title: Activity Recognition on Streaming Sensor Data

abstract: Many real-world applications that focus on addressing needs of a human, require information about the activities being performed by the human in real-time. While advances in pervasive computing have led to the development of wireless and non-intrusive sensors that can capture the necessary activity information, current activity recognition approaches have so far experimented on either a scripted or pre-segmented sequence of sensor events related to activities. In this paper we propose and evaluate a sliding window based approach to perform activity recognition in an on line or streaming fashion; recognizing activities as and when new sensor events are recorded. To account for the fact that different activities can be best characterized by different window lengths of sensor events, we incorporate the time decay and mutual information based weighting of sensor events within a window. Additional contextual information in the form of the previous activity and the activity of the previous window is also appended to the feature describing a sensor window. The experiments conducted to evaluate these techniques on real-world smart home datasets suggests that combining mutual information based weighting of sensor events and adding past contextual information to the feature leads to best performance for streaming activity recognition.


title: Combined active wrist and hand orthosis for home use: Lessons learned

abstract: In this paper, we present our design of an active hand and wrist orthosis, SCRIPT Active Orthosis Iteration 3 (SAO-i3), for rehabilitation after stroke at home. Impairments in the hand decrease the quality of life of stroke patients drastically by limiting activities of daily living such as eating, drinking, buttoning, etc. Since there is a high correlation between therapy intensity and functional recovery, it is logical to bring robot-assisted therapy to patients' homes in order to let them customize their own therapy schedules in a more time-effective, affordable and motivational way. In the EUFP7 SCRIPT project, we developed a passive hand and wrist orthosis and tested it with 24 patients from three countries (NL, IT, UK) at their homes for six weeks. Based on the lessons we learned, we developed an active hand and wrist orthosis for home use to enable better control over the assist-as-needed characteristics of the orthosis, which is beneficial to adjust the assistance according to patients' recovery progress. The results are mixed. Passive assistive forces, combined with compliant independent interaction with fingers and wrist, create a solid gaming interface for motivated rehabilitation at home, which was one of the primary goals of the project. However, complex mechanics and mechatronics provide theoretical benefits but result in significant practical limitations. Perhaps, having a compliant active wrist and finger orthosis that is useable independently at home, is a step too far.


title: NON-rigid structure from motion via sparse self-expressive representation

abstract: To simultaneously recover 3D shapes of non-rigid object and camera motions from 2D corresponding points is a difficult task in computer vision. This task is called Non-rigid Structure from motion(NRSfM). To solve this ill-posed problem, many existing methods rely on low rank assumption. However, the value of rank has to be accurately predefined because incorrect value can largely degrade the reconstruction performance. Unfortunately, these is no automatic solution to determine this value. In this paper, we present a self-expressive method that models 3D shapes with a sparse combination of other 3D shapes from the same structure. One of the biggest advantages is that it doesn't need the rank to be predefined. Also, unlike other learning-based methods, our method doesn't need learning step. Experimental results validate the efficiency of our method.


title: Multicriteria-Based Active Discriminative Dictionary Learning for Scene Recognition

abstract: Scene recognition is a significant and challenging problem in the field of computer vision. One of the principal bottlenecks in applying machine learning techniques to scene recognition tasks is the requirement of a large number of labeled training data. However, labeling massive training data manually (especially labeling images and videos) is very expensive in terms of human time and effort. In this paper, we present a novel multicriteria-based active discriminative dictionary learning (M-ADDL) algorithm to reduce the human annotation effort and create a robust scene recognition model. The M-ADDL algorithm possesses three advantages. First, M-ADDL introduces an active learning strategy into the discriminative dictionary learning model so that the performance of discriminative dictionary learning can be improved when the number of labeled samples is small. Second, different from most existing active learning methods that measure either the informativeness or representativeness of unlabeled samples to select useful samples for expanding the training dataset, M-ADDL employs both informativeness and representativeness to query useful unlabeled samples and utilizes the manifold-preserving ability of unlabeled samples as an additional sample selection criterion. Finally, a more effective representativeness criterion is presented based on the reconstruction coefficients of the samples. The experimental results of four standard scene recognition databases demonstrate the feasibility and validity of the proposed M-ADDL algorithm.


title: Neuroeconomics: an overview from an economic perspective

abstract: Until now, economic theory has not systematically integrated the influence of emotions on decision-making. Since evidence from neuroscience suggests that decision-making as hypothesized in economic theory depends on prior emotional processing, interdisciplinary research under the label of “neuroeconomics” arose. The key idea of this approach is to employ recent neuroscientific methods in order to analyze economically relevant brain processes. This paper aims to provide an overview of the current state of neuroeconomic research by giving a brief description of the concept of neuroeconomics, outlining methods commonly used and describing current studies in this new research area. Finally, some future prospects and limitations are discussed.


title: Tracking the Digital Footprints of Personality

abstract: A growing portion of offline and online human activities leave digital footprints in electronic databases. Resulting big social data offers unprecedented insights into population-wide patterns and detailed characteristics of the individuals. The goal of this paper is to review the literature showing how pervasive records of digital footprints, such as Facebook profile, or mobile device logs, can be used to infer personality, a major psychological framework describing differences in individual behavior. We briefly introduce personality and present a range of works focusing on predicting it from digital footprints and conclude with a discussion of the implications of these results in terms of privacy, data ownership, and opportunities for future research in computational social science.


title: A Compact High-Gain and Front-to-Back Ratio Elliptically Tapered Antipodal Vivaldi Antenna With Trapezoid-Shaped Dielectric Lens

abstract: A modified compact antipodal Vivaldi antenna is proposed with good performance for different applications including microwave and millimeter wave imaging. A step-by-step procedure is applied in this design including conventional antipodal Vivaldi antenna (AVA), AVA with a periodic slit edge, and AVA with a trapezoid-shaped dielectric lens to feature performances including wide bandwidth, small size, high gain, front-to-back ratio and directivity, modification on E-plane beam tilt, and small sidelobe levels. By adding periodic slit edge at the outer brim of the antenna radiators, lower-end limitation of the conventional AVA extended twice without changing the overall dimensions of the antenna. The optimized antenna is fabricated and tested, and the results show that S11 <; -10 dB frequency band is from 3.4 to 40 GHz, and it is in good agreement with simulation one. Gain of the antenna has been elevated by the periodic slit edge and the trapezoid dielectric lens at lower frequencies up to 8 dB and at higher frequencies up to 15 dB, respectively. The E-plane beam tilts and sidelobe levels are reduced by the lens.


title: The Role of Relevance in Sponsored Search

abstract: Sponsored search aims at retrieving the advertisements that in the one hand meet users' intent reflected in their search queries, and in the other hand attract user clicks to generate revenue. Advertisements are typically ranked based on their expected revenue that is computed as the product between their predicted probability of being clicked (i.e., namely clickability) and their advertiser provided bid. The relevance of an advertisement to a user query is implicitly captured by the predicted clickability of the advertisement, assuming that relevant advertisements are more likely to attract user clicks. However, this approach easily biases the ranking toward advertisements having rich click history. This may incorrectly lead to showing irrelevant advertisements whose clickability is not accurately predicted due to lack of click history. Another side effect consists of never giving a chance to new advertisements that may be highly relevant to be printed due to their lack of click history. To address this problem, we explicitly measure the relevance between an advertisement and a query without relying on the advertisement's click history, and present different ways of leveraging this relevance to improve user search experience without reducing search engine revenue. Specifically, we propose a machine learning approach that solely relies on text-based features to measure the relevance between an advertisement and a query. We discuss how the introduced relevance can be used in four important use cases: pre-filtering of irrelevant advertisements, recovering advertisements with little history, improving clickability prediction, and re-ranking of the advertisements on the final search result page. Offine experiments using large-scale query logs and online A/B tests demonstrate the superiority of the proposed click-oblivious relevance model and the important roles that relevance plays in sponsored search.


Development and application of an autonomous unmanned aerial vehicle for precise aerobiological sampling above agricultural fields


title: A distance measure between attributed relational graphs for pattern recognition

abstract: A method to determine a distance measure between two nonhierarchical attributed relational graphs is presented. In order to apply this distance measure, the graphs are characterised by descriptive graph grammars (DGG). The proposed distance measure is based on the computation of the minimum number of modifications required to transform an input graph into the reference one. Specifically, the distance measure is defined as the cost of recognition of nodes plus the number of transformations which include node insertion, node deletion, branch insertion, branch deletion, node label substitution and branch label substitution. The major difference between the proposed distance measure and the other ones is the consideration of the cost of recognition of nodes in the distance computation. In order to do this, the principal features of the nodes are described by one or several cost functions which are used to compute the similarity between the input nodes and the reference ones. Finally, an application of this distance measure to the recognition of lower case handwritten English characters is presented.


title: Linear Feedback Control: Analysis and Design with MATLAB (Advances in Design and Control)

abstract: A discussion of analysis and design techniques for linear feedback control systems using MATLAB software. By reducing the mathematics, increasing MATLAB working examples, and inserting short scripts and plots within the text, the authors have created a text suitable for almost any type of user. Suitable for beginners entering the field; for students who wish to bridge the gap between control theory and the use of MATLAB for control systems; and as a handy reference for practising engineers. The MATLAB companion package, CtrlLAB, allows readers to learn quickly while simply clicking through options with a mouse. Each chapter ends with a set of problems to help readers strengthen their understanding of the material. All example scripts within the book, as well as the CtrlLAB package, are freely downloadable from MATLAB Central, an open exchange for the MATLAB and Simulink community (www.mathworks.com/matlabcentral/index.shtml).


title: Towards personalized medicine in sepsis: Quest for Shangri-La?

abstract: Boomer JS, To K, Chang KC, Takasu O, Osborne DF, Walton AH, Bricker TL, Jarman SD 2nd, Kreisel D, Krupnick AS, Srivastava A, Swanson PE, Green JM, Hotchkiss RS: Immunosuppression in patients who dieof sepsis and multiple organ failure. JAMA 2011, 306: 2594-2605. Severe sepsis is typically characterized by initial cytokine-mediated hyper-inflammation. Whether this hyper-inflammatory phase is followed by immunosuppression is a subject of controversy. Animal studies suggest that multiple immune defects occur in sepsis, but data from humans remain conflicting. Objectives To determine the association of sepsis with changes in host innate and adaptive immunity and to examine potential mechanisms for putative immunosuppression. Design Rapid prospective postmortem spleen and lung tissue harvest and analysis within 120 minutes of death. Setting Intensive care units (ICUs) of academic medical centers. Subjects A convenience sample of 40 patients who died with active severe sepsis was taken over the course of 2 years to characterize their immune status at the time of death. Control spleens (n = 29) were obtained from patients who were declared brain dead or had emergent splenectomy due to trauma; control lungs (n = 20) were obtained from transplant donors or from lung cancer resections. Interventions None. Outcomes Cytokine secretion assays and immunophenotyping of cell surface receptor-ligand expression profiles were performed to identify potential mechanisms of immune dysfunction. Immunohistochemical staining was performed to evaluate the loss of immune effector cells. The mean ages (standard deviations) of patients with sepsis and controls were 71.7 (15.9) and 52.7 (15.0) years, respectively. Patients with sepsis were in the ICU for a median of 8 days (range of 1 to 195 days), whereas control patients were in the ICU for not more than 4 days. The median duration of sepsis was 4 days (range of 1 to 40 days). Anti-CD3/anti-CD28-stimulated splenocytes from patients with sepsis, compared with those from controls, had significant reductions in cytokine secretion at 5 hours: tumor necrosis factor, 5,361 (95% confidence interval (CI) 3,327 to 7,485) pg/mL versus 418 (95% CI 98 to 738) pg/mL; interferon-gamma, 1,374 (95% CI 550 to 2,197) pg/mL versus 37.5 (95% CI -5 to 80) pg/mL; interleukin-6, 3,691 (95% CI 2,313 to 5,070) versus 365 (95% CI 87 to 642) pg/mL; and interleukin-10, 633 (95% CI -269 to 1,534) versus 58 (95% CI -39 to 156) pg/mL (P < 0.001 for all). There were similar reductions in 5-hour lipopolysaccharidestimulated cytokine secretion. Cytokine secretion in patients with sepsis was generally less than 10% of that in controls, independently of age, duration of sepsis, corticosteroid use, and nutritional status. Despite differences between spleen and lung, flow cytometric analysis showed increased expression of selected inhibitory receptors and ligands and expansion of suppressor cell populations in both organs. Unique differences in cellular inhibitory molecule expression existed in immune cells isolated from lungs of patients with sepsis versus patients with cancer and versus transplant donors. Immunohistochemical staining showed extensive depletion of splenic CD4, CD8, and HLA-DR cells and expression of ligands for inhibitory receptors on lung epithelial cells. Patients who die in the ICU following sepsis compared with patients who die of non-sepsis etiologies have bio-chemical, flow cytometric, and immunohistochemical findings consistent with those of immunosuppression. Targeted immune-enhancing therapy may be a valid approach in selected patients with sepsis.


Title : Three-Dimensional Printing and Its Applications in Otorhinolaryngology – Head and Neck Surgery


title: Analyzing the Resource Utilization of AES Encryption on IoT Devices

abstract: With the prosperity of the Internet of Things (IoT), the implied security issues demand heightened attention. However, conventional cryptographic security solutions often lead to heavy computations and high energy costs, which can hardly be afforded by resource-constrained IoT edge devices. Therefore, it is essential to retrieve real testing data and investigate the tradeoff between information security and its resource consumption on IoT edge devices. This paper explores the duration and energy consumption of the Advanced Encryption Standard (AES), implemented through both software and hardware with various key and buffer size settings on two resource-constrained IoT edge devices. In particular, we observe that (1) compared to software, the hardware implementation is more sensitive to buffer size settings and only consumes lower overall time and energy when the buffer size is sufficiently large; (2) the security premium provided by an increase in key size leads to increased resource consumption in all cases; and (3) comparing the two IoT boards, the CYW board, which is designed with faster default CPU clock rate and more memory, consumes fewer resources overall than the BCM board. These observations not only help advance the understanding of the trade-off between IoT devices' security needs and resource consumption, but also shed light on future improvement of light-weight security designs.


title: Three-Dimensional Intravascular Reconstruction Techniques Based on Intravascular Ultrasound: A Technical Review

abstract: Intravascular ultrasound (IVUS) imaging provides two-dimensional (2-D) real-time luminal and transmural cross-sectional images of intravascular vessels with detailed pathological information. It has offered significant advantages in terms of diagnosis and guidance and has been increasingly introduced from coronary interventions into more generalized endovascular surgery. However, IVUS itself does not provide spatial pose information for its generated images, making it difficult to construct a 3-D intravascular visualization. To address this limitation, IVUS imaging-driven 3-D intravascular reconstruction techniques have been developed. These techniques enable accurate diagnosis and quantitative measurements of intravascular diseases to facilitate optimal treatment determination. Such reconstruction extends the IVUS imaging modality from pure diagnostic assistance to intraoperative navigation and guidance and supports both therapeutic options and interventional operations. This paper presents a comprehensive survey of technological advances and recent progress on IVUS imaging-based 3-D intravascular reconstruction and its state-of-the-art applications. Limitations of existing technologies and prospects of new technologies are also discussed.


title: Hierarchical Structured Model for Fine-to-coarse Manifesto Text Analysis

abstract: Election manifestos document the intentions, motives, and views of political parties. They are often used for analysing a party's fine-grained position on a particular issue, as well as for coarse-grained positioning of a party on the left--right spectrum. In this paper we propose a two-stage model for automatically performing both levels of analysis over manifestos. In the first step we employ a hierarchical multi-task structured deep model to predict fine- and coarse-grained positions, and in the second step we perform post-hoc calibration of coarse-grained positions using probabilistic soft logic. We empirically show that the proposed model outperforms state-of-art approaches at both granularities using manifestos from twelve countries, written in ten different languages.


title: An iterative semi-explicit rating method for building collaborative recommender systems

abstract: Collaborative filtering plays the key role in recent recommender systems. It uses a user-item preference matrix rated either explicitly (i.e., explicit rating) or implicitly (i.e., implicit feedback). Despite the explicit rating captures the preferences better, it often results in a severely sparse matrix. The paper presents a novel iterative semi-explicit rating method that extrapolates unrated elements in a semi-supervised manner. Extrapolation is simply an aggregation of neighbor ratings, and iterative extrapolations result in a dense preference matrix. Preliminary simulation results show that the recommendation using the semi-explicit rating data outperforms that of using the pure explicit data only.


title: 6 Years of SMT-COMP

abstract: The annual Satisfiability Modulo Theories Competition (SMT-COMP) was initiated in 2005 in order to stimulate the advance of state-of-the-art techniques and tools developed by the Satisfiability Modulo Theories (SMT) community. This paper summarizes the first six editions of the competition. We present the evolution of the competition's organization and rules, show how the state of the art has improved over the course of the competition, and discuss the impact SMT-COMP has had on the SMT community and beyond. Additionally, we include an exhaustive list of all competitors, and present experimental results showing significant improvement in SMT solvers during these six years. Finally, we analyze to what extent the initial goals of the competition have been achieved, and sketch future directions for the competition.


title: Tasks Scheduling Technique Using League Championship Algorithm for Makespan Minimization in IaaS Cloud

abstract: Makespan minimization in tasks scheduling of infrastructure as a service (IaaS) cloud is an NP-hard problem. A number of techniques had been used in the past to optimize the makespan time of scheduled tasks in IaaS cloud, which is propotional to the execution cost billed to customers. In this paper, we proposed a League Championship Algorithm (LCA) based makespan time minimization scheduling technique in IaaS cloud. The LCA is a sports-inspired population based algorithmic framework for global optimization over a continuous search space. Three other existing algorithms that is, First Come First Served (FCFS), Last Job First (LJF) and Best Effort First (BEF) were used to evaluate the performance of the proposed algorithm. All algorithms under consideration assumed to be non-preemptive. The results obtained shows that, the LCA scheduling technique perform moderately better than the other algorithms in minimizing the makespan time of scheduled tasks in IaaS cloud.


title: Ultra-wideband 20-way coaxial power combiner

abstract: In this paper we present a new ultra-wideband 20-way coaxial power combiner. In this new structure, there is a radially extended coaxial cavity and 20 coaxial coupling probes. It can not only be used as power combiner but also power divider. There are several critical parameters we'll discuss here that ultimately determine the performance of the new power combiner. The new structure can efficiently work in ultra-wideband 6GHz–18GHz range. ANSYS HFSS simulation results show that the insertion loss is less than −1dB and the return loss is below −15dB. With relatively compacted body, space is sufficiently used and manufacturing process is easy for industry production.


Exploration of Attention in Question Answering


title: Design of a Q-learning-based client quality selection algorithm for HTTP adaptive video streaming

abstract: Over the past decades, the importance of multimedia services such as video streaming has increased considerably. HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for adaptive video streaming services. In HAS, a video is split into multiple segments and encoded at multiple quality levels. State-of-the-art HAS clients employ deterministic heuristics to dynamically adapt the requested quality level based on the perceived network and device conditions. Current HAS client heuristics are however hardwired to fit specific network configurations, making them less flexible to fit a vast range of settings. In this article, an adaptive Q-Learning-based HAS client is proposed. In contrast to existing heuristics, the proposed HAS client dynamically learns the optimal behavior corresponding to the current network environment. Considering multiple aspects of video quality, a tunable reward function has been constructed, giving the opportunity to focus on different aspects of the Quality of Experience, the quality as perceived by the end-user. The proposed HAS client has been thoroughly evaluated using a network-based simulator, investigating multiple reward configurations and Reinforcement Learning specific settings. The evaluations show that the proposed client can outperform standard HAS in the evaluated networking environments.


title: Ontology-based Extraction of Structured Information from Publications on Preclinical Experiments for Spinal Cord Injury Treatments

abstract: Preclinical research in the field of central nervous system trauma advances at a fast pace, currently yielding over 8,000 new publications per year, at an exponentially growing rate. This amount of published information by far exceeds the capacity of individual scientists to read and understand the relevant literature. So far, no clinical trial has led to therapeutic approaches which achieve functional recovery in human patients. In this paper, we describe a first prototype of an ontology-based information extraction system that automatically extracts relevant preclinical knowledge about spinal cord injury treatments from natural language text by recognizing participating entity classes and linking them to each other. The evaluation on an independent test corpus of manually annotated full text articles shows a macroaverage F1 measure of 0.74 with precision 0.68 and recall 0.81 on the task of identifying entities participating in relations.


title: Agent Modeling Language (AML): A Comprehensive Approach to Modeling MAS

abstract: The Agent Modeling Language (AML) is a semi-formal visual modeling language for specifying, modeling and documenting systems that incorporate features drawn from multi-agent systems theory. It is specified as an extension to UML 2.0 in accordance with major OMG modeling frameworks (MDA, MOF, UML, and OCL). The ultimate objective of AML is to provide software engineers with a ready-to-use, complete and highly expressive modeling language suitable for the development of commercial software solutions based on multi-agent technologies. This paper presents an overview of AML. The scope of the language, its structure and extensibility mechanisms are discussed, and the core AML modeling constructs and mechanisms are introduced and demonstrated by examples.


title: A case study of user participation in the information systems development process

abstract: There are many in the information systems discipline who believe that user participation is necessary for successful systems development. However, it has been suggested that this belief is neither grounded in theory nor substantiated by research data. This may indicate that researchers have not addressed fully the underlying complexity of the concept. If so, this is indicative of a deficiency in understanding user participation in information systems development as it occurs in organizations. In order to enhance the extant understanding of participative information systems development, the present study adopts a qualitative, case-based approach to research so as to provide an in-depth description of the complex social nature of the phenomenon as manifested in one organization. The results of the study illustrate that a high degree of direct and indirect user participation did not guarantee the successful implementation and use of information systems in the organization studied. Such participatory development practices did, however, result in the development of systems that adequately captured user requirements and hence satisfied user informational needs. It was clear that despite the perceived negative impact, which the new systems would have on user work-related roles and activities, the existence of an organization-wide participative policy, and associated participative structures, coupled with a favorable organization culture, generated a participatory development climate that was conducive to the successful development of information systems, while not guaranteeing it. That said, the central conclusion of this study was that user dissatisfaction with developed systems centered on the poor management of change in the organization.


title: Parental Socialization of Emotion Expression: Gender Differences and Relations to Child Adjustment.

abstract: The present study examined gender differences in children’s submissive and disharmonious emotions and parental attention to these emotions. Sixty children and their mothers and fathers participated when children were 4 and 6 years old. Children’s emotion expression and parental responses during a game were coded. Girls expressed more submissive emotion than boys. Fathers attended more to girls’ submissive emotion than to boys’ at preschool age. Fathers attended more to boys’ disharmonious emotion than to girls’ at early school age. Parental attention at preschool age predicted later submissive expression level. Child disharmonious emotion predicted later externalizing symptoms. Gender differences in these emotions may occur as early as preschool age and may be subject to differential responding, particularly by fathers.


title: Stochastic Multistage Co-optimization of Generation and Transmission Expansion Planning

abstract: This paper describes a generation and transmission (GT (ii) an SDDP-based "operation module" calculates the expected operation costs for the trial plan and Benders cuts to the investment module based on the expected marginal costs of the capacity constraints at the optimal solution. ::: The integrated G&T planning approach is illustrated with a realistic planning study for the Bolivian power system.


title: Ka-Band 5-Bit MMIC Phase Shifter Using InGaAs PIN Switching Diodes

abstract: The design and performance of a Ka-band 5 b MMIC phase shifter using InGaAs PIN switching diodes is presented. In order to achieve low insertion loss and good phase shifting characteristics at Ka-band, a switched reactance type InGaAs PIN-diode phase shifter topology has been employed with a compact bias network. The fabricated InGaAs PIN MMIC phase shifter has demonstrated good performance characteristics such as a low insertion loss of less than 7.8 dB and a high P1 dB of 21.0 dBm compared to the previous results.


title: Through mold vias for stacking of mold embedded packages

abstract: The constant drive towards further miniaturization and heterogeneous system integration leads to a need for new packaging technologies which also allow large area processing and 3D integration with potential for low cost applications. Large area mold embedding technologies and embedding of active components into printed circuit boards (Chip-in-Polymer) are two major packaging trends in this area. This paper describes the use of a novel S2iP (Stacked System in Package) interconnect technique using advanced molding process for multi chip embedding in combination with large area and low cost redistribution technology derived from printed circuit board manufacturing with a focus on integration of through mold vias for package stacking. The use of compression molding equipment with liquid or granular epoxy molding compounds for the targeted integration process flow is a new technology that has been especially developed to allow large area embedding of single chips but also of multiple chips or heterogeneous systems on wafer scale, typically 8” to 12”. Future developments will deal with panel sizes up to 470 × 370 mm². The wiring of the embedded components in this novel type of SiP is done using PCB manufacturing technologies, i.e. a resin coated copper (RCC) film is laminated over the embedded components — whichever no matter which shape they are: a compression molded wafer or a larger rectangular area of a Molded Array Package (MAP). Interconnects are formed by laser drilling to die pads and electroplating — all of them making use of standard PCB processes. Thus, through vias which are standard features in PCB manufacturing and can be also integrated in the proposed process flow for mold embedding in combination with RCC based redistribution. Vias were drilled by laser or mechanically after RCC lamination and were metalized together with the vias for chip interconnection. Within this study different liquid and granular molding compounds have been intensively evaluated on their processability. Via drilling process by laser and mechanical drilling is systematically developed and analyzed with focus on via diameter, pitch, mold thickness and molding compound composition and here especially on filler particle sizes and distribution. The feasibility of the entire process chain is demonstrated by fabrication of a Ball Grid Array (BGA) type of system package with two embedded dies and through mold vias allowing the stacking of these BGA packages. Finally, a technology demonstrator is described consisting of two BGAs stacked on each other and mounted on a base substrate enabling the electrical test of a daisy chain structure through the stacked module, allowing the evaluation of the technology and the applied processes.


title: Supporting privacy impact assessment by model-based privacy analysis

abstract: According to Article 35 of the General Data Protection Regulation (GDPR), data controllers are obligated to conduct a privacy impact assessment (PIA) to ensure the protection of sensitive data. Failure to properly protect sensitive data may affect data subjects negatively, and damage the reputation of data processors. Existing PIA approaches cannot be easily conducted, since they are mainly abstract or imprecise. Moreover, they lack a methodology to conduct the assessment concerning the design of IT systems. We propose a novel methodology to support PIA by performing model-based privacy and security analyses in the early phases of the system development. In our methodology, the design of a system is analyzed and, where necessary, appropriate security and privacy controls are suggested to improve the design. Hence, this methodology facilitates privacy by design as prescribed in Article 25 of the GDPR. We evaluated our methodology based on three industrial case studies and a quality-based comparison to the state of the art.


title: INVITED: Bandwidth-Efficient Deep Learning

abstract: Deep learning algorithms are achieving increasingly higher prediction accuracy on many machine learning tasks. However, applying brute-force programming to data demands a huge amount of machine power to perform training and inference, and a huge amount of manpower to design the neural network models, which is inefficient. In this paper, we provide techniques to solve these bottlenecks: saving memory bandwidth for inference by model compression, saving networking bandwidth for training by gradient compression, and saving engineer bandwidth for model design by using AI to automate the design of models.


title: Tense and Aspect Assignment in Narrative Discourse

abstract: We describe a method for assigning English tense and aspect in a system that realizes surface text for symbolically encoded narratives. Our testbed is an encoding interface in which propositions that are attached to a timeline must be realized from several temporal viewpoints. This involves a mapping from a semantic encoding of time to a set of tense/aspect permutations. The encoding tool realizes each permutation to give a readable, precise description of the narrative so that users can check whether they have correctly encoded actions and statives in the formal representation. Our method selects tenses and aspects for individual event intervals as well as subintervals (with multiple reference points), quoted and unquoted speech (which reassign the temporal focus), and modal events such as conditionals.


title: ANALYZING INFORMATION AND VALUE FLOWS IN HIGH-FREQUENCY CAPITAL MARKETS

abstract: High-frequency trading has significant influence on today’s capital markets and has received massive attention in the media. This research aims to provide a conceptual understanding of high-frequency capital markets by analysing information and value flows between relevant high-frequency trading market participants. In a first step, market participants including traders, brokers, market platforms, technology providers, information providers, and clearing agencies are introduced. Second, the trading process is described focusing on the three most important phases, namely the information phase, order routing phase, and order matching phase. Furthermore, we review widely adopted high-frequency trading strategies such as market making, arbitrage trading, and pinging. Expert interviews are used to provide practical insights on the perception of high-frequency trading and the necessity for improved regulation. We merge theoretical knowledge and our findings from practice to develop the HFT Value Information Framework visualizing information and value flows between market participants. We discuss the interrelations between market participants in current high-frequency capital markets and describe implications for different stakeholders. Finally, the implications for regulatory bodies are discussed and possible future research opportunities are identified.


title: Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods

abstract: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, automated analysis of dermoscopy images has become an important research area. Border detection is often the first step in this analysis. In many cases, the lesion can be roughly separated from the background skin using a thresholding method applied to the blue channel. However, no single thresholding method appears to be robust enough to successfully handle the wide variety of dermoscopy images encountered in clinical practice. In this paper, we present an automated method for detecting lesion borders in dermoscopy images using ensembles of thresholding methods. Experiments on a difficult set of 90 images demonstrate that the proposed method is robust, fast, and accurate when compared to nine state-of-the-art methods.


title: Discovery of a short-necked sauropod dinosaur from the Late Jurassic period of Patagonia

abstract: Sauropod dinosaurs are one of the most conspicuous groups of Mesozoic terrestrial vertebrates. They show general trends towards an overall increase in size and elongation of the neck, by means of considerable elongation of the length of individual vertebrae and a cervical vertebra count that, in some cases, increases to 19 (ref. 1). The long neck is a particular hallmark of sauropod dinosaurs and is usually regarded as a key feeding adaptation. Here we describe a new dicraeosaurid sauropod, from the latest Jurassic period of Patagonia, that has a particularly short neck. With a neck that is about 40% shorter than in other known dicraeosaurs, this taxon demonstrates a trend opposite to that seen in most sauropods and indicates that the ecology of dicraeosaurids might have differed considerably from that of other sauropods. The new taxon indicates that there was a rapid radiation and dispersal of dicraeosaurids in the Late Jurassic of the Southern Hemisphere, after the separation of Gondwana from the northern continents by the late Middle Jurassic.


title: A flexible and low-cost tactile sensor for robotic applications

abstract: For humans, the sense of touch is essential for interactions with the environment. With robots slowly starting to emerge as a human-centric technology, tactile information becomes increasingly important. Tactile sensors enable robots to gain information about contacts with the environment, which is required for safe interaction with humans or tactile exploration. Many sensor designs for the application on robots have been presented in literature so far. However, most of them are complex in their design and require high-tech tools for their manufacturing. In this paper, we present a novel design for a tactile sensor that can be built with low-cost, widely available materials, and low effort. The sensor is flexible, may be cut to arbitrary shapes and may have a customized spatial resolution. Both pressure distribution and absolute pressure on the sensor are detected. An experimental evaluation of our design shows low detection thresholds as well as high sensor accuracy. We seek to accelerate research on tactile feedback methods with this easy to replicate design. We consider our design a starting point for the integration of multiple sensor units to a large-scale tactile skin for robots.


title: An improved PSO-based ANN with simulated annealing technique

abstract: This paper presents a modified particle swarm optimization (PSO) with simulated annealing (SA) technique. An improved PSO-based artificial neural network (ANN) is developed. The results show that the proposed SAPSO-based ANN has a better ability to escape from a local optimum and is more effective than the conventional PSO-based ANN.


title: A peer-to-peer spatial cloaking algorithm for anonymous location-based service

abstract: This paper tackles a major privacy threat in current location-based services where users have to report their exact locations to the database server in order to obtain their desired services. For example, a mobile user asking about her nearest restaurant has to report her exact location. With untrusted service providers, reporting private location information may lead to several privacy threats. In this paper, we present a peer-to-peer (P2P)spatial cloaking algorithm in which mobile and stationary users can entertain location-based services without revealing their exact location information. The main idea is that before requesting any location-based service, the mobile user will form a group from her peers via single-hop communication and/or multi-hop routing. Then,the spatial cloaked area is computed as the region that covers the entire group of peers. Two modes of operations are supported within the proposed P2P s patial cloaking algorithm, namely, the on-demand mode and the proactive mode. Experimental results show that the P2P spatial cloaking algorithm operated in the on-demand mode has lower communication cost and better quality of services than the proactive mode, but the on-demand incurs longer response time.


title: Visual Tracking with Fully Convolutional Networks

abstract: We propose a new approach for general object tracking with fully convolutional neural network. Instead of treating convolutional neural network (CNN) as a black-box feature extractor, we conduct in-depth study on the properties of CNN features offline pre-trained on massive image data and classification task on ImageNet. The discoveries motivate the design of our tracking system. It is found that convolutional layers in different levels characterize the target from different perspectives. A top layer encodes more semantic features and serves as a category detector, while a lower layer carries more discriminative information and can better separate the target from distracters with similar appearance. Both layers are jointly used with a switch mechanism during tracking. It is also found that for a tracking target, only a subset of neurons are relevant. A feature map selection method is developed to remove noisy and irrelevant feature maps, which can reduce computation redundancy and improve tracking accuracy. Extensive evaluation on the widely used tracking benchmark [36] shows that the proposed tacker outperforms the state-of-the-art significantly.


title: Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction

abstract: Within the data envelopment analysis context, problems of discrimination between efficient and inefficient decision-making units often arise, particularly if there are a relatively large number of variables with respect to observations. This paper applies Monte Carlo simulation to generalize and compare two discrimination improving methods; principal component analysis applied to data envelopment analysis (PCA-DEA) and variable reduction based on partial covariance (VR). Performance criteria are based on the percentage of observations incorrectly classified; efficient decision-making units mistakenly defined as inefficient and inefficient units defined as efficient. A trade-off was observed with both methods improving discrimination by reducing the probability of the latter error at the expense of a small increase in the probability of the former error. A comparison of the methodologies demonstrates that PCA-DEA provides a more powerful tool than VR with consistently more accurate results. PCA-DEA is applied to all basic DEA models and guidelines for its application are presented in order to minimize misclassification and prove particularly useful when analyzing relatively small datasets, removing the need for additional preference information.


Nehovah: A Neologism Creator Nomen Ipsum


title: A brain–computer interface using electrocorticographic signals in humans

abstract: Brain–computer interfaces (BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the brain can enable users to control a one-dimensional computer cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3–24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74–100% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the brain. M This article features online multimedia enhancements


title: Antecedents and effects of individual absorptive capacity: a micro-foundational perspective on open innovation

abstract: Purpose ::: ::: ::: ::: ::: The paper aims to understand how individuals differ in individual absorptive capacity – their ability to recognize, assimilate, transform and exploit external knowledge. These individual absorptive capacities are a key knowledge management building block for an organization’s open innovation practices. The study examines individual antecedents – human capital, social capital and cognition – and innovation outcomes of individual absorptive capacity. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: This is a quantitative study of 147 employees in a single medium-sized Dutch industrial firm. Based on a survey and structural equation modeling, the antecedents’ prior knowledge diversity, network diversity and cognitive style are examined in relation to individual absorptive capacity. Further, the mediating effects of individual absorptive capacity on its antecedents and innovation outcome are investigated. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The main findings are that prior knowledge diversity, external network diversity and a bisociative cognitive style explain differences in individual absorptive capacity. A bisociative cognitive style appears to be the most important factor. Also, this study finds that individual absorptive capacity mediates between its antecedents and individual innovation performance and is therefore a relevant factor to capture value from external knowledge sources. ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: ::: The study extends open innovation theory by exploring individual-level factors that explain the ability to capture value from external knowledge. It suggests that differences in open innovation practices are explained by heterogeneity at the individual level. Further, it explains how individuals’ potentials for open innovation are mediated by their absorptive capacities. These insights enable future researchers to further examine individual-level factors in knowledge management practices and to explore cross-level individual-organizational interactions for open innovation. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: This paper highlights that individuals’ engagements in open innovation practices are explained not only by individuals’ motivations but also by their abilities to absorb external knowledge. Further, it helps managers to design knowledge management practices to promote employees’ absorptive capacities, to improve open innovation processes. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: This study investigates the neglected individual-level factors of open innovation practices from a micro-foundational and knowledge management perspective. To our best knowledge, this is the first study to examine individual-level antecedents and outcomes of individual absorptive capacity.


title: AX.25 protocol compatible reconfigurable 2/4 FSK modulator design for nano/micro-satellites

abstract: In this paper, implementation of a FPGA-based and VHDL coded reconfigurable frequency shift keying (FSK) modulator for nano-satellite communication systems is presented. Reconfigurable FSK modulator includes 2-FSK and 4-FSK modulators. The system is compatible with AX.25 data link layer protocol for general amateur radio communication which is popular in micro/nano satellite communication. ROM based direct digital synthesizer (DDS) generates a sinusoidal wave at the output with a specified frequency and phase.


Formal and Informal Learning Experiences in Second Life: An Overview


title: An accurate algorithm for head detection based on XYZ and HSV hair and skin color models

abstract: Head detection in images and videos plays an important role in a wide range of computer vision and multimedia applications. In this paper, we propose a new head detection algorithm that is capable of handling significantly variable conditions in terms of viewpoint (i.e. frontal, profile, back view, from -180 degrees to +180 degrees), tilt angle (i.e. from horizontal to aerial), scale and resolution. To this aim, we built a new model for the head based on appearance distributions and shape constraints. The appearance distribution models the colors of hair and skin by sets of Gaussian mixtures in the XYZ and HSV color spaces. The shape constraint fits an elliptical model to the candidate region and compares its parameters with priors based on the human anatomy. This presents a pixel-level measurement of accuracy for the proposed algorithm both prior and after applying the spatial constraints referenced by the elliptical model. The excellent accuracy at both levels confirms the accuracy of the appearance model and the appropriateness of the spatial and topological process.


title: Duplicate Record Detection for Database Cleansing

abstract: Many organizations collect large amounts of data to support their business and decision making processes. The data collected from various sources may have data quality problems in it. These kinds of issues become prominent when various databases are integrated. The integrated databases inherit the data quality problems that were present in the source database. The data in the integrated systems need to be cleaned for proper decision making. Cleansing of data is one of the most crucial steps. In this research, focus is on one of the major issue of data cleansing i.e. “duplicate record detection” which arises when the data is collected from various sources. As a result of this research study, comparison among standard duplicate elimination algorithm (SDE), sorted neighborhood algorithm (SNA), duplicate elimination sorted neighborhood algorithm (DE-SNA), and adaptive duplicate detection algorithm (ADD) is provided. A prototype is also developed which shows that adaptive duplicate detection algorithm is the optimal solution for the problem of duplicate record detection. For approximate matching of data records, string matching algorithms (recursive algorithm with word base and recursive algorithm with character base) have been implemented and it is concluded that the results are much better with recursive algorithm with word base.


title: Predicting Future Scientific Discoveries Based on a Networked Analysis of the Past Literature

abstract: We present KnIT, the Knowledge Integration Toolkit, a system for accelerating scientific discovery and predicting previously unknown protein-protein interactions. Such predictions enrich biological research and are pertinent to drug discovery and the understanding of disease. Unlike a prior study, KnIT is now fully automated and demonstrably scalable. It extracts information from the scientific literature, automatically identifying direct and indirect references to protein interactions, which is knowledge that can be represented in network form. It then reasons over this network with techniques such as matrix factorization and graph diffusion to predict new, previously unknown interactions. The accuracy and scope of KnIT's knowledge extractions are validated using comparisons to structured, manually curated data sources as well as by performing retrospective studies that predict subsequent literature discoveries using literature available prior to a given date. The KnIT methodology is a step towards automated hypothesis generation from text, with potential application to other scientific domains.


title: Modular elliptic curves and Fermat’s Last Theorem

abstract: When Andrew John Wiles was 10 years old, he read Eric Temple Bell’s The Last Problem and was so impressed by it that he decided that he would be the first person to prove Fermat’s Last Theorem. This theorem states that there are no nonzero integers a, b, c, n with n > 2 such that an + bn = cn. The object of this paper is to prove that all semistable elliptic curves over the set of rational numbers are modular. Fermat’s Last Theorem follows as a corollary by virtue of previous work by Frey, Serre and Ribet.


title: Junction Tree Variational Autoencoder for Molecular Graph Generation

abstract: We seek to automate the design of molecules based on specific chemical properties. In computational terms, this task involves continuous embedding and generation of molecular graphs. Our primary contribution is the direct realization of molecular graphs, a task previously approached by generating linear SMILES strings instead of graphs. Our junction tree variational autoencoder generates molecular graphs in two phases, by first generating a tree-structured scaffold over chemical substructures, and then combining them into a molecule with a graph message passing network. This approach allows us to incrementally expand molecules while maintaining chemical validity at every step. We evaluate our model on multiple tasks ranging from molecular generation to optimization. Across these tasks, our model outperforms previous state-of-the-art baselines by a significant margin.


title: Application of Enhanced Phase-Locked Loop System to the Computation of Synchrophasors

abstract: This paper introduces the application of an enhanced phase-locked loop (EPLL) system to the estimation of synchrophasors in a phasor measurement unit (PMU). The major concern is accurate estimation within off-nominal frequency operation of the system. The well-known technique based on discrete Fourier transform (DFT) can provide accurate estimation of the phasors in a three-phase balanced system. However, the negative-sequence component causes errors to the DFT estimates. The DFT cannot accomplish this task in a single-phase system. The EPLL is shown to be a solution for those shortcomings of the DFT technique, both in single-phase and in unbalanced three-phase systems, at the expense of some more complicated structure. Extensive steady-state and dynamic tests are performed and the results are presented and discussed.


title: Bargaining Through Agents: An Experimental Study of Delegation and Commitment

abstract: Abstract While face-to-face bargaining has proven itself to be extremely efficient in the laboratory, it appears to break down often in the real world. This discrepancy, we assert, is explained by the fact that in the real world face-to-face bargaining is usually conducted not between principals but between the agents of principals. We find a substantial increase in inefficiency when bargaining is conducted through agents rather than through principals and offer an explanation for this rise in inefficiency. As such, this paper helps to shed light on the growing literature on delegation, commitment, and preference distortion games. Journal of Economic Literature Classification Numbers: C78, C92.


title: Surviving wi-fi interference in low power ZigBee networks

abstract: Frequency overlap across wireless networks with different radio technologies can cause severe interference and reduce communication reliability. The circumstances are particularly unfavorable for ZigBee networks that share the 2.4 GHz ISM band with WiFi senders capable of 10 to 100 times higher transmission power. Our work first examines the interference patterns between ZigBee and WiFi networks at the bit-level granularity. Under certain conditions, ZigBee activities can trigger a nearby WiFi transmitter to back off, in which case the header is often the only part of the Zig-Bee packet being corrupted. We call this the symmetric interference regions, in comparison to the asymmetric regions where the ZigBee signal is too weak to be detected by WiFi senders, but WiFi activity can uniformly corrupt any bit in a ZigBee packet. With these observations, we design BuzzBuzz to mitigate WiFi interference through header and payload redundancy. Multi-Headers provides header redundancy giving ZigBee nodes multiple opportunities to detect incoming packets. Then, TinyRS, a full-featured Reed Solomon library for resource-constrained devices, helps decoding polluted packet payload. On a medium-sized testbed, BuzzBuzz improves the ZigBee network delivery rate by 70%. Furthermore, BuzzBuzz reduces ZigBee retransmissions by a factor of three, which increases the WiFi throughput by 10%.


SideNoter: Scholarly Paper Browsing System based on PDF Restructuring and Text Annotation


title: Authorship verification for short messages using stylometry

abstract: Authorship verification can be checked using stylometric techniques through the analysis of linguistic styles and writing characteristics of the authors. Stylometry is a behavioral feature that a person exhibits during writing and can be extracted and used potentially to check the identity of the author of online documents. Although stylometric techniques can achieve high accuracy rates for long documents, it is still challenging to identify an author for short documents, in particular when dealing with large authors populations. These hurdles must be addressed for stylometry to be usable in checking authorship of online messages such as emails, text messages, or twitter feeds. In this paper, we pose some steps toward achieving that goal by proposing a supervised learning technique combined with n-gram analysis for authorship verification in short texts. Experimental evaluation based on the Enron email dataset involving 87 authors yields very promising results consisting of an Equal Error Rate (EER) of 14.35% for message blocks of 500 characters.


title: Creation and Management of Social Network Honeypots for Detecting Targeted Cyber Attacks

abstract: Reconnaissance is the initial and essential phase of a successful advanced persistent threat (APT). In many cases, attackers collect information from social media, such as professional social networks. This information is used to select members that can be exploited to penetrate the organization. Detecting such reconnaissance activity is extremely hard because it is performed outside the organization premises. In this paper, we propose a framework for management of social network honeypots to aid in detection of APTs at the reconnaissance phase. We discuss the challenges that such a framework faces, describe its main components, and present a case study based on the results of a field trial conducted with the cooperation of a large European organization. In the case study, we analyze the deployment process of the social network honeypots and their maintenance in real social networks. The honeypot profiles were successfully assimilated into the organizational social network and received suspicious friend requests and mail messages that revealed basic indications of a potential forthcoming attack. In addition, we explore the behavior of employees in professional social networks, and their resilience and vulnerability toward social network infiltration.


title: CFCM: Segmentation via Coarse to Fine Context Memory

abstract: Recent neural-network-based architectures for image segmentation make extensive usage of feature forwarding mechanisms to integrate information from multiple scales. Although yielding good results, even deeper architectures and alternative methods for feature fusion at different resolutions have been scarcely investigated for medical applications. In this work we propose to implement segmentation via an encoder-decoder architecture which differs from any other previously published method since (i) it employs a very deep architecture based on residual learning and (ii) combines features via a convolutional Long Short Term Memory (LSTM), instead of concatenation or summation. The intuition is that the memory mechanism implemented by LSTMs can better integrate features from different scales through a coarse-to-fine strategy; hence the name Coarse-to-Fine Context Memory (CFCM). We demonstrate the remarkable advantages of this approach on two datasets: the Montgomery county lung segmentation dataset, and the EndoVis 2015 challenge dataset for surgical instrument segmentation.


title: Australian SME Micro-Offshoring Opportunities in the Philippines: An Expanding Niche Market?

abstract: This paper examines how Filipino-based business process outsourcing centers have been developing services, including offshore "staff leasing" and "co-managed services" arrangements, that are helping to overcome traditional small and medium enterprise (SME) resource constraints. These "micro-offshoring" models greatly reduce transaction costs for Australian SMEs seeking to outsource/offshore former in-house work and appear to be supporting a rapid increase in the number of Australian-based SMEs offshoring professional services to the Philippines. Micro-offshoring further provides job opportunities for Filipino tertiary graduates and entrepreneurial opportunities for local Filipino SMEs looking to enter and tap this market. This research also suggests that it may encourage Australian SMEs to shift to more long-term offshoring models over time, such as local incorporation, and therefore foster foreign direct investment.


title: Analysis of numerical methods for the simulation of deformable models

abstract: Simulating deformable objects based on physical laws has become the most popular technique for modeling textiles, skin, or volumetric soft objects like human tissue. The physical model leads to an ordinary differential equation. Recently, several approaches to fast algorithms have been proposed. ::: ::: In this work, more profound numerical background about numerical stiffness is provided. Stiff equations impose stability restrictions on a numerical integrator. Some one-step and multistep methods with adequate stability properties are presented. For an efficient implementation, the inexact Newton method is discussed. Applications to 2D and 3D elasticity problems show that the discussed methods are faster and give higher-quality solutions than the commonly used linearized Euler method.


title: People Do Not Feel Guilty About Exploiting Machines

abstract: Guilt and envy play an important role in social interaction. Guilt occurs when individuals cause harm to others or break social norms. Envy occurs when individuals compare themselves unfavorably to others and desire to benefit from the others’ advantage. In both cases, these emotions motivate people to act and change the status quo: following guilt, people try to make amends for the perceived transgression, and following envy, people try to harm envied others. In this article, we present two experiments that study participants’ experience of guilt and envy when engaging in social decision making with machines and humans. The results showed that, though experiencing the same level of envy, people felt considerably less guilt with machines than with humans. These effects occurred both with subjective and behavioral measures of guilt and envy, and in three different economic games: public goods, ultimatum, and dictator game. This poses an important challenge for human-computer interaction because, as shown here, it leads people to systematically exploit machines, when compared to humans. We discuss theoretical and practical implications for the design of human-machine interaction systems that hope to achieve the kind of efficiency -- cooperation, fairness, reciprocity, etc. -- we see in human-human interaction.


title: A Markov Chain Model of Temporal Behavior for Anomaly Detection

abstract: This paper presents an anomaly detection technique to detect intrusions into computer and network systems. In this technique, a Markov chain model is used to represent a temporal profile of normal behavior in a computer and network system. The Markov chain model of the norm profile is learned from historic data of the system’s normal behavior. The observed behavior of the system is analyzed to infer the probability that the Markov chain model of the norm profile supports the observed behavior. A low probability of support indicates an anomalous behavior that may result from intrusive activities. The technique was implemented and tested on the audit data of a Sun Solaris system. The testing results showed that the technique clearly distinguished intrusive activities from normal activities in the testing data.


title: The 'true' L-xylulose reductase of filamentous fungi identified in Aspergillus niger.

abstract: L-Xylulose reductase is part of the eukaryotic pathway for l-arabinose catabolism. A previously identified L-xylulose reductase in Hypocrea jecorina turned out to be not the 'true' one since it was not upregulated during growth on L-arabinose and the deletion strain showed no reduced L-xylulose reductase activity but instead lost the D-mannitol dehydrogenase activity. In this communication we identified the 'TRUE'L-xylulose reductase in Aspergillus niger. The gene, lxrA (JGI177736), is upregulated on L-arabinose and the deletion results in a strain lacking the NADPH-specific L-xylulose reductase activity and having reduced growth on l-arabinose. The purified enzyme had a K(m) for L-xylulose of 25 mM and a nu(max) of 650 U/mg.


title: LaneLoc: Lane marking based localization using highly accurate maps

abstract: Precise and robust localization in real-world traffic scenarios is a new challenge arising in the context of autonomous driving and future driver assistance systems. The required precision is in the range of a few centimeters. In urban areas this precision cannot be achieved by standard global navigation satellite systems (GNSS). Our novel approach achieves this requirement using a stereo camera system and a highly accurate map containing curbs and road markings. The maps are created beforehand using an extended sensor setup. GNSS position is used for initialization only and is not required during the localization process. In the paper we present the localization process and provide an evaluation on a test track under known conditions as well as a long term evaluation on approximately 50 km of rural roads, where a precision in centimeter-range is achieved.


title: Harissa: A flexible and efficient Java environment mixing bytecode and compiled code

abstract: The Java language provides a promising solution to the design of safe programs, with an application spectrum ranging from Web services to operating system components. The well-known tradeoff of Java's portability is the inefficiency of its basic execution model, which relies on the interpretation of an object-based virtual machine. Many solutions have been proposed to overcome this problem, such as just-in-time (JIT) and off-line bytecode compilers. However, most compilers trade efficiency for either portability or the ability to dynamically load bytecode. ::: ::: In this paper, we present an approach which reconciles portability and efficiency, and preserves the ability to dynamically load bytecode. We have designed and implemented an efficient environment for the execution of Java programs, named Harissa. Harissa permits the mixing of compiled and interpreted methods. Harissa's compiler translates Java bytecode to C, incorporating aggressive optimizations such as virtual-method call optimization based on the Class Hierarchy Analysis. To evaluate the performance of Harissa, we have conducted an extensive experimental study aimed at comparing the various existing alternatives to execute Java programs. The C code produced by Harissa's compiler is more efficient than all other alternative ways of executing Java programs (that were available to us): it is up to 140 times faster than the JDK interpreter, up to 13 times faster than the Softway Guava JIT, and 30% faster than the Toba bytecode to C compiler.


title: Causal Inference by Direction of Information

abstract: We focus on data-driven causal inference. In particular, we propose a new principle for causal inference based on algorithmic information theory, i.e. Kolmogorov complexity. In a nutshell, we determine how much information one data object gives about the other, and vice versa, and identify the most likely causal direction by the strongest direction of information. To apply this principle in practice, we propose ERGO, an efficient instantiation for inferring the causal direction between multivariate real-valued data pairs. ERGO is based on cumulative and Shannon entropy. Therewith, we do not have to assume distributions, nor have to restrict the type of correlation. Extensive empirical evaluation on synthetic, benchmark, and real-world data shows that ERGO is robust against both noise and dimensionality, efficient, and outperforms the state of the art by a wide margin.


title: Measuring the Influence of Perceived Cybercrime Risk on Online Service Avoidance

abstract: Cybercrime is a pervasive threat for today's Internet-dependent society. While the real extent and economic impact is hard to quantify, scientists and officials agree that cybercrime is a huge and still growing problem. A substantial fraction of cybercrime's overall costs to society can be traced to indirect opportunity costs, resulting from unused online services. This paper presents a parsimonious model that builds on technology acceptance research and insights from criminology to identify factors that reduce Internet users’ intention to use online services. We hypothesize that avoidance of online banking, online shopping and online social networking is increased by cybercrime victimization and media reports. The effects are mediated by the perceived risk of cybercrime and moderated by the user's confidence online. We test our hypotheses using a structural equation modeling analysis of a representative pan-European sample. Our empirical results confirm the negative impact of perceived risk of cybercrime on the use of all three online service categories and support the role of cybercrime experience as an antecedent of perceived risk of cybercrime. We further show that more confident Internet users perceive less cybercriminal risk and are more likely to use online banking and online shopping, which highlights the importance of consumer education.


title: Mobile Edutainment System -- National Palace Museum as an Example

abstract: In order to achieve highest level of knowledge dissemination, many museums have been digitalizing their collections and disseminated them to the world through the Internet. On the other hand, cloud computing and mobile communication technologies will further enhance the penetration capability of digital information, enabling mobile users surfing the wave of information world at anytime and anywhere through mobile devices such as smart phones or tablet PC. Nevertheless, both technologies have their own limitations that may hinder their applicability to the digital museums. We use the National Palace Museum (NPM) as a reference museum for the application of cloud computing and mobile communication technologies to mobilize digital museums. A set of design guidelines specific to the NPM's services is formulated and a few edutainment systems were implemented based on these guidelines: (1) Qing Ming Painting and (2) Mao Gong Ding Inscription graphical exhibition systems. With mobilization, the NPM and other museums will be able to lift their world-class services to another level for the benefit of all.


title: CycloidGrid: A proximity-aware P2P-based resource discovery

abstract: Volunteer computing which benefits from idle cycles of volunteer resources over the Internet can integrate the power of hundreds to thousands of resources to achieve high computing power. In such an environment the resources are heterogeneous in terms of CPU speed, RAM, disk capacity, and network bandwidth. So finding a suitable resource to run a particular job becomes difficult. Resource discovery architecture is a key factor for overall performance of peer-to-peer based volunteer computing systems. The main contribution of this paper is to develop a proximity-aware resource discovery architecture for peer-to-peer based volunteer computing systems. The proposed resource discovery algorithm consists of two stages. In the first stage, it selects resources based on the requested quality of service and current load of peers. In the second stage, a resource with higher priority to communication delay is selected among the discovered resources. Communication delay between two peers is computed by a network model based on queuing theory, taking into account the background traffic of the Internet. Simulation results show that the proposed resource discovery algorithm improves the response time of user's requests by a factor of 4.04 under a moderate load.


title: Robust Blind Separation of Statistically Dependent Sources using Dual Tree Wavelets

abstract: Blind source separation (BSS) problem is commonly solved by means of independent component analysis (ICA) assuming statistically independent and non-Gaussian sources. The strict independence assumption can be relaxed to existence of subbands where signals are less dependent. In this paper, we use dual tree complex wavelets for the subband decomposition of observed signals and small cummulant based approximation of mutual information for finding the most independent subband(s). We compare the proposed method to previously reported shift invariant and decimated wavelet packet based approach, as well as to innovations based approach. We found proposed dual tree wavelets scheme as an efficient and robust solution of the BSS problem of statistically dependent sources. One important application of the proposed method is related to unsupervised segmentation of medical and remotely sensed multispectral images.


title: Approximate analytic solutions to non-symmetric stance trajectories of the passive Spring-Loaded Inverted Pendulum with damping

abstract: This paper introduces an accurate yet analytically simple approximation to the stance dynamics of the Spring-Loaded Inverted Pendulum (SLIP) model in the presence of non-negligible damping and non-symmetric stance trajectories. Since the SLIP model has long been established as an accurate descriptive model for running behaviors, its careful analysis is instrumental in the design of successful locomotion controllers. Unfortunately, none of the existing analytic methods in the literature explicitly take damping into account, resulting in degraded predictive accuracy when they are used for dissipative runners. We show that the methods we propose not only yield average predictive errors below 2% in the presence of significant damping, but also outperform existing alternatives to approximate the trajectories of a lossless model. Finally, we exploit both the predictive performance and analytic simplicity of our approximations in the design of a gait-level running controller, demonstrating their practical utility and performance benefits.


title: Relative Parts: Distinctive Parts for Learning Relative Attributes

abstract: The notion of relative attributes as introduced by Parikh and Grauman (ICCV, 2011) provides an appealing way of comparing two images based on their visual properties (or attributes) such as "smiling" for face images, "naturalness" for outdoor images, etc. For learning such attributes, a Ranking SVM based formulation was proposed that uses globally represented pairs of annotated images. In this paper, we extend this idea towards learning relative attributes using local parts that are shared across categories. First, instead of using a global representation, we introduce a part-based representation combining a pair of images that specifically compares corresponding parts. Then, with each part we associate a locally adaptive "significance-coefficient" that represents its discriminative ability with respect to a particular attribute. For each attribute, the significance-coefficients are learned simultaneously with a max-margin ranking model in an iterative manner. Compared to the baseline method, the new method is shown to achieve significant improvement in relative attribute prediction accuracy. Additionally, it is also shown to improve relative feedback based interactive image search.


title: A Blind Source Separation Technique Using Second-Order Statistics

abstract: Separation of sources consists of recovering a set of signals of which only instantaneous linear mixtures are observed. In many situations, no a priori information on the mixing matrix is available: The linear mixture should be "blindly" processed. This typically occurs in narrowband array processing applications when the array manifold is unknown or distorted. This paper introduces a new source separation technique exploiting the time coherence of the source signals. In contrast with other previously reported techniques, the proposed approach relies only on stationary second-order statistics that are based on a joint diagonalization of a set of covariance matrices. Asymptotic performance analysis of this method is carried out; some numerical simulations are provided to illustrate the effectiveness of the proposed method.


title: Design of a hexapod robot

abstract: This paper introduces a new hexapod legged robot that can autonomously adapt to a variety of terrain; its wheel leg designs specially to make it has a good obstacle surmounting capacity. This robot uses the modularized design method, using the interrupt to detect the position of the wheel leg, PWM to control motor speed, Bus to control motor, gait program to control the robot motion, wireless data transmission to control the robot's gait, host computer to real-time monitor the robot status. The host computer or user handheld devices can regulate the state of the robot, at the same time the host computer can monitor the state of the robot. Finally, the principle demonstrator verifies the feasibility of the software design and hardware design method, thus ensuring the validity and reliability of the robot design.


title: Evaluation of conducting capital structure arbitrage using the multi-period extended Geske–Johnson model

abstract: This study utilizes a multi-period structural model developed by Chen and Yeh (Pricing credit default swaps with the extended Geske–Johnson Model. Working paper, 2006), which extends the Geske and Johnson (J Financ Quant Anal 19:231–232, 1984) compound option model to evaluate the performance of capital structure arbitrage. In the paper, first of all, we predict the default probability for each firm using the multi-period Geske–Johnson model that assumes endogenous default barriers. Second, based on the arbitrage performance of 369 North American obligators from 2004 to 2008, we find that the extended Geske–Johnson model is more suitable than the CreditGrades model for exploiting the mispricing between equity prices and credit default swap spreads. Finally, the Geske–Johnson model also performs well in extreme market condition, such as the financial crisis around 2008.


title: Strategies for training large scale neural network language models

abstract: We describe how to effectively train neural network based language models on large data sets. Fast convergence during training and better overall performance is observed when the training data are sorted by their relevance. We introduce hash-based implementation of a maximum entropy model, that can be trained as a part of the neural network model. This leads to significant reduction of computational complexity. We achieved around 10% relative reduction of word error rate on English Broadcast News speech recognition task, against large 4-gram model trained on 400M tokens.


title: Learning Outcomes in a Model-Based Approach to Curriculum Design

abstract: Curriculum or program design has received considerable attention recently. The EQF initiative, the CDIO curriculum or other integrated program developments based on learning outcomes and/or competencies have increased the complexity of program design, appropriation, diffusion and implementation. Various model-based or standardized approaches have been proposed, including IMS-LD, CDM, XCRI or MLO. In this paper, we propose a general model-based approach to curriculum design that has been successfully apply to course syllabi. We show how learning outcomes (and other features) could be incorporated either in a top-down or bottom-up approach. Our approach merges also seamlessly with learning outcome assessment in courses or in portfolios. We also discuss how accreditation reports can be automatically generated.


title: Evaluating Business Intelligence Platforms: a case study

abstract: The paper examines some common platforms supporting Business Intelligence activities in order to state evaluation criteria for the system choice. The evaluation considers a software measurement method based on the analysis of the functional complexity of the platforms. The study has been performed on an academic warehouse that uses historical data available in legacy databases. Experimental results are reported which show the advantages and the drawbacks of each considered system.


title: A 5 GHz, 1.5 V and very low-power CMOS frequency synthesizer for wireless communications

abstract: A 5 GHz, 1.5 V and low power CMOS frequency synthesizer with LC-tuned VCO (voltage controlled oscillator) is presented. The synthesizer consists of an LC-tuned VCO, an injection-lock frequency divider, a very low power prescaler which brings 2.5 GHz range frequency to 11 MHz, a phase-frequency detector and charge-pump with improved architecture. Spiral inductors with Q-factor of 9 are used for the LC-tuned 5 GHz VCO. A fixed 2-frequency divider based on injection-locking phenomenon is used to bring 5 GHz VCO frequency to 2.5 GHz. Both VCO and injection-locked divider's oscillators are tuned with NMOS varactors with their bodies grounded, resulting in about 700 MHz tuning range. Implemented in 0.25/spl mu/m standard CMOS process, the synthesizer consumes only 13.5 mW with a single 1.5 V supply.


title: Machine Learning for Biometrics

abstract: Biometrics aims at reliable and robust identication of humans from their personal traits, mainly for security and authentication purposes, but also for identifying and tracking the users of smarter applications. Frequently considered modalities are ngerprint, face, iris, palmprint and voice, but there are many other possible biometrics, including gait, ear image, retina, DNA, and even behaviours. This chapter presents a survey of machine learning methods used for biometrics applications, and identies relevant research issues. The author focuses on three areas of interest: ofine methods for biometric template construction and recognition, information fusion methods for integrating multiple biometrics to obtain robust results, and methods for dealing with temporal information. By introducing exemplary and inuential machine learning approaches in the context of specic biometrics applications, the author hopes to provide the reader with the means to create novel machine learning solutions to challenging biometrics problems.


title: Fostering knowledge construction in university students through asynchronous discussion groups

abstract: Does collaborative learning in asynchronous discussion groups result in enhancing academic discourse and knowledge construction? This general research question has been researched in a study involving 300 students, working during six months in 38 electronic discussion groups. The transcripts of the discussions were coded and analysed to test hypotheses related to the impact on knowledge construction. Coding of the units of analysis was based on the models of Fahy, P. J., Crawford, G., Ally, M., Cookson, P., Keller, V., & Prosser, F. (2000). The development and testing of a tool for analysis of computer mediated conferencing transcripts. Alberta Journal of Educational Research, 46(1), 85-88 and Veerman, A., & Veldhuis-Diermanse E. (2001). Collaborative learning through computer-mediated communication in academic education. In P. Dillenbourg, A. Eurelings, & K. Hakkarainen (Eds.), European Perspectives on Computer-Supported Collaborative Learning. Proceedings of the First European Conference on CSCL. Maastricht: McLuhan Institute, University of Maastricht. The results confirm that students in the discussion groups are very task-oriented and that higher proportions of high phases of knowledge construction are observed. Significant increases in the cognitive interaction, task-orientation and higher phases of knowledge construction are detected. Group size is a significant interaction variable. Discussion in smaller groups reflects larger proportions of higher levels of knowledge construction. The results point at the critical impact of structure in the task environment. In the discussion section, methodological issues are presented. The article concludes with directions for future research and some implications for instructional practice.


Discovering and Exploiting Additive Structure for Bayesian Optimization


title: Upper limb amputees can be induced to experience a rubber hand as their own

abstract: We describe how upper limb amputees can be made to experience a rubber hand as part of their own body. This was accomplished by applying synchronous touches to the stump, which was out of view, and to the index finger of a rubber hand, placed in full view (26 cm medial to the stump). This elicited an illusion of sensing touch on the artificial hand, rather than on the stump and a feeling of ownership of the rubber hand developed. This effect was supported by quantitative subjective reports in the form of questionnaires, behavioural data in the form of misreaching in a pointing task when asked to localize the position of the touch, and physiological evidence obtained by skin conductance responses when threatening the hand prosthesis. Our findings outline a simple method for transferring tactile sensations from the stump to a prosthetic limb by tricking the brain, thereby making an important contribution to the field of neuroprosthetics where a major goal is to develop artificial limbs that feel like a real parts of the body.


title: Digital beamforming for Passive Coherent Location radar

abstract: The paper presents digital beamforming for Passive Coherent Location (PCL) radar. The considered circular antenna array is a part of a passive system developed at Warsaw University of Technology. The system is based on FM radio transmitters. The array consists of eight half-wave dipoles arranged in a circular array covering 360deg with multiple beams. The digital beamforming procedure is presented, including mutual coupling correction and antenna pattern optimization. The results of field calibration and measurements are also shown.


title: Computational Intelligence in Sports: A Systematic Literature Review

abstract: Recently, data mining studies are being successfully conducted to estimate several parameters in a variety of domains. Data mining techniques have attracted the attention of the information industry and society as a whole, due to a large amount of data and the imminent need to turn it into useful knowledge. However, the effective use of data in some areas is still under development, as is the case in sports, which in recent years, has presented a slight growth; consequently, many sports organizations have begun to see that there is a wealth of unexplored knowledge in the data extracted by them. Therefore, this article presents a systematic review of sports data mining. Regarding years 2010 to 2018, 31 types of research were found in this topic. Based on these studies, we present the current panorama, themes, the database used, proposals, algorithms, and research opportunities. Our findings provide a better understanding of the sports data mining potentials, besides motivating the scientific community to explore this timely and interesting topic.


title: Privee: an architecture for automatically analyzing web privacy policies

abstract: Privacy policies on websites are based on the notice-and-choice principle. They notify Web users of their privacy choices. However, many users do not read privacy policies or have difficulties understanding them. In order to increase privacy transparency we propose Privee--a software architecture for analyzing essential policy terms based on crowdsourcing and automatic classification techniques. We implement Privee in a proof of concept browser extension that retrieves policy analysis results from an online privacy policy repository or, if no such results are available, performs automatic classifications. While our classifiers achieve an overall F-1 score of 90%, our experimental results suggest that classifier performance is inherently limited as it correlates to the same variable to which human interpretations correlate--the ambiguity of natural language. This finding might be interpreted to call the notice-and-choice principle into question altogether. However, as our results further suggest that policy ambiguity decreases over time, we believe that the principle is workable. Consequently, we see Privee as a promising avenue for facilitating the notice-and-choice principle by accurately notifying-Web users of privacy practices and increasing privacy transparency on the Web.


title: A wireless sensor network based low cost and energy efficient frame work for precision agriculture

abstract: Agriculture happens to be the key source of livelihood for more or less every occupant in developing country including India. Agriculture by and large determines the economic growth of nation and is known to be its backbone. It is due to the population explosion, which resulted in disproportion of demand/ supply ratio for agricultural products. To satisfy ever increasing demand for agricultural products, either the agricultural productivity has to be nearly doubled or the methods used in traditional agriculture have to modified in a way that would cut short the wastage of agricultural resources to nearly half. The proposed framework for precision agriculture employs low cost environmental sensors, an Arduino Uno prototyping board and a pair of wireless transceivers (XBee ZB S2) along with actuating circuit to provide automated irrigation and monitoring of crops. The proposed prototype uses XBee protocol which is based on ZigBee technology and is built over IEEE 802.15.4 standard. The important characteristics of ZigBee technology favorable for precision agriculture are; low data rate, low power consumption and larger coverage area. Thus, due to aforesaid characteristics, ZigBee technology happens to be the first choice for implementing precision agriculture. The prototype developed not only provides real time monitoring of crops by sensing parameters like temperature, humidity and moisture content of the soil which are essentially required for the healthy growth of the crops but also provides automated irrigation. It also uses the resources only when demanded by the crops and that too in right quantity, thereby reducing the resource wastage.


title: Attentive Memory Networks: Efficient Machine Reading for Conversational Search

abstract: Recent advances in conversational systems have changed the search paradigm. Traditionally, a user poses a query to a search engine that returns an answer based on its index, possibly leveraging external knowledge bases and conditioning the response on earlier interactions in the search session. In a natural conversation, there is an additional source of information to take into account: utterances produced earlier in a conversation can also be referred to and a conversational IR system has to keep track of information conveyed by the user during the conversation, even if it is implicit. We argue that the process of building a representation of the conversation can be framed as a machine reading task, where an automated system is presented with a number of statements about which it should answer questions. The questions should be answered solely by referring to the statements provided, without consulting external knowledge. The time is right for the information retrieval community to embrace this task, both as a stand-alone task and integrated in a broader conversational search setting. In this paper, we focus on machine reading as a stand-alone task and present the Attentive Memory Network (AMN), an end-to-end trainable machine reading algorithm. Its key contribution is in efficiency, achieved by having an hierarchical input encoder, iterating over the input only once. Speed is an important requirement in the setting of conversational search, as gaps between conversational turns have a detrimental effect on naturalness. On 20 datasets commonly used for evaluating machine reading algorithms we show that the AMN achieves performance comparable to the state-of-the-art models, while using considerably fewer computations.


title: Effective Listings of Function Stop words for Twitter

abstract: Many words in documents recur very frequently but are essentially meaningless as they are used to join words together in a sentence. It is commonly understood that stop words do not contribute to the context or content of textual documents. Due to their high frequency of occurrence, their presence in text mining presents an obstacle to the understanding of the content in the documents. To eliminate the bias effects, most text mining software or approaches make use of stop words list to identify and remove those words. However, the development of such top words list is difficult and inconsistent between textual sources. This problem is further aggravated by sources such as Twitter which are highly repetitive or similar in nature. In this paper, we will be examining the original work using term frequency, inverse document frequency and term adjacency for developing a stop words list for the Twitter data source. We propose a new technique using combinatorial values as an alternative measure to effectively list out stop words.


title: Video Frame’s Background Modeling: Reviewing the Techniques

abstract: Background modeling is a technique for extracting moving objects in video frames. This technique can be used in ma-chine vision applications, such as video frame compression and monitoring. To model the background in video frames, initially, a model of scene background is constructed, then the current frame is subtracted from the background. Even-tually, the difference determines the moving objects. This paper evaluates a number of existing background modeling techniques in term of accuracy, speed and memory requirement.


title: ScanIndel: a hybrid framework for indel detection via gapped alignment, split reads and de novo assembly

abstract: Comprehensive identification of insertions/deletions (indels) across the full size spectrum from second generation sequencing is challenging due to the relatively short read length inherent in the technology. Different indel calling methods exist but are limited in detection to specific sizes with varying accuracy and resolution. We present ScanIndel, an integrated framework for detecting indels with multiple heuristics including gapped alignment, split reads and de novo assembly. Using simulation data, we demonstrate ScanIndel’s superior sensitivity and specificity relative to several state-of-the-art indel callers across various coverage levels and indel sizes. ScanIndel yields higher predictive accuracy with lower computational cost compared with existing tools for both targeted resequencing data from tumor specimens and high coverage whole-genome sequencing data from the human NIST standard NA12878. Thus, we anticipate ScanIndel will improve indel analysis in both clinical and research settings. ScanIndel is implemented in Python, and is freely available for academic use at https://github.com/cauyrd/ScanIndel.