in arbitrary orders, and propose a novel state-of-the-art algorithm by reinterpreting the random walk concept on the hyper-graph in a probabilistic manner. Adopting personalized jumps with a reweighting scheme, the algorithm effectively reflects the one-to-one matching constraints during the random walk process. Comparative experiments on synthetic data and real images show that the proposed method clearly outperforms existing algorithms especially in the presence of noise and outliers.


title: Internet of things security: challenges and perspectives

abstract: No one can deny that the Internet of Things (IOT) will revolutionize our daily thanks to its many benefits in order to improve and simplify people's lives. Us any new technology the internet of things has a number of problems that prevents it to reach its full potential. Security is the major issues that face the IOT. In this article, we discuss about the Internet of things and its security challenges. We also discuss some major attacks and solutions that can be implemented against these attacks. Finally, the paper presents future directions for securing the IoT.


title: A practical soft type system for Scheme

abstract: Soft typing is a generalization of static type checking that accommodates both dynamic typing and static typing in one framework. A soft type checker infers types for identifiers and inserts explicit run-time checks to transform untypable programs into typable form. Soft Scheme is a practical soft type system for R4RS Scheme. The type checker uses a representation for types that is expressive, easy to interpret, and supports efficient type inference. Soft Scheme supports all of R4RS Scheme, including uncurried procedures of fixed and variable arity, assignment, and continuations.


title: The repurposed social brain

abstract: Human social intelligence depends on a diverse array of perceptual, cognitive, and motivational capacities. Some of these capacities depend on neural systems that may have evolved through modification of ancestral systems with non-social or more limited social functions (evolutionary repurposing). Social intelligence, in turn, enables new forms of repurposing within the lifetime of an individual (cultural and instrumental repurposing), which entail innovating over and exploiting pre-existing circuitry to meet problems our brains did not evolve to solve. Considering these repurposing processes can provide insight into the computations that brain regions contribute to social information processing, generate testable predictions that usefully constrain social neuroscience theory, and reveal biologically imposed constraints on cultural inventions and our ability to respond beneficially to contemporary challenges.


title: Minimum classification error rate methods for speech recognition

abstract: A critical component in the pattern matching approach to speech recognition is the training algorithm, which aims at producing typical (reference) patterns or models for accurate pattern comparison. In this paper, we discuss the issue of speech recognizer training from a broad perspective with root in the classical Bayes decision theory. We differentiate the method of classifier design by way of distribution estimation and the discriminative method of minimizing classification error rate based on the fact that in many realistic applications, such as speech recognition, the real signal distribution form is rarely known precisely. We argue that traditional methods relying on distribution estimation are suboptimal when the assumed distribution form is not the true one, and that "optimality" in distribution estimation does not automatically translate into "optimality" in classifier design. We compare the two different methods in the context of hidden Markov modeling for speech recognition. We show the superiority of the minimum classification error (MCE) method over the distribution estimation method by providing the results of several key speech recognition experiments. In general, the MCE method provides a significant reduction of recognition error rate.


title: Cluster validity functions for categorical data: a solution-space perspective

abstract: For categorical data, there are three widely-used internal validity functions: the $$k$$k-modes objective function, the category utility function and the information entropy function, which are defined based on within-cluster information only. Many clustering algorithms have been developed to use them as objective functions and find their optimal solutions. In this paper, we study the generalization, effectiveness and normalization of the three validity functions from a solution-space perspective. First, we present a generalized validity function for categorical data. Based on it, we analyze the generality and difference of the three validity functions in the solution space. Furthermore, we address the problem whether the between-cluster information is ignored when these validity functions are used to evaluate clustering results. To the end, we analyze the upper and lower bounds of the three validity functions for a given data set, which can help us estimate the clustering difficulty on a data set and compare the performance of a clustering algorithm on different data sets.


title: Why can LDA be performed in PCA transformed space?

abstract: Abstract PCA plus LDA is a popular framework for linear discriminant analysis (LDA) in high dimensional and singular case. In this paper, we focus on building a theoretical foundation for this framework. Moreover, we point out the weakness of the previous LDA based methods, and suggest a complete PCA plus LDA algorithm. Experimental results on ORL face image database indicate that the proposed method is more effective than the previous ones.


title: On the Likes and Dislikes of YouTube's Educational Videos: A Quantitative Study

abstract: As major product of information technology, YouTube is a ubiquitous source for education, also in the field of information technology. Learners, however, are facing the increasing problem of finding appropriate videos on YouTube efficiently. Users' rating in terms of Likes and Dislikes could provide a starting point. However, it is unclear what the number of Likes and Dislikes reveal about the video. This paper tries to create links between different video features and users' rating of YouTube's educational content. For this purpose, 300 educational videos were collected and analyzed and regression models were established that describe the number of Likes per view and the number of Dislikes per view as functions of different video features and production styles. Results show that the number of Likes per view can be predicted more reliably than the number of Dislikes per view. The number of Likes per view increases with higher video resolution and higher talking rate (words per second), and when the instructor or tutor speaks English as a native language. Videos using explanations on paper or whiteboard as well as videos that use more than one style attract more Likes per view. In contrast, the model that describes the number of Dislikes per view has a low adjusted R-squared and the contribution of its significant variables is rather difficult to interpret. This suggests that further research is required to understand users' behavior in terms of disliking an educational video.


title: A Novel EA-based Memetic Approach for Efficiently Mapping Complex Fitness Landscapes

abstract: Recent work in computational structural biology focuses on modeling intrinsically dynamic proteins important to human biology and health. The energy landscapes of these proteins are rich in minima that correspond to alternative structures with which a dynamic protein binds to molecular partners in the cell. On such landscapes, evolutionary algorithms that switch their objective from classic optimization to mapping are more informative of protein structure function relationships. While techniques for mapping energy landscapes have been developed in computational chemistry and physics, protein landscapes are more difficult for mapping due to their high dimensionality and multimodality. In this paper, we describe a memetic evolutionary algorithm that is capable of efficiently mapping complex landscapes. In conjunction with a hall of fame mechanism, the algorithm makes use of a novel, lineage- and neighborhood-aware local search procedure or better exploration and mapping of complex landscapes. We evaluate the algorithm on several benchmark problems and demonstrate the superiority of the novel local search mechanism. In addition, we illustrate its effectiveness in mapping the complex multimodal landscape of an intrinsically dynamic protein important to human health.


title: Fast in-memory transaction processing using RDMA and HTM

abstract: We present DrTM, a fast in-memory transaction processing system that exploits advanced hardware features (i.e., RDMA and HTM) to improve latency and throughput by over one order of magnitude compared to state-of-the-art distributed transaction systems. The high performance of DrTM are enabled by mostly offloading concurrency control within a local machine into HTM and leveraging the strong consistency between RDMA and HTM to ensure serializability among concurrent transactions across machines. We further build an efficient hash table for DrTM by leveraging HTM and RDMA to simplify the design and notably improve the performance. We describe how DrTM supports common database features like read-only transactions and logging for durability. Evaluation using typical OLTP workloads including TPC-C and SmallBank show that DrTM scales well on a 6-node cluster and achieves over 5.52 and 138 million transactions per second for TPC-C and SmallBank Respectively. This number outperforms a state-of-the-art distributed transaction system (namely Calvin) by at least 17.9X for TPC-C.


title: The moderating effects of performance ambiguity on the relationship between self-efficacy and performance.

abstract: Recent research (e.g., Vancouver & Kendall, 2006; Vancouver, Thompson, Tischner, & Putka, 2002; Vancouver, Thompson, & Williams, 2001) has challenged the conventional view of self-efficacy as a positive influence on performance, finding a negative within-person relationship between self-efficacy and performance. In the current study, performance ambiguity is examined as a potential boundary condition for this negative self-efficacy effect. As hypothesized, self-efficacy was negatively related to subsequent performance under conditions of high ambiguity but was positively related to performance when performance ambiguity was low. Additionally, the study evaluates key mediating processes underlying the relationship between self-efficacy and performance, finding support for the role of performance perceptions and effort allocation. The results of this study help to establish the scope of the phenomenon and suggest potential means of inhibiting the negative self-efficacy effects.


A Survey of Knowledge Representation and Retrieval for Learning in Service Robotics


title: Fast Wavenet Generation Algorithm

abstract: This paper presents an efficient implementation of the Wavenet generation process called Fast Wavenet. Compared to a naive implementation that has complexity O(2^L) (L denotes the number of layers in the network), our proposed approach removes redundant convolution operations by caching previous calculations, thereby reducing the complexity to O(L) time. Timing experiments show significant advantages of our fast implementation over a naive one. While this method is presented for Wavenet, the same scheme can be applied anytime one wants to perform autoregressive generation or online prediction using a model with dilated convolution layers. The code for our method is publicly available.


Fundamentals of MOSFET and IGBT Gate Driver Circuits


title: Effectiveness of Manual Therapy Combined With Physical Therapy in Treatment of Patellofemoral Pain Syndrome: Systematic Review

abstract: OBJECTIVES ::: The purpose of this study was to conduct a review of randomized controlled trials (RCTs) to determine the treatment effectiveness of the combination of manual therapy (MT) with other physical therapy techniques. ::: ::: ::: METHODS ::: Systematic searches of scientific literature were undertaken on PubMed and the Cochrane Library (2004-2014). The following terms were used: "patellofemoral pain syndrome," "physical therapy," "manual therapy," and "manipulation." RCTs that studied adults diagnosed with patellofemoral pain syndrome (PFPS) treated by MT and physical therapy approaches were included. The quality of the studies was assessed by the Jadad Scale. ::: ::: ::: RESULTS ::: Five RCTs with an acceptable methodological quality (Jadad ≥ 3) were selected. The studies indicated that MT combined with physical therapy has some effect on reducing pain and improving function in PFPS, especially when applied on the full kinetic chain and when strengthening hip and knee muscles. ::: ::: ::: CONCLUSIONS ::: The different combinations of MT and physical therapy programs analyzed in this review suggest that giving more emphasis to proximal stabilization and full kinetic chain treatments in PFPS will help better alleviation of symptoms.


“Body coupled FingerRing”: wireless wearable keyboard


title: A comparison of RDB-to-RDF mapping languages

abstract: Mapping Relational Databases (RDB) to RDF is an active field of research. The majority of data on the current Web is stored in RDBs. Therefore, bridging the conceptual gap between the relational model and RDF is needed to make the data available on the Semantic Web. In addition, recent research has shown that Semantic Web technologies are useful beyond the Web, especially if data from different sources has to be exchanged or integrated. Many mapping languages and approaches were explored leading to the ongoing standardization effort of the World Wide Web Consortium (W3C) carried out in the RDB2RDF Working Group (WG). The goal and contribution of this paper is to provide a feature-based comparison of the state-of-the-art RDB-to-RDF mapping languages. It should act as a guide in selecting a RDB-to-RDF mapping language for a given application scenario and its requirements w.r.t. mapping features. Our comparison framework is based on use cases and requirements for mapping RDBs to RDF as identified by the RDB2RDF WG. We apply this comparison framework to the state-of-the-art RDB-to-RDF mapping languages and report the findings in this paper. As a result, our classification proposes four categories of mapping languages: direct mapping, read-only general-purpose mapping, read-write general-purpose mapping, and special-purpose mapping. We further provide recommendations for selecting a mapping language.


title: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization : NSGA-II

abstract: Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) O(MN3) computational complexity (where M is the number of objectives and N is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing parameter. In this paper, we suggest a non-dominated sorting based multi-objective evolutionary algorithm (we called it the Non-dominated Sorting GA-II or NSGA-II) which alleviates all the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN2) computational complexity is presented. Second, a selection operator is presented which creates a mating pool by combining the parent and child populations and selecting the best (with respect to fitness and spread) N solutions. Simulation results on five difficult test problems show that the proposed NSGA-II, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to PAES and SPEA--two other elitist multi-objective EAs which pay special attention towards creating a diverse Pareto-optimal front. Because of NSGA-II's low computational requirements, elitist approach, and parameter-less sharing approach, NSGA-II should find increasing applications in the years to come.


title: Vision Processing for Realtime 3-D Data Acquisition Based on Coded Structured Light

abstract: Structured light vision systems have been successfully used for accurate measurement of 3D surfaces in computer vision. However, their applications are mainly limited to scanning stationary objects so far since tens of images have to be captured for recovering one 3D scene. This paper presents an idea for real-time acquisition of 3D surface data by a specially coded vision system. To achieve 3D measurement for a dynamic scene, the data acquisition must be performed with only a single image. A principle of uniquely color-encoded pattern projection is proposed to design a color matrix for improving the reconstruction efficiency. The matrix is produced by a special code sequence and a number of state transitions. A color projector is controlled by a computer to generate the desired color patterns in the scene. The unique indexing of the light codes is crucial here for color projection since it is essential that each light grid be uniquely identified by incorporating local neighborhoods so that 3D reconstruction can be performed with only local analysis of a single image. A scheme is presented to describe such a vision processing method for fast 3D data acquisition. Practical experimental performance is provided to analyze the efficiency of the proposed methods.


title: On Teams, Teamwork, and Team Performance: Discoveries and Developments

abstract: Objective: We highlight some of the key discoveries and developments in the area of team performance over the past 50 years, especially as reflected in the pages of Human Factors.Background: Teams increasingly have become a way of life in many organizations, and research has kept up with the pace. Method: We have characterized progress in the field in terms of eight discoveries and five challenges. Results: Discoveries pertain to the importance of shared cognition, the measurement of shared cognition, advances in team training, the use of synthetic task environments for research, factors influencing team effectiveness, models of team effectiveness, a multidisciplinary perspective, and training and technological interventions designed to improve team effectiveness. Challenges that are faced in the coming decades include an increased emphasis on team cognition; reconfigurable, adaptive teams; multicultural influences; and the need for naturalistic study and better measurement. Conclusion: Work in human fact...


title: A mobile-bearing knee prosthesis can reduce strain at the proximal tibia.

abstract: Mobile and fixed-bearing knee prostheses are likely to generate distinct strain gradients in the proximal tibia. The resulting strain distribution in the proximal tibia governs bone remodeling and affects implant integration and stability. We determined the effects of fixed and mobile-bearing total knee prostheses on strain distribution at the proximal tibia. This mobile-bearing prosthesis was evaluated in cadaveric specimens under axial and torsional loading. Strain on the proximal tibial cortex was measured with rosette strain gages and an optical full-field strain acquisition system. Tibial torsion in response to combined axial and torsional loading was documented. There was no difference in cortex strain between the fixed and the mobile-bearing prostheses under 1.5 kN axial loading. Superimposing 10° tibial internal rotation induced 22% less compressive strain in the mobile-bearing prosthesis compared with the fixed-bearing prosthesis. Under 10° tibial external rotation, the mobile-bearing prosthesis induced 33% less compressive strain than the fixed-bearing prosthesis. Optically acquired strain fields showed peak compressive strain at the anteromedial aspect 30 mm below the joint line. The mobile-bearing prosthesis reduced torque in the proximal tibia during knee rotation by 68-73% compared with the fixed-bearing prosthesis. Our data suggest that the particular mobile-bearing prosthesis tested potentially reduces elevated strain levels in the proximal tibia.


title: Fast convolutional sparse coding using matrix inversion lemma

abstract: Convolutional sparse coding is an interesting alternative to standard sparse coding in modeling shift-invariant signals, giving impressive results for example in unsupervised learning of visual features. In state-of-the-art methods, the most time-consuming parts include inversion of a linear operator related to convolution. In this article we show how these inversions can be computed non-iteratively in the Fourier domain using the matrix inversion lemma. This greatly speeds up computation and makes convolutional sparse coding computationally feasible even for large problems. The algorithm is derived in three variants, one of them especially suitable for parallel implementation. We demonstrate algorithms on two-dimensional image data but all results hold for signals of arbitrary dimension. Interesting alternative to sparse coding for shift-invariant signals (images, audio).New fast algorithm, which makes sparse coding feasible even for large problems.3 versions of the algorithm, one of them designed for parallel implementation.Convolution kernels can be learned at several scales simultaneously.Algorithms are demonstrated on images but can be used for arbitrary signals.


title: A Latent Variable Model for Geographic Lexical Variation

abstract: The rapid growth of geotagged social media raises new computational possibilities for investigating geographic linguistic variation. In this paper, we present a multi-level generative model that reasons jointly about latent topics and geographical regions. High-level topics such as "sports" or "entertainment" are rendered differently in each geographic region, revealing topic-specific regional distinctions. Applied to a new dataset of geotagged microblogs, our model recovers coherent topics and their regional variants, while identifying geographic areas of linguistic consistency. The model also enables prediction of an author's geographic location from raw text, outperforming both text regression and supervised topic models.


title: A Case Study on Modeling of Complex Event Processing in Enterprise Architecture.

abstract: Over the decades, Enterprise Architecture (EA) has been researched to supply all the necessary components for enterprise system modelling including taxonomies, meta-models, architecture development methods, and modelling tools. The main benefits of EA are the knowledge infrastructure for analysis and reporting by all stakeholders and the possibility of designing new conditions in an organized manner. However, EA now faces a big challenge with the growing dynamic of market demands and the rapid changes in business environments, which requires agile system response and self-evolutionary behaviour to support quick decision-making. In technology side, there are already matured, promising paradigm to tackle this challenges, which is Complex Event Processing, however it has not been fully dealt with EA. No standardized method for applying event driven approach in business and IT systems modelling has been developed yet. The paper investigates a possibility of integration of EDA by adding ev ent process layer between business operating and business process layers in the EA stack. Complex event patterns are identified and an event meta-model extending ArchiMate is also proposed to integrate complex event modelling to the business modelling. Using a case study, we modeled a business scenario with event driven approach.


title: Wideband 5 W Ka-Band GaAs Power Amplifier

abstract: This letter presents a Ka-Band 0.1- $\mu\text{m}$ GaAs pHEMT Power Amplifier with broad bandwidth. The isolating backvia wall (IBVW) has been proposed to improve the stability and performance. Over the frequency band of 31–40 GHz, implemented PA delivers 5 W saturated output power, 28% maximum power added efficiency (PAE) and 20 dB maximum small-signal gain. The chip size of the PA is 11.9 mm2. To the best of authors knowledge, the presented PA demonstrates widest bandwidth in Ka-band GaAs PAs while maintaining compact size.


title: Interactive problem solving via algorithm visualization

abstract: COMIND is a tool for conceptual design of industrial products. It helps designers define and evaluate the initial design space by using search algorithms to generate sets of feasible solutions. Two algorithm visualization techniques, Kaleidoscope and Lattice, and one visualization of n-dimensional data, MAP, are used to externalize the machine's problem solving strategies and the tradeoffs as a result of using these strategies. After a short training period, users are able to discover tactics to explore design space effectively, evaluate new design solutions, and learn important relationships among design criteria, search speed and solution quality. We thus propose that visualization can serve as a tool for interactive intelligence, ie., human-machine collaboration for solving complex problems.


title: Multi-Exemplar Affinity Propagation

abstract: The affinity propagation (AP) clustering algorithm has received much attention in the past few years. AP is appealing because it is efficient, insensitive to initialization, and it produces clusters at a lower error rate than other exemplar-based methods. However, its single-exemplar model becomes inadequate when applied to model multisubclasses in some situations such as scene analysis and character recognition. To remedy this deficiency, we have extended the single-exemplar model to a multi-exemplar one to create a new multi-exemplar affinity propagation (MEAP) algorithm. This new model automatically determines the number of exemplars in each cluster associated with a super exemplar to approximate the subclasses in the category. Solving the model is NP--hard and we tackle it with the max-sum belief propagation to produce neighborhood maximum clusters, with no need to specify beforehand the number of clusters, multi-exemplars, and superexemplars. Also, utilizing the sparsity in the data, we are able to reduce substantially the computational time and storage. Experimental studies have shown MEAP's significant improvements over other algorithms on unsupervised image categorization and the clustering of handwritten digits.


title: Formal concept analysis: current trends and directions

abstract: Formalization of human thinking helps in fostering the process of learning by giving an explicit representation to human thoughts. Formal Concept Analysis (FCA) finds it's core here. It considers a "concept" as a formal unit of human thought. A concept is represented as a set of inter related objects called the extent and the set of the properties of these objects, called the intent. Making use of the mathematical principles of Lattice Theory and Map Theory of Abstract Algebra, a set of tools and algorithms have been developed in FCA. These helps us to analyze and represent any context as a relation between it's extent and intent. Concepts drawn from the subsets of the extent and intent can be organized in the form of a lattice giving a subsumption hierarchy. Such concept lattices could be maintained by different operations on the lattice like scaling, pruning, navigating etc. A host of applications and software have been developed over the years which serves the usage of FCA tools and processes for specific purposes in various fields. This paper reviews the theoretical foundation, research and applications of FCA in different areas. The paper projects current trends in FCA and concludes with a discussion on open issues and limitations of FCA.


title: Transaction processing on confidential data using cipherbase

abstract: Cipherbase is a comprehensive database system that provides strong end-to-end data confidentiality through encryption. Cipherbase is based on a novel architecture that combines an industrial strength database engine (SQL Server) with lightweight processing over encrypted data that is performed in secure hardware. The overall architecture provides significant benefits over the state-of-the-art in terms of security, performance, and functionality. This paper presents a prototype of Cipherbase that uses FPGAs to provide secure processing and describes the system engineering details implemented to achieve competitive performance for transactional workloads. This includes hardware-software co-design issues (e.g. how to best offer parallelism), optimizations to hide the latency between the secure hardware and the main system, and techniques to cope with space inefficiencies. All these optimizations were carefully designed not to affect end-to-end data confidentiality. Our experiments with the TPC-C benchmark show that in the worst case when all data are strongly encrypted, Cipherbase achieves 40% of the throughput of plaintext SQL Server. In more realistic cases, if only critical data such as customer names are encrypted, the Cipherbase throughput is more than 90% of plaintext SQL Server.


title: Subspace histograms for outlier detection in linear time

abstract: Outlier detection algorithms are often computationally intensive because of their need to score each point in the data. Even simple distance-based algorithms have quadratic complexity. High-dimensional outlier detection algorithms such as subspace methods are often even more computationally intensive because of their need to explore different subspaces of the data. In this paper, we propose an exceedingly simple subspace outlier detection algorithm, which can be implemented in a few lines of code, and whose complexity is linear in the size of the data set and the space requirement is constant. We show that this outlier detection algorithm is much faster than both conventional and high-dimensional algorithms and also provides more accurate results. The approach uses randomized hashing to score data points and has a neat subspace interpretation. We provide a visual representation of this interpretability in terms of outlier sensitivity histograms. Furthermore, the approach can be easily generalized to data streams, where it provides an efficient approach to discover outliers in real time. We present experimental results showing the effectiveness of the approach over other state-of-the-art methods.


title: Designing a wellness self-management tool for older adults: results from a field trial of YourWellness

abstract: It is recognized that empowering individuals to manage their own health and wellbeing will result in more cost-effective healthcare systems, improved health outcomes and will encourage healthy individuals to remain that way. With the advent of the quantified-self movement in recent years, there has been an increase in technology applications supporting wellness self-management. Such applications allow people to self-track and self-report, with many providing feedback. However, little research in this area has examined how best to support older adults in health self-management. This paper reports findings from a 5-month home deployment of YourWellness -- an application that supports older adults in self-reporting on their wellbeing and provides feedback to promote positive wellbeing management. Our findings contribute to a greater understanding of older adults' attitudes and behaviours in relation to wellbeing self-management that can facilitate the creation of new, personalized health and wellbeing interventions for this population.


title: Stable-Aware Evolutionary Routing Protocol for Wireless Sensor Networks

abstract: In real life scenario for wireless sensor networks (WSNs), energy heterogeneity among the sensor nodes due to uneven terrain, connectivity failure, and packet dropping is a crucial factor that triggered the race for developing robust and reliable routing protocols. Prolonging the time interval before the death of the first sensor node, viz. the stability period, is critical for many applications where the feedback from the WSN must be reliable. Although Low Energy Adaptive Clustering Hierarchy (LEACH) and LEACH-like protocols are fundamental and popular clustering protocols to manage the system's energy and thus to prolong the lifespan of the network, they assume a near to a perfect energy homogeneous system where a node failure, drainage and re-energizing are typically not considered. More recent protocols like Stable Election Protocol (SEP) considers the reverse, i.e., energy heterogeneity, and properly utilizes the extra energy to guarantee a stable and reliable performance of the network system. While paradigms of computational intelligence such as evolutionary algorithms (EAs) have attracted significant attention in recent years to address various WSN's challenges such as nodes deployment and localization, data fusion and aggregation, security and routing, they did not (to the best of our knowledge) explore the possibility of maintaining heterogeneous-aware energy consumption to guarantee a reliable and robust routing protocol design. By this, a new protocol named stable-aware evolutionary routing protocol (SAERP), is proposed in this paper to ensure maximum stability and minimum instability periods for both homogeneous/heterogeneous WSNs. SAERP introduces an evolutionary modeling, where the cluster head election probability becomes more efficient, to well maintain balanced energy consumption in both energy homogeneous and heterogeneous settings. The performance of SAERP over simulation for 90 WSNs is evaluated and compared to well known LEACH and SEP protocols. We found that SAERP is more robust and always ensures longer stability period and shorter instability period.


title: Trainable classifier-fusion schemes: An application to pedestrian detection

abstract: This work proposes a novel classifier-fusion scheme using learning algorithms, i.e. syntactic models, instead of the usual Bayesian or heuristic rules. Moreover, this paper complements the previous comparative studies on DaimlerChrysler Automotive Dataset, offering a set of complementary experiments using feature extractor and classifier combinations. The experimental results provide evidence of the effectiveness of our methods regarding false positive rate, AUC, and accuracy, which reached 96.67%.


title: Underground Wireless Communication Using Magnetic Induction

abstract: Underground is a challenging environment for wireless communication since the propagation medium is no longer air but soil, rock and water. The well established wireless communication techniques using electromagnetic (EM) waves do not work well in this environment due to three problems: high path loss, dynamic channel condition and large antenna size. New techniques using magnetic induction (MI) can solve two of the three problems (dynamic channel condition and large antenna size), but may still cause even higher path loss. In this paper, a complete characterization of the underground MI communication channel is provided. Based on the channel model, the MI waveguide technique for communication is developed in order to reduce the MI path loss. The performance of the traditional EM wave systems, the current MI systems and our improved MI waveguide system are quantitatively compared. The results reveal that our MI waveguide system has much lower path loss than the other two cases for any channel conditions.


title: Comparison of continuous thoracic epidural analgesia with bilateral erector spinae plane block for perioperative pain management in cardiac surgery

abstract: Objective ::: Continuous thoracic epidural analgesia (TEA) is compared with erector spinae plane (ESP) block for the perioperative pain management in patients undergoing cardiac surgery for the quality of analgesia, incentive spirometry, ventilator duration, and intensive care unit (ICU) duration. ::: ::: ::: Methodology ::: A prospective, randomized comparative clinical study was conducted. A total of 50 patients were enrolled, who were randomized to either Group A: TEA (n = 25) or Group B: ESP block (n = 25). Visual analog scale (VAS) was recorded in both the groups during rest and cough at the various time intervals postextubation. Both the groups were also compared for incentive spirometry, ventilator, and ICU duration. Statistical analysis was performed using the independent Student's t-test. A value of P < 0.05 was considered statistically significant. ::: ::: ::: Results: C ::: omparable VAS scores were revealed at 0 h, 3 h, 6 h, and 12 h (P > 0.05) at rest and during cough in both the groups. Group A had a statistically significant VAS score than Group B (P ≤ 0.05) at 24 h, 36 h, and 48 h but mean VAS in either of the Group was ≤4 both at rest and during cough. Incentive spirometry, ventilator, and ICU duration were comparable between the groups. ::: ::: ::: Conclusion ::: ESP block is easy to perform and can serve as a promising alternative to TEA in optimal perioperative pain management in cardiac surgery.


title: Predictions, perception, and a sense of self

abstract: In recent years there has been a paradigm shift in theoretical neuroscience in which the brain—as a passive processor of sensory information—is now considered an active organ of inference, generating predictions and hypotheses about the causes of its sensations. In this commentary, we try to convey the basic ideas behind this perspective, describe their neurophysiologic underpinnings, and highlight the potential importance of this formulation for clinical neuroscience. The formalism it provides—and the implementation of active inference in the brain—may have the potential to reveal aspects of functional neuroanatomy that are compromised in conditions ranging from Parkinson disease to schizophrenia. In particular, many neurologic and neuropsychiatric conditions may be understandable in terms of a failure to modulate the postsynaptic gain of neuronal populations reporting prediction errors during action and perception. From the perspective of the predictive brain, this represents a failure to encode the precision of—or confidence in—sensory information. We propose that the predictive or inferential perspective on brain function offers novel insights into brain diseases.


title: Sensitivity of fNIRS to cognitive state and load

abstract: Functional near-infrared spectroscopy (fNIRS) is an emerging low-cost noninvasive neuroimaging technique that measures cortical bloodflow. While fNIRS has gained interest as a potential alternative to fMRI for use with clinical and pediatric populations, it remains unclear whether fNIRS has the necessary sensitivity to serve as a replacement for fMRI. The present study set out to examine whether fNIRS has the sensitivity to detect linear changes in activation and functional connectivity in response to cognitive load, and functional connectivity changes when transitioning from a task-free resting state to a task. Sixteen young adult subjects were scanned with a continuous-wave fNIRS system during a 10-minute resting-state scan followed by a letter n-back task with three load conditions. Five optical probes were placed over frontal and parietal cortices, covering bilateral dorsolateral PFC (dlPFC), bilateral ventrolateral PFC (vlPFC), frontopolar cortex (FP), and bilateral parietal cortex. Activation was found to scale linearly with working memory load in bilateral prefrontal cortex. Functional connectivity increased with increasing n-back loads for fronto-parietal, interhemispheric dlPFC, and local connections. Functional connectivity differed between the resting state scan and the n-back scan, with fronto-parietal connectivity greater during the n-back, and interhemispheric vlPFC connectivity greater during rest. These results demonstrate that fNIRS is sensitive to both cognitive load and state, suggesting that fNIRS is well-suited to explore the full complement of neuroimaging research questions and will serve as a viable alternative to fMRI.


title: Removing Rain from Single Images via a Deep Detail Network

abstract: We propose a new deep network architecture for removing rain streaks from individual images based on the deep convolutional neural network (CNN). Inspired by the deep residual network (ResNet) that simplifies the learning process by changing the mapping form, we propose a deep detail network to directly reduce the mapping range from input to output, which makes the learning process easier. To further improve the de-rained result, we use a priori image domain knowledge by focusing on high frequency detail during training, which removes background interference and focuses the model on the structure of rain in images. This demonstrates that a deep architecture not only has benefits for high-level vision tasks but also can be used to solve low-level imaging problems. Though we train the network on synthetic data, we find that the learned network generalizes well to real-world test images. Experiments show that the proposed method significantly outperforms state-of-the-art methods on both synthetic and real-world images in terms of both qualitative and quantitative measures. We discuss applications of this structure to denoising and JPEG artifact reduction at the end of the paper.


title: Induction of galectin-1 by TLR-dependent PI3K activation enhances epithelial-mesenchymal transition of metastatic ovarian cancer cells

abstract: The expression of different toll-like receptors (TLRs) on tumor cells has been associated with disease aggressiveness, treatment resistance, and poor prognosis. The phosphatidylinositol 3-kinase (PI3K)/AKT pathway is considered critical for cancer cell survival and proliferation. Thus, we investigated the effect of TLR-stimulated PI3K activation on the epithelial-to-mesenchymal transition (EMT) of primary (Caov-3) and metastatic (SK‑OV‑3) epithelial ovarian cancer cell lines in this study. TLR engagement with various ligands promoted the expression of class IA PI3K (p110α, p110β, and p110δ) and increased the expression of mesenchymal markers (N-cadherin, Slug, Vimentin, Snail, α-SMA, and TCF) in SK‑OV‑3 cells. The migratory activity and secretion of EMT-related cytokines of SK‑OV‑3 were significantly higher compared to those of Caov-3 after activation with TLR agonist. Although the invasive capacity and production of EMT-related cytokines of LPS-stimulated SK‑OV‑3 cells were significantly suppressed by all pharmacological inhibitors of the p110 isoform, the Syk/Src-dependent p110β isoform prominently attenuated migration activity. In contrast, the production of IL-10 and galectin-1 was mainly affected by the p110δ isoform. Gene silencing of TLR4 and galectin-1 with siRNA decreased the expression of matrix metalloproteinase-2 (MMP2) and MMP9 and reduced mesenchymal markers in LPS-treated SK‑OV‑3 cells. This study demonstrated that TLR-mediated PI3K activation modulated the invasion and metastasis of ovarian cancer through the production of galectin-1, suggesting that inhibition of the p110 isoform is a promising therapeutic approach against metastatic ovarian cancer.


title: Short text classification in twitter to improve information filtering

abstract: In microblogging services such as Twitter, the users may become overwhelmed by the raw data. One solution to this problem is the classification of short text messages. As short texts do not provide sufficient word occurrences, traditional classification methods such as "Bag-Of-Words" have limitations. To address this problem, we propose to use a small set of domain-specific features extracted from the author's profile and text. The proposed approach effectively classifies the text to a predefined set of generic classes such as News, Events, Opinions, Deals, and Private Messages.


title: ASCERTAIN: Emotion and Personality Recognition Using Commercial Sensors

abstract: We present ASCERTAIN—a multimodal databa AS e for impli C it p ER sonali T y and A ffect recognit I o N using commercial physiological sensors. To our knowledge, ASCERTAIN is the first database to connect personality traits and emotional states via physiological responses . ASCERTAIN contains big-five personality scales and emotional self-ratings of 58 users along with their Electroencephalogram (EEG), Electrocardiogram (ECG), Galvanic Skin Response (GSR) and facial activity data, recorded using off-the-shelf sensors while viewing affective movie clips. We first examine relationships between users’ affective ratings and personality scales in the context of prior observations, and then study linear and non-linear physiological correlates of emotion and personality. Our analysis suggests that the emotion-personality relationship is better captured by non-linear rather than linear statistics. We finally attempt binary emotion and personality trait recognition using physiological features. Experimental results cumulatively confirm that personality differences are better revealed while comparing user responses to emotionally homogeneous videos, and above-chance recognition is achieved for both affective and personality dimensions.


title: Multivariate LSTM-FCNs for Time Series Classification

abstract: Abstract Over the past decade, multivariate time series classification has received great attention. We propose transforming the existing univariate time series classification models, the Long Short Term Memory Fully Convolutional Network (LSTM-FCN) and Attention LSTM-FCN (ALSTM-FCN), into a multivariate time series classification model by augmenting the fully convolutional block with a squeeze-and-excitation block to further improve accuracy. Our proposed models outperform most state-of-the-art models while requiring minimum preprocessing. The proposed models work efficiently on various complex multivariate time series classification tasks such as activity recognition or action recognition. Furthermore, the proposed models are highly efficient at test time and small enough to deploy on memory constrained systems.


title: Review of the main developments in the analytic hierarchy process

abstract: In this paper the authors review the developments of the analytic hierarchy process (AHP) since its inception. The focus of this paper is a neutral review on the methodological developments rather than reporting its applications that have appeared since its introduction. In particular, we discuss problem modelling, pair-wise comparisons, judgement scales, derivation methods, consistency indices, incomplete matrix, synthesis of the weights, sensitivity analysis and group decisions. All have been important areas of research in AHP.


title: Algorithm Runtime Prediction: Methods&Evaluation

abstract: Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on a previously unseen input, using machine learning techniques to build a model of the algorithm's runtime as a function of problem-specific instance features. Such models have important applications to algorithm analysis, portfolio-based algorithm selection, and the automatic configuration of parameterized algorithms. Over the past decade, a wide variety of techniques have been studied for building such models. Here, we describe extensions and improvements of existing models, new families of models, and -- perhaps most importantly -- a much more thorough treatment of algorithm parameters as model inputs. We also comprehensively describe new and existing features for predicting algorithm runtime for propositional satisfiability (SAT), travelling salesperson (TSP) and mixed integer programming (MIP) problems. We evaluate these innovations through the largest empirical analysis of its kind, comparing to a wide range of runtime modelling techniques from the literature. Our experiments consider 11 algorithms and 35 instance distributions; they also span a very wide range of SAT, MIP, and TSP instances, with the least structured having been generated uniformly at random and the most structured having emerged from real industrial applications. Overall, we demonstrate that our new models yield substantially better runtime predictions than previous approaches in terms of their generalization to new problem instances, to new algorithms from a parameterized space, and to both simultaneously.


title: UWB microstrip antenna based on circular patch with wing stub loaded and Defect Ground Structure

abstract: A compact Ultra Wideband (UWB) microstrip antenna consists of a circular patch added with a three different length of open-end stub loaded Wing and partial ground plane with dumbbell shape defected ground structure (DGS) is proposed in this paper. This antenna was an improvement from the previous research, marked size reduction of 25% of the antenna size with comparable performance. The introduction of the DGS technique enables improvements in terms of bandwidth, gain and return loss characteristics. Using the model of transmission line based on 50Ω characteristic impedance, this antenna obtained a 10 dB return loss bandwidth from 3.21 GHz to 11.92 GHz.


Leader ’ s Authenticity Influence on Followers ’ Organizational Commitment


title: When Subjects Interpret the Data: Social Media Non-use as a Case for Adapting the Delphi Method to CSCW

abstract: This paper describes the use of the Delphi method as a means of incorporating study participants into the processes of data analysis and interpretation. As a case study, it focuses on perceptions about use and non-use of the social media site Facebook. The work presented here involves three phases. First, a large survey included both a demographically representative sample and a convenience sample. Second, a smaller follow-up survey presented results from that survey back to survey respondents. Third, a series of qualitative member checking interviews with additional survey respondents served to validate the findings of the follow-up survey. This paper demonstrates the utility of Delphi by highlighting the ways that it enables us to synthesize across these three study phases, advancing understanding of perceptions about social media use and non-use. The paper concludes by discussing the broader applicability of the Delphi method across CSCW research.


title: Text Simplification using Typed Dependencies: A Comparision of the Robustness of Different Generation Strategies

abstract: We present a framework for text simplification based on applying transformation rules to a typed dependency representation produced by the Stanford parser. We test two approaches to regeneration from typed dependencies: (a) gen-light, where the transformed dependency graphs are linearised using the word order and morphology of the original sentence, with any changes coded into the transformation rules, and (b) gen-heavy, where the Stanford dependencies are reduced to a DSyntS representation and sentences are generating formally using the RealPro surface realiser. The main contribution of this paper is to compare the robustness of these approaches in the presence of parsing errors, using both a single parse and an n-best parse setting in an overgenerate and rank approach. We find that the gen-light approach is robust to parser error, particularly in the n-best parse setting. On the other hand, parsing errors cause the realiser in the gen-heavy approach to order words and phrases in ways that are disliked by our evaluators.


title: Convolutional neural networks for histopathology image classification: Training vs. Using pre-trained networks

abstract: We explore the problem of classification within a medical image data-set based on a feature vector extracted from the deepest layer of pre-trained Convolution Neural Networks. We have used feature vectors from several pre-trained structures, including networks with/without transfer learning to evaluate the performance of pre-trained deep features versus CNNs which have been trained by that specific dataset as well as the impact of transfer learning with a small number of samples. All experiments are done on Kimia Path24 dataset which consists of 27,055 histopathology training patches in 24 tissue texture classes along with 1,325 test patches for evaluation. The result shows that pre-trained networks are quite competitive against training from scratch. As well, fine-tuning does not seem to add any tangible improvement for VGG16 to justify additional training while we observed considerable improvement in retrieval and classification accuracy when we fine-tuned the Inception structure.


July 2007Building Robust Wireless Mesh Networks Using Directional Antennas: How Many Radios are Enough?


title: Changes in nutritional composition, functional, and sensory properties of yam flour as a result of presoaking

abstract: This study investigated the effect of soaking pretreatments on some of the properties of flour obtained from two varieties of yam namely;Dioscorea alata andDioscorea rotundata with a view of providing information that will enhance their end use. The yam varieties were washed, chipped, parboiled at 50°C, soaked for different periods (0, 6, 12, and 18 h), dried at 60°C, and milled into flour. The flour samples were analyzed for their nutritional composition, pH, color, and functional properties. The flour samples were also made into pastes and were sensorially analyzed and 0 h soaked samples were used as control. The protein content of 18 h-soakedD. rotundata andD. alata flour samples was significantly different from the control and soaking had no effect (P > 0.05) on the fat and ash content but the carbohydrate content of the flour samples ranged from 83.08% to 86.13%. The 18 h-soakedD. rotundata flour sample had the lowest peak viscosity, breakdown value, and final viscosity among theD. rotundata variety samples. Pasting temperature ranged from 79.80 to 83.60°C and 6-h soakedD. alata flour sample had the lowest water absorption capacity and the highest bulk density. On the basis of sensory analysis, the panelist preferred the taste, texture, color, and appearance of paste made from the 18-h soakedD. rotundata flour to the paste of other flour samples. The results of this study show that D.rotundata should be soaked for 18 h prior to drying and milling in order to obtain a good-quality flour and paste.


title: Cladogenesis and morphological diversification in passerine birds

abstract: Morphological diversity tends to increase within evolving lineages over time1,2, but the relative roles of gradual evolutionary change (anagenesis)3 and abrupt shifts associated with speciation events (cladogenesis, or ‘punctuated equilibrium’)4 have not been resolved for most groups of organisms5. However, these two modes of evolution can be distinguished by the fact that morphological variance increases in proportion to time under anagenesis6, and in proportion to the logarithm of the number of species under cladogenesis7. Although species and time are themselves correlated, multiple regression analysis provides a statistical framework for partitioning their relative contributions. In this study, I use multiple regressions to evaluate the effects of time and species number on morphological diversity within clades of passerine birds. The results show clearly that number of species exerts a strong influence on morphological variance independent of time, but that time has no unique effect. Thus, morphological evolution in birds seems to be associated with cladogenesis. How lineage splitting promotes morphological diversification poses an important challenge to ecologists and evolutionary biologists.


title: Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms

abstract: Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. The paper closes with some discussion of ongoing and future work.


title: Scan2Mesh: From Unstructured Range Scans to 3D Meshes

abstract: We introduce Scan2Mesh, a novel data-driven generative approach which transforms an unstructured and potentially incomplete range scan into a structured 3D mesh representation. The main contribution of this work is a generative neural network architecture whose input is a range scan of a 3D object and whose output is an indexed face set conditioned on the input scan. In order to generate a 3D mesh as a set of vertices and face indices, the generative model builds on a series of proxy losses for vertices, edges, and faces. At each stage, we realize a one-to-one discrete mapping between the predicted and ground truth data points with a combination of convolutional- and graph neural network architectures. This enables our algorithm to predict a compact mesh representation similar to those created through manual artist effort using 3D modeling software. Our generated mesh results thus produce sharper, cleaner meshes with a fundamentally different structure from those generated through implicit functions, a first step in bridging the gap towards artist-created CAD models.


title: Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?

abstract: Recently, several techniques have been explored to detect unusual behaviour in surveillance videos. Nevertheless, few studies leverage features from pre-trained CNNs and none of then present a comparison of features generate by different models. Motivated by this gap, we compare features extracted by four state-of-the-art image classification networks as a way of describing patches from security video frames. We carry out experiments on the Ped1 and Ped2 datasets and analyze the usage of different feature normalization techniques. Our results indicate that choosing the appropriate normalization is crucial to improve the anomaly detection performance when working with CNN features. Also, in the Ped2 dataset our approach was able to obtain results comparable to the ones of several state-of-the-art methods. Lastly, as our method only considers the appearance of each frame, we believe that it can be combined with approaches that focus on motion patterns to further improve performance.


title: A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization

abstract: In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., ozone, particle matter ( ::: PM ::: 2.5 ::: ) and sulfur dioxide). Machine learning, as one of the most popular techniques, is able to efficiently train a model on big data by using large-scale optimization algorithms. Although there exist some works applying machine learning to air quality prediction, most of the prior studies are restricted to several-year data and simply train standard regression models (linear or nonlinear) to predict the hourly air pollution concentration. In this work, we propose refined models to predict the hourly air pollution concentration on the basis of meteorological data of previous days by formulating the prediction over 24 h as a multi-task learning (MTL) problem. This enables us to select a good model with different regularization techniques. We propose a useful regularization by enforcing the prediction models of consecutive hours to be close to each other and compare it with several typical regularizations for MTL, including standard Frobenius norm regularization, nuclear norm regularization, and l ::: 2 ::: , ::: 1 ::: -norm regularization. Our experiments have showed that the proposed parameter-reducing formulations and consecutive-hour-related regularizations achieve better performance than existing standard regression models and existing regularizations.


title: A Tour of Reinforcement Learning: The View from Continuous Control

abstract: This manuscript surveys reinforcement learning from the perspective of optimization and control with a focus on continuous control applications. It surveys the general formulation, terminology, and typical experimental implementations of reinforcement learning and reviews competing solution paradigms. In order to compare the relative merits of various techniques, this survey presents a case study of the Linear Quadratic Regulator (LQR) with unknown dynamics, perhaps the simplest and best-studied problem in optimal control. The manuscript describes how merging techniques from learning theory and control can provide non-asymptotic characterizations of LQR performance and shows that these characterizations tend to match experimental behavior. In turn, when revisiting more complex applications, many of the observed phenomena in LQR persist. In particular, theory and experiment demonstrate the role and importance of models and the cost of generality in reinforcement learning algorithms. This survey concludes with a discussion of some of the challenges in designing learning systems that safely and reliably interact with complex and uncertain environments and how tools from reinforcement learning and control might be combined to approach these challenges.


title: CyberTwitter: Using Twitter to generate alerts for cybersecurity threats and vulnerabilities

abstract: In order to secure vital personal and organizational system we require timely intelligence on cybersecurity threats and vulnerabilities. Intelligence about these threats is generally available in both overt and covert sources like the National Vulnerability Database, CERT alerts, blog posts, social media, and dark web resources. Intelligence updates about cybersecurity can be viewed as temporal events that a security analyst must keep up with so as to secure a computer system. We describe CyberTwitter, a system to discover and analyze cybersecurity intelligence on Twitter and serve as a OSINT (Open-source intelligence) source. We analyze real time information updates, in form of tweets, to extract intelligence about various possible threats. We use the Semantic Web RDF to represent the intelligence gathered and SWRL rules to reason over extracted intelligence to issue alerts for security analysts.


title: User Profile Based Research Paper Recommendation

abstract: We design a recommender system for research papers based on topic-modeling. The users feedback to the results is used to make the results more relevant the next time they fire a query. The user's needs are understood by observing the change in the themes that the user shows a preference for over time.


title: Interpreting Deep Visual Representations via Network Dissection

abstract: The success of recent deep convolutional neural networks (CNNs) depends on learning hidden representations that can summarize the important factors of variation behind the data. However, CNNs often criticized as being black boxes that lack interpretability, since they have millions of unexplained model parameters. In this work, we describe Network Dissection, a method that interprets networks by providing labels for the units of their deep visual representations. The proposed method quantifies the interpretability of CNN representations by evaluating the alignment between individual hidden units and a set of visual semantic concepts. By identifying the best alignments, units are given human interpretable labels across a range of objects, parts, scenes, textures, materials, and colors. The method reveals that deep representations are more transparent and interpretable than expected: we find that representations are significantly more interpretable than they would be under a random equivalently powerful basis. We apply the method to interpret and compare the latent representations of various network architectures trained to solve different supervised and self-supervised training tasks. We then examine factors affecting the network interpretability such as the number of the training iterations, regularizations, different initializations, and the network depth and width. Finally we show that the interpreted units can be used to provide explicit explanations of a prediction given by a CNN for an image. Our results highlight that interpretability is an important property of deep neural networks that provides new insights into their hierarchical structure.


title: Collaborative personalized Twitter search with topic-language models

abstract: The vast amount of real-time and social content in microblogs results in an information overload for users when searching microblog data. Given the user's search query, delivering content that is relevant to her interests is a challenging problem. Traditional methods for personalized Web search are insufficient in the microblog domain, because of the diversity of topics, sparseness of user data and the highly social nature. In particular, social interactions between users need to be considered, in order to accurately model user's interests, alleviate data sparseness and tackle the cold-start problem. In this paper, we therefore propose a novel framework for Collaborative Personalized Twitter Search. At its core, we develop a collaborative user model, which exploits the user's social connections in order to obtain a comprehensive account of her preferences. We then propose a novel user model structure to manage the topical diversity in Twitter and to enable semantic-aware query disambiguation. Our framework integrates a variety of information about the user's preferences in a principled manner. A thorough evaluation is conducted using two personalized Twitter search query logs, demonstrating a superior ranking performance of our framework compared with state-of-the-art baselines.


title: Hierarchical Self-Tuning of Concurrency and Resource Units in Data-Analytics Frameworks

abstract: Many big-data processing applications use dataanalytics frameworks such as Apache Hadoop. Such frameworks have tunable configuration parameters set by experienced system administrators and/or application developers. However, tuning parameters manually can be hard and time-consuming because it requires domain-specific knowledge and understanding of complex inter-dependencies amongst parameters. Most of the frameworks seek efficient resource management by using slots or containers as resource units to be assigned to jobs or tasks, the maximum number of slots or containers in a system being part of the static configuration of the system. This static resource management has limited effectiveness in coping with jobs’ diversity and workload dynamics, even in the case of a single job. Seeking to improve performance (e.g., multiple-jobs makespan and job completion time) without modification of the framework, this paper proposes a hierarchical approach using a fuzzy-logic controller to dynamically adjust the number of concurrent jobs and additional controllers (one for each cluster node) to adjust the number of resource units assigned to jobs on each node. The fuzzylogic controller uses fuzzy rules based on a concave downward relationship between aggregate CPU usage and the number of concurrent jobs. The other controllers use a simple heuristic algorithm to adjust the number of resource units on the basis of resource usage by job tasks. A prototype of our approach was implemented for Apache Hadoop on a cluster running at CloudLab. The prototype was evaluated using realistic workloads generated by SWIM, a statistical workload injector for MapReduce. The evaluation shows that the proposed approach yields up to a 42% reduction of the jobs makespan that results from using Hadoop default settings.


title: Structural Organization of the Cytokine Gene Cluster on Human Chromosome 5

abstract: Cytokines make up a family of glycoprotein growth factors that have been shown to support clonal proliferation of hematopoietic progenitor cells [1]. Interleukin-3 (IL-3) and granulocyte-macrophage colony-stimulating factor (CSF-2) stimulate differentiation and proliferation of cell progenitors along multiple (myeloid and erythroid) pathways [2–4]. Interleukin-4 (IL-4) can affect B cell, T cell, mast cell and macrophage functions [5]. Interleukin-5 (IL-5) is a more specific cytokine, acting on later stages of eosinophil differentiation [6].


title: Real-time Rumor Debunking on Twitter

abstract: In this paper, we propose the first real time rumor debunking algorithm for Twitter. We use cues from 'wisdom of the crowds', that is, the aggregate 'common sense' and investigative journalism of Twitter users. We concentrate on identification of a rumor as an event that may comprise of one or more conflicting microblogs. We continue monitoring the rumor event and generate real time updates dynamically based on any additional information received. We show using real streaming data that it is possible, using our approach, to debunk rumors accurately and efficiently, often much faster than manual verification by professionals.


title: Biodiversity and Microbial Ecosystems Functioning

abstract: All ecosystems are composed of multiple species performing numerous functions. This plurality identified as biodiversity has become a research topic of general importance for understanding how ecosystems function. The word “biodiversity” has subsequently received different interpretations we aim to describe. Microbial systems can be used to illustrate these definitions and to clarify paradigms that have emerged in general ecology. Microbial biodiversity can be characterized by the kind of biodiversity (taxa or functional groups present), the representativeness of samples, and the culturability of samples or taxa, in terms of genetic, functional, or physiological characteristics. Biodiversity is a major driver of ecosystem functioning, and this relation is described in the case of several biotopes. Several mathematical approaches have been used to quantify microbial diversity, and the various indices are described and discussed.


title: Multiresolution Graph Attention Networks for Relevance Matching

abstract: A large number of deep learning models have been proposed for the text matching problem, which is at the core of various typical natural language processing (NLP) tasks. However, existing deep models are mainly designed for the semantic matching between a pair of short texts, such as paraphrase identification and question answering, and do not perform well on the task of relevance matching between short-long text pairs. This is partially due to the fact that the essential characteristics of short-long text matching have not been well considered in these deep models. More specifically, these methods fail to handle extreme length discrepancy between text pieces and neither can they fully characterize the underlying structural information in long text documents. In this paper, we are especially interested in relevance matching between a piece of short text and a long document, which is critical to problems like query-document matching in information retrieval and web searching. To extract the structural information of documents, an undirected graph is constructed, with each vertex representing a keyword and the weight of an edge indicating the degree of interaction between keywords. Based on the keyword graph, we further propose a Multiresolution Graph Attention Network to learn multi-layered representations of vertices through a Graph Convolutional Network (GCN), and then match the short text snippet with the graphical representation of the document with the attention mechanisms applied over each layer of the GCN. Experimental results on two datasets demonstrate that our graph approach outperforms other state-of-the-art deep matching models.


title: Hierarchical multi-class segmentation of glioma images using networks with multi-level activation function

abstract: For many segmentation tasks, especially for the biomedical image, the topological prior is vital information which is useful to exploit. The containment/nesting is a typical inter-class geometric relationship. In the MICCAI Brain tumor segmentation challenge, with its three hierarchically nested classes 'whole tumor', 'tumor core', 'active tumor', the nested classes relationship is introduced into the 3D-residual-Unet architecture. The network comprises a context aggregation pathway and a localization pathway, which encodes increasingly abstract representation of the input as going deeper into the network, and then recombines these representations with shallower features to precisely localize the interest domain via a localization path. The nested-class-prior is combined by proposing the multi-class activation function and its corresponding loss function. The model is trained on the training dataset of Brats2018, and 20% of the dataset is regarded as the validation dataset to determine parameters. When the parameters are fixed, we retrain the model on the whole training dataset. The performance achieved on the validation leaderboard is 86%, 77% and 72% Dice scores for the whole tumor, enhancing tumor and tumor core classes without relying on ensembles or complicated post-processing steps. Based on the same start-of-the-art network architecture, the accuracy of nested-class (enhancing tumor) is reasonably improved from 69% to 72% compared with the traditional Softmax-based method which blind to topological prior.


title: Tablet-based activity schedule for children with autism in mainstream environment

abstract: Including children with Autism Spectrum Disorders (ASD) in mainstreamed environments creates a need for new interventions whose efficacy must be assessed in situ. This paper presents a tablet-based application for activity schedules that has been designed following a participatory design approach involving mainstream teachers, special-education teachers and school aides. This applications addresses two domains of activities: classroom routines and verbal communications. We assessed the efficiency of our application with a study involving 10 children with ASD in mainstream inclusion (5 children are equipped and 5 are not equipped). We show that (1) the use of the application is rapidly self-initiated (after two months for almost all the participants) and that (2) the tablet-supported routines are differently executed over time according to the activity domain conditions. Importantly, compared to the control children, the equipped children exhibited more classroom and communication routines correctly performed after three month of intervention.


title: A simple platform of brain-controlled mobile robot and its implementation by SSVEP

abstract: Brain-computer interfacing (BCI) is an emerging technology to translate non-invasively measured brain signals into commands, providing an additional communication channel. This paper develops a BCI platform to remotely control a mobile robot through the Internet. The platform consists of a server computer for EEG signal acquisition and signal processing/classification and a bluetooth controlled robot carrying an Android smartphone. The smartphone can give a visual feedback via the Internet to the user with its phone camera. The Android smartphone transfers the command from the server to the robot and transmits video stream to the server for the feedback to the user. It is also shown to employ the steady state visual evoked potentials (SSVEP) to classify commands. We propose a method for detecting the idle state when a user does not gaze any visual stimuli.


Security in Infancy , Childhood , and Adulthood : A Move to the Level of Representation


title: Authorized Private Keyword Search over Encrypted Data in Cloud Computing

abstract: In cloud computing, clients usually outsource their data to the cloud storage servers to reduce the management costs. While those data may contain sensitive personal information, the cloud servers cannot be fully trusted in protecting them. Encryption is a promising way to protect the confidentiality of the outsourced data, but it also introduces much difficulty to performing effective searches over encrypted information. Most existing works do not support efficient searches with complex query conditions, and care needs to be taken when using them because of the potential privacy leakages about the data owners to the data users or the cloud server. In this paper, using on line Personal Health Record (PHR) as a case study, we first show the necessity of search capability authorization that reduces the privacy exposure resulting from the search results, and establish a scalable framework for Authorized Private Keyword Search (APKS) over encrypted cloud data. We then propose two novel solutions for APKS based on a recent cryptographic primitive, Hierarchical Predicate Encryption (HPE). Our solutions enable efficient multi-dimensional keyword searches with range query, allow delegation and revocation of search capabilities. Moreover, we enhance the query privacy which hides users' query keywords against the server. We implement our scheme on a modern workstation, and experimental results demonstrate its suitability for practical usage.


title: PaperLens: advanced magic lens interaction above the tabletop

abstract: In order to improve the three-dimensional (3D) exploration of virtual spaces above a tabletop, we developed a set of navigation techniques using a handheld magic lens. These techniques allow for an intuitive interaction with two-dimensional and 3D information spaces, for which we contribute a classification into volumetric, layered, zoomable, and temporal spaces. The proposed PaperLens system uses a tracked sheet of paper to navigate these spaces with regard to the Z-dimension (height above the tabletop). A formative user study provided valuable feedback for the improvement of the PaperLens system with respect to layer interaction and navigation. In particular, the problem of keeping the focus on selected layers was addressed. We also propose additional vertical displays in order to provide further contextual clues.


title: A Survey of Artificial Cognitive Systems: Implications for the Autonomous Development of Mental Capabilities in Computational Agents

abstract: This survey presents an overview of the autonomous development of mental capabilities in computational agents. It does so based on a characterization of cognitive systems as systems which exhibit adaptive, anticipatory, and purposive goal-directed behavior. We present a broad survey of the various paradigms of cognition, addressing cognitivist (physical symbol systems) approaches, emergent systems approaches, encompassing connectionist, dynamical, and enactive systems, and also efforts to combine the two in hybrid systems. We then review several cognitive architectures drawn from these paradigms. In each of these areas, we highlight the implications and attendant problems of adopting a developmental approach, both from phylogenetic and ontogenetic points of view. We conclude with a summary of the key architectural features that systems capable of autonomous development of mental capabilities should exhibit


title: Traffic light mapping and detection

abstract: The outdoor perception problem is a major challenge for driver-assistance and autonomous vehicle systems. While these systems can often employ active sensors such as sonar, radar, and lidar to perceive their surroundings, the state of standard traffic lights can only be perceived visually. By using a prior map, a perception system can anticipate and predict the locations of traffic lights and improve detection of the light state. The prior map also encodes the control semantics of the individual lights. This paper presents methods for automatically mapping the three dimensional positions of traffic lights and robustly detecting traffic light state onboard cars with cameras. We have used these methods to map more than four thousand traffic lights, and to perform onboard traffic light detection for thousands of drives through intersections.


title: Key lessons in achieving widespread inspection use

abstract: Hewlett-Packard has distilled its experience in promoting software inspections into a model of how technology adoption occurs and a metric of where it stands. Its managers know when and how to accelerate efforts to adopt inspections and other best practices. Experience has shown that the return on investment in technology adoption efforts can be huge. At HP, inspection technology was adopted across the entire company in four recognizable stages, which are defined as: experimental, initial guidelines, widespread belief and adoption, and standardization. HP's inspections program has progressed through three of these stages. We address several key questions: What characterizes the four stages? What were the most important lessons learned? What situations led to failure or success? And most important, how can we apply what we learned to speed the adoption of other proven practices? The lessons related will give many who are directly or indirectly responsible for software process improvement more confidence that inspections apply to all software-development organizations. >


title: A Survey of Research on Cloud Robotics and Automation

abstract: The Cloud infrastructure and its extensive set of Internet-accessible resources has potential to provide significant benefits to robots and automation systems. We consider robots and automation systems that rely on data or code from a network to support their operation, i.e., where not all sensing, computation, and memory is integrated into a standalone system. This survey is organized around four potential benefits of the Cloud: 1) Big Data: access to libraries of images, maps, trajectories, and descriptive data; 2) Cloud Computing: access to parallel grid computing on demand for statistical analysis, learning, and motion planning; 3) Collective Robot Learning: robots sharing trajectories, control policies, and outcomes; and 4) Human Computation: use of crowdsourcing to tap human skills for analyzing images and video, classification, learning, and error recovery. The Cloud can also improve robots and automation systems by providing access to: a) datasets, publications, models, benchmarks, and simulation tools; b) open competitions for designs and systems; and c) open-source software. This survey includes over 150 references on results and open challenges. A website with new developments and updates is available at: http://goldberg.berkeley.edu/cloud-robotics/


title: Disaster Recovery for Cloud-Hosted Enterprise Applications

abstract: We describe disaster protection and recovery of cloud-hosted enterprise applications both at the cloud infrastructure level and at the application level. We explore scenarios which favor one option over the other, and scenarios where a combination of both are required for effective and end-to-end protection. Through case studies grounded in the experience of implementing disaster recovery for IBM's Cloud Managed Services (CMS) platform, we highlight the complexities of protecting enterprise applications on the cloud. For recovery planning and execution, we present a scheduling algorithm that recovers machines hosted on cloud by taking into account application-level logical dependencies and the business criticalities of the applications.


title: Sentiment analysis: Approaches and open issues

abstract: Sentiment Analysis is the process which helps to identify and classifying the opinions or feelings expressed in opinioned data, in order to ascertain whether the attitude of the writer towards a particular service, product etc. is negative, positive or neutral. Sentiment analysis also helps the consumers to identify if the information in the neighborhood of the product or service is satisfactory or not before the customers buy it. The interest of the scientific communities and business world is increasing day by day to gather, process and extract the knowledge from the public opinions available on different types of social media. The main problems that exist in the current techniques are: inability to perform well in different domains, inadequate accuracy and performance in sentiment analysis based on insufficient labeled data, incapability to deal with complex sentences that require more than sentiment words and simple analyzing. This article discusses the different approaches for sentiment analysis and open problems and issues present in performing sentiment analysis.


title: Bus as a sensor: A mobile sensor nodes network for the air quality monitoring

abstract: Air pollution is an important issue due to its direct impact on human health. To cope with this problem, in urban areas a “monitor and react” approach, based on measurements provided use of static monitoring stations, is commonly adopted introducing constrains to the amount of areas that can be monitored within the city. With the rise of the Internet of Things paradigm, new air monitoring models based on mobile sensors networks have been proposed. In this paper, we present the concept of Bus as a Sensor (BaaS): an air quality monitoring system based on mobile sensor nodes placed upon buses. This work aims to provide a high resolution air quality map to report pollutants measurements and facilitate the analysis to support the government decisions in order to reduce the pollution. The proposed paper describes the system architecture and the requirements of sensor nodes to depict the implementation concepts and lay the foundations for future experiments on field. A preliminary end-to-end prototype of the BaaS solution has been already developed at the TIM Joint Open Lab based in Catania and some tests have been successfully carried out within the University campus.


title: Texture Classification Approach Based on Combination of Edge&Co-occurrence and Local Binary Pattern

abstract: Texture classification is one of the problems which has been paid much attention on by computer scientists since late 90s. If texture classification is done correctly and accurately, it can be used in many cases such as Pattern recognition, object tracking, and shape recognition. So far, there have been so many methods offered to solve this problem. Near all these methods have tried to extract and define features to separate different labels of textures really well. This article has offered an approach which has an overall process on the images of textures based on Local binary pattern and Gray Level Co-occurrence matrix and then by edge detection, and finally, extracting the statistical features from the images would classify them. Although, this approach is a general one and is could be used in different applications, the method has been tested on the stone texture and the results have been compared with some of the previous approaches to prove the quality of proposed approach.


title: A dual-band circularly polarized aperture-coupled stacked microstrip antenna for global positioning satellite

abstract: This paper describes the design and testing of an aperture-coupled circularly polarized antenna for Global Positioning System (GPS) applications. The antenna operates at both the L1 and L2 frequencies of 1575 and 1227 MHz, which is required for differential GPS systems in order to provide maximum positioning accuracy. Electrical performance, low-profile, and cost were equally important requirements for this antenna. The design procedure is discussed, and measured results are presented. Results from a manufacturing sensitivity analysis are also included.


Benign vulvar vestibular papillomatosis: An underreported condition in Indian dermatological literature


Churn Prediction System for Telecom using Filter–Wrapper and Ensemble Classification


title: Towards Symbolic Reinforcement Learning with Common Sense

abstract: Deep Reinforcement Learning (deep RL) has made several breakthroughs in recent years in applications ranging from complex control tasks in unmanned vehicles to game playing. Despite their success, deep RL still lacks several important capacities of human intelligence, such as transfer learning, abstraction and interpretability. Deep Symbolic Reinforcement Learning (DSRL) seeks to incorporate such capacities to deep Q-networks (DQN) by learning a relevant symbolic representation prior to using Q-learning. In this paper, we propose a novel extension of DSRL, which we call Symbolic Reinforcement Learning with Common Sense (SRL+CS), offering a better balance between generalization and specialization, inspired by principles of common sense when assigning rewards and aggregating Q-values. Experiments reported in this paper show that SRL+CS learns consistently faster than Q-learning and DSRL, achieving also a higher accuracy. In the hardest case, where agents were trained in a deterministic environment and tested in a random environment, SRL+CS achieves nearly 100% average accuracy compared to DSRL's 70% and DQN's 50% accuracy. To the best of our knowledge, this is the first case of near perfect zero-shot transfer learning using Reinforcement Learning.


Brassau : Automatically Generating Graphical User Interfaces for Virtual Assistants


title: Reinforcement Learning with a Gaussian mixture model

abstract: Recent approaches to Reinforcement Learning (RL) with function approximation include Neural Fitted Q Iteration and the use of Gaussian Processes. They belong to the class of fitted value iteration algorithms, which use a set of support points to fit the value-function in a batch iterative process. These techniques make efficient use of a reduced number of samples by reusing them as needed, and are appropriate for applications where the cost of experiencing a new sample is higher than storing and reusing it, but this is at the expense of increasing the computational effort, since these algorithms are not incremental. On the other hand, non-parametric models for function approximation, like Gaussian Processes, are preferred against parametric ones, due to their greater flexibility. A further advantage of using Gaussian Processes for function approximation is that they allow to quantify the uncertainty of the estimation at each point. In this paper, we propose a new approach for RL in continuous domains based on Probability Density Estimations. Our method combines the best features of the previous methods: it is non-parametric and provides an estimation of the variance of the approximated function at any point of the domain. In addition, our method is simple, incremental, and computationally efficient. All these features make this approach more appealing than Gaussian Processes and fitted value iteration algorithms in general.


1 Seismic and energy renovation . A review of the de-2 sign approach in Italy and in Romania . 3


title: Decrease torque ripple for SynRM using barrier arrangement design

abstract: SynRM (Synchronous Reluctance Motor) is a high efficient motor with simple structure and lower price than induction motor. However, the torque ripple of Synchronous Reluctance Motor is very large. This paper analyzes the SynRM output characteristics with a new rotor flux barriers arrangement design. And the stator of SynRM is as similar as an induction motor. A method for reducing the torque ripple is described by designed the relative positions between outer edges of flux barriers and slots correspond. Then, devised several new models and compared with a basic model. As a result torque ripple can be reduced.


title: Community-based bayesian aggregation models for crowdsourcing

abstract: This paper addresses the problem of extracting accurate labels from crowdsourced datasets, a key challenge in crowdsourcing. Prior work has focused on modeling the reliability of individual workers, for instance, by way of confusion matrices, and using these latent traits to estimate the true labels more accurately. However, this strategy becomes ineffective when there are too few labels per worker to reliably estimate their quality. To mitigate this issue, we propose a novel community-based Bayesian label aggregation model, CommunityBCC, which assumes that crowd workers conform to a few different types, where each type represents a group of workers with similar confusion matrices. We assume that each worker belongs to a certain community, where the worker's confusion matrix is similar to (a perturbation of) the community's confusion matrix. Our model can then learn a set of key latent features: (i) the confusion matrix of each community, (ii) the community membership of each user, and (iii) the aggregated label of each item. We compare the performance of our model against established aggregation methods on a number of large-scale, real-world crowdsourcing datasets. Our experimental results show that our CommunityBCC model consistently outperforms state-of-the-art label aggregation methods, requiring, on average, 50% less data to pass the 90% accuracy mark.