title: A Semisupervised Approach to the Detection and Characterization of Outliers in Categorical Data

abstract: In this paper, we introduce a new approach of semisupervised anomaly detection that deals with categorical data. Given a training set of instances (all belonging to the normal class), we analyze the relationship among features for the extraction of a discriminative characterization of the anomalous instances. Our key idea is to build a model that characterizes the features of the normal instances and then use a set of distance-based techniques for the discrimination between the normal and the anomalous instances. We compare our approach with the state-of-the-art methods for semisupervised anomaly detection. We empirically show that a specifically designed technique for the management of the categorical data outperforms the general-purpose approaches. We also show that, in contrast with other approaches that are opaque because their decision cannot be easily understood, our proposed approach produces a discriminative model that can be easily interpreted and used for the exploration of the data.


title: Real-time general object recognition for indoor robot based on PCL

abstract: We present a system for real-time general object recognition (gor) for indoor robot in complex scenes. A point cloud image containing the object to be recognized from a Kinect sensor, for general object at will, must be extracted a point cloud model of the object with the Cluster Extraction method, and then we can compute the global features of the object model, making up the model database after processing many frame images. Here the global feature we used is Clustered Viewpoint Feature Histogram (CVFH) feature from Point Cloud Library (PCL). For real-time gor we must preprocess all the point cloud images streamed from the Kinect into clusters based on a clustering threshold and the min-max cluster sizes related to the size of the model, for reducing the amount of the clusters and improving the processing speed, and also compute the CVFH features of the clusters. For every cluster of a frame image, we search the several nearer features from the model database with the KNN method in the feature space, and we just consider the nearest model. If the strings of the model name contain the strings of the object to be recognized, it can be considered that we have recognized the general object; otherwise, we compute another cluster again and perform the above steps. The experiments showed that we had achieved the real-time recognition, and ensured the speed and accuracy for the gor.


title: Semantic Representation

abstract: In recent years, there has been renewed interest in the NLP community in genuine language understanding and dialogue. Thus the long-standing issue of how the semantic content of language should be represented is reentering the communal discussion. This paper provides a brief "opinionated survey" of broad-coverage semantic representation (SR). It suggests multiple desiderata for such representations, and then outlines more than a dozen approaches to SR-some longstanding, and some more recent, providing quick characterizations, pros, cons, and some comments on implementations.


title: Inferring Networks of Substitutable and Complementary Products

abstract: To design a useful recommender system, it is important to understand how products relate to each other. For example, while a user is browsing mobile phones, it might make sense to recommend other phones, but once they buy a phone, we might instead want to recommend batteries, cases, or chargers. In economics, these two types of recommendations are referred to as substitutes and complements: substitutes are products that can be purchased instead of each other, while complements are products that can be purchased in addition to each other. Such relationships are essential as they help us to identify items that are relevant to a user's search. Our goal in this paper is to learn the semantics of substitutes and complements from the text of online reviews. We treat this as a supervised learning problem, trained using networks of products derived from browsing and co-purchasing logs. Methodologically, we build topic models that are trained to automatically discover topics from product reviews that are successful at predicting and explaining such relationships. Experimentally, we evaluate our system on the Amazon product catalog, a large dataset consisting of 9 million products, 237 million links, and 144 million reviews.


The Use of Modified Unified Theory of Acceptance and Use of Technology 2 Model to Analyze Factors Influencing Continuance Intention of E-Payment Adoption ( A Case Study of Go-Pay from Indonesia )


Operations of a Self-Reconfigurable


Towards an Understanding of Fake News


title: E-WEHP: A Batteryless Embedded Sensor-Platform Wirelessly Powered From Ambient Digital-TV Signals

abstract: The use of digital television broadcasting standards has resulted in transmission of perpetually on wireless digital-TV signals over the air at wider bandwidths in ultrahigh-frequency bands for high-definition video and audio broadcasts to TV and smart phones. This paper presents a unique embedded wireless energy-harvesting prototype (E-WEHP) that exploits the unique makeup of ambient digital-TV signals, and scavenges wireless power from them at distance of over 6.3 km from the TV broadcast source. The harvested wireless power is successfully used to power and sustain a 16-bit embedded microcontroller for sensing and machine-to-machine applications without the use of batteries. The E-WEHP uses a miniaturized planar log-periodic antenna and RF-dc charge-pump circuit with maximum sensitivities of - 14.6 and -18.86 dBm and an embedded firmware-based power management scheme to power microcontroller peripherals from different types of ambient digital-TV signals.


Development 199 Advantages of the Modified Euler Angles in the Design and Control of PKMs


title: Robust optimization of robotic pick and place operations for deformable objects through simulation

abstract: This paper discusses various optimization schemes for partly stochastic and bound optimization, particular with application to solve robotic optimization problems, where robustness of the solutions is crucial. The use case revolves around grasping and manipulation of deformable objects. These kinds of tasks are difficult to tune to a satisfactory extent by expert users, and therefore optimization by simulation is a good alternative to achieve satisfactory solutions. In order to apply the optimization, a dynamic simulation framework has been used to model the performance of a given solution for the task. The solutions are parameterized in terms of the robot motion and the gripper configuration, and after each simulation various objective scores are determined and combined. This enables the use of various optimization strategies. Based on visual inspection of the most robust solution found, it is determined that 50 out of 50 simulations, with different meat properties, produce satisfactory manipulations.


title: Cogging Torque Mitigation in Axial Flux Magnetic Gear System Based on Skew Effects Using an Improved Quasi 3-D Analytical Method

abstract: Since the flux leakage phenomenon is dominant in the inner and outer radii of axial flux magnetic gears (AFMGs), a prominent overestimation of torque prediction is obtained by the 2-D analytical method (AM) in comparison with the 3-D finite-element method (FEM) simulations. The main motivation of this paper is to present an improved quasi-3-D AM (IQAM), which is an improvement and amendment of the 2-D AM by a simple and effective radial dependence modeling of the axial and tangential magnetic field distribution in AFMGs. This IQAM plays an important role in a reliable and accurate prediction of static and transient behavior of AFMGs. The cogging torque of the AFMG is predicted using the obtained IQAM and Maxwell’s stress tensor. However, the proposed IQAM will become more prominent by focusing on the effects of single- and dual-skews’ designs for ferromagnetic pole-pieces and permanent magnets through suitable step changes in a combined skewed-MG model, as an effective passive method, in order to mitigate the cogging torque and thereby intensively augmenting torque quality in an exemplary AFMG. The accuracy of the developed skewing method is verified using 3-D FEM simulations.


title: High-Sensitivity Low-Noise Miniature Fluxgate Magnetometers Using a Flip Chip Conceptual Design

abstract: This paper presents a novel class of miniature fluxgate magnetometers fabricated on a print circuit board (PCB) substrate and electrically connected to each other similar to the current "flip chip" concept in semiconductor package. This sensor is soldered together by reversely flipping a 5 cm × 3 cm PCB substrate to the other identical one which includes dual magnetic cores, planar pick-up coils, and 3-D excitation coils constructed by planar Cu interconnections patterned on PCB substrates. Principles and analysis of the fluxgate sensor are introduced first, and followed by FEA electromagnetic modeling and simulation for the proposed sensor. Comprehensive characteristic experiments of the miniature fluxgate device exhibit favorable results in terms of sensitivity (or "responsivity" for magnetometers) and field noise spectrum. The sensor is driven and characterized by employing the improved second-harmonic detection technique that enables linear V-B correlation and responsivity verification. In addition, the double magnitude of responsivity measured under very low frequency (1 Hz) magnetic fields is experimentally demonstrated. As a result, the maximum responsivity of 593 V/T occurs at 50 kHz of excitation frequency with the second harmonic wave of excitation; however, the minimum magnetic field noise is found to be 0.05 nT/Hz(1/2) at 1 Hz under the same excitation. In comparison with other miniature planar fluxgates published to date, the fluxgate magnetic sensor with flip chip configuration offers advances in both device functionality and fabrication simplicity. More importantly, the novel design can be further extended to a silicon-based micro-fluxgate chip manufactured by emerging CMOS-MEMS technologies, thus enriching its potential range of applications in modern engineering and the consumer electronics market.


title: Design and Analysis of Soft-Error Resilience Mechanisms for GPU Register File

abstract: Modern graphics processing units (GPUs) are using increasingly larger register file (RF) which occupies a large fraction of GPU core area and is very frequently accessed. This makes RF vulnerable to soft-errors (SE). In this paper, we present two techniques for improving SE resilience of GPU RF. First, we propose compressing the RF values for reducing the number of vulnerable bits. We leverage value similarity and the presence of narrow-width values to perform compression at warp or thread-level, respectively. Second, we propose selective hardening to design a portion of register entry with SE immune circuits. By collectively using these techniques, higher resilience can be provided with lower overhead. Without hardening, our warp and thread-level compression techniques bring 47.0% and 40.8% reduction in SE vulnerability, respectively.


title: Face recognition with support vector machines: global versus component-based approach

abstract: We present a component-based method and two global methods for face recognition and evaluate them with respect to robustness against pose changes. In the component system we first locate facial components, extract them and combine them into a single feature vector which is classified by a Support Vector Machine (SVM). The two global systems recognize faces by classifying a single feature vector consisting of the gray values of the whole face image. In the first global system we trained a single SVM classifier for each person in the database. The second system consists of sets of viewpoint-specific SVM classifiers and involves clustering during training. We performed extensive tests on a database which included faces rotated up to about 40/spl deg/ in depth. The component system clearly outperformed both global systems on all tests.


title: On analysis of time-series data with preserved privacy

abstract: Time-series data analysis with privacy preservation is an open and challenging issue. To name a few are like analyzing company’s confidential financial data, individual’s health-related data, electricity consumption of individual’s households and so on. Due to the complex nature of time-series data, analyzing such data without any revelation of sensitive information to adversaries is a pervasive task. Here, we have addressed the issue of analyzing numerical time-series of equal length with preserved privacy. Considering the Discrete Wavelet Transform as a suitable technique for transforming time-series in frequency–time representation, we have applied the concept in privacy-preserving analysis of such data. Experimental results show that our proposed method is superior to the existing methods in preserving the trade-off between data utility and privacy. The privacy models developed using the proposed method are also evaluated in terms of clustering and classification accuracies obtained from perturbed time-series data.


title: Mining SQL injection and cross site scripting vulnerabilities using hybrid program analysis

abstract: In previous work, we proposed a set of static attributes that characterize input validation and input sanitization code patterns. We showed that some of the proposed static attributes are significant predictors of SQL injection and cross site scripting vulnerabilities. Static attributes have the advantage of reflecting general properties of a program. Yet, dynamic attributes collected from execution traces may reflect more specific code characteristics that are complementary to static attributes. Hence, to improve our initial work, in this paper, we propose the use of dynamic attributes to complement static attributes in vulnerability prediction. Furthermore, since existing work relies on supervised learning, it is dependent on the availability of training data labeled with known vulnerabilities. This paper presents prediction models that are based on both classification and clustering in order to predict vulnerabilities, working in the presence or absence of labeled training data, respectively. In our experiments across six applications, our new supervised vulnerability predictors based on hybrid (static and dynamic) attributes achieved, on average, 90% recall and 85% precision, that is a sharp increase in recall when compared to static analysis-based predictions. Though not nearly as accurate, our unsupervised predictors based on clustering achieved, on average, 76% recall and 39% precision, thus suggesting they can be useful in the absence of labeled training data.


title: Multi-species simulation of porous sand and water mixtures

abstract: We present a multi-species model for the simulation of gravity driven landslides and debris flows with porous sand and water interactions. We use continuum mixture theory to describe individual phases where each species individually obeys conservation of mass and momentum and they are coupled through a momentum exchange term. Water is modeled as a weakly compressible fluid and sand is modeled with an elastoplastic law whose cohesion varies with water saturation. We use a two-grid Material Point Method to discretize the governing equations. The momentum exchange term in the mixture theory is relatively stiff and we use semi-implicit time stepping to avoid associated small time steps. Our semi-implicit treatment is explicit in plasticity and preserves symmetry of force linearizations. We develop a novel regularization of the elastic part of the sand constitutive model that better mimics plasticity during the implicit solve to prevent numerical cohesion artifacts that would otherwise have occurred. Lastly, we develop an improved return mapping for sand plasticity that prevents volume gain artifacts in the traditional Drucker-Prager model.


title: Monotonic Wide-Range Digitally Controlled Oscillator Compensated for Supply Voltage Variation

abstract: A digitally controlled oscillator (DCO) to be used in an all-digital phase-locked loop (PLL) is presented which offers a wide operating frequency range, a monotonic gain curve, and compensation for instantaneous supply voltage variation. The monotonic and wide oscillation frequency is achieved by interpolating at the fine tuning block between two nodes selected from a coarse delay line. Supply voltage compensation is obtained by dynamically adjusting the strength of the feedback latch of the delay cell in response to the change of the supply voltage.


title: Data models in database management

abstract: It is a combination of three components: 1) a collection of data structure types (the building blocks of any database that conforms to the model); ::: 2) a collection of operators or inferencing rules, which can be applied to any valid instances of the data types listed in (1), to retrieve or derive data from any parts of those structures in any combinations desired; ::: 3) a collection of general integrity rules, which implicitly or explicitly define the set of consistent database states or changes of state or both—these rules may sometimes be expressed as insert-update-delete rules.


title: Germacrone and sesquiterpene-enriched extracts from Curcuma aeruginosa Roxb. increase skin penetration of minoxidil, a hair growth promoter

abstract: Minoxidil is approved for topical treatment of androgenic alopecia but hampered by poor cutaneous absorption. Recently, the randomized control trial showed that hair loss treatment of minoxidil was improved by co-application of the anti-androgen, Curcuma aeruginosa Roxb. extract. Here, we aimed to show that the apparent synergism arises from improved cutaneous penetration of minoxidil by bioactive compound, germacrone or C. aeruginosa (as an n-hexane extract, or essential oil). The partition coefficient of germacrone was determined by HPLC. Skin penetration was measured ex vivo on Franz diffusion cells using full thickness human foreskin as membranes. The receiver solution was sampled hourly for 8 h after which the skin was removed, the stratum corneum separated, and minoxidil assayed in this and in the remaining viable skin layer by HPLC. Skin penetration of minoxidil with 0.2 and 2% extract was increased ~ 4-fold (accumulated amount in receiver + skin viable layer after 8 h). Furthermore, germacrone enhanced minoxidil flux by ~ 10-fold and C. aeruginosa essential oil by ~ 20-fold. This work suggests three clinical consequences: (i) minoxidil efficacy is promoted, (ii) lower doses of minoxidil suffice, and (iii) C. aeruginosa extract/essential oil or germacrone can supplement treatment outcomes by acting as anti-androgen, thereby introducing a more effective topical treatment strategy for androgenic alopecia.


title: Anterograde degeneration study of the superior colliculus in Tupaia glis: evidence for a subdivision between superficial and deep layers.

abstract: The large, well developed superior colliculus of the tree shrew with its highly differentiated layers is ideal for analyzing the connections of individual layers. Our most significant finding concerns the differences between the projections of the superficial and deep layers. Lesions limited to those strata which receive projections from the retina and striate cortex (superficial 3 layers) result in terminal degeneration almost exclusively within the pulvinar, the pretectal area, and the dorsal and ventral lateral geniculate nuclei. In each case, the greatest amount of degeneration was present in the pulvinar, supporting previous suggestions that the tecto-pulvinar pathway conveys visual information. In sharp contrast, lesions limited to the deep layers which receive multimodal input from nonstriate areas of the neocortex and from a variety of subcortical centers, produce terminal degeneration in entirely different thalamic nuclei — certain intralaminar nuclei, the subthalamic region, and a region homologous to the posterior nuclear group of Rose and Woolsey ('49). The deep lesions also result in terminal degeneration within the inferior colliculus, the parabigeminal nucleus, the reticulo-tegmental nucleus, and the inferior olivary nucleus, as well as in the brainstem reticular formation. Finally, deep lesions produced scattered degenerating fibers in the motor facial nucleus. Our results favor a division of the tree shrew superior colliculus into superficial and deep portions based on strikingly different projection patterns and may be useful in resolving certain problems of thalamic homology.


title: Pooling-based continuous evaluation of information retrieval systems

abstract: The dominant approach to evaluate the effectiveness of information retrieval (IR) systems is by means of reusable test collections built following the Cranfield paradigm. In this paper, we propose a new IR evaluation methodology based on pooled test-collections and on the continuous use of either crowdsourcing or professional editors to obtain relevance judgements. Instead of building a static collection for a finite set of systems known a priori, we propose an IR evaluation paradigm where retrieval approaches are evaluated iteratively on the same collection. Each new retrieval technique takes care of obtaining its missing relevance judgements and hence contributes to augmenting the overall set of relevance judgements of the collection. We also propose two metrics: Fairness Score, and opportunistic number of relevant documents, which we then use to define new pooling strategies. The goal of this work is to study the behavior of standard IR metrics, IR system ranking, and of several pooling techniques in a continuous evaluation context by comparing continuous and non-continuous evaluation results on classic test collections. We both use standard and crowdsourced relevance judgements, and we actually run a continuous evaluation campaign over several existing IR systems.


WIDE BANDGAP SEMICONDUCTORS FOR UTILITY APPLICATIONS


title: Failing to forget: prospective memory commission errors can result from spontaneous retrieval and impaired executive control.

abstract: Investigations of prospective memory (PM) have typically examined the ability to remember to perform an intended action, such as remembering to take one's medication. Similarly important, but rarely studied, is the ability to forget, deactivate, or inhibit finished PM intentions. Failing to forget a finished intention could lead to the erroneous repetition of an irrelevant intention, referred to as a commission error, and cause embarrassment (e.g., taking the same poster handout from a conference presenter twice), confusion and disorganization (e.g., an air traffic controller attempting to re-route an already re-routed aircraft), or even a medical emergency (e.g., overdosing on medication). ::: ::: Conceptualizations of PM Forgetting ::: Conceptualizations of failures in PM forgetting, including commission errors, are only beginning to emerge, and they mostly stem from existing theories of successful prospective remembering. The monitoring view argues that after intention formation, effortful preparatory monitoring processes are needed to maintain the intention in consciousness and search the environment for PM cues (e.g., Smith, 2003). By this view, failures in PM forgetting should emerge if the monitoring process is not discontinued upon intention completion. A second view arises from the intention superiority hypothesis (Goschke & Kuhl, 1993), which argues that encoded intentions have an elevated level of activation, which may persist following intention completion. Walser, Fischer, and Goschke (2012) proposed that the extent to which no-longer-relevant PM cues trigger spurious retrievals depends on the intention's residual level of activation and whether participants are engaging preparatory monitoring for PM intentions (i.e., similar to the monitoring view). ::: ::: A third view arises from the finding that, in the absence of preparatory monitoring, PM cues that are strongly linked to an intention can spontaneously trigger intention retrieval (e.g., Einstein et al., 2005). Several studies have shown that individuals will not unnecessarily monitor during contexts in which a PM task need not be performed (Knight et al., 2011; Marsh, Hicks, & Cook, 2006), and by the spontaneous retrieval view, commission errors may occur in such contexts if the PM cue—action link remains strong following intention completion. ::: ::: ::: Laboratory Method for Investigating PM Forgetting ::: The primary approach to investigating whether finished PM intentions are deactivated is depicted in Figure 1. First, participants encode a PM intention such as remembering to press the Q key when the word corn appears during a lexical decision task. Following completion of this Active-PM Block,1 participants are instructed that the PM task is finished. In a later phase (the Finished-PM Block), the PM cue is (re)presented multiple times, and researchers examine responding to the finished PM cue. If response times (RTs) are greater to the finished PM cue than to matched control items, that suggests a failure in deactivation of the PM intention (Cohen, Dixon, & Lindsay, 2005; Scullin, Bugg, McDaniel, & Einstein, 2011). Additionally, some participants will actually repeat the PM response—that is, produce a commission error—during the Finished-PM Block (Scullin, Bugg, & McDaniel, 2012; Walser et al., 2012).2 ::: ::: ::: ::: Figure 1 ::: ::: Schematic of the commission error procedure. The procedure was identical in the no-PM control group except those participants never encoded the PM task. ::: ::: ::: ::: ::: Empirical Work on PM Forgetting ::: Early research evaluated whether finished intentions are deactivated, with some findings favoring intention deactivation (Marsh, Hicks, & Bink, 1998; Meilan, 2008; Scullin, Einstein, & McDaniel, 2009), other findings not supporting deactivation (Penningroth, 2011; West, McNerney, & Travers, 2007), and some findings producing mixed results (Cohen, Kantner, Dixon, & Lindsay, 2011). Of current interest are factors that promote spurious retrievals versus successful intention deactivation. One factor is the strength of the PM cue—action link. Failures in PM deactivation may be more likely if finished PM cues are (re)presented shortly after intention completion (Walser et al.; West et al.), that is, before the cue—action link is significantly weakened. In addition, Scullin et al. (2012) found that 25% of younger adults produced commission errors when the PM cue was salient and was encountered during the same ongoing task context as the original Active-PM Block context; commission errors were eliminated in a nonsalient-cue/task-mismatch condition. ::: ::: A second factor that is important to PM forgetting is executive control integrity. Scullin et al. (2011; 2012) found that individual differences in Stroop interference, Trail Making B, and Wisconsin Card Sorting Task perseveration predicted failures in PM deactivation. Older age, which covaries with executive control declines (e.g., Braver & Barch, 2002), was also associated with increased failures to deactivate finished intentions (Scullin et al., 2011; 2012; but see Cohen et al., 2005). Because Scullin et al. (2012) observed commission errors during a Finished-PM Block in which participants were assumed not to be monitoring, and the likelihood of a commission error was associated with low executive control, they proposed that “commission errors occur when a completed intention is spontaneously retrieved and individuals fail to suppress executing the intention” (p. 52). ::: ::: ::: The Present Research ::: The present research had several goals. First, we aimed to replicate Scullin et al.'s (2012) finding of commission errors in younger adults using a refined approach in which the Finished-PM Block included only a single PM cue to reduce possible confusion effects related to repeating no-longer-relevant cues (Scullin et al. presented 10 PM cues and 10 salient control cues). Second, we investigated the effect of delay on commission errors, and we did so by presenting the PM target cue either early (short-delay condition) or late (long-delay condition) during the Finished-PM Block. Third, we acquired qualitative data after the Finished-PM Block by asking participants whether they ever made a commission error (and to explain why). ::: ::: A fourth goal was to rigorously test whether commission errors arise from spontaneous retrieval versus preparatory monitoring processes. No study of finished PM intentions has utilized a between-subjects, no-PM control group to determine whether participants are monitoring for their PM intentions (inferred from slowed lexical decision responding; Smith, 2003). Specifically, we were interested in a) replicating the finding of monitoring during an Active-PM Block (e.g., Harrison & Einstein, 2010), b) determining whether such monitoring predicts later commission errors (proposed by Walser et al., 2012), and c) determining whether PM group participants were monitoring during the Finished-PM Block. The theoretical relevance here is that the spontaneous retrieval view (e.g., Scullin et al., 2012), but not the monitoring view (e.g., Smith, 2003), predicts that commission errors can occur in the absence of monitoring. The residual activation view predicts that commission errors will be minimal in the absence of monitoring and that commission errors will correlate positively with degree of monitoring (Walser et al.).


title: A Computational Intelligence Framework for Length of Stay Prediction in Emergency Healthcare Services Department

abstract: Estimating Patient Length of Stay (LOS) in healthcare systems is significant for seemly decision making regarding capacity planning, resource allocation and scheduling. In most Emergency Healthcare Services Departments, the commonly used indicators to measuring performance include length of stay, waiting time, resource utilization and number of patients treated. The existing challenge of Prolonged Hospital Length of Stay (PHLOS), usually experienced in most healthcare system indicates constant surge, resulting in over demand of Healthcare resources (facilities and personnel). In this paper, a Machine Learning (ML) framework is proposed to predict patient Length of Stay (LOS) in emergency health care services departments. First, a study of the emergency health care services in the University of Uyo Teaching Hospital was conducted to gain insights into the operations of the emergency department and the contributions as well as limitations of important system parameters. Second, an analysis of several relevant factors such as Severity of Illness or Emergency Cases (SIC) was carried out to assess its performance. Third, the proposed ML framework was then implemented using the Intuitionistic Type-2 Fuzzy Logic System (IT2-FLS). Results of the model demonstrate the importance of ML in evaluating the performance of healthcare systems for efficient LOS Prediction Provisioning.


title: How to win the battle of ideas in corporate social responsibility: the International Pyramid Model of CSR

abstract: This paper reviews the definitions of Corporate Social Responsibility (CSR) as they have evolve over time. It traces the origins of the concept and creates a theoretical framework for international use, thus having the benefit of applicability in both developing as well as developed economies. The models of Carroll and Visser are integrated to produce The International Pyramid Model of CSR, which acknowledges the relative importance of economic, glocal, legal and ethical, and philanthropic aspects of the CSR concept. The primary innovation in the International Pyramid is the development of ‘glocal’ responsibilities, relating to the environment, socio-cultural matters, technology users, and political rights. Additionally, the International Pyramid condenses Carroll (Business Horizons 34(4):39–48, 1991) pyramid such that the separate legal and ethical responsibilities are merged into one ‘legal and ethical’ obligation. Furthermore, it offers flexibility by acknowledging that the various responsibilities it embodies can shift up or down the pyramid as priorities change, which is inevitable as businesses and economies differ cross-sectionally, and over time.


title: Extracting Search Query Patterns via the Pairwise Coupled Topic Model

abstract: A fundamental yet new challenge in information retrieval is the identification of patterns behind search queries. For example, the query "NY restaurant" and "boston hotel" shares the common pattern "LOCATION SERVICE". However, because of the diversity of real queries, existing approaches require data preprocessing by humans or specifying the target query domains, which hinders their applicability. We propose a probabilistic topic model that assumes that each term (e.g., "NY") has a topic (LOCATION). The key idea is that we consider topic co-occurrence in a query rather than a topic sequence, which significantly reduces computational cost yet enables us to acquire coherent topics without the preprocessing. Using two real query datasets, we demonstrate that the obtained topics are intelligible by humans, and are highly accurate in keyword prediction and query generation tasks.


title: Comparison of orthodontic treatment outcomes in adults with skeletal open bite between conventional edgewise treatment and implant-anchored orthodontics

abstract: Introduction Open bites are known to be difficult malocclusions to treat. Generally, with conventional edgewise treatment, incisor extrusion rather than molar intrusion is observed. Recently, the use of miniscrews as anchorage has markedly increased. In this study, orthodontic treatment outcomes after conventional edgewise treatment and implant-anchored treatment were investigated by cephalometric analysis and several occlusal indexes. In addition, the stability of these patients 2 years after the retention phase was also analyzed. Methods Thirty adults (15 for nonimplant treatment [non-IA group] and 15 for implant-anchored treatment [IA group]) were our subjects. Cephalometric analysis, peer assessment rating, discrepancy index, and objective grading system were used. Results From the cephalometric values in the non-IA group, open-bite patients were generally treated by extrusion of the maxillary and mandibular incisors that resulted in clockwise rotation of the mandibular plane angle. In the IA group, intrusion of the maxillary and mandibular molars that resulted in counterclockwise rotation was noted. Furthermore, in the IA group, the soft-tissue analysis showed decreases in the facial convexity and the inferior labial sulcus angle that resulted in the disappearance of incompetent lips. In the retention phase, extrusion of the mandibular molars was observed in the IA group. From the objective grading system evaluation, significant reductions of overbite in canines and premolars were observed in both groups. Furthermore, less stability was observed in the IA group compared with the non-IA group according to the total objective grading system score. Conclusions Ideal occlusion can be achieved in adults with severe open bite with both conventional edgewise and implant-anchored orthodontic treatment. However, absolute intrusion of the molars and improvement in esthetics might be achieved more effectively by using miniscrews as an anchorage device. In addition, since a significant amount of tooth movement occurs with miniscrews, careful attention is required during the retention phase.


title: Thermal conductivity of individual silicon nanowires

abstract: The thermal conductivities of individual single crystalline intrinsic Si nanowires with diameters of 22, 37, 56, and 115 nm were measured using a microfabricated suspended device over a temperature range of 20–320 K. Although the nanowires had well-defined crystalline order, the thermal conductivity observed was more than two orders of magnitude lower than the bulk value. The strong diameter dependence of thermal conductivity in nanowires was ascribed to the increased phonon-boundary scattering and possible phonon spectrum modification.


AN INDEX-BASED SHADOW EXTRACTION APPROACH ON HIGH-RESOLUTION IMAGES


title: CytonRL: an Efficient Reinforcement Learning Open-source Toolkit Implemented in C++

abstract: This paper presents an open-source enforcement learning toolkit named CytonRL (https://github.com/arthurxlw/cytonRL). The toolkit implements four recent advanced deep Q-learning algorithms from scratch using C++ and NVIDIA's GPU-accelerated libraries. The code is simple and elegant, owing to an open-source general-purpose neural network library named CytonLib. Benchmark shows that the toolkit achieves competitive performances on the popular Atari game of Breakout.


Combining Models from Multiple Sources for RGB-D Scene Recognition


title: A review of alpha activity in integrative brain function: fundamental physiology, sensory coding, cognition and pathology.

abstract: Abstract Aim of the review Questions related to the genesis and functional correlates of the brain's alpha oscillations around 10 Hz (Alpha) are one of the fundamental research areas in neuroscience. In recent decades, analysis of this activity has been not only the focus of interest for description of sensory‐cognitive processes, but has also led to trials for establishing new hypotheses. The present review and the companion review aim to constitute an ensemble of “reasonings and suggestions” to understand alpha oscillations based on a wide range of accumulated findings rather than a trial to launch a new “alpha theory”. Surveyed descriptions related to physiology and brain function The review starts with descriptions of earlier extracellular recordings, field potentials and also considers earlier alpha hypotheses. Analytical descriptions of evoked and event-related responses, event-related desynchronization, the relationship between spontaneous activity and evoked potentials, aging brain, pathology and alpha response in cognitive impairment are in the content of this review. In essence, the gamut of the survey includes a multiplicity of evidence on functional correlates in sensory processing, cognition, memory and vegetative system, including the spinal cord and heart .


title: Prediction of Satellite Image Sequence for Weather Nowcasting Using Cluster-Based Spatiotemporal Regression

abstract: The flawed characterization of transitions between different meteorological structures is often regarded as one of the largest sources of error in weather forecasting. This paper attempts to improve upon the satellite-image-based nowcasting capability of models by coupling a clustering technique into a spatiotemporal autoregression method. Experimental results indicate the superiority of clustering-based regression algorithm in terms of statistically significant skill scores. The tests show an improvement in probability of detection with a decrease in false alarm rate as compared to unclassified predictions. The developed model has also been demonstrated to be useful in nowcasting of convective systems.


title: Evolution of avian flight: muscles and constraints on performance

abstract: Competing hypotheses about evolutionary origins of flight are the 'fundamental wing-stroke' and 'directed aerial descent' hypotheses. Support for the fundamental wing-stroke hypothesis is that extant birds use flapping of their wings to climb even before they are able to fly; there are no reported examples of incrementally increasing use of wing movements in gliding transitioning to flapping. An open question is whether locomotor styles must evolve initially for efficiency or if they might instead arrive due to efficacy. The proximal muscles of the avian wing output work and power for flight, and new research is exploring functions of the distal muscles in relation to dynamic changes in wing shape. It will be useful to test the relative contributions of the muscles of the forearm compared with inertial and aerodynamic loading of the wing upon dynamic morphing. Body size has dramatic effects upon flight performance. New research has revealed that mass-specific muscle power declines with increasing body mass among species. This explains the constraints associated with being large. Hummingbirds are the only species that can sustain hovering. Their ability to generate force, work and power appears to be limited by time for activation and deactivation within their wingbeats of high frequency. Most small birds use flap-bounding flight, and this flight style may offer an energetic advantage over continuous flapping during fast flight or during flight into a headwind. The use of flap-bounding during slow flight remains enigmatic. Flap-bounding birds do not appear to be constrained to use their primary flight muscles in a fixed manner. To improve understanding of the functional significance of flap-bounding, the energetic costs and the relative use of alternative styles by a given species in nature merit study.This article is part of the themed issue 'Moving in a moving medium: new perspectives on flight'.


title: Tattoo reactions as a sign of sarcoidosis.

abstract: A 34-year-old man was referred to the dermatology clinic with a five-month history of his tattoos becoming red, scaly and painful. Over the same period, he noted fluctuating blurry vision in both eyes with associated pain and photophobia, along with shortness of breath. He had no history of pain in


Application of Remote Sensing and GIS in Solid Waste Management : A Case Study of Surroundings of River Yamuna , India


title: Cyber ethics and cyber crime: A deep dwelved study into legality, ransomware, underground web and bitcoin wallet

abstract: Future wars will be cyber wars and the attacks will be a sturdy amalgamation of cryptography along with malware to distort information systems and its security. The explosive Internet growth facilitates cyber-attacks. Web threats include risks, that of loss of confidential data and erosion of consumer confidence in e-commerce. The emergence of cyber hack jacking threat in the new form in cyberspace is known as ransomware or crypto virus. The locker bot waits for specific triggering events, to become active. It blocks the task manager, command prompt and other cardinal executable files, a thread checks for their existence every few milliseconds, killing them if present. Imposing serious threats to the digital generation, ransomware pawns the Internet users by hijacking their system and encrypting entire system utility files and folders, and then demanding ransom in exchange for the decryption key it provides for release of the encrypted resources to its original form. We present in this research, the anatomical study of a ransomware family that recently picked up quite a rage and is called CTB locker, and go on to the hard money it makes per user, and its source C&C server, which lies with the Internet's greatest incognito mode-The Dark Net. Cryptolocker Ransomware or the CTB Locker makes a Bitcoin wallet per victim and payment mode is in the form of digital bitcoins which utilizes the anonymity network or Tor gateway. CTB Locker is the deadliest malware the world ever encountered.


title: Process Mining: A Research Agenda

abstract: Enterprise information systems support and control operational business processes ranging from simple internal back-office process to complex interorganizational processes. Technologies such as workflow management (WFM), enterprise, application integration (EAI), enterprise resource planning (ERP), and web services (WS) typically focus on the realization of IT support rather than monitoring the operational business processes. Process mining aims at extracting information from event logs to capture the business process as it is being executed. In this paper, we put the topic of process mining into context, discuss the main issues around process mining, and finally we introduce the papers in this special issue.


title: Deep learning-based electroencephalography analysis: a systematic review

abstract: Electroencephalography (EEG) is a complex signal and can require several years of training to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question. In this work, we review 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches in order to inform future research and formulate recommendations. Various data items were extracted for each study pertaining to 1) the data, 2) the preprocessing methodology, 3) the DL design choices, 4) the results, and 5) the reproducibility of the experiments. Our analysis reveals that the amount of EEG data used across studies varies from less than ten minutes to thousands of hours. As for the model, 40% of the studies used convolutional neural networks (CNNs), while 14% used recurrent neural networks (RNNs), most often with a total of 3 to 10 layers. Moreover, almost one-half of the studies trained their models on raw or preprocessed EEG time series. Finally, the median gain in accuracy of DL approaches over traditional baselines was 5.4% across all relevant studies. More importantly, however, we noticed studies often suffer from poor reproducibility: a majority of papers would be hard or impossible to reproduce given the unavailability of their data and code. To help the field progress, we provide a list of recommendations for future studies and we make our summary table of DL and EEG papers available and invite the community to contribute.


title: Enhancement of Spectral Tilt in Synthesized Speech

abstract: The research in statistical parametric speech synthesis is towards improving naturalness and intelligibility. In this work, the deviation in spectral tilt of the natural and synthesized speech is analyzed and observed a large gap between the two. Furthermore, the same is analyzed for different classes of sounds, namely low-vowels, mid-vowels, high-vowels, semi-vowels, nasals, and found to be varying with category of sound units. Based on variation, a novel method for spectral tilt enhancement is proposed, where the amount of enhancement introduced is different for different classes of sound units. The proposed method yields improvement in terms of intelligibility, naturalness, and speaker similarity of the synthesized speech.


title: Bayesian Multi-Task Reinforcement Learning

abstract: We consider the problem of multi-task reinforcement learning where the learner is provided with a set of tasks, for which only a small number of samples can be generated for any given policy. As the number of samples may not be enough to learn an accurate evaluation of the policy, it would be necessary to identify classes of tasks with similar structure and to learn them jointly. We consider the case where the tasks share structure in their value functions, and model this by assuming that the value functions are all sampled from a common prior. We adopt the Gaussian process temporal-difference value function model and use a hierarchical Bayesian approach to model the distribution over the value functions. We study two cases, where all the value functions belong to the same class and where they belong to an undefined number of classes. For each case, we present a hierarchical Bayesian model, and derive inference algorithms for (i) joint learning of the value functions, and (ii) efficient transfer of the information gained in (i) to assist learning the value function of a newly observed task.


title: Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes from 2D Ones in RGB-Depth Images

abstract: This paper addresses the problem of amodal perception of 3D object detection. The task is to not only find object localizations in the 3D world, but also estimate their physical sizes and poses, even if only parts of them are visible in the RGB-D image. Recent approaches have attempted to harness point cloud from depth channel to exploit 3D features directly in the 3D space and demonstrated the superiority over traditional 2.5D representation approaches. We revisit the amodal 3D detection problem by sticking to the 2.5D representation framework, and directly relate 2.5D visual appearance to 3D objects. We propose a novel 3D object detection system that simultaneously predicts objects 3D locations, physical sizes, and orientations in indoor scenes. Experiments on the NYUV2 dataset show our algorithm significantly outperforms the state-of-the-art and indicates 2.5D representation is capable of encoding features for 3D amodal object detection. All source code and data is on https://github.com/phoenixnn/Amodal3Det.


Mobile Traffic Sign Recognition


title: Developmentally advanced EEG alpha power in gifted male and female adolescents

abstract: Abstract An electroencephalographic (EEG) study of gifted and average ability male and female adolescents, as well as college students of both sexes, was conducted to investigate further the relative contributions the left and right cerebral hemispheres during an eyes open (baseline) task in all groups. A total of 90 subjects had baseline EEG recorded in three groups with equal numbers of males and females: 30 gifted adolescents, 30 average ability adolescents, and 30 college-age subjects. Overall alpha power (8–12 Hz resting potential) was significantly greater in average ability subjects compared to both college-age and gifted adolescent subjects. Moreover, there were no significant differences in overall alpha power between college-age and gifted adolescent subjects. However, college-age and gifted adolescent subjects had different RH/LH patterns of activation such that at temporal and parietal locations college-age subjects had greater LH alpha power levels whereas gifted adolescents had greater RH alpha power. These findings suggest that gifted adolescents may have a developmentally enhanced state of brain activity, one that more closely resembles that of college-age adults to whom they also resemble in terms of cognitive development.


title: Controlled Procedural Terrain Generation Using Software Agents

abstract: Procedural terrain generation is used to create landforms for applications such as computer games and flight simulators. While most of the existing work has concentrated on algorithms that generate terrain without input from the user, we explore a more controllable system that uses intelligent agents to generate terrain elevation heightmaps according to designer-defined constraints. This allows the designer to create procedural terrain that has specific properties.


title: A Smart ECG Measurement System Based on Web-Service-Oriented Architecture for Telemedicine Applications

abstract: The opportunity for cardiac patients to have constantly monitored their health state at home is now possible by means of telemedicine applications. In fact, today, portable and simple-to-use devices allow one to get preliminary domestic diagnoses of the heart status. In this paper, the authors present an original ECG measurement system based on web-service-oriented architecture to monitor the heart health of cardiac patients. The projected device is a smart patient-adaptive system able to provide personalized diagnoses by using personal data and clinical history of the monitored patient. In the presence of a pathology occurrence, the system is able to call the emergency service for assistance. An ECG sensor has the task to acquire, condition, and sample the heart electrical impulses, whereas a personal digital assistant (PDA) performs the diagnosis according to the measurement uncertainty and, in case of a critical situation, calls the medical staff. The system has two removable and updatable memory devices: the first memory device stores the clinical and personal data of the patient, and the second memory device stores information on the metrological status of the measurement system. This way, according to the personal data and historical information of the patient, the measurement system adapts itself by selecting the best fitted ECG model as a reference to configure the computing algorithm. Further information on the measurement uncertainty is used to qualify the reliability of the final clinical response to reduce the occurrence of a faulty diagnosis. Through the PDA graphic interface, the user can display his personal data, observe the graph of his ECG signal, and read diagnosis information with the relative reliability level. Moreover, the patient can choose to print his ECG graph through a Bluetooth printer or to send it to a specialist by a General Packet Radio Service (GPRS) modem.


State-of-the-art MEMS and microsystem tools for brain research


title: Advanced hybrid system for solar car

abstract: A three-input hybrid system for solar car is designed in this project. It consists of one unidirectional input power port and two bidirectional power ports with a storage element. Depending on utilization state of the battery, three different power operation modes are defined for the converter. Battery charging in the system is carried out from the amorphous solar panel mounted on the body and a solar energy harvesting charging station. Since the solar energy is directly given to the DC load, the efficiency of the system will improve. The capacitor which is connected to the lead acid battery will charge at off peak hours and discharge during the acceleration time of the car. In this proposed system energy wasted in the brakes are also recovered and used to charge the lead acid battery. Hence competent Hybrid Electric Vehicle was developed by using super capacitor and regenerative braking scheme.


title: NEURAL NETWORK BASED ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORKS : A SURVEY

abstract: The main concern in Wireless Sensor Networks is how to handle with their limited energy resources. The performance of Wireless Sensor Networks strongly depends on their lifetime. As a result, Dynamic Power Management approaches with the purpose of reduction of energy consumption in sensor nodes, after deployment and designing of the network, have drawn attentions of many research studies. Recently, there have been a strong interest to use intelligent tools especially Neural Networks in energy efficient approaches of Wireless Sensor Networks, due to their simple parallel distributed computation, distributed storage, data robustness, auto-classification of sensor nodes and sensor reading. Dimensionality reduction and prediction of sensor data obtained simply from the outputs of the neural-networks algorithms can lead to lower communication costs and energy conservation. All these characteristics show great analogy and compatibility between wireless sensor networks and neural networks. This paper aims to present the most important possible application of neural networks in reduction of energy consumption according to research studies have been done in the literature so far through a general classification of them.


title: A design perspective on modularity

abstract: Modularity is essential to software development. Without it, large software systems simply could not be realized. Designers typically strive to achieve a high degree of modularity by separating different concerns over different modules, a process called modularization. Modularity, however, is but one of the goals that the designer has. Furthermore, design does not just take place when the designer decides upon the structure of the code. Instead, design is a multi-dimensional activity that permeates the software life cycle. In this paper, we examine modularity as seen through the lens of design as it takes place across the life cycle, particularly dissecting typical activities in which designers engage to study how these activities involve and relate to modularity. The result is an affirmation that modularity is and always should be an integral part of designers' work, but that our current techniques to achieve modularity fall short in a number of critical ways. A research agenda is laid out toward overcoming these shortcomings.


title: The social and psychological aspects of smart home technology within the care sector

abstract: The invention provides novel 5- and 6-benzoxazolyl alkanoic acids, optionally substituted in the 2-position, and derivatives thereof which possess anti-inflammatory, anti-pyretic and analgesic activity. Also provided is a process for preparing such compounds by cyclising an appropriately substituted o-aminophenol and, if necessary, converting the resultant benzoxazole to the desired compound.


ARTIFICIAL INTELLIGENCE IN POWER SYSTEMS


1 Factors Causing Mathematics Anxiety among Undergraduate Students


title: Logistics information systems adoption: an empirical investigation in Brazil

abstract: Purpose – The purpose of this paper is to examine whether the level of logistics information systems (LIS) adoption in manufacturing companies is influenced by organizational profile variables, such as the company's size, the nature of its operations and their subsectors.Design/methodology/approach – A review of the mainstream literature on LIS was carried out to identify the factors influencing the adoption of such information systems and also some research gaps. The empirical study's strategy is based on a survey research in Brazilian manufacturing firms from the capital goods industry. Data collected were analyzed through Kruskall‐Wallis and Mann Whitney's non‐parametric tests.Findings – The analysis indicates that characteristics such as the size of companies and the nature of their operations influence the levels of LIS adoption, whilst comparisons regarding the subsectors appeared to be of little influence.Originality/value – This is the first known study to examine the influence of organizational p...


title: Pulse stream VLSI circuits and systems: the EPSILON neural network chipset.

abstract: An analogue CMOS VLSI neural processing chip has been designed and fabricated. The device employs "pulse-stream" neural state signalling and is capable of computing some 360 million synaptic connections per second. In addition to basic characterisation results, the performance of the chip in solving "real-world" problems is also demonstrated. The experience gained from the development of this device has resulted in the design of a second "pulse-stream" chip with improved performance and features. It is anticipated that this second device will be integrated into a standard bus-based system and find early application in robotic control.


title: A wall climbing robot using propulsive force of propeller

abstract: A robot capable of moving on a vertical wall of high-rise buildings can be used for rescue, wall inspection, fire fighting, etc.. A wall climbing robot using thrust force of propellers has been developed. The thrust force is inclined a little to the wall side to produce the frictional force between the wheels and wall surface. As the strong wind is predicted on the wall surface of buildings, the direction of thrust force is controlled to compensate the wind force acting on the robot. A frictional force augmentor is also considered, which is an airfoil to produce the lift force directed to the wall side by the cross wind. Its effect is tested in the wind tunnel. The overall performance of the robot is examined by computer simulation and a model was constructed and tested on the wall. >


title: SOFORT: a hybrid SCM-DRAM storage engine for fast data recovery

abstract: Storage Class Memory (SCM) has the potential to significantly improve database performance. This potential has been well documented for throughput [4] and response time [25, 22]. In this paper we show that SCM has also the potential to significantly improve restart performance, a shortcoming of traditional main memory database systems. We present SOFORT, a hybrid SCM-DRAM storage engine that leverages full capabilities of SCM by doing away with a traditional log and updating the persisted data in place in small increments. We show that we can achieve restart times of a few seconds independent of instance size and transaction volume without significantly impacting transaction throughput.


title: Outdoors augmented reality on mobile phone using loxel-based visual feature organization

abstract: We have built an outdoors augmented reality system for mobile phones that matches camera-phone images against a large database of location-tagged images using a robust image retrieval algorithm. We avoid network latency by implementing the algorithm on the phone and deliver excellent performance by adapting a state-of-the-art image retrieval algorithm based on robust local descriptors. Matching is performed against a database of highly relevant features, which is continuously updated to reflect changes in the environment. We achieve fast updates and scalability by pruning of irrelevant features based on proximity to the user. By compressing and incrementally updating the features stored on the phone we make the system amenable to low-bandwidth wireless connections. We demonstrate system robustness on a dataset of location-tagged images and show a smart-phone implementation that achieves a high image matching rate while operating in near real-time.


title: Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations

abstract: Deployment of deep learning models in robotics as sensory information extractors can be a daunting task to handle, even using generic GPU cards. Here, we address three of its most prominent hurdles, namely, i) the adaptation of a single model to perform multiple tasks at once (in this work, we consider depth estimation and semantic segmentation crucial for acquiring geometric and semantic understanding of the scene), while ii) doing it in real-time, and iii) using asymmetric datasets with uneven numbers of annotations per each modality. To overcome the first two issues, we adapt a recently proposed real-time semantic segmentation network, making changes to further reduce the number of floating point operations. To approach the third issue, we embrace a simple solution based on hard knowledge distillation under the assumption of having access to a powerful ‘teacher’ network. We showcase how our system can be easily extended to handle more tasks, and more datasets, all at once, performing depth estimation and segmentation both indoors and outdoors with a single model. Quantitatively, we achieve results equivalent to (or better than) current state-of-the-art approaches with one forward pass costing just 13ms and 6.5 GFLOPs on 640×480 inputs. This efficiency allows us to directly incorporate the raw predictions of our network into the SemanticFusion framework [1] for dense 3D semantic reconstruction of the scene.33The models are available here: https://github.com/drsleep/ multi–task–refinenet


title: Locomotion control for many-muscle humanoids

abstract: We present a biped locomotion controller for humanoid models actuated by more than a hundred Hill-type muscles. The key component of the controller is our novel algorithm that can cope with step-based biped locomotion balancing and the coordination of many nonlinear Hill-type muscles simultaneously. Minimum effort muscle activations are calculated based on muscle contraction dynamics and online quadratic programming. Our controller can faithfully reproduce a variety of realistic biped gaits (e.g., normal walk, quick steps, and fast run) and adapt the gaits to varying conditions (e.g., muscle weakness, tightness, joint dislocation, and external pushes) and goals (e.g., pain reduction and efficiency maximization). We demonstrate the robustness and versatility of our controller with examples that can only be achieved using highly-detailed musculoskeletal models with many muscles.


title: Efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems

abstract: This work presents and analyzes three convolutional neural network (CNN) models for efficient pixelwise classification of images. When using convolutional neural networks to classify single pixels in patches of a whole image, a lot of redundant computations are carried out when using sliding window networks. This set of new architectures solve this issue by either removing redundant computations or using fully convolutional architectures that inherently predict many pixels at once. ::: The implementations of the three models are accessible through a new utility on top of the Caffe library. The utility provides support for a wide range of image input and output formats, pre-processing parameters and methods to equalize the label histogram during training. The Caffe library has been extended by new layers and a new backend for availability on a wider range of hardware such as CPUs and GPUs through OpenCL. ::: On AMD GPUs, speedups of $54\times$ (SK-Net), $437\times$ (U-Net) and $320\times$ (USK-Net) have been observed, taking the SK equivalent SW (sliding window) network as the baseline. The label throughput is up to one megapixel per second. ::: The analyzed neural networks have distinctive characteristics that apply during training or processing, and not every data set is suitable to every architecture. The quality of the predictions is assessed on two neural tissue data sets, of which one is the ISBI 2012 challenge data set. Two different loss functions, Malis loss and Softmax loss, were used during training. ::: The whole pipeline, consisting of models, interface and modified Caffe library, is available as Open Source software under the working title Project Greentea.


title: Web Usage Mining Tools & Techniques: A survey

abstract: The Quest for knowledge has led to new discoveries and invention. That leads to amelioration of various technologies. As years passed World Wide Web became overloaded with information and it became hard to retrieve data according to the need .Web mining came as a violence to provide solution of above problem. Web usage mining is category of web mining. Web usage mining mainly circulation with discovery and analyzing of usage patterns in order to serve the needs of web based applications. The web usage mining mainly consist of three stages: data preprocessing, pattern discovery and pattern analysis. This paper is focused with the study of different tools and techniques for web usage mining


The Impact of Combinations of Alcohol, Nicotine, and Cannabis on Dynamic Brain Connectivity


title: Glucose-dependent dielectric properties of blood plasma

abstract: In this study, we show a correlation between electrical properties (relative permittivity-e r and conductivity-σ) of blood plasma and plasma glucose concentration. In order to formulate that correlation, we performed electrical property measurements on blood samples collected from 10 adults between the ages of 18 and 40 at University of Alabama Birmingham (UAB) Children's hospital. The measurements are conducted between 500 MHz and 20 GHz band. Using the data obtained from measurements, we developed a single-pole Cole-Cole model for e r and σ as a function of plasma blood glucose concentration. To provide an application, we designed a microstrip patch antenna that can be used to predict the glucose concentration within a given plasma sample. Simulation results regarding antenna design and its performance are also presented.


title: The evaluation of nitric oxide scavenging activity of certain Indian medicinal plants in vitro: a preliminary study.

abstract: The plant extracts of 17 commonly used Indian medicinal plants were examined for their possible regulatory effect on nitric oxide (NO) levels using sodium nitroprusside as an NO donor in vitro. Most of the plant extracts tested demonstrated direct scavenging of NO and exhibited significant activity. The potency of scavenging activity was in the following order: Alstonia scholaris > Cynodon dactylon > Morinda citrifolia > Tylophora indica > Tectona grandis > Aegle marmelos (leaf) > Momordica charantia > Phyllanthus niruri > Ocimum sanctum > Tinospora cordifolia (hexane extract) = Coleus ambonicus > Vitex negundo (alcoholic) > T. cordifolia (dichloromethane extract) > T. cordifolia (methanol extract) > Ipomoea digitata > V. negundo (aqueous) > Boerhaavia diffusa > Eugenia jambolana (seed) > T. cordifolia (aqueous extract) > V. negundo (dichloromethane/methanol extract) > Gingko biloba > Picrorrhiza kurroa > A. marmelos (fruit) > Santalum album > E. jambolana (leaf). All the extracts evaluated exhibited a dose-dependent NO scavenging activity. The A. scholaris bark showed its greatest NO scavenging effect of 81.86% at 250 microg/mL, as compared with G. biloba, where 54.9% scavenging was observed at a similar concentration. The present results suggest that these medicinal plants might be potent and novel therapeutic agents for scavenging of NO and the regulation of pathological conditions caused by excessive generation of NO and its oxidation product, peroxynitrite.


title: Motion estimation and segmentation in depth and intensity videos

abstract: This paper investigates motion estimation and segmentation of independently moving objects in video sequences that contain depth and intensity information, such as videos captured by a Time of Flight camera. Specifically, we present a motion estimation algorithm which is based on integration of depth and intensity data. The resulting motion information is used to derive long-term point trajectories. A segmentation technique groups the trajectories according to their motion and depth similarity into spatio-temporal segments. Quantitative and qualitative analysis of synthetic and real world videos verify the proposed motion estimation and segmentation approach. The proposed framework extracts independently moving objects from videos recorded by a Time of Flight camera.


On the Actions of Muscles passing over more than One Joint.


CML: A higher concurrent language


title: Traffic Surveillance System for Vehicle Flow Detection

abstract: Robust and reliable traffic surveillance system is an urgent need to improve traffic control and management. Vehicle flow detection appears to be an important part in surveillance system. The traffic flow shows the traffic state in fixed time interval and helps to manage and control especially when there’s a traffic jam. In this paper, we propose a traffic surveillance system for vehicle detection and counting. First, we extract ROI (region of interest) from the traffic video using the combination of background subtraction and edge detection. Second, after detection of the traffic, an automatic lane-dividing method is presented to acquire several lanes. At last, based on the width of lanes, we set several self-adapting windows for vehicle counting instead of the traditional fixed window method. The self-adapting windows can eliminate the effect of vehicle sight distance on road because their width are adjusted by the width of lanes. Experiments in real traffic video have proved the robust and effectiveness of our system.


title: Interactive organ segmentation using graph cuts

abstract: An N-dimensional image is divided into “object” and “background” segments using a graph cut approach. A graph is formed by connecting all pairs of neighboring image pixels (voxels) by weighted edges. Certain pixels (voxels) have to be a priori identified as object or background seeds providing necessary clues about the image content. Our objective is to find the cheapest way to cut the edges in the graph so that the object seeds are completely separated from the background seeds. If the edge cost is a decreasing function of the local intensity gradient then the minimum cost cut should produce an object/background segmentation with compact boundaries along the high intensity gradient values in the image. An efficient, globally optimal solution is possible via standard min-cut/max-flow algorithms for graphs with two terminals. We applied this technique to interactively segment organs in various 2D and 3D medical images.


title: Collaborative Work in 3D Virtual Environments: A Research Agenda and Operational Framework

abstract: We propose a conceptual framework based on input-process-output models adapted from traditional group research for the systematic analysis of virtual teamwork. A research agenda contains a list of research questions that will be investigated in a controlled field study in the context of "The ShanghAI Lectures", a global teaching and international student collaboration project. The research questions are formulated regarding processes and outcomes of global virtual teamwork and focus on usability and sociability issues in collaborative work in 3D virtual environments. An operational framework is provided for collecting the relevant data in a structured manner by using qualitative and quantitative process measures of group behavior.


title: Comparison of envelope extraction algorithms for cardiac sound signal segmentation

abstract: This paper describes a comparative study of the envelope extraction algorithms for the cardiac sound signal segmentation. In order to extract the envelope curves based on the time elapses of the first and the second heart sounds of cardiac sound signals, three representative algorithms such as the normalized average Shannon energy, the envelope information of Hilbert transform, and the cardiac sound characteristic waveform (CSCW) are introduced. Performance comparison of the envelope extraction algorithms, and the advantages and disadvantages of the methods are examined by some parameters.


title: Omnidirectional Wideband E-Shaped Cylindrical Patch Antennas

abstract: Two novel wideband cylindrical microstrip array antennas (CMAAs) are proposed. They involve two symmetrically located probe-fed E-shaped radiating elements mounted on a cylindrical ground platform. The first design consists of two E-shaped patches located on a textile circular dielectric substrate. The second design involves a radiating element consisting of four strongly coupled E-shaped patches. For both CMAAs, the design has been performed using CST Microwave Studio. In both cases, an omnidirectional radiation pattern (ORP) in the horizontal plane with a deviation of less than 3 dB (maximum over minimum) is obtained. The two antennas are operational at 3.4 and 3.3 GHz with realized bandwidths of 33% and 21% and peak gain values of 2.98 and 4.56 dBi, respectively. A – 10\;dB sidelobe level is reached in the vertical plane for the second antenna. The CMAA with the higher gain has been fabricated and measured. Above the – 10\;dB reflection level, simulations and measurements are in good agreement.


title: Quantitative Analysis of VIIRS DNB Nightlight Point Source for Light Power Estimation and Stability Monitoring

abstract: The high sensitivity and advanced onboard calibration on the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) enables accurate measurements of low light radiances which leads to enhanced quantitative applications at night. The finer spatial resolution of DNB also allows users to examine social economic activities at urban scales. Given the growing interest in the use of the DNB data, there is a pressing need for better understanding of the calibration stability and absolute accuracy of the DNB at low radiances. The low light calibration accuracy was previously estimated at a moderate 15% using extended sources while the long-term stability has yet to be characterized. There are also several science related questions to be answered, for example, how the Earth’s atmosphere and surface variability contribute to the stability of the DNB measured radiances; how to separate them from instrument calibration stability; whether or not SI (International System of Units) traceable active light sources can be designed and installed at selected sites to monitor the calibration stability, radiometric and geolocation accuracy, and point spread functions of the DNB; furthermore, whether or not such active light sources can be used for detecting environmental changes, such as aerosols. This paper explores the quantitative analysis of nightlight point sources, such as those from fishing vessels, bridges, and cities, using fundamental radiometry and radiative transfer, which would be useful for a number of applications including search and rescue in severe weather events, as well as calibration/validation of the DNB. Time series of the bridge light data are used to assess the stability of the light measurements and the calibration of VIIRS DNB. It was found that the light radiant power computed from the VIIRS DNB data matched relatively well with independent assessments based on the in situ light installations, although estimates have to be made due to limited ground truth data and lack of suitable radiative transfer models. Results from time series analysis are encouraging in potentially being able to detect anomalies in the DNB calibration. The study also suggests that accurate ground based active lights, when properly designed and installed, can be used to monitor the stability of the VIIRS DNB calibration at near the specified minimum radiances (3 nW∙cm−2∙sr−1), and potentially can be used to monitor the environmental changes as well.


title: Supervised Learning Based on Temporal Coding in Spiking Neural Networks

abstract: Gradient descent training techniques are remarkably successful in training analog-valued artificial neural networks (ANNs). Such training techniques, however, do not transfer easily to spiking networks due to the spike generation hard nonlinearity and the discrete nature of spike communication. We show that in a feedforward spiking network that uses a temporal coding scheme where information is encoded in spike times instead of spike rates, the network input-output relation is differentiable almost everywhere. Moreover, this relation is piecewise linear after a transformation of variables. Methods for training ANNs thus carry directly to the training of such spiking networks as we show when training on the permutation invariant MNIST task. In contrast to rate-based spiking networks that are often used to approximate the behavior of ANNs, the networks we present spike much more sparsely and their behavior cannot be directly approximated by conventional ANNs. Our results highlight a new approach for controlling the behavior of spiking networks with realistic temporal dynamics, opening up the potential for using these networks to process spike patterns with complex temporal information.


title: A robust 2D point-sequence curve offset algorithm with multiple islands for contour-parallel tool path

abstract: An offset algorithm is important to the contour-parallel tool path generation process. Usually, it is necessary to offset with islands. In this paper a new offset algorithm for a 2D point-sequence curve (PS-curve) with multiple islands is presented. The algorithm consists of three sub-processes, the islands bridging process, the raw offset curve generation and the global invalid loops removal. The input of the algorithm is a set of PS-curves, in which one of them is the outer profile and the others are islands. The bridging process bridges all the islands to the outer profile with the Delaunay triangulation method, forming a single linked PS-curve. With the fact that local problems are caused by intersections of adjacent bisectors, the concept of stuck circle is proposed. Based on stuck circle, local problems are fixed by updating the original profile with the proposed basic rule and append rule, so that a raw offset curve can be generated. The last process first reports all the self-intersections on the raw offset PS-curve, and then a procedure called tree analysis puts all the self-intersections into a tree. All the points between the nodes in even depth and its immediate children are collected using the collecting rule. The collected points form the valid loops, which is the output of the proposed algorithm. Each sub-process can be complete in near linear time, so the whole algorithm has a near linear time complexity. This can be proved by the examples tested in the paper.


title: Cultural Competence Models and Cultural Competence Assessment Instruments in Nursing

abstract: The author reviewed cultural competence models and cultural competence assessment instruments developed and published by nurse researchers since 1982. Both models and instruments were examined in terms of their components, theoretical backgrounds, empirical validation, and psychometric evaluation. Most models were not empirically tested; only a few models developed model-based instruments. About half of the instruments were tested with varying levels of psychometric properties. Other related issues were discussed, including the definition of cultural competence and its significance in model and instrument development, limitations of existing models and instruments, impact of cultural competence on health disparities, and further work in cultural competence research and practice.


title: Permutation inference for the general linear model

abstract: Permutation methods can provide exact control of false positives and allow the use of non-standard statistics, making only weak assumptions about the data. With the availability of fast and inexpensive computing, their main limitation would be some lack of flexibility to work with arbitrary experimental designs. In this paper we report on results on approximate permutation methods that are more flexible with respect to the experimental design and nuisance variables, and conduct detailed simulations to identify the best method for settings that are typical for imaging research scenarios. We present a generic framework for permutation inference for complex general linear models (glms) when the errors are exchangeable and/or have a symmetric distribution, and show that, even in the presence of nuisance effects, these permutation inferences are powerful while providing excellent control of false positives in a wide range of common and relevant imaging research scenarios. We also demonstrate how the inference on glm parameters, originally intended for independent data, can be used in certain special but useful cases in which independence is violated. Detailed examples of common neuroimaging applications are provided, as well as a complete algorithm – the “randomise” algorithm – for permutation inference with the glm.


title: Learning Features For Relational Data

abstract: Feature engineering is one of the most important but tedious tasks in data science projects. This work studies automation of feature learning for relational data. We first theoretically proved that learning relevant features from relational data for a given predictive analytics problem is NP-hard. However, it is possible to empirically show that an efficient rule based approach predefining transformations as a priori based on heuristics can extract very useful features from relational data. Indeed, the proposed approach outperformed the state of the art solutions with a significant margin. We further introduce a deep neural network which automatically learns appropriate transformations of relational data into a representation that predicts the target variable well instead of being predefined as a priori by users. In an extensive experiment with Kaggle competitions, the proposed methods could win late medals. To the best of our knowledge, this is the first time an automation system could win medals in Kaggle competitions with complex relational data.


title: An Interactive Many Objective Evolutionary Algorithm with Cascade Clustering and Reference Point Incremental Learning

abstract: Researches have shown difficulties in obtaining proximity while maintaining diversity for solving many-objective optimization problems (MaOPs). The complexities of the true Pareto Front (PF) also pose serious challenges for the pervasive algorithms for their insufficient ability to adapt to the characteristics of the true PF with no priori. This paper proposes a cascade Clustering and reference point incremental Learning based Interactive Algorithm (CLIA) for many-objective optimization. In the cascade clustering process, using reference lines provided by the learning process, individuals are clustered and intraclassly sorted in a bi-level cascade style for better proximity and diversity. In the reference point incremental learning process, using the feedbacks from the clustering process, the proper generation of reference points is gradually obtained by incremental learning and the reference lines are accordingly repositioned. The advantages of the proposed interactive algorithm CLIA lie not only in the proximity obtainment and diversity maintenance but also in the versatility for the diverse PFs which uses only the interactions between the two processes without incurring extra evaluations. The experimental studies on the CEC'2018 MaOP benchmark functions have shown that the proposed algorithm CLIA has satisfactory covering of the true PFs, and is competitive, stable and efficient compared with the state-of-the-art algorithms.


title: Manticore: a heterogeneous parallel language

abstract: The Manticore project is an effort to design and implement a new functional language for parallel programming. Unlike many earlier parallel languages, Manticore is a heterogeneous language that supports parallelism at multiple levels. Specifically, we combine CML-style explicit concurrency with NESL/Nepal-style data-parallelism. In this paper, we describe and motivate the design of the Manticore language. We also describe a flexible runtime model that supports multiple scheduling disciplines (e.g., for both fine-grain and course-grain parallelism) in a uniform framework. Work on a prototype implementation is ongoing and we give a status report.


title: Rhizosphere bacteria help plants tolerate abiotic stress.

abstract: Plant-growth-promoting rhizobacteria (PGPR) are associated with plant roots and augment plant productivity and immunity; however, recent work by several groups shows that PGPR also elicit so-called 'induced systemic tolerance' to salt and drought. As we discuss here, PGPR might also increase nutrient uptake from soils, thus reducing the need for fertilizers and preventing the accumulation of nitrates and phosphates in agricultural soils. A reduction in fertilizer use would lessen the effects of water contamination from fertilizer run-off and lead to savings for farmers.


title: Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images

abstract: Segmentation of pulmonary X-ray computed tomography (CT) images is a precursor to most pulmonary image analysis applications. This paper presents a fully automatic method for identifying the lungs in three-dimensional (3-D) pulmonary X-ray CT images. The method has three main steps. First, the lung region is extracted from the CT images by gray-level thresholding. Then, the left and right lungs are separated by identifying the anterior and posterior junctions by dynamic programming. Finally, a sequence of morphological operations is used to smooth the irregular boundary along the mediastinum in order to obtain results consistent with those obtained by manual analysis, in which only the most central pulmonary arteries are excluded from the lung region. The method has been tested by processing 3-D CT data sets from eight normal subjects, each imaged three times at biweekly intervals with lungs at 90% vital capacity. We present results by comparing our automatic method to manually traced borders from two image analysts. Averaged over all volumes, the root mean square difference between the computer and human analysis is 0.8 pixels (0.54 mm). The mean intrasubject change in tissue content over the three scans was 2.75% +/- 2.29% (mean +/- standard deviation).


title: ELF radio emission associated with strong M6.0 earthquake

abstract: This article presents the results of observation of Extremely Low Frequency (ELF, 300–3000 Hz) electromagnetic radiation related to seismic activity in the period before and after strong M6.0 earthquake. Proper ELF radiation of the system of tectonic faults may indicate the state of activity and help to understand and interpret the data of ionosphere sounding by radio wave propagation study other technique. ELF and ionosphere sounding methods should complement each other in studies of radio precursors of earthquakes.


title: Evolving large-scale neural networks for vision-based reinforcement learning

abstract: The idea of using evolutionary computation to train artificial neural networks, or neuroevolution (NE), for reinforcement learning (RL) tasks has now been around for over 20 years. However, as RL tasks become more challenging, the networks required become larger, as do their genomes. But, scaling NE to large nets (i.e. tens of thousands of weights) is infeasible using direct encodings that map genes one-to-one to network components. In this paper, we scale-up our compressed network encoding where network weight matrices are represented indirectly as a set of Fourier-type coefficients, to tasks that require very-large networks due to the high-dimensionality of their input space. The approach is demonstrated successfully on two reinforcement learning tasks in which the control networks receive visual input: (1) a vision-based version of the octopus control task requiring networks with over 3 thousand weights, and (2) a version of the TORCS driving game where networks with over 1 million weights are evolved to drive a car around a track using video images from the driver's perspective.


title: BPMN-based conceptual modeling of ETL processes

abstract: Business Intelligence (BI) solutions require the design and implementation of complex processes (denoted ETL) that extract, transform, and load data from the sources to a common repository. New applications, like for example, real-time data warehousing, require agile and flexible tools that allow BI users to take timely decisions based on extremely up-to-date data. This calls for new ETL tools able to adapt to constant changes and quickly produce and modify executable code. A way to achieve this is to make ETL processes become aware of the business processes in the organization, in order to easily identify which data are required, and when and how to load them in the data warehouse. Therefore, we propose to model ETL processes using the standard representation mechanism denoted BPMN (Business Process Modeling and Notation). In this paper we present a BPMN-based metamodel for conceptual modeling of ETL processes. This metamodel is based on a classification of ETL objects resulting from a study of the most used commercial and open source ETL tools.


title: Bagging Support Vector Machines for Leukemia Classification

abstract: Leukemia is one of the most common cancer type, and its diagnosis and classification is becoming increasingly complex and important. Here, we used a gene expression dataset and adapted bagging support vector machines (bSVM) for leukemia classification. bSVM trains each SVM seperately using bootstrap technique, then aggregates the performances of each SVM by majority voting. bSVM showed accuracy between 87.5% - 92.5%, area under ROC curve between 98.0% - 99.2%, F-measure between 90.5% - 92.7% and outperformed single SVM and other classification methods. We also compared our results with other study results which used the same dataset for leukemia classification. Experimental results revealed that bSVM showed the best performance and can be used as a biomarker for the diagnose of leukemia disease.


title: Vision and Control for UAVs: A Survey of General Methods and of Inexpensive Platforms for Infrastructure Inspection

abstract: Unmanned aerial vehicles (UAVs) have gained significant attention in recent years. Low-cost platforms using inexpensive sensor payloads have been shown to provide satisfactory flight and navigation capabilities. In this report, we survey vision and control methods that can be applied to low-cost UAVs, and we list some popular inexpensive platforms and application fields where they are useful. We also highlight the sensor suites used where this information is available. We overview, among others, feature detection and tracking, optical flow and visual servoing, low-level stabilization and high-level planning methods. We then list popular low-cost UAVs, selecting mainly quadrotors. We discuss applications, restricting our focus to the field of infrastructure inspection. Finally, as an example, we formulate two use-cases for railway inspection, a less explored application field, and illustrate the usage of the vision and control techniques reviewed by selecting appropriate ones to tackle these use-cases. To select vision methods, we run a thorough set of experimental evaluations.


title: Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks

abstract: We use deep max-pooling convolutional neural networks to detect mitosis in breast histology images. The networks are trained to classify each pixel in the images, using as context a patch centered on the pixel. Simple postprocessing is then applied to the network output. Our approach won the ICPR 2012 mitosis detection competition, outperforming other contestants by a significant margin.


title: On the existence of a spectrum of policies that subsumes the least recently used (LRU) and least frequently used (LFU) policies

abstract: Sam H. Nohs Sang Lyul Mint t Department of Computer Engineering Seoul National University Seoul 151-742, Korea http://ssrnet.snu.ac.kr http://archi.snu.ac.kr We show that there exists a spectrum of block replacement policies that subsumes both the Least Recently Used (LRU) and the Least Frequently Used (LFU) policies. The spectrum is formed according to how much more weight we give to the recent history than to the older history, and is referred to as the LRFU (Least Recently/Frequently Used) policy. Unlike many previous policies that use limited history to make block replacement decisions, the LRFU policy uses the complete reference history of blocks recorded during their cache residency. Nevertheless, the LRFU requires only a few words for each block to maintain such history. This paper also describes an implementation of the LRFU that again subsumes the LRU and LFU implementations. The LRFU policy is applied to buffer caching, and results from trace-driven simulations show that the LRFU performs better than previously known policies for the workloads we considered. This point is reinforced by results from our integration of the LRFU into the FreeBSD operating system.


title: Radio Link Frequency Assignment

abstract: The problem of radio frequency assignment is to provide communication channels from limited spectral resources whilst keeping to a minimum the interference suffered by those whishing to communicate in a given radio communication network. This problem is a combinatorial (NP-hard) optimization problem. In 1993, the CELAR (the French “Centre d'Electronique de l'Armement”) built a suite of simplified versions of Radio Link Frequency Assignment Problems (RLFAP) starting from data on a real network Roisnel93. Initially designed for assessing the performances of several Constraint Logic Programming languages, these benchmarks have been made available to the public in the framework of the European EUCLID project CALMA (Combinatorial Algorithms for Military Applications). These problems should look very attractive to the CSP community: the problem is simple to represent, all constraints are binary and involve finite domain variables. They nevertheless have some of the flavors of real problems (including large size and several optimization criteria). This paper gives essential facts about the CELAR instances and also introduces the GRAPH instances which were generated during the CALMA project.


title: Data on higher education student ethics model

abstract: Abstract This article describes data collected between July 2018 and December 2018 in Yogyakarta, Indonesia. The data were collected from 566 Indonesian higher education students who completed a survey. The data were analysed using structural equational modelling (SEM) to develop a model of student ethics.


title: Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks

abstract: Most ad hoc mobile devices today operate on batteries. Hence, power consumption becomes an important issue. To maximize the lifetime of ad hoc mobile networks, the power consumption rate of each node must be evenly distributed, and the overall transmission power for each connection request must be minimized. These two objectives cannot be satisfied simultaneously by employing routing algorithms proposed in previous work. We present a new power-aware routing protocol to satisfy these two constraints simultaneously; we also compare the performance of different types of power-related routing algorithms via simulation. Simulation results confirm the need to strike a balance in attaining service availability performance of the whole network vs. the lifetime of ad hoc mobile devices.


title: Electrochemical Sensors for Clinic Analysis

abstract: Demanded by modern medical diagnosis, advances in microfabrication technology have led to the development of fast, sensitive and selective electrochemical sensors for clinic analysis. This review addresses the principles behind electrochemical sensor design and fabrication, and introduces recent progress in the application of electrochemical sensors to analysis of clinical chemicals such as blood gases, electrolytes, metabolites, DNA and antibodies, including basic and applied research. Miniaturized commercial electrochemical biosensors will form the basis of inexpensive and easy to use devices for acquiring chemical information to bring sophisticated analytical capabilities to the non-specialist and general public alike in the future.


Development and evaluation of a spatial decision support system for malaria elimination in Bhutan


Towards Robust Semantic Role Labeling