title: Autonomous vehicle ultrasonic sensor vulnerability and impact assessment

abstract: Vehicles today are relying more on technologies to bring about fully autonomous features. The conventional wirings within are being simplified into a network of electronic components, and this network is controlled via advanced sensing of the environment to make decisions in real-time. However, with the heavy reliance on the sensor readings, any inaccurate reading from the sensors could result in decisions that may cause life-threatening incidents. As such, this research focuses on the in-depth assessment of potential vulnerabilities of an important and commonly used obstacle sensing device, which is the ultrasonic sensor, in modern as well as autonomous vehicles. This research will help bring awareness to the car manufacturers and AV researchers so as to mitigate such issues.


title: Effect of probiotic supplements in women with gestational diabetes mellitus on inflammation and oxidative stress biomarkers: a randomized clinical trial

abstract: Background and Objectives: Very little is known about the use of probiotics among pregnant women with gestational diabetes mellitus (GDM) especially its effect on oxidative stress and inflammatory indices. The aim of present study was to measure the effect of a probiotic supplement capsule on inflammation and oxidative stress biomarkers in women with newly-diagnosed GDM. Methods and Study Design: 64 pregnant women with GDM were enrolled in a double-blind placebo controlled randomized clinical trial in the spring and summer of 2014. They were randomly assigned to receive either a probiotic containing four bacterial strains of Lactobacillus acidophilus LA-5, Bifidobacterium BB-12, Streptococcus Thermophilus STY-31 and Lactobacillus delbrueckii bulgaricus LBY-27 or placebo capsule for 8 consecutive weeks. Blood samples were taken pre- and post-treatment and serum indices of inflammation and oxidative stress were assayed. The measured mean response scales were then analyzed using mixed effects model. All statistical analysis was performed using Statistical Package for Social Sciences (SPSS) software (version 16). Results: Serum highsensitivity C-reactive protein and tumor necrosis factor-α levels improved in the probiotic group to a statistically significant level over the placebo group. Serum interleukin-6 levels decreased in both groups after intervention; however, neither within group nor between group differences interleukin-6 serum levels was statistically significant. Malondialdehyde, glutathione reductase and erythrocyte glutathione peroxidase levels improved significantly with the use of probiotics when compared with the placebo. Conclusions: The probiotic supplement containing L.acidophilus LA- 5, Bifidobacterium BB-12, S.thermophilus STY-31 and L.delbrueckii bulgaricus LBY-2 appears to improve several inflammation and oxidative stress biomarkers in women with GDM.


LIPN-UAM at EmoInt-2017: Combination of Lexicon-based features and Sentence-level Vector Representations for Emotion Intensity Determination.


title: Certain sequence of arithmetic progressions and a new key sharing method

abstract: We consider a special type of sequence of arithmetic progressions, in which consecutive progressions are related by the property: ithterms ofjth, (j + 1)thprogressions of the sequence are multiplicative inverses of each other modulo(i + 1)thterm ofjthprogression. Such a sequence is uniquely defined for any pair of co-prime numbers. A computational problem, defined in the context of such a sequence and its generalization, is shown to be equivalent to the integer factoring problem. The proof is probabilistic. As an application of the equivalence result, we propose a method for how users securely agree upon secret keys, which are ensured to be random. We compare our method with factoring based public key cryptographic systems: RSA (Rivest et al., ACM 21, 120–126, 1978) and Rabin systems (Rabin 1978). We discuss the advantages of the method, and its potential use-case in the post quantum scenario.


Nursing Care in Heart Disease


title: Model of knowledge transfer within an organisation

abstract: Many studies show that the acquisition of knowledge is the key to build competitive advantage of companies. We propose a simple model of knowledge transfer within the organisation and we implement the proposed model using cellular automata technique. In this paper the organisation is considered in the context of complex systems. In this perspective, the main role in organisation is played by the network of informal contacts (informal communication). The goal of this paper is to check which factors influence the efficiency and effectiveness of knowledge transfer. Our studies indicate a significant role of initial distribution of chunks of knowledge for knowledge transfer process, and the results suggest taking action in the organisation to shorten the distance (social distance) between people with different levels of knowledge, or working out incentives to share knowledge.


title: Analysis and Design of Phase-Shifted Dual H-Bridge Converter With a Wide ZVS Range and Reduced Output Filter

abstract: In this paper, a phase-shifted dual H-bridge converter, which can solve the drawbacks of existing phase-shifted full-bridge converters such as narrow zero-voltage-switching (ZVS) range, large circulating current, large duty-cycle loss, and serious secondary-voltage overshoot and oscillation, is analyzed and evaluated. The proposed topology is composed of two symmetric half-bridge inverters that are placed in parallel on the primary side and are driven in a phase-shifting manner to regulate the output voltage. At the rectifier stage, a center-tap-type rectifier with two additional low-current-rated diodes is employed. This structure allows the proposed converter to have the advantages of a wide ZVS range, no problems related to duty-cycle loss, no circulating current, and the reduction of secondary-voltage oscillation and overshoot. Moreover, the output filter's size becomes smaller compared to the conventional phase-shift full-bridge converters. This paper describes the operation principle of the proposed converter and the analysis and design consideration in depth. A 1-kW 320-385-V input 50-V output laboratory prototype operating at a 100-kHz switching frequency is designed, built, and tested to verify the effectiveness of the presented converter.


title: THE ROLE OF SERVICE AGENT , SERVICE QUALITY , AND USER SATISFACTION IN SELF -SERVICE TECHNOLOGY

abstract: This paper seeks to answer two questions: (1) Do consumers perceive the four service quality dimensions (reliability, responsiveness, assurance, and empathy) differently when interacting with Self-Service Technologies (SSTs) and human agents? and (2) How do these four dimensions affect consumers’ satisfaction when using SSTs versus using human agents? Based on an extensive literature review, we propose that consumers will perceive higher service quality from human agents compared to from SSTs. Additionally, we propose that higher service quality will have a positive impact on consumer satisfaction. We tested our propositions using experimental data collected from 181 subjects. The results indicated that consumers perceived higher levels of reliability, assurance, and empathy from human agents compared to SSTs, but did not perceive a significant difference in responsiveness. Furthermore, the service quality dimensions affected consumer satisfaction differently when receiving service from human agents versus SSTs. This study raises important implications for research and practice.


title: Tracking down software bugs using automatic anomaly detection

abstract: This paper introduces DIDUCE, a practical and effective tool that aids programmers in detecting complex program errors and identifying their root causes. By instrumenting a program and observing its behavior as it runs, DIDUCE dynamically formulates hypotheses of invariants obeyed by the program. DIDUCE hypothesizes the strictest invariants at the beginning, and gradually relaxes the hypothesis as violations are detected to allow for new behavior. The violations reported help users to catch software bugs as soon as they occur. They also give programmers new visibility into the behavior of the programs such as identifying rare corner cases in the program logic or even locating hidden errors that corrupt the program's results.We implemented the DIDUCE system for Java programs and applied it to four programs of significant size and complexity. DIDUCE succeeded in identifying the root causes of programming errors in each of the programs quickly and automatically. In particular, DIDUCE is effective in isolating a timing-dependent bug in a released JSSE (Java Secure Socket Extension) library, which would have taken an experienced programmer days to find. Our experience suggests that detecting and checking program invariants dynamically is a simple and effective methodology for debugging many different kinds of program errors across a wide variety of application domains.


title: Self-Organizing Scheme Based on NFV and SDN Architecture for Future Heterogeneous Networks

abstract: Future cellular networks will be of high capacity and heterogeneity. The structure and architecture will require high efficiency and scalability in network operation and management. In this paper, we address main requirements and challenges of future cellular networks and introduce network function virtualisation (NFV) with software defined networking (SDN) to realize the self-organizing (SO) scheme. NFV integrates the hardware appliances together in industry standard servers. And SDN performs as core controller of the network. The proposed SO scheme is based on soft fractional frequency reuse (SFFR) framework. The scheme takes different traffic demands into consideration and allocates the power adaptively. Finally the system is proved to be more scalable, energy-saving, and intelligent.


title: Development of a kW class microwave wireless power supply system to a vehicle roof

abstract: In recent years, there has been increased interest in contactless power transfer technologies in various fields such as home electric appliances or electric vehicles. In particular, researches on contactless power transfer for electric vehicles have been examined all over the world and attracted a great deal of public attention in terms of global warming prevention, energy conservation and so forth [1].


title: A 0.2 V 32-Kb 10T SRAM With 41 nW Standby Power for IoT Applications

abstract: This paper proposes a new 10-transistor (10T) bitcell for robust operations at subthreshold voltages without any boost circuitry. Features of the proposed bitcell include: 1) differential pre-discharged bit-lines and a pair of decoupled access ports to resist disturbance; 2) footed latch for write assist; 3) spare latch-foot to fight against half-select disturbances; and 4) highly stacked pull-down structure to reduce both leakage and power. A bit-interleaved SRAM macro, constructed with the proposed 10T bitcell, is fabricated in 28 nm CMOS. The power consumption has been measured as 90 nW for 30 kHz access at 0.25 V, and 41 nW for retention at 0.20 V, of which the leakage is 31% better than the state-of-the-art design.


title: 11-023 Sequential stability analysis and observer design for distributed TS fuzzy systems ∗

abstract: Many complex physical systems are the interconnection of lower-dimensional subsystems. For such systems, distributed stability analysis and observer design presents several advantages with respect to centralized approaches, such as modularity, easier analysis and design, and reduced computational complexity. Applications include distributed process control, traffic and communication networks, and economic systems. In this paper, we propose sequential stability analysis and observer design for distributed systems where the subsystems are represented by Takagi-Sugeno (TS) fuzzy models. The analysis and design are done sequentially for the subsystems, allowing for the online addition of new subsystems. The conditions are formulated as LMIs and are therefore easy to solve. The approach is illustrated on simulation examples.


New approach to the RIA of prostaglandins and related compounds using iodinated tracers.


title: Avoidit: A cyber attack taxonomy

abstract: Cyber-attacks have greatly increased over the years, and the attackers have progressively improved in devising attacks towards specific targets. To aid in identifying and defending against cyber-attacks we propose a cyber attack taxonomy called AVOIDIT (Attack Vector, Operational Impact, Defense, Information Impact, and Target). We use five major classifiers to characterize the nature of an attack: classification by attack vector, classification by operational impact, classification by defense, classification by informational impact, and classification by attack target. Classification by defense is oriented towards providing information to the network administrator regarding attack mitigation or remediation strategies. Contrary to the existing taxonomies, AVOIDIT efficiently classifies blended attacks. We further propose an efficient cause, action, defense, analysis, and target (CADAT) process used to facilitate attack classification. AVOIDIT and CADAT are used by an issue resolution system (IRS) to educate the defender on possible cyber-attacks and the development of potential security policies. We validate the proposed AVOIDIT taxonomy using cyber-attacks scenarios and highlight future work intended to simulate AVOIDIT's use within the IRS.


title: The effect of selective REM-sleep deprivation on the consolidation and affective evaluation of emotional memories

abstract: Abstract Emotion boosts the consolidation of events in the declarative memory system. Rapid eye movement (REM) sleep is believed to foster the memory consolidation of emotional events. On the other hand, REM sleep is assumed to reduce the emotional tone of the memory. Here, we investigated the effect of selective REM-sleep deprivation, SWS deprivation, or wake on the affective evaluation and consolidation of emotional and neutral pictures. Prior to an 9-h retention interval, sixty-two healthy participants (23.5 ± 2.5 years, 32 female, 30 male) learned and rated their affect to 80 neutral and 80 emotionally negative pictures. Despite rigorous deprivation of REM sleep or SWS, the residual sleep fostered the consolidation of neutral and negative pictures. Furthermore, emotional arousal helped to memorize the pictures. The better consolidation of negative pictures compared to neutral ones was most pronounced in the SWS-deprived group where a normal amount of REM sleep was present. This emotional memory bias correlated with REM sleep only in the SWS-deprived group. Furthermore, emotional arousal to the pictures decreased over time, but neither sleep nor wake had any differential effect. Neither the comparison of the affective ratings (arousal, valence) during encoding and recognition, nor the affective ratings of the recognized targets and rejected distractors supported the hypothesis that REM sleep dampens the emotional reaction to remembered stimuli. The data suggest that REM sleep fosters the consolidation of emotional memories but has no effect on the affective evaluation of the remembered contents.


title: Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates

abstract: Reaching and grasping in primates depend on the coordination of neural activity in large frontoparietal ensembles. Here we demonstrate that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain–machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters (i.e., hand position, velocity, gripping force, and the EMGs of multiple arm muscles) from the electrical activity of frontoparietal neuronal ensembles. As single neurons typically contribute to the encoding of several motor parameters, we observed that high BMIc accuracy required recording from large neuronal ensembles. Continuous BMIc operation by monkeys led to significant improvements in both model predictions and behavioral performance. Using visual feedback, monkeys succeeded in producing robot reach-and-grasp movements even when their arms did not move. Learning to operate the BMIc was paralleled by functional reorganization in multiple cortical areas, suggesting that the dynamic properties of the BMIc were incorporated into motor and sensory cortical representations.


title: A high efficiency solar array simulator implemented by an LLC resonant DC/DC converter

abstract: In order to save the cost and energy for PV system testing, a high efficiency solar array simulator (SAS) implemented by an LLC resonant DC/DC converter is proposed. This converter has zero voltage switching (ZVS) operation of the primary switches and zero current switching (ZCS) operation of the rectifier diodes. By frequency modulation control, the output impedance of an LLC converter can be regulated from zero to infinite without shunt or series resistor; hence, the efficiency of the proposed SAS can be significantly increased. According to the provided operation principles and design considerations of an LLC converter, a prototype is implemented to demonstrate the feasibility of the proposed SAS.


title: Fibronectin promotes the phorbol 12-myristate 13-acetate-induced macrophage differentiation in myeloid leukemia cells.

abstract: The extracellular matrix plays a critical role in macrophage maturation. In this study, the HL60 cell line was used as a model of leukemic myeloid cell differentiation. We assessed the ability of HL60 cells cultured on fibronectin substratum prior to phorbol 12-myristate 13-acetate (PMA) induction to differentiate into terminally differentiated macrophages. Beside their distinctive macrophage morphology, they expressed antigen receptors CD14, TLR2, TLR4 and CD68, and displayed enhanced phagocytic activity and production of reactive oxygen species. Expression of CD13, CD33, CD15 and alpha-naphthyl-acetate esterase was also maintained, however, differentiated HL60 cells were HLA-DR and CD1a negative. Here, we describe the enhanced capacity of fibronectin-adherent HL60 cells to differentiate into macrophages in response to PMA.


title: Toward a Better Understanding of Causality between Verbal Events: Extraction and Analysis of the Causal Power of Verb-Verb Associations

abstract: The identification of causal relations between verbal events is important for achieving natural language understanding. However, the problem has proven notoriously difficult since it is not clear which types of knowledge are necessary to solve this challenging problem close to human level performance. Instead of employing a large set of features proved useful in other NLP tasks, we split the problem in smaller sub problems. Since verbs play a very important role in causal relations, in this paper we harness, explore, and evaluate the predictive power of causal associations of verb-verb pairs. More specifically, we propose a set of knowledge-rich metrics to learn the likelihood of causal relations between verbs. Employing these metrics, we automatically generate a knowledge base (KBc) which identifies three categories of verb pairs: Strongly Causal, Ambiguous, and Strongly Non-causal. The knowledge base is evaluated empirically. The results show that our metrics perform significantly better than the state-of-the-art on the task of detecting causal verbal events.


title: Are we barking up the right tree ? Evaluating a comparative approach to personality

abstract: Animal studies can enrich the field of human personality psychology by addressing questions that are difficult or impossible to address with human studies alone. However, the benefits of a comparative approach to personality cannot be reaped until the tenability of the personality construct has been established in animals. Using criteria established in the wake of the person–situation debate (Kenrick & Funder, 1988), the authors evaluate the status of personality traits in animals. The animal literature provides strong evidence that personality does exist in animals. That is, personality ratings of animals: (a) show strong levels of interobserver agreement, (b) show evidence of validity in terms of predicting behaviors and real-world outcomes, and (c) do not merely reflect the implicit theories of observers projected onto animals. Although much work remains to be done, the preliminary groundwork has been laid for a comparative approach to personality.


title: Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach

abstract: Cloud computing provides resources as services in pay-as-you-go mode to customers by using virtualization technology. As virtual machine (VM) is hosted on physical server, great energy is consumed by maintaining the servers in data center. More physical servers means more energy consumption and more money cost. Therefore, the VM placement (VMP) problem is significant in cloud computing. This paper proposes an approach based on ant colony optimization (ACO) to solve the VMP problem, named as ACO-VMP, so as to effectively use the physical resources and to reduce the number of running physical servers. The number of physical servers is the same as the number of the VMs at the beginning. Then the ACO approach tries to reduce the physical server one by one. We evaluate the performance of the proposed ACO-VMP approach in solving VMP with the number of VMs being up to 600. Experimental results compared with the ones obtained by the first-fit decreasing (FFD) algorithm show that ACO-VMP can solve VMP more efficiently to reduce the number of physical servers significantly, especially when the number of VMs is large.


title: Cloud Services Orchestration: A Comparative Study of Existing Approaches

abstract: Cloud Computing is emerging today as a service model used to relocate locally-based data and applications to virtualized services available via Internet at a lower cost. A key to exploit the benefits of this model is orchestration which consists in coordinating effectively the deployment of a set of virtualized services in order to fulfill operational and quality objectives of end users and Cloud providers. Cloud orchestration can be carried out at two levels: hardware level orchestration and software level orchestration. In this paper, we highlight the main challenging points about the Cloud orchestration concept. Then, we carry on a comparative study of some existing research works involved with this concept at hardware and software levels.


Dynamics analysis of an amended HBV infection model with a simulation for anti-HBV infection therapy


title: Positive results receive more citations, but only in some disciplines

abstract: Negative results are commonly assumed to attract fewer readers and citations, which would explain why journals in most disciplines tend to publish too many positive and statistically significant findings. This study verified this assumption by counting the citation frequencies of papers that, having declared to "test" a hypothesis, reported a "positive" (full or partial) or a "negative" (null or negative) support. Controlling for various confounders, positive results were cited on average 32 % more often. The citation advantage, however, was unequally distributed across disciplines (classified as in the Essential Science Indicators database). Using Space Science as the reference category, the citation differential was positive and formally statistically significant only in Neuroscience & Behaviour, Molecular Biology & Genetics, Clinical Medicine, and Plant and Animal Science. Overall, the effect was significantly higher amongst applied disciplines, and in the biological compared to the physical and the social sciences. The citation differential was not a significant predictor of the actual frequency of positive results amongst the 20 broad disciplines considered. Although future studies should attempt more fine-grained assessments, these results suggest that publication bias may have different causes and require different solutions depending on the field considered.


title: Embedding Deep Networks into Visual Explanations

abstract: In this paper, we propose a novel explanation module to explain the predictions made by deep learning. Explanation modules work by embedding a high-dimensional deep network layer nonlinearly into a low-dimensional explanation space, while retaining faithfulness in that the original deep learning predictions can be constructed from the few concepts extracted by the explanation module. We then visualize such concepts so that human can learn about the high-level concepts deep learning is using to make decisions. We propose an algorithm called Sparse Reconstruction Autoencoder (SRAE) for learning the embedding to the explanation space, SRAE aims to reconstruct part of the original feature space while retaining faithfulness. A visualization system is then introduced for human understanding of features in the explanation space. The proposed method is applied to explain CNN models in image classification tasks, and several novel metrics are introduced to evaluate the performance of explanations quantitatively without human involvement. Experiments show that the proposed approach could generate better explanations of the mechanisms CNN use for making predictions in the task.


Causes of Employee Turnover Intention : A Study on Banking Industry of Pakistan


title: Local Free-Style Perforator Flaps in Head and Neck Reconstruction: An Update and a Useful Classification.

abstract: BACKGROUND ::: Any standard skin flap of the body including a detectable or identified perforator at its axis can be safely designed and harvested in a free-style fashion. ::: ::: ::: METHODS ::: Fifty-six local free-style perforator flaps in the head and neck region, 33 primary and 23 recycle flaps, were performed in 53 patients. The authors introduced the term "recycle" to describe a perforator flap harvested within the borders of a previously transferred flap. A Doppler device was routinely used preoperatively for locating perforators in the area adjacent to a given defect. The final flap design and degree of mobilization were decided intraoperatively, depending on the location of the most suitable perforator and the ability to achieve primary closure of the donor site. Based on clinical experience, the authors suggest a useful classification of local free-style perforator flaps. ::: ::: ::: RESULTS ::: All primary and 20 of 23 recycle free-style perforator flaps survived completely, providing tension-free coverage and a pleasing final contour for patients. In the remaining three recycle cases, the skeletonization of the pedicle resulted in pedicle damage, because of surrounding postradiotherapy scarring and flap failure. All donor sites except one were closed primarily, and all of them healed without any complications. ::: ::: ::: CONCLUSIONS ::: The free-style concept has significantly increased the potential and versatility of the standard local and recycled head and neck flap alternatives for moderate to large defects, providing a more robust, custom-made, tissue-sparing, and cosmetically superior outcome in a one-stage procedure, with minimal donor-site morbidity. ::: ::: ::: CLINICAL QUESTION/LEVEL OF EVIDENCE ::: Therapeutic, V.


title: Observing a volume rendered fetus within a pregnant patient

abstract: Augmented reality systems with see-through head-mounted displays have been used primarily for applications that are possible with today's computational capabilities. We explore possibilities for a particular application---in-place, real-time 3D ultrasound visualization---without concern for such limitations. The question is not "How well could we currently visualize the fetus in real time," but "How well could we see the fetus if we had sufficient compute power?"Our video sequence shows a 3D fetus within a pregnant woman's abdomen---the way this would look to a HMD user. Technical problems in making the sequence are discussed. This experience exposed limitations of current augmented reality systems; it may help define the capabilities of future systems needed for applications as demanding as real-time medical visualization.


title: Short-Term Power Load Forecasting with Deep Belief Network and Copula Models

abstract: The complexity and uncertainty in scheduling and operation of the power system are prominently increasing with the penetration of smart grid. An essential task for the effective operation of power systems is the power load forecasting. In this paper, a tandem data-driven method is studied in this research based on deep learning. A deep belief network (DBN) embedded with parametric Copula models is proposed to forecast the hourly load of a power grid. Data collected over a whole year from an urbanized area in Texas, United States is utilized. Forecasting hourly power load in four different seasons in a selected year is examined. Two forecasting scenarios, day-ahead and week-ahead forecasting are conducted using the proposed methods and compared with classical neural networks (NN), support vector regression machine (SVR), extreme learning machine (ELM), and classical deep belief networks (DBN). The accuracy of the forecasted power load is assessed by mean absolute percentage error (MAPE) and root mean square error (RMSE). Computational results confirm the effectiveness of the proposed semi-parametric data-driven method.


title: Burnout syndrome among critical care healthcare workers

abstract: Purpose of reviewBurnout syndrome is a psychological state resulting from prolonged exposure to job stressors. Because ICUs are characterized by a high level of work-related stress, a factor known to increase the risk of burnout syndrome, we sought to review the available literature on burnout syndr


title: Evidence for gene-gene epistatic interactions between susceptibility genes for Mycobacterium avium subsp. paratuberculosis infection in cattle

abstract: Abstract Johne's disease is a chronic granulomatous inflammatory disease caused by Mycobacterium avium subsp. paratuberculosis (MAP), with a particularly negative impact on the economy of the dairy industry. In recent years, several whole genome and candidate gene association studies have been published reporting MAP susceptibility genes, but the putative interaction between them remains unknown. Here, twenty-four single nucleotide polymorphisms in the bovine SLC11A1 , NOD2 , SP110 , TLR2 , TLR4 , and CD209 genes, previously related with paratuberculosis disease, have been analyzed. Several significant ( P CD209-TLR4 , CD209-TLR2, TLR4-TLR2 , SP110-SLC11A1 , SP110-TLR2, and SP110-NOD2 . The statistical interaction described here between bovine CD209 and TLR4 genes may be indicative of the biological interaction between their protein products upon infection by mycobacteria, as has been reported to occur in humans. Overall, this is the first evidence of epistasis among bovine innate immunity genes affecting susceptibility to MAP infection, corroborating, in turn, their implication in the disease.


title: Optimal Operation of Residential Energy Hubs in Smart Grids

abstract: This paper presents mathematical optimization models of residential energy hubs which can be readily incorporated into automated decision making technologies in smart grids, and can be solved efficiently in a real-time frame to optimally control all major residential energy loads, storage and production components while properly considering the customer preferences and comfort level. Novel mathematical models for major household demand, i.e., fridge, freezer, dishwasher, washer and dryer, stove, water heater, hot tub, and pool pumps are formulated. Also, mathematical models of other components of a residential energy system including lighting, heating, and air-conditioning are developed, and generic models for solar PV panels and energy storage/generation devices are proposed. The developed mathematical models result in Mixed Integer Linear Programming (MILP) optimization problems with the objective functions of minimizing energy consumption, total cost of electricity and gas, emissions, peak load, and/or any combination of these objectives, while considering end-user preferences. Several realistic case studies are carried out to examine the performance of the mathematical model, and experimental tests are carried out to find practical procedures to determine the parameters of the model. The application of the proposed model to a real household in Ontario, Canada is presented for various objective functions. The simulation results show that savings of up to 20% on energy costs and 50% on peak demand can be achieved, while maintaining the household owner's desired comfort levels.


title: Implementation of Cyclic Redundancy Check in Data Communication

abstract: A cyclic redundancy check (CRC) is an error-detecting code commonly used in data communication and storage devices to detect accidental changes to raw data. This paper provides an overview and principle of CRC. Emphasis is placed on the implementation of the CRC algorithm by method of hardware as well as software. It is proved that the implementation method present has high practical value.


title: Variational mesh decomposition

abstract: The problem of decomposing a 3D mesh into meaningful segments (or parts) is of great practical importance in computer graphics. This article presents a variational mesh decomposition algorithm that can efficiently partition a mesh into a prescribed number of segments. The algorithm extends the Mumford-Shah model to 3D meshes that contains a data term measuring the variation within a segment using eigenvectors of a dual Laplacian matrix whose weights are related to the dihedral angle between adjacent triangles and a regularization term measuring the length of the boundary between segments. Such a formulation simultaneously handles segmentation and boundary smoothing, which are usually two separate processes in most previous work. The efficiency is achieved by solving the Mumford-Shah model through a saddle-point problem that is solved by a fast primal-dual method. A preprocess step is also proposed to determine the number of segments that the mesh should be decomposed into. By incorporating this preprocessing step, the proposed algorithm can automatically segment a mesh into meaningful parts. Furthermore, user interaction is allowed by incorporating the user's inputs into the variational model to reflect the user's special intention. Experimental results show that the proposed algorithm outperforms competitive segmentation methods when evaluated on the Princeton Segmentation Benchmark.


title: A global test for groups of genes : testing association with a clinical outcome

abstract: Motivation: This paper presents a global test to be used for the analysis of microarray data. Using this test it can be determined whether the global expression pattern of a group of genes is significantly related to some clinical outcome of interest. Groups of genes may be any size from a single gene to all genes on the chip (e.g. known pathways, specific areas of the genome or clusters from a cluster analysis). ::: ::: Result: The test allows groups of genes of different size to be compared, because the test gives one p-value for the group, not a p-value for each gene. Researchers can use the test to investigate hypotheses based on theory or past research or to mine gene ontology databases for interesting pathways. Multiple testing problems do not occur unless many groups are tested. Special attention is given to visualizations of the test result, focussing on the associations between samples and showing the impact of individual genes on the test result. ::: ::: Availability: An R-package globaltest is available from http://www.bioconductor.org


title: Analysis and Design of Millimeter-Wave Power Amplifier Using Stacked-FET Structure

abstract: A new analysis methodology for millimeter-wave stacked-FET power amplifier design is proposed with a focus on the output power improvement by adjusting the complex load-admittance of each stacked-transistor. From this analysis, it is shown that there exist fundamental limitations on the maximum FET-stacking number and the operation frequency with given FET characteristics. Moreover, comprehensive analysis is performed to understand the effect of each FET parameter in limiting the number of stacks and output power. Based on this, a simple design method of ${\rm C} _{\rm gs}$ compensation is proposed to further increase the number of the stacks and enhance the output power. To verify analyses, various high-frequency stacked-FET PA MMICs are designed and fabricated at different frequencies with pHEMTs, mHEMTs, and CMOSFETs, which all have different maximum transition frequencies ( ${\rm f} _{\rm T}$ 's). This paper presents comprehensive analysis to identify the limitation of the stacked-FET amplifiers at millimeter-wave frequencies and presents a new design methodology to further improve the output power performance at high frequencies.


title: Degeneracy-Based Real-Time Sub-Event Detection in Twitter Stream

abstract: In this paper, we deal with the task of sub-event detection in evolving events using posts collected from the Twitter stream. By representing a sequence of successive tweets in a short time interval as a weighted graph-of-words, we are able to identify the key moments (sub-events) that compose an event using the concept of graph degeneracy. We then select a tweet to best describe each sub-event using a simple yet effective heuristic. We evaluated our approach using humangenerated summaries containing the actual important sub-events within each event and compare it to two baseline approaches using several performance metrics such as DET curves and precision/recall performance. Extensive experiments on recent sporting event streams indicate that our approach outperforms the dominant sub-event detection methods and constructs a humanreadable event summary by aggregating the most representative tweets of each sub-event.


Homomorphic Mini-blockchain Scheme


title: Ageing rate of paper insulation used in power transformers Part 2: Oil/paper system with medium and high oxygen concentration

abstract: Utility asset managers and engineers are concerned about the life and reliability of their power transformers which depends on the continued life of the paper insulation. The ageing rate of the paper is affected by water, oxygen and acids. Traditionally, the ageing rate of paper has been studied in sealed vessels however this approach does not allow the possibility to assess the affect of oxygen on paper with different water content. The ageing rate of paper has been studied for dry paper in air (excess oxygen). In these experiments we studied the ageing rate of Kraft and thermally upgraded Kraft paper in medium and high oxygen with varying water content. Furthermore, the oxygen content of the oil in sealed vessels is low which represents only sealed transformers. The ageing rate of the paper has not been determined for free breathing transformers with medium or high oxygen content and for different wetness of paper. In these ageing experiments the water and oxygen content was controlled using a special test rig to compare the ageing rate to previous work and to determine the ageing effect of paper by combining temperature, water content of paper and oxygen content of the oil. We found that the ageing rate of paper with the same water content increased with oxygen content in the oil. Hence, new life curves were developed based on the water content of the paper and the oxygen content of the oil.


title: Human-like generalization in a machine through predicate learning

abstract: Humans readily generalize, applying prior knowledge to novel situations and stimuli. Advances in machine learning and artificial intelligence have begun to approximate and even surpass human performance, but machine systems reliably struggle to generalize information to untrained situations. We describe a neural network model that is trained to play one video game (Breakout) and demonstrates one-shot generalization to a new game (Pong). The model generalizes by learning representations that are functionally and formally symbolic from training data, without feedback, and without requiring that structured representations be specified a priori. The model uses unsupervised comparison to discover which characteristics of the input are invariant, and to learn relational predicates; it then applies these predicates to arguments in a symbolic fashion, using oscillatory regularities in network firing to dynamically bind predicates to arguments. We argue that models of human cognition must account for far-reaching and flexible generalization, and that in order to do so, models must be able to discover symbolic representations from unstructured data, a process we call predicate learning. Only then can models begin to adequately explain where human-like representations come from, why human cognition is the way it is, and why it continues to differ from machine intelligence in crucial ways.


title: Smart Papers: Dynamic Publications on the Blockchain

abstract: Distributed Ledgers (DLs), also known as blockchains, provide decentralised, tamper-free registries of transactions among partners that distrust each other. For the scientific community, DLs have been proposed to decentralise and make more transparent each step of the scientific workflow. For the particular case of dissemination and peer-reviewing, DLs can provide the cornerstone to realise open decentralised publishing systems where social interactions between peers are tamper-free, enabling trustworthy computation of bibliometrics. In this paper, we propose the use of DL-backed smart contracts to track a subset of social interactions for scholarly publications in a decentralised and reliable way, yielding Smart Papers. We show how our Smart Papers approach complements current models for decentralised publishing, and analyse cost implications.


title: Edge-centric Computing: Vision and Challenges

abstract: In many aspects of human activity, there has been a continuous struggle between the forces of centralization and decentralization. Computing exhibits the same phenomenon; we have gone from mainframes to PCs and local networks in the past, and over the last decade we have seen a centralization and consolidation of services and applications in data centers and clouds. We position that a new shift is necessary. Technological advances such as powerful dedicated connection boxes deployed in most homes, high capacity mobile end-user devices and powerful wireless networks, along with growing user concerns about trust, privacy, and autonomy requires taking the control of computing applications, data, and services away from some central nodes (the "core") to the other logical extreme (the "edge") of the Internet. We also position that this development can help blurring the boundary between man and machine, and embrace social computing in which humans are part of the computation and decision making loop, resulting in a human-centered system design. We refer to this vision of human-centered edge-device based computing as Edge-centric Computing. We elaborate in this position paper on this vision and present the research challenges associated with its implementation.


QUINT: Interpretable Question Answering over Knowledge Bases


title: Pattern-Based Classification: A Unifying Perspective

abstract: The use of patterns in predictive models is a topic that has received a lot of attention in recent years. Pattern mining can help to obtain models for structured domains, such as graphs and sequences, and has been proposed as a means to obtain more accurate and more interpretable models. Despite the large amount of publications devoted to this topic, we believe however that an overview of what has been accomplished in this area is missing. This paper presents our perspective on this evolving area. We identify the principles of pattern mining that are important when mining patterns for models and provide an overview of pattern-based classification methods. We categorize these methods along the following dimensions: (1) whether they post-process a pre-computed set of patterns or iteratively execute pattern mining algorithms; (2) whether they select patterns model-independently or whether the pattern selection is guided by a model. We summarize the results that have been obtained for each of these methods.


title: Understanding and Predicting Graded Search Satisfaction

abstract: Understanding and estimating satisfaction with search engines is an important aspect of evaluating retrieval performance. Research to date has modeled and predicted search satisfaction on a binary scale, i.e., the searchers are either satisfied or dissatisfied with their search outcome. However, users' search experience is a complex construct and there are different degrees of satisfaction. As such, binary classification of satisfaction may be limiting. To the best of our knowledge, we are the first to study the problem of understanding and predicting graded (multi-level) search satisfaction. We ex-amine sessions mined from search engine logs, where searcher satisfaction was also assessed on multi-point scale by human annotators. Leveraging these search log data, we observe rich and non-monotonous changes in search behavior in sessions with different degrees of satisfaction. The findings suggest that we should predict finer-grained satisfaction levels. To address this issue, we model search satisfaction using features indicating search outcome, search effort, and changes in both outcome and effort during a session. We show that our approach can predict subtle changes in search satisfaction more accurately than state-of-the-art methods, affording greater insight into search satisfaction. The strong performance of our models has implications for search providers seeking to accu-rately measure satisfaction with their services.


title: Speeding up Monte-Carlo Simulation for Statistical Timing Analysis of Digital Integrated Circuits

abstract: This paper presents a pair of novel techniques to speed-up path-based Monte-Carlo simulation for statistical timing analysis of digital integrated circuits with no loss of accuracy. The presented techniques can be used in isolation or they could be used together. Both techniques can be readily implemented in any statistical timing framework. We compare our proposed Monte-Carlo simulation with traditional Monte-Carlo simulation in a rigorous framework and show that the new method is up to 2 times as efficient as the traditional method


title: Double-Gate Tunnel FET With High-$\kappa$ Gate Dielectric

abstract: In this paper, we propose and validate a novel design for a double-gate tunnel field-effect transistor (DG tunnel FET), for which the simulations show significant improvements compared with single-gate devices using a gate dielectric. For the first time, DG tunnel FET devices, which are using a high-gate dielectric, are explored using realistic design parameters, showing an on-current as high as 0.23 mA for a gate voltage of 1.8 V, an off-current of less than 1 fA (neglecting gate leakage), an improved average subthreshold swing of 57 mV/dec, and a minimum point slope of 11 mV/dec. The 2D nature of tunnel FET current flow is studied, demonstrating that the current is not confined to a channel at the gate-dielectric surface. When varying temperature, tunnel FETs with a high-kappa gate dielectric have a smaller threshold voltage shift than those using SiO2, while the subthreshold slope for fixed values of Vg remains nearly unchanged, in contrast with the traditional MOSFET. Moreover, an Ion/Ioff ratio of more than 2 times 1011 is shown for simulated devices with a gate length (over the intrinsic region) of 50 nm, which indicates that the tunnel FET is a promising candidate to achieve better-than-ITRS low-standby-power switch performance.


title: The Timing of Resource Development and Sustainable Competitive Advantage

abstract: We develop a formal model of the timing of resource development by competing firms. Our aim is to deepen and extend resource-level theorizing about sustainable competitive advantage. Our analysis formalizes the notion of barriers to imitation, particularly those based on time compression diseconomies where the faster a firm develops a resource the greater the cost. Time compression diseconomies are derived from a micro-model of resource development with diminishing returns to effort. We use a continuous time model of the flows of development costs and market revenues, which allows us to integrate strategic and financial analyses of firm investment problems. We examine two dimensions of sustainability: whether the resources underlying a firm's competitive advantage are economically imitable and, if so, how long imitation takes. Surprisingly, we show that sustainable competitive advantage does not necessarily lead to superior performance. We find that imitators sometimes benefit from reductions in their absorptive capacity and that innovators should license either all or none of their knowledge. Despite recent criticisms, we reaffirm the usefulness of a resource-level of analysis for strategy research, especially when the focus is on resources developed through internal projects with identifiable stopping times.


title: Enhancements to constrained novelty search: two-population novelty search for generating game content

abstract: Novelty search is a recent algorithm geared to explore search spaces without regard to objectives; minimal criteria novelty search is a variant of this algorithm for constrained search spaces. For large search spaces with multiple constraints, however, it is hard to find a set of feasible individuals that is both large and diverse. In this paper, we present two new methods of novelty search for constrained spaces, Feasible-Infeasible Novelty Search and Feasible-Infeasible Dual Novelty Search. Both algorithms keep separate populations of feasible and infeasible individuals, inspired by the FI-2pop genetic algorithm. These algorithms are applied to the problem of creating diverse and feasible game levels, representative of a large class of important problems in procedural content generation for games. Results show that the new algorithms under certain conditions can produce larger and more diverse sets of feasible strategy game maps than existing algorithms. However, the best algorithm is contingent on the particularities of the search space and the genetic operators used. It is also shown that the proposed enhancement of offspring boosting increases performance in all cases.


title: Challenges for multi-agent coordination theory based on empirical observations

abstract: Significant research progress and understanding about the nature of coordination has been made over the years. Development of the DCOP and DEC-MDP frameworks in the past decade has been especially important. Although these advances are very important for multi-agent coordination theory, they overlook a set of coordination behaviors and phenomena that have been observed empirically by many researchers since the early years of the field. The goal of this paper is to challenge researchers in multi-agent coordination to develop a comprehensive formal framework that explains these empirical observations.


title: Fuzzy logic control for an electric vehicles fast charging station

abstract: This paper presents a novel fuzzy logic based control for the energy management of electric vehicles (EV) fast charging stations (FCS). In addition to the proposed control, this work shows the viability of a hybrid FCS composed of a renewable energy source, a battery as energy storage system (ESS) and a connection with the grid. All these components are connected to a common medium voltage DC (MVDC) bus through power converters. The new control is based on a fuzzy controller, which uses two control variables, the MVDC voltage and the state-of-charge (SOC) of the ESS. The simulation results, obtained under different operating conditions, show that the proposed fuzzy logic control maintains the voltage in the MVDC bus and controls satisfactorily the ESS SOC.


Distributed Consensus Algorithms in Blockchains


title: POWER4 system microarchitecture

abstract: The IBM POWER4 is a new microprocessor organized in a system structure that includes new technology to form systems. The name POWER4 as used in this context refers not only to a chip, but also to the structure used to interconnect chips to form systems. In this paper we describe the processor microarchitecture as well as the interconnection architecture employed to form systems up to a 32-way symmetric multiprocessor.


title: Municipal Solid Waste Management in Ulaanbaatar, Mongolia: Systems Analysis

abstract: Research was conducted in Ulaanbaatar (UB), Mongolia with a view of finding ways of making its municipal solid waste management (MSWM) more efficient by minimizing the negative impact of waste on the environment and public health whilst increasing its resource efficiency in a manner that is economically and financially viable. In this study, “Wasteaware” benchmark indicators were applied to assess the current system for MSWM in UB according to its physical and governance features. Data were obtained from site visits, interviews with the key stakeholders, and consulting of official documents and reports. The results of benchmark indicators showed that, in terms of Public Health, Environmental Control and Institutional Aspects, UB had surpassed the levels of low- and lower-middle-income countries and sufficed the prerequisites for modernizing its waste management system. However, there are still some major steps ahead to fully transition to a modern system. Our study brought significant contributions by filling the existing literature gaps for UB and identified its key strengths and areas for improvement. We conclude that an improvement in data collection and reporting, and widespread consultation with all stakeholders would impact positively on the improvement of the efficiency of the MSWM in UB and other developing countries.


title: K-band FMCW radar CMOS front-end ICs with 13.3 dBm output power

abstract: This paper presents CMOS front-end ICs with 13.3 dBm output power for K-band FMCW radar, which is integrated in 0.13-μm CMOS technology. The transmitter consists of a voltage controlled oscillator, divider chain, power amplifier, and additional buffers. The receiver consists of a low-noise amplifier, IQ mixers, an IQ generator, and buffers. The leakage problem can be mitigated by adopting differential topology and ground shielding. As a result, the receiver achieves a conversion gain of 35.7 dB, a P1dB of -31.6 dBm, and a DSB noise figure of 5.5 dB. The transmitter achieves the tuning range of 23.8~24.5 GHz and the phase noise of -104 dBc/Hz @ 1MHz offset. The receiver and transmitter chips consume 121.5 mW and 373.5 mW from a 1.5 V power supply, respectively. Using these two chips, the K-band FMCW radar module is implemented and verified by measuring the distance of an object.


Comparing Network Centrality Measures as Tools for Identifying Key Concepts in Complex Networks : A Case of Wikipedia


title: A Mixed-Methods Study of Personality Conceptions in the Levant: Jordan, Lebanon, Syria, and the West Bank

abstract: Personality taxonomies are investigated using either etic-style studies that test whether Western-developed models fit in a new culture, or emic-style studies that derive personality dimensions from a local culture, using a psycholexical approach. Recent studies have incorporated strengths from both approaches. We combine the 2 approaches in the first study of personality descriptors in spoken Arabic. In Study 1, we collected 17,283 responses from a sample of adults in Lebanon, Syria, Jordan, and the West Bank (N = 545). Qualitative analysis revealed 9 personality dimensions: Soft-Heartedness, Positive Social Relatedness, Integrity, Humility versus Dominance, Conscientiousness, Extraversion, Emotional Stability, Intellect, and Openness. In Study 2, we converted the qualitative model into an indigenous personality inventory and obtained self-ratings of a sample of adults in the same region (N = 395). We also simultaneously obtained self-ratings on an adapted etic inventory that measures the lexical Big Five (N = 325). Psychometric and conceptual considerations yielded a robust 7-factor indigenous model: Agreeableness/Soft Heartedness, Honesty/Integrity, Unconventionality, Emotional Stability, Conscientiousness, Extraversion/Positive Social Relatedness, and Intellect. Initial validation evidence shows that 5 of the 7 factors overlapped with the Big Five, whereas Honesty/Integrity and Unconventionality did not overlap. Also, scores on the indigenous tools were better predicted by relevant demographic variables than scores on the etic tool. Our study demonstrated the viability of combining etic and emic approaches as key to the understanding of personality in its cultural context. (PsycINFO Database Record (c) 2017 APA, all rights reserved)


title: Knowledge Distillation with Adversarial Samples Supporting Decision Boundary

abstract: Many recent works on knowledge distillation have provided ways to transfer the knowledge of a trained network for improving the learning process of a new one, but finding a good technique for knowledge distillation is still an open problem. In this paper, we provide a new perspective based on a decision boundary, which is one of the most important component of a classifier. The generalization performance of a classifier is closely related to the adequacy of its decision boundary, so a good classifier bears a good decision boundary. Therefore, transferring information closely related to the decision boundary can be a good attempt for knowledge distillation. To realize this goal, we utilize an adversarial attack to discover samples supporting a decision boundary. Based on this idea, to transfer more accurate information about the decision boundary, the proposed algorithm trains a student classifier based on the adversarial samples supporting the decision boundary. Experiments show that the proposed method indeed improves knowledge distillation and achieves the state-of-the-arts performance.


title: Face anti-spoofing based on color texture analysis

abstract: Research on face spoofing detection has mainly been focused on analyzing the luminance of the face images, hence discarding the chrominance information which can be useful for discriminating fake faces from genuine ones. In this work, we propose a new face anti-spoofing method based on color texture analysis. We analyze the joint color-texture information from the luminance and the chrominance channels using a color local binary pattern descriptor. More specifically, the feature histograms are extracted from each image band separately. Extensive experiments on two benchmark datasets, namely CASIA face anti-spoofing and Replay-Attack databases, showed excellent results compared to the state-of-the-art. Most importantly, our inter-database evaluation depicts that the proposed approach showed very promising generalization capabilities.


title: On Learning 3D Face Morphable Model from In-the-wild Images

abstract: As a classic statistical model of 3D facial shape and albedo, 3D Morphable Model (3DMM) is widely used in facial analysis, e.g., model fitting, image synthesis. Conventional 3DMM is learned from a set of well-controlled 2D face images with associated 3D face scans, and represented by two sets of PCA basis functions. Due to the type and amount of training data, as well as, the linear bases, the representation power of 3DMM can be limited. To address these problems, this paper proposes an innovative framework to learn a nonlinear 3DMM model from a large set of unconstrained face images, without collecting 3D face scans. Specifically, given a face image as input, a network encoder estimates the projection, illumination, shape and albedo parameters. Two decoders serve as the nonlinear 3DMM to map from the shape and albedo parameters to the 3D shape and albedo, respectively. With the projection parameter, lighting, 3D shape, and albedo, a novel analytically-differentiable rendering layer is designed to reconstruct the original input face. The entire network is end-to-end trainable with only weak supervision. We demonstrate the superior representation power of our nonlinear 3DMM over its linear counterpart, and its contribution to face alignment and 3D reconstruction.


title: Learning Deep Off-the-Person Heart Biometrics Representations

abstract: Since the beginning of the new millennium, the electrocardiogram (ECG) has been studied as a biometric trait for security systems and other applications. Recently, with devices such as smartphones and tablets, the acquisition of ECG signal in the off-the-person category has made this biometric signal suitable for real scenarios. In this paper, we introduce the usage of deep learning techniques, specifically convolutional networks, for extracting useful representation for heart biometrics recognition. Particularly, we investigate the learning of feature representations for heart biometrics through two sources: on the raw heartbeat signal and on the heartbeat spectrogram. We also introduce heartbeat data augmentation techniques, which are very important to generalization in the context of deep learning approaches. Using the same experimental setup for six methods in the literature, we show that our proposal achieves state-of-the-art results in the two off-the-person publicly available databases.


title: Few-Example Object Detection with Model Communication

abstract: In this paper, we study object detection using a large pool of unlabeled images and only a few labeled images per category, named “few-example object detection”. The key challenge consists in generating trustworthy training samples as many as possible from the pool. Using few training examples as seeds, our method iterates between model training and high-confidence sample selection. In training, easy samples are generated first and, then the poorly initialized model undergoes improvement. As the model becomes more discriminative, challenging but reliable samples are selected. After that, another round of model improvement takes place. To further improve the precision and recall of the generated training samples, we embed multiple detection models in our framework, which has proven to outperform the single model baseline and the model ensemble method. Experiments on PASCAL VOC'07, MS COCO'14, and ILSVRC'13 indicate that by using as few as three or four samples selected for each category, our method produces very competitive results when compared to the state-of-the-art weakly-supervised approaches using a large number of image-level labels.


title: Apprenticeship learning using linear programming

abstract: In apprenticeship learning, the goal is to learn a policy in a Markov decision process that is at least as good as a policy demonstrated by an expert. The difficulty arises in that the MDP's true reward function is assumed to be unknown. We show how to frame apprenticeship learning as a linear programming problem, and show that using an off-the-shelf LP solver to solve this problem results in a substantial improvement in running time over existing methods---up to two orders of magnitude faster in our experiments. Additionally, our approach produces stationary policies, while all existing methods for apprenticeship learning output policies that are "mixed", i.e. randomized combinations of stationary policies. The technique used is general enough to convert any mixed policy to a stationary policy.


title: An incentive mechanism with privacy protection in mobile crowdsourcing systems

abstract: In order to improve the efficiency and utility of mobile crowdsourcing systems, this paper proposes an incentive mechanism with privacy protection in mobile crowdsourcing systems. Combining the advantages of offline incentive mechanisms and online incentive mechanisms, this paper proposes an incentive mechanism that selects the worker candidates statically, and then dynamically selects winners after bidding. The proposed incentive mechanism includes two algorithms which are an improved two-stage auction algorithm (ITA) and a truthful online reputation updating algorithm (TORU). Through simulations, we verify the efficiency and effectiveness of the proposed incentive mechanism, which can solve the free-riding problem and improve the efficiency and utility of mobile crowdsourcing systems effectively.


title: Muscle-plotter: An Interactive System based on Electrical Muscle Stimulation that Produces Spatial Output

abstract: We explore how to create interactive systems based on electrical muscle stimulation that offer expressive output. We present muscle-plotter, a system that provides users with input and output access to a computer system while on the go. Using pen-on-paper interaction, muscle-plotter allows users to engage in cognitively demanding activities, such as writing math. Users write formulas using a pen and the system responds by making the users' hand draw charts and widgets. While Anoto technology in the pen tracks users' input, muscle-plotter uses electrical muscle stimulation (EMS) to steer the user's wrist so as to plot charts, fit lines through data points, find data points of interest, or fill in forms. We demonstrate the system at the example of six simple applications, including a wind tunnel simulator. The key idea behind muscle-plotter is to make the user's hand sweep an area on which muscle-plotter renders curves, i.e., series of values, and to persist this EMS output by means of the pen. This allows the system to build up a larger whole. Still, the use of EMS allows muscle-plotter to achieve a compact and mobile form factor. In our user study, muscle-plotter made participants draw random plots with an accuracy of ±4.07 mm and preserved the frequency of functions to be drawn up to 0.3 cycles per cm.


Feature-based Malicious URL and Attack Type Detection Using Multi-class Classification


IT Capabilities, Process-Oriented Dynamic Capabilities, and Firm Financial Performance


title: Pornography and Sexual Aggression: Are There Reliable Effects and Can We Understand Them?

abstract: Abstract In response to some recent critiques, we (a) analyze the arguments and data presented in those commentaries, (b) integrate the findings of several meta-analytic summaries of experimental and naturalistic research, and (c) conduct statistical analyses on a large representative sample. All three steps support the existence of reliable associations between frequent pornography use and sexually aggressive behaviors, particularly for violent pornography and/or for men at high risk for sexual aggression. We suggest that the way relatively aggressive men interpret and react to the same pornography may differ from that of nonaggressive men, a perspective that helps integrate the current analyses with studies comparing rapists and nonrapists as well as with cross-cultural research.


title: Complications of Injectable Soft Tissue Filler

abstract: Soft tissue fillers for soft tissue augmentation and the correction of wrinkles have been used widely in recent years. Various kinds of products have been developing and the demand for these products is growing more and more rapidly. The popularity of soft tissue fillers was based on the safety profile, easy application and low complications rate [1]. As the use of fillers increases, many complications have arisen recently. The range of complications varies widely. Local and temporary reaction to the filler is common and self-limited. Ozturk et al. [1] named the minimal and self-limited complication simply as adverse sequelae. Misplacement of filler, allergic reaction, infection and biofilm formation, and delayed granulomatous reaction can occur and proper treatments are necessary [1-3]. Skin necrosis and visual impairment are not common but it is a disaster to both the patient and the clinician [4,5]. These complications have to be treated immediately and properly. Clinicians are familiar with the nature and property of most commonly used fillers and their procedure technique. Enough practice is necessary to prevent various complications because injection technique is critical. Although clinicians take care of above-mentioned factors, complications can develop unexpectedly. Clinicians should be aware of the method to deal with the complications. In this article, we introduce the three different groups of fillers according to their origin and discuss properties of each filler and possible complications. We also review how to avoid or manage the complications of soft tissue fillers.


title: Natural Language Comprehension with the EpiReader

abstract: We present the EpiReader, a novel model for machine comprehension of text. Machine comprehension of unstructured, real-world text is a major research goal for natural language processing. Current tests of machine comprehension pose questions whose answers can be inferred from some supporting text, and evaluate a model's response to the questions. The EpiReader is an end-to-end neural model comprising two components: the first component proposes a small set of candidate answers after comparing a question to its supporting text, and the second component formulates hypotheses using the proposed candidates and the question, then reranks the hypotheses based on their estimated concordance with the supporting text. We present experiments demonstrating that the EpiReader sets a new state-of-the-art on the CNN and Children's Book Test machine comprehension benchmarks, outperforming previous neural models by a significant margin.


title: An approach to handle concept drift in financial time series based on Extreme Learning Machines and explicit Drift Detection

abstract: Financial markets are very important to the economical and social organization of modern society. Due to they importance, several researchers have investigated how to predict future market movements by using both statistical and soft computing methods based on historical time series data. However, as a typical data stream, financial time series frequently present concept drift, which is a change in the relationship between input data and the target variable over time. The concept drift phenomenon affects negatively the forecasting accuracy since the learned model becomes outdated after a change in the current concept. In this paper we investigate how to handle concept drift in financial time series prediction in order to improve the forecasting accuracy. Two explicit drift detector mechanisms, namely the Drift Detection Mechanism (DDM) and the Exponentially Weighted Moving Average for Concept Drift Detection Mechanism (ECDD), were investigated. The main contribution of this work is an approach that combines Online Sequential Extreme Learning Machines (OS-ELM) with explicit drift detection, in which the OS-ELM updates the decision model just in the presence of concept drift in data. Experimental results showed that the use of drift detection was able to speed up the prediction time of OS-ELM maintaining equivalent accuracy.


title: Distant supervision for emotion detection using Facebook reactions

abstract: We exploit the Facebook reaction feature in a distant supervised fashion to train a support vector machine classifier for emotion detection, using several feature combinations and combining different Facebook pages. We test our models on existing benchmarks for emotion detection and show that employing only information that is derived completely automatically, thus without relying on any handcrafted lexicon as it's usually done, we can achieve competitive results. The results also show that there is large room for improvement, especially by gearing the collection of Facebook pages, with a view to the target domain.


title: CrackDex: Universal and automatic DEX extraction method

abstract: With Android application packing technology evolving, there are more and more ways to harden APPs. Manually unpacking APPs becomes more difficult as the time needed for analyzing increase exponentially. At the beginning, the packing technology is designed to prevent APPs from being easily decompiled, tampered and re-packed. But unfortunately, many malicious APPs start to use packing service to protect themselves. At present, most of the antivirus software focus on APPs that are unpacked, which means if malicious APPs apply the packing service, they can easily escape from a lot of antivirus software. Therefore, we should not only emphasize the importance of packing, but also concentrate on the unpacking technology. Only by doing this can we protect the normal APPs, and not miss any harmful APPs at the same time. In this paper, we first systematically study a lot of DEX packing and unpacking technologies, then propose and develop a universal unpacking system, named CrackDex, which is capable of extracting the original DEX file from the packed APP. We propose three core technologies: simulation execution, DEX reassembling, and DEX restoration, to get the unpacked DEX file. CrackDex is a part of the Dalvik virtual machine, and it monitors the execution of functions to locate the unpacking point in the portable interpreter, then launches the simulation execution, collects the data of original DEX file through corresponding structure pointer, finally fulfills the unpacking process by reassembling the data collected. The results of our experiments show that CrackDex can be used to effectively unpack APPs that are packed by packing service in a universal approach without any other knowledge of packing service.


title: DroidAlarm: an all-sided static analysis tool for Android privilege-escalation malware

abstract: Since smartphones have stored diverse sensitive privacy information, including credit card and so on, a great deal of malware are desired to tamper them. As one of the most prevalent platforms, Android contains sensitive resources that can only be accessed via corresponding APIs, and the APIs can be invoked only when user has authorized permissions in the Android permission model. However, a novel threat called privilege escalation attack may bypass this watchdog. It's presented as that an application with less permissions can access sensitive resources through public interfaces of a more privileged application, which is especially useful for malware to hide sensitive functions by dispersing them into multiple programs. We explore privilege-escalation malware evolution techniques on samples from Android Malware Genome Project. And they have showed great effectiveness against a set of powerful antivirus tools provided by VirusTotal. The detection ratios present different and distinguished reduction, compared to an average 61% detection ratio before transformation. In order to conquer this threat model, we have developed a tool called DroidAlarm to conduct a full-spectrum analysis for identifying potential capability leaks and present concrete capability leak paths by static analysis on Android applications. And we can still alarm all these cases by exposing capability leak paths in them.


title: What is Research Collaboration

abstract: Although there have been many previous studies of research collaboration, comparatively little attention has been given to the concept of ‘collaboration’ or to the adequacy of attempting to measure it through co-authorship. In this paper, we distinguish between collaboration at different levels and show that inter-institutional and international collaboration need not necessarily involve inter-individual collaboration. We also show that co-authorship is no more than a partial indicator of collaboration. Lastly, we argue for a more symmetrical approach in comparing the costs of collaboration with the undoubted benefits when considering policies towards research collaboration.


title: Long-Term Optical Access to an Estimated One Million Neurons in the Live Mouse Cortex

abstract: Summary A major technological goal in neuroscience is to enable the interrogation of individual cells across the live brain. By creating a curved glass replacement to the dorsal cranium and surgical methods for its installation, we developed a chronic mouse preparation providing optical access to an estimated 800,000–1,100,000 individual neurons across the dorsal surface of neocortex. Post-surgical histological studies revealed comparable glial activation as in control mice. In behaving mice expressing a Ca 2+ indicator in cortical pyramidal neurons, we performed Ca 2+ imaging across neocortex using an epi-fluorescence macroscope and estimated that 25,000–50,000 individual neurons were accessible per mouse across multiple focal planes. Two-photon microscopy revealed dendritic morphologies throughout neocortex, allowed time-lapse imaging of individual cells, and yielded estimates of >1 million accessible neurons per mouse by serial tiling. This approach supports a variety of optical techniques and enables studies of cells across >30 neocortical areas in behaving mice.


title: Automating hierarchical document classification for construction management information systems

abstract: The widespread use of information technologies for construction is considerably increasing the number of electronic text documents stored in construction management information systems. Consequently, automated methods for organizing and improving the access to the information contained in these types of documents become essential to construction information management. This paper describes a methodology developed to improve information organization and access in construction management information systems based on automatic hierarchical classification of construction project documents according to project components. A prototype system for document classification is presented, as well as the experiments conducted to verify the feasibility of the proposed approach.


title: Inferring Air Quality for Station Location Recommendation Based on Urban Big Data

abstract: This paper tries to answer two questions. First, how to infer real-time air quality of any arbitrary location given environmental data and historical air quality data from very sparse monitoring locations. Second, if one needs to establish few new monitoring stations to improve the inference quality, how to determine the best locations for such purpose? The problems are challenging since for most of the locations (>99%) in a city we do not have any air quality data to train a model from. We design a semi-supervised inference model utilizing existing monitoring data together with heterogeneous city dynamics, including meteorology, human mobility, structure of road networks, and point of interests (POIs). We also propose an entropy-minimization model to suggest the best locations to establish new monitoring stations. We evaluate the proposed approach using Beijing air quality data, resulting in clear advantages over a series of state-of-the-art and commonly used methods.


title: No Escape From Reality: Security and Privacy of Augmented Reality Browsers

abstract: Augmented reality (AR) browsers are an emerging category of mobile applications that add interactive virtual objects to the user's view of the physical world. This paper gives the first system-level evaluation of their security and privacy properties. We start by analyzing the functional requirements that AR browsers must support in order to present AR content. We then investigate the security architecture of Junaio, Layar, and Wikitude browsers, which are running today on over 30 million mobile devices, and identify new categories of security and privacy vulnerabilities unique to AR browsers. Finally, we provide the first engineering guidelines for securely implementing AR functionality.


title: Who Wrote the Web? Revisiting Influential Author Identification Research Applicable to Information Retrieval

abstract: In this paper, we revisit author identification research by conducting a new kind of large-scale reproducibility study: we select 15 of the most influential papers for author identification and recruit a group of students to reimplement them from scratch. Since no open source implementations have been released for the selected papers to date, our public release will have a significant impact on researchers entering the field. This way, we lay the groundwork for integrating author identification with information retrieval to eventually scale the former to the web. Furthermore, we assess the reproducibility of all reimplemented papers in detail, and conduct the first comparative evaluation of all approaches on three well-known corpora.


title: An adaptation algorithm for an intelligent natural language tutoring system

abstract: The focus of computerised learning has shifted from content delivery towards personalised online learning with Intelligent Tutoring Systems (ITS). Oscar Conversational ITS (CITS) is a sophisticated ITS that uses a natural language interface to enable learners to construct their own knowledge through discussion. Oscar CITS aims to mimic a human tutor by dynamically detecting and adapting to an individual's learning styles whilst directing the conversational tutorial. Oscar CITS is currently live and being successfully used to support learning by university students. The major contribution of this paper is the development of the novel Oscar CITS adaptation algorithm and its application to the Felder-Silverman learning styles model. The generic Oscar CITS adaptation algorithm uniquely combines the strength of an individual's learning style preference with the available adaptive tutoring material for each tutorial question to decide the best fitting adaptation. A case study is described, where Oscar CITS is implemented to deliver an adaptive SQL tutorial. Two experiments are reported which empirically test the Oscar CITS adaptation algorithm with students in a real teaching/learning environment. The results show that learners experiencing a conversational tutorial personalised to their learning styles performed significantly better during the tutorial than those with an unmatched tutorial.


NIR spectroscopy in Nepal


title: Using rule-based natural language processing to improve disease normalization in biomedical text

abstract: Background and objective In order for computers to extract useful information from unstructured text, a concept normalization system is needed to link relevant concepts in a text to sources that contain further information about the concept. Popular concept normalization tools in the biomedical field are dictionary-based. In this study we investigate the usefulness of natural language processing (NLP) as an adjunct to dictionary-based concept normalization. ::: ::: Methods We compared the performance of two biomedical concept normalization systems, MetaMap and Peregrine, on the Arizona Disease Corpus, with and without the use of a rule-based NLP module. Performance was assessed for exact and inexact boundary matching of the system annotations with those of the gold standard and for concept identifier matching. ::: ::: Results Without the NLP module, MetaMap and Peregrine attained F-scores of 61.0% and 63.9%, respectively, for exact boundary matching, and 55.1% and 56.9% for concept identifier matching. With the aid of the NLP module, the F-scores of MetaMap and Peregrine improved to 73.3% and 78.0% for boundary matching, and to 66.2% and 69.8% for concept identifier matching. For inexact boundary matching, performances further increased to 85.5% and 85.4%, and to 73.6% and 73.3% for concept identifier matching. ::: ::: Conclusions We have shown the added value of NLP for the recognition and normalization of diseases with MetaMap and Peregrine. The NLP module is general and can be applied in combination with any concept normalization system. Whether its use for concept types other than disease is equally advantageous remains to be investigated.


title: Will the experience of playing a violent role in a video game influence people's judgments of violent crimes?

abstract: The present study investigates the impact of the experience of role playing a violent character in a video game on attitudes towards violent crimes and criminals. People who played the violent game were found to be more acceptable of crimes and criminals compared to people who did not play the violent game. More importantly, interaction effects were found such that people were more acceptable of crimes and criminals outside the game if the criminals were matched with the role they played in the game and the criminal actions were similar to the activities they perpetrated during the game. The results indicate that people's virtual experience through role-playing games can influence their attitudes and judgments of similar real-life crimes, especially if the crimes are similar to what they conducted while playing games. Theoretical and practical implications are discussed.


title: Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehension

abstract: We present a new dataset and models for comprehending paragraphs about processes (e.g., photosynthesis), an important genre of text describing a dynamic world. The new dataset, ProPara, is the first to contain natural (rather than machine-generated) text about a changing world along with a full annotation of entity states (location and existence) during those changes (81k datapoints). The end-task, tracking the location and existence of entities through the text, is challenging because the causal effects of actions are often implicit and need to be inferred. We find that previous models that have worked well on synthetic data achieve only mediocre performance on ProPara, and introduce two new neural models that exploit alternative mechanisms for state prediction, in particular using LSTM input encoding and span prediction. The new models improve accuracy by up to 19%. The dataset and models are available to the community at http://data.allenai.org/propara.


title: Computational multicopter design

abstract: We present an interactive system for computational design, optimization, and fabrication of multicopters. Our computational approach allows non-experts to design, explore, and evaluate a wide range of different multicopters. We provide users with an intuitive interface for assembling a multicopter from a collection of components (e.g., propellers, motors, and carbon fiber rods). Our algorithm interactively optimizes shape and controller parameters of the current design to ensure its proper operation. In addition, we allow incorporating a variety of other metrics (such as payload, battery usage, size, and cost) into the design process and exploring tradeoffs between them. We show the efficacy of our method and system by designing, optimizing, fabricating, and operating multicopters with complex geometries and propeller configurations. We also demonstrate the ability of our optimization algorithm to improve the multicopter performance under different metrics.


TCS-TRA-1476 TCS Technical Report Frontier-based Search for Enumerating All Constrained Subgraphs with Compressed Representation by


title: Broadbeam Cross-Dipole Antenna for GPS Applications

abstract: A broadbeam cross-dipole antenna with improved low-elevation gain for global positioning system (GPS) applications is reported. The cross-dipole consists of two perpendicular dipoles, which are printed on two separate substrates that are vertically placed inside a cylindrical back cavity. Circularly polarized fields are generated by applying two quadrature signals with equal amplitude to the dipoles. To enhance the low-elevation gain, circular dipoles, curved ground planes, and corrugated back cavity are deployed in our design. A prototype operating in GPS L1 band (1.575 GHz) was designed with ANSYS HFSS, and measurement was done to verify the simulations. The reflection coefficient, axial ratio (AR), radiation pattern, antenna gain, and antenna efficiency are studied. It is found that the antenna has a wide 3 dB AR beamwidth of over 230° and a 3 dB gain beamwidth of 150°. The measured and simulated antenna gains at $\theta = 85^{\circ }$ are 0.11 and 0.68 dBic, respectively, which are higher than those of existing designs.


title: To Isolate, or to Share?: That is a Question for Intel SGX

abstract: One cornerstone of computer security is hardware-based isolation mechanisms, among which an emerging technology named Intel Software Guard Extensions (SGX) offers arguably the strongest security on x86 architecture. Intel SGX enables user-level code to create trusted memory regions named enclaves, which are isolated from the rest of the system, including privileged system software. This strong isolation of SGX, however, forbids sharing any trusted memory between enclaves, making it difficult to implement any features or techniques that must share code or data between enclaves. This dilemma between isolation and sharing is especially challenging to system software for SGX (e.g., library OSes), to which both properties are highly desirable. To resolve the tension between isolation and sharing in system software for SGX, especially library OSes, we propose a single-address-space approach, which runs all (user-level) processes and the library OS in a single enclave. This single-enclave architecture enables various memory-sharing features or techniques, thus improving both performance and usability. To enforce inter-process isolation and user-privilege isolation inside the enclave, we design a multi-domain software fault isolation (SFI) scheme, which is unique in its support for two types of domains: 1) data domains, which enable process isolation, and 2) code domains, which enable shared libraries. Our SFI is implemented efficiently by leveraging Intel Memory Protection Extensions (MPX). Experimental results show an average overhead of 10%, thus demonstrating the practicality of our approach.


title: Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation

abstract: Event extraction is of practical utility in natural language processing. In the real world, it is a common phenomenon that multiple events existing in the same sentence, where extracting them are more difficult than extracting a single event. Previous works on modeling the associations between events by sequential modeling methods suffer a lot from the low efficiency in capturing very long-range dependencies. In this paper, we propose a novel Jointly Multiple Events Extraction (JMEE) framework to jointly extract multiple event triggers and arguments by introducing syntactic shortcut arcs to enhance information flow and attention-based graph convolution networks to model graph information. The experiment results demonstrate that our proposed framework achieves competitive results compared with state-of-the-art methods.


title: A conceptual framework for geographic knowledge engineering

abstract: In many applications, the management of geographic knowledge is very important especially not only for urban and environmental planning, but also for any application in territorial intelligence. However there are several practical problems hindering the efficiency, some of them being technical and other being more conceptual. The goal of this paper is to present a tentative conceptual framework for managing practical geographic knowledge taking account of accuracy, rotundity of earth, the mobility of objects, multiple-representation, multi-scale, existence of sliver polygons, differences in classifying real features (ontologies), the many-to-many relationship of place names (gazetteers) and the necessity of interoperability. In other words, this framework must be robust against scaling, generalization and small measurement errors. Therefore, geographic objects must be distinguished into several classes of objects with different properties, namely geodetic objects, administrative objects, manmade objects and natural objects. Regarding spatial relations, in addition to conventional topological and projective relations, other relations including tessellations and ribbon topology relations are presented in order to help model geographic objects by integrating more practical semantics. Any conceptual framework is based on principles which are overall guidelines and rules; moreover, principles allow at making predictions and drawing implications and are finally the basic building blocks of theoretical models. But before identifying the principles, one needs some preliminary considerations named prolegomena. In our case, principles will be essentially rules for transforming geographic knowledge whereas prolegomena will be assertions regarding more the foundations of geographic science. Based on those considerations, 12 principles are given, preceded by 12 prolegomena. For instance, some principles deal with the transformation of spatial relationships based on visual acuity and granularity of interest, with the influence of neighboring information and cross-boundary interoperability. New categories of geographic knowledge types are presented, spatial facts, cluster of areas, flows of persons, goods, etc., topological constraints and co-location rules. To represent knowledge chunks, three styles are presented, based respectively on descriptive logics, XML and visual languages. To conclude this paper, after having defined contexts of interpretation, an example of visual language to manage geographic knowledge is proposed.


Arabic Textual Entailment with Word Embeddings


title: Geographic information systems as a marketing information system technology

abstract: Marketing information systems (MKIS) are decision support systems targeted at marketing-specific decisions. One of the most widely disseminated MKIS models divides the marketing decision universe into four domains and links these domains to each other and to other marketing activities. Unfortunately, there is little guidance on the construction of specific MKIS targeted at problems in these domains or to the construction of integrated MKIS that span domains. This paper advocates the use of geographic information systems (GIS) as a DSS generator for constructing MKIS. The paper reviews the technical capabilities of GIS and shows how these capabilities align with accepted elements of MKIS. We see that a unique advantage of GIS over other MKIS technologies is its ability to integrate information from disparate sources and spanning multiple decision domains when a single decision requires this capability. The paper then uses a decision making resource-based approach and the four elements of the marketing mix to propose a research agenda for increasing our understanding of GIS as an MKIS technology.


Applying the Kill Chain and Diamond Models to Microsoft Advanced Threat Analytics


title: Power consumption modeling of different base station types in heterogeneous cellular networks

abstract: In wireless communications micro cells are potentially more energy efficient than conventional macro cells due to the high path loss exponent. Also, heterogeneous deployments of both cell types can be used to optimize the energy efficiency. Energy efficiency of any deployment is impacted by the power consumption of each individual network element and the dependency of transmit power and load. In this paper we developed such power models for macro and micro base stations relying on data sheets of several GSM and UMTS base stations with focus on component level, e.g., power amplifier and cooling equipment. In a first application of the model a traditional macro cell deployment and a heterogeneous deployment are compared.


title: Neural network applications in automated optical inspection: state of the arts

abstract: Optical inspection techniques have been widely adopted in industrial areas since they provide fast and accurate information on product quality, process status, and machine conditions. The technologies include sensing using vision, laser scattering and imaging, x-ray imaging, and other optical sensing, and data processing for classification and recognition problems. Frequently, data processing tasks are very difficult, which is mainly due to the large volume, the complexity, and the noise of the raw data acquired. Artificial neural networks have been proven to be an effective means to cope with the problems difficult to solve or inefficient to solve by convectional methodologies. This paper presents the applications of neural networks in optical inspection tasks. Among the variety of industrial areas, this paper focuses on the inspection tasks involved in printed circuit board manufacturing processes and semiconductor manufacturing processes, which are the most competing industries in the world today. In this paper, the inspection problems are addressed and the optical techniques together with neural networks to solve such problems are reviewed. The application cases to which neural networks are applied are also presented with their effects.


title: High-Efficiency Differential-Drive CMOS Rectifier for UHF RFIDs

abstract: A high-efficiency CMOS rectifier circuit for UHF RFIDs was developed. The rectifier has a cross-coupled bridge configuration and is driven by a differential RF input. A differential-drive active gate bias mechanism simultaneously enables both low ON-resistance and small reverse leakage of diode-connected MOS transistors, resulting in large power conversion efficiency (PCE), especially under small RF input power conditions. A test circuit of the proposed differential-drive rectifier was fabricated with 0.18 mu m CMOS technology, and the measured performance was compared with those of other types of rectifiers. Dependence of the PCE on the input RF signal frequency, output loading conditions and transistor sizing was also evaluated. At the single-stage configuration, 67.5% of PCE was achieved under conditions of 953 MHz, - 12.5 dBm RF input and 10 KOmega output load. This is twice as large as that of the state-of-the-art rectifier circuit. The peak PCE increases with a decrease in operation frequency and with an increase in output load resistance. In addition, experimental results show the existence of an optimum transistor size in accordance with the output loading conditions. The multi-stage configuration for larger output DC voltage is also presented.


Detecting anxiety on Reddit