title: The relationship between the big-five model of personality and self-regulated learning strategies

abstract: Abstract The study examined the relationship between the big-five model of personality and the use of self-regulated learning strategies. Measures of self-regulated learning strategies and big-five personality traits were administered to a sample of undergraduate students. Results from canonical correlation analysis indicated an overlap between the big-five personality factors and the set of self-regulatory learning strategies. The study also compared the relative contributions of the personality factors and the self-regulated learning strategies in predicting academic achievement. The results from hierarchical multiple regressions suggest that the personality trait of Intellect made an independent contribution to the variance in student GPA, whereas effort regulation mediated the effects of Conscientiousness and Agreeableness. The relevance of personality constructs in the learning context is discussed in terms of dispositions for active learning.


title: Drowsiness Detection Based on Eyelid Movement

abstract: Fatigue driving easily causes traffic accidents. drowsiness is an important hint of Fatigue. A novel drowsiness detection algorithms based on eyelid movement is proposed in the paper. The cascaded classifiers algorithm is used to detect driver's face and the diamond searching algorithm used to trace the face. A simple feature is then extracted from temporal difference image and used to analyze rules of eyelid movement in drowsiness. Furthermore, three criterions are also presented and used to judge whether a driver is drowsy or not. Experimental results show that this new algorithm achieves a satisfied performance for drowsiness detection.


title: Drivers and Consequences of Frustration When Using Social Networking Services: A Quantitative Analysis of Facebook Users

abstract: In this study drivers and consequences of frustration, a negative emotion when using information technology (IT), are theorized and empirically evaluated in a social networking services (SNS) usage context. For example, when users are frustrated by using SNS they might stop using these services. As the number of users mainly determines the value of SNS this paper focuses on frustration while using SNS. It is assumed that both technology and social aspects of SNS usage determine whether users feel frustrated. Empirical evidence can be provided that perceived enjoyment, envy, information overload, and social overload are antecedents of the sentiment frustration. It is also argued that frustration while using SNS will lead to dissatisfaction and discontinued usage. Based on the empirical evidence for this cohesion the paper discusses its theoretical contribution in terms of that discontinuous usage behavior is a coping strategy applied by users to minimize the frustration sentiment.


title: An empirical study of community and sub-community detection in social networks applying Newman-Girvan algorithm

abstract: A social network can be represented by a set of human beings in which one member is connected to one or more members from the same set. We can obtain visual and mathematical models of human relationship by analysing a social network. There are several inherent properties of social networks such as power law distribution, centrality, small world network, modularity etc. Community structure is another important property of social network and it has gained tremendous popularity in terms of current research trends. With the increasing popularity, community structure is also getting equally complex within online social network services like Facebook, Google+, MySpace and Twitter. Newman-Girvan algorithm is the widely used community detection algorithm in social networks. This paper reflects the structure of communities as well as sub-communities occurring in a social network by applying Newman-Girvan algorithm. We have implemented this community detection algorithm on real world networks. We have given a new concept to detect sub-communities in real world networks in this paper. This paper is mainly focused on an empirical study of the Newman-Girvan algorithm.


title: The Evolution of Happiness

abstract: An evolutionary perspective offers novel insights into some major obstacles to achieving happiness. Impediments include large discrepancies between modern and ancestral environments, the existence of evolved mechanisms "designed" to produce subjective distress, and the fact that evolution by selection has produced competitive mechanisms that function to benefit one person at the expense of others. On the positive side, people also possess evolved mechanisms that produce deep sources of happiness: those for mating bonds, deep friendship, close kinship, and cooperative coalitions. Understanding these psychological mechanisms--the selective processes that designed them, their evolved functions, and the contexts governing their activation--offers the best hope for holding some evolved mechanisms in check and selectively activating others to produce an overall increment in human happiness.


title: A 10-point plan for avoiding hyaluronic acid dermal filler-related complications during facial aesthetic procedures and algorithms for management

abstract: The recent rapid growth in dermal filler use, in conjunction with inadequate product and injector control, has heralded a concerning increase in filler complications. The 10-point plan has been developed to minimize complications through careful preconsideration of causative factors, categorized as patient, product, and procedure related. Patient-related factors include history, which involves a preprocedural consultation with careful elucidation of skin conditions, systemic disease, medications, and previous cosmetic procedures. Other exclusion criteria include autoimmune diseases and multiple allergies. The temporal proximity of dental or routine medical procedures is discouraged. Insightful patient assessment, with the consideration of ethnicity, gender, and generational needs, is of paramount importance. Specified informed consent is vital due to the concerning increase in vascular complications, which carry the risk for skin compromise and loss of vision. Informed consent should be signed for both adverse events and their treatment. Product-related factors include reversibility, which is a powerful advantage when using hyaluronic acid (HA) products. Complications from nonreversible or minimally degradable products, especially when layered over vital structures, are more difficult to control. Product characteristics such as HA concentration and proprietary cross-linking should be understood in the context of ideal depth, placement, and expected duration. Product layering over late or minimally degradable fillers is discouraged, while layering of HA of over the same brand, or even across brands, seems to be feasible. Procedural factors such as photographic documentation, procedural planning, aseptic technique, and anatomical and technical knowledge are of pivotal importance. A final section is dedicated to algorithms and protocols for the management and treatment of complications such as hypersensitivity, vascular events, infection, and late-onset nodules. The 10-point plan is a systematic, effective strategy aimed at reducing the risk of dermal filler complications.


title: Computational Social Science to Gauge Online Extremism

abstract: Recent terrorist attacks carried out on behalf of ISIS on American and European soil by lone wolf attackers or sleeper cells remind us of the importance of understanding the dynamics of radicalization mediated by social media communication channels. In this paper, we shed light on the social media activity of a group of twenty-five thousand users whose association with ISIS online radical propaganda has been manually verified. By using a computational tool known as dynamical activity-connectivity maps, based on network and temporal activity patterns, we investigate the dynamics of social influence within ISIS supporters. We finally quantify the effectiveness of ISIS propaganda by determining the adoption of extremist content in the general population and draw a parallel between radical propaganda and epidemics spreading, highlighting that information broadcasters and influential ISIS supporters generate highly-infectious cascades of information contagion. Our findings will help generate effective countermeasures to combat the group and other forms of online extremism.


title: TaDA: A Logic for Time and Data Abstraction

abstract: To avoid data races, concurrent operations should either be at distinct times or on distinct data. Atomicity is the abstraction that an operation takes effect at a single, discrete instant in time, with linearisability being a well-known correctness condition which asserts that concurrent operations appear to behave atomically. Disjointness is the abstraction that operations act on distinct data resource, with concurrent separation logics enabling reasoning about threads that appear to operate independently on disjoint resources. ::: ::: We present TaDA, a program logic that combines the benefits of abstract atomicity and abstract disjointness. Our key contribution is the introduction of atomic triples, which offer an expressive approach to specifying program modules. By building up examples, we show that TaDA supports elegant modular reasoning in a way that was not previously possible.


title: Improving smart card security using self-timed circuits

abstract: We demonstrate how 1-of-n encoded speed-independent circuits provide a good framework for constructing smart card functions that are resistant to side channel attacks and fault injection. A novel alarm propagation technique is also introduced. These techniques have been used to produce a prototype smart card chip: a 16-bit secure processor with Montgomery modular exponentiator and smart card UART.


title: Cloth Animation with Adaptively Refined Meshes

abstract: Cloth animation is a very expensive process in terms of computational cost, due to the flexible nature of cloth objects. Since wrinkles and smooth areas co-exist commonly in cloth, it is tempting to reduce computational cost by avoiding redundant tessellation at the smooth areas. In this paper we present a method for dynamic adaptation of triangular meshes suitable for cloth simulation. A bottom-up approach is used for mesh refinement, which does not require precomputation and storage of multiresolution hierarchy. The hierarchy is constructed in runtime and allows reverting of the refinement locally. Local mesh refinement and simplification are triggered by curvature-induced criterion, where the curvature is estimated using methods of discrete differential geometry. The results presented are the realistic animation of garment worn by a walking mannequin generated with Baraff-Witkin type cloth solver enhanced with the mesh adaptation scheme.


title: A Survey on Gene Selection for Microarray Cancer Classification Based on Soft Computing Techniques

abstract: Recent developments in the field of soft computing have affected a large number of research works of data mining in a positive way. Processing and analysis of large data become an easy task when using soft computing techniques and especially for NP-Complete problems. This paper focuses on the cancer classification field which is an important research field in Bioinformatics. The efficiency in cancer classification depends on the competence in selecting the most promising genes using a suitable gene selection algorithm. By using soft computing techniques, the performances of gene selection algorithms are enhanced and efficient results are obtained. In this paper, a detailed literature has been done on gene selection and cancer classification and a detailed review have been made on the various gene selection algorithms based on soft computing techniques. A comparison is made based on different aspects of the existing gene selection algorithms. Apart from this, the future of gene selection and classification along with the various research gaps has been discussed.


title: High efficiency two-stage 48V VRM with PCB winding matrix transformer

abstract: High efficiency power supply solutions for data centers are gaining more attention, in order to minimize the fast growing power demands of such loads, the 48V Voltage Regulator Module (VRM) for powering CPU is a promising solution replacing the legacy 12V VRM by which the bus distribution loss, cost and size can be dramatically minimized. In this paper, a two-stage 48V/12V/1.8V–250W VRM is proposed, the first stage is a high efficiency, high power density isolated — unregulated DC/DC converter (DCX) based on LLC resonant converter stepping the input voltage from 48V to 12V. The Matrix transformer concept was utilized for designing the high frequency transformer of the first stage, an enhanced termination loop for the synchronous rectifiers and a non-uniform winding structure is proposed resulting in significant increase in both power density and efficiency of the first stage converter. The second stage is a 4-phases buck converter stepping the voltage from 12V to 1.8V to the CPU. Since the CPU runs in the sleep mode most of the time a light load efficiency improvement method by changing the bus voltage from 12V to 6 V during light load operation is proposed showing more than 8% light load efficiency enhancement than fixed bus voltage. Experimental results demonstrate the high efficiency of the proposed solution reaching peak of 91% with a significant light load efficiency improvement.


title: Hospital information systems : Measuring end user computing satisfaction ( EUCS )

abstract: Graphical abstractDisplay Omitted Highlights? The new EUCS model proposed is a valid and reliable instrument for measuring computing satisfaction. ? System quality directly and indirectly contributes to the observed explanatory power of EUCS. ? System quality incorporates system ease of use, speed, documentation, user interface and training. ? Information quality directly and positively affects EUCS. ? Information quality incorporates content, accuracy, format and timeliness. Over the past decade, hospitals in Greece have made significant investments in adopting and implementing new hospital information systems (HISs). Whether these investments will prove beneficial for these organizations depends on the support that will be provided to ensure the effective use of the information systems implemented and also on the satisfaction of its users, which is one of the most important determinants of the success of these systems. Measuring end-user computing satisfaction has a long history within the IS discipline. A number of attempts have been made to evaluate the overall post hoc impact of HIS, focusing on the end-users and more specifically on their satisfaction and the parameters that determine it. The purpose of this paper is to build further upon the existing body of the relevant knowledge by testing past models and suggesting new conceptual perspectives on how end-user computing satisfaction (EUCS) is formed among hospital information system users.All models are empirically tested using data from hospital information system (HIS) users (283). Correlation, explanatory and confirmation factor analysis was performed to test the reliability and validity of the measurement models. The structural equation modeling technique was also used to evaluate the causal models.The empirical results of the study provide support for the EUCS model (incorporating new factors) and enhance the generalizability of the EUCS instrument and its robustness as a valid measure of computing satisfaction and a surrogate for system success in a variety of cultural and linguistic settings.Although the psychometric properties of EUCS appear to be robust across studies and user groups, it should not be considered as the final chapter in the validation and refinement of these scales. Continuing efforts should be made to validate and extend the instrument.


title: Efficient Visualization of Lagrangian Coherent Structures by Filtered AMR Ridge Extraction

abstract: This paper presents a method for filtered ridge extraction based on adaptive mesh refinement. It is applicable in situations where the underlying scalar field can be refined during ridge extraction. This requirement is met by the concept of Lagrangian coherent structures which is based on trajectories started at arbitrary sampling grids that are independent of the underlying vector field. The Lagrangian coherent structures are extracted as ridges in finite Lyapunov exponent fields computed from these grids of trajectories. The method is applied to several variants of finite Lyapunov exponents, one of which is newly introduced. High computation time due to the high number of required trajectories is a main drawback when computing Lyapunov exponents of 3-dimensional vector fields. The presented method allows a substantial speed-up by avoiding the seeding of trajectories in regions where no ridges are present or do not satisfy the prescribed filter criteria such as a minimum finite Lyapunov exponent.


title: 100W X-band GaN SSPA for medium power TWTA replacement

abstract: This paper reports an X-band solid-state power amplifier with an initial output power of 180W at 9.5 GHz and greater than 100 watts measured over an 8 to 11 GHz bandwidth after burn-in. These SOA results were achieved by combining 16 GaN amplifier modules in a low-loss spatial power combiner network. The combining structure has shown a port to port loss of 0.25–0.75 dB from 8GHz to 12 GHz. The 16-way combiner demonstrates an overall combining efficiency of 90%. The size of the 16-way amplifier/combiner is 2.2″ dia. x 6″ length. This work represents the widest bandwidth operation of a GaN combiner technology to applications at X-band, and establishes new levels of power and efficiency for solid-state power amplifiers operating at these bandwidths.


title: Scalable multicasting with multiple shared trees in software defined networking

abstract: With Software Defined Networking (SDN), IP multicast becomes promising again. For IPTV applications over SDN, existing works would not scale well since they are based on per-source trees. As control-plane in SDN is logically centralized, constructing multiple shared trees is more feasible than that in traditional IP networks. Thus, in this work, we present a locality-aware multicast approach (LAMA) to construct multi-group shared trees in SDN, where each shared tree covers multiple multicast groups. In LAMA, the controller first clusters the multicast sources located in the vicinity into the same multicast cluster. For each multicast cluster, the controller selects the center switch which has the minimum distance to all multicast sources as its rendezvous point (RP) and then constructs a shortest-path multicast tree from the RP to its hosts. Finally, based on the multi-group shared trees, the controller can establish coarse-grained flow entries into on-tree switches to reduce the number of installed flow entries. Emulations on the Ryu controller and the Mininet emulator show that only 2-5 shared trees would suffice. The computation time in the controller using LAMA is around 70ms, much less than hundreds ms required for per-source trees. Moreover, LAMA only establishes 2300 flow entries, 4% of that with per-source trees in a large topology.


title: Bandwidth-Efficient Multipath Transport Protocol for Quality-Guaranteed Real-Time Video Over Heterogeneous Wireless Networks

abstract: Recent technological advancements in wireless infrastructures and handheld devices enable mobile users to concurrently receive multimedia contents with different radio interfaces (e.g., cellular and Wi-Fi). However, multipath video transport over the resource-limited and error-prone wireless networks is challenged with key technical issues: 1) conventional multipath protocols are throughput-oriented, and video data are scheduled in a content-agnostic fashion and 2) high-quality real-time video is bandwidth-intensive and delay-sensitive. To address these critical problems, this paper proposes a bandwidth-efficient multipath streaming (BEMA) protocol featured by the priority-aware data scheduling and forward error correction-based reliable transmission. First, we present a mathematical framework to formulate the delay-constrained distortion minimization problem for concurrent video transmission over multiple wireless access networks. Second, we develop a joint Raptor coding and data distribution framework to achieve target video quality with minimum bandwidth consumption. The proposed BEMA is able to effectively mitigate packet reordering and path asymmetry to improve network utilization. We conduct performance evaluation through extensive emulations in Exata involving real-time H.264 video streaming. Compared with the existing multipath protocols, BEMA achieves appreciable improvements in terms of video peak signal-to-noise ratio, end-to-end delay, bandwidth utilization, and goodput. Therefore, BEMA is recommended for streaming high-quality real-time video to multihomed terminals in heterogeneous wireless networks.


title: Good and safe uses of AI Oracles

abstract: It is possible that powerful and potentially dangerous artificial intelligence (AI) might be developed in the future. An Oracle is a design which aims to restrain the impact of a potentially dangerous AI by restricting the agent to no actions besides answering questions. Unfortunately, most Oracles will be motivated to gain more control over the world by manipulating users through the content of their answers, and Oracles of potentially high intelligence might be very successful at this \citep{DBLP:journals/corr/AlfonsecaCACAR16}. In this paper we present two designs for Oracles which, even under pessimistic assumptions, will not manipulate their users into releasing them and yet will still be incentivised to provide their users with helpful answers. The first design is the counterfactual Oracle -- which choses its answer as if it expected nobody to ever read it. The second design is the low-bandwidth Oracle -- which is limited by the quantity of information it can transmit.


title: Audio scene segmentation using multiple features, models and time scales

abstract: We present an algorithm for audio scene segmentation. An audio scene is a semantically consistent sound segment that is characterized by a few dominant sources of sound. A scene change occurs when a majority of the sources present in the data change. Our segmentation framework has three parts: a definition of an audio scene; multiple feature models that characterize the dominant sources; and a simple, causal listener model, which mimics human audition using multiple time-scales. We define a correlation function that determines correlation with past data to determine segmentation boundaries. The algorithm was tested on a difficult data set, a 1 hour audio segment of a film, with impressive results. It achieves an audio scene change detection accuracy of 97%.


title: A single-fed element antenna for tri-band anti-jamming GPS arrays

abstract: A small tri-band GPS antenna is proposed as part of an anti-jamming GPS array. A key aspect of the element is its single feed to achieve circular polarization even when integrated in a small array. This is done via a Wilkinson powder divider together with a quarter-wavelength delay line. Different array configurations are studied to minimize coupling. Design procedures are presented along with the measurement validations.


title: A Comparative Study of Demographic Attribute Inference in Twitter

abstract: Social media platforms have become a major gateway to receive and analyze public opinions. Understanding users can provide invaluable context information of their social media posts and significantly improve traditional opinion analysis models. Demographic attributes, such as ethnicity, gender, age, among others, have been extensively applied to characterize social media users. While studies have shown that user groups formed by demographic attributes can have coherent opinions towards political issues, these attributes are often not explicitly coded by users through their profiles. Previous work has demonstrated the effectiveness of different user signals such as users’ posts and names in determining demographic attributes. Yet, these efforts mostly evaluate linguistic signals from users’ posts and train models from artificially balanced datasets. In this paper, we propose a comprehensive list of user signals: self-descriptions and posts aggregated from users’ friends and followers, users’ profile images, and users’ names. We provide a comparative study of these signals side-by-side in the tasks on inferring three major demographic attributes, namely ethnicity, gender, and age. We utilize a realistic unbalanced datasets that share similar demographic makeups in Twitter for training models and evaluation experiments. Our experiments indicate that self-descriptions provide the strongest signal for ethnicity and age inference and clearly improve the overall performance when combined with tweets. Profile images for gender inference have the highest precision score with overall score close to the best result in our setting. This suggests that signals in self-descriptions and profile images have potentials to facilitate demographic attribute inferences in Twitter, and are promising for future investigation.


title: Linux kernel projects for an undergraduate operating systems course

abstract: In this paper, we present a series of programming projects based on the Linux kernel for students in a senior-level undergraduate operating systems course. The projects we describe cover several key operating systems concepts, including process scheduling, I/O scheduling, memory management, and device drivers. In addition, we assess these projects along several dimensions, from their difficulty to their capacity to help students understand operating systems concepts, based on six terms (three years) of detailed student exit surveys along with observations and anecdotal evidence. Through this assessment, we conclude that our Linux-based projects are an effective means by which to teach operating systems concepts and, additionally, that students' response to these projects is overwhelmingly positive.


title: The State of the Heart: Emotional Labor as Emotion Regulation Reviewed and Revised

abstract: Emotional labor has been an area of burgeoning research interest in occupational health psychology in recent years. Emotional labor was conceptualized in the early 1980s by sociologist Arlie Hochschild (1983) as occupational requirements that alienate workers from their emotions. Almost 2 decades later, a model was published in Journal of Occupational Health Psychology (JOHP) that viewed emotional labor through a psychological lens, as emotion regulation strategies that differentially relate to performance and wellbeing. For this anniversary issue of JOHP, we review the emotional labor as emotion regulation model, its contributions, limitations, and the state of the evidence for its propositions. At the heart of our article, we present a revised model of emotional labor as emotion regulation, that incorporates recent findings and represents a multilevel and dynamic nature of emotional labor as emotion regulation. (PsycINFO Database Record


title: Results from Data Mining in a Radiology Department: The Relevance of Data Quality

abstract: This work is part of an ongoing effort to examine and improve clinical workflows in radiology. Classical workflow analysis is time consuming and expensive. Here we present a purely data-driven approach using data mining techniques to detect causes for poor data quality and areas with poor workflow performance. Data has been taken from a operational RIS system. We defined a set of four key indicators for both data quality and workflow performance. Using several mining techniques such as cluster analysis and correlation tests we were able to detect interesting effects regarding data quality and an abnormality in the workflow for some organizational units of the examined radiology departments. We conclude that data-driven data mining approaches may act as a valuable tool to support workflow analysis and can narrow down the problem space for a manual on-site workflow analysis. This can save time and effort and leads to less strain for clinicians and workflow analysts during interviews.


title: A Pixel Pitch-Matched Ultrasound Receiver for 3-D Photoacoustic Imaging With Integrated Delta-Sigma Beamformer in 28-nm UTBB FD-SOI

abstract: This paper presents a pixel pitch-matched readout chip for 3-D photoacoustic (PA) imaging, featuring a dedicated signal conditioning and delta-sigma modulation integrated within a pixel area of 250 $\mu \text{m}$ by 250 $\mu \text{m}$ . The proof-of-concept receiver was implemented in an STMicroelectronics’s 28-nm Fully Depleted Silicon On Insulator technology, and interfaces to a $4 \times 4$ subarray of capacitive micromachined ultrasound transducers (CMUTs). The front-end signal conditioning in each pixel employs a coarse/fine gain tuning architecture to fulfill the 90-dB dynamic range requirement of the application. The employed delta-sigma beamforming architecture obviates the need for area-consuming Nyquist ADCs and thereby enables an efficient in-pixel A/D conversion. The per-pixel switched-capacitor $\Delta \Sigma $ modulator leverages slewing-dominated and area-optimized inverter-based amplifiers. It occupies only 1/4th of the pixel, and its area compares favorably with state-of-the-art designs that offer the same SNR and bandwidth. The modulator’s measured peak signal-to-noise-and-distortion ratio is 59.9 dB for a 10-MHz input bandwidth, and it consumes 6.65 mW from a 1-V supply. The overall subarray beamforming approach improves the area per channel by 7.4 times and the single-channel SNR by 8 dB compared to prior art with similar delay resolution and power dissipation. The functionality of the designed chip was evaluated within a PA imaging experiment, employing a flip-chip bonded 2-D CMUT array.


title: A network project course based on network processors

abstract: A difficult problem in networking courses is to find hands-on projects that have the right balance between the level of realism and complexity. This is especially true for projects that focus on the internal functionality of routers and other network devices. We developed a capstone course called "Network Design and Evaluation" that uses a network processor-based platform for networking projects. This platform is more realistic than traditional approaches based on software emulation environments or PC-based routers running Unix, but it is significantly less complex to work with than real commercial routers or even PC-based routers. We are currently teaching this course for the third year, and our experience has been extremely positive. Students enjoy the realism of the platform and not only learn a lot about the internal operation of the network, but also about network configuration and management.


title: Gold nanoparticles probe for recognition of specific DNA sequence based on non-cross-link system

abstract: Simpler, cheaper method for DNA sequence recognition is of a greater scientific and economic interest. In this paper, we develop a novel colorimetric detection method for recognition of specific DNA. The detection method bases on the strong distance-dependent optical properties of gold nanoparticles (Au-nps) and the DNA sequences' electrostatic interactions with the Au-nps. As a result of the single-stranded DNA (ssDNA) displaying far higher affinities than double-stranded DNA (dsDNA) to negatively charged Au-nps, when the concentration of salt is added, Au-nps in ssDNA keep stable because of the absorption of large amount of ssDNA, the mixture's color is red. But Au-nps in dsDNA aggregate immediately, and the mixture's color turns out to be blue. Therefore, we can judge whether the hybridization occurs by the mixture's color or absorption spectrums. Au-nps aggregation in this detection mode is not induced by hybridization of target DNA which cross-links the nanoparticles but by salt, then the color transition is rapid and clearly visible. This method is simple, cheap, and we can detect about 20pmol specific DNA sequences.


title: Input/Output Current Ripple Cancellation and RHP Zero Elimination in a Boost Converter using an Integrated Magnetic Technique

abstract: This paper presents a novel integrated magnetic boost converter (IMBC) with both input/output current ripple cancellation and right-half-plane (RHP) zero elimination. The input inductor, output inductor, and the ripple cancellation network auxiliary inductor of the proposed IMBC have been integrated in one magnetic core. Two extra capacitors were added to achieve input and output current ripple cancellation. Therefore, the input current ripple of the IMBC dropped to one-twelfth of the original in a conventional boost converter, and the output current worked in continuous-conduction-mode with very small ripple. Meanwhile, the proposed IMBC has eliminated the RHP zero of the boost converter, which means higher bandwidth can be reached. The using of the integrated magnetic technique not only performs above advantages but also shows great potential for reducing the weight and volume of dc-dc converter. Finally, three 36 V input, 50 V output and 500 W prototypes operating at 100 kHz are implemented to verify the expected performance. The experimental results show that the proposed IMBC can achieve both input and output current ripple cancellation and RHP zero elimination with the maximum efficiency of 96.8%. All these advantages of the IMBC are very important especially in high dynamic response, high efficiency, and high-power application.


title: Detecting Clickbait in Online Social Media: You Won't Believe How We Did It

abstract: In this paper, we propose an approach for the detection of clickbait posts in online social media (OSM). Clickbait posts are short catchy phrases that attract a user's attention to click to an article. The approach is based on a machine learning (ML) classifier capable of distinguishing between clickbait and legitimate posts published in OSM. The suggested classifier is based on a variety of features, including image related features, linguistic analysis, and methods for abuser detection. In order to evaluate our method, we used two datasets provided by Clickbait Challenge 2017. The best performance obtained by the ML classifier was an AUC of 0.8, an accuracy of 0.812, precision of 0.819, and recall of 0.966. In addition, as opposed to previous studies, we found that clickbait post titles are statistically significant shorter than legitimate post titles. Finally, we found that counting the number of formal English words in the given content is useful for clickbait detection.


title: Segmentation of Liver Lesions with Reduced Complexity Deep Models

abstract: We propose a computationally efficient architecture that learns to segment lesions from CT images of the liver. The proposed architecture uses bilinear interpolation with sub-pixel convolution at the last layer to upscale the course feature in bottle neck architecture. Since bilinear interpolation and sub-pixel convolution do not have any learnable parameter, our overall model is faster and occupies less memory footprint than the traditional U-net. We evaluate our proposed architecture on the highly competitive dataset of 2017 Liver Tumor Segmentation (LiTS) Challenge. Our method achieves competitive results while reducing the number of learnable parameters roughly by a factor of 13.8 compared to the original UNet model.


Using epigenomics data to predict gene expression in lung cancer


title: Moral Resilience: A Capacity for Navigating Moral Distress in Critical Care

abstract: E challenges are commonplace in critical care settings. Questions about the boundaries of ethically permissible treatment, assessment of decisionmaking capacity, determining who ought to decide on the ultimate treatment plan, or potentially medically inappropriate treatment are part of everyday practice. Contradictory views can result in lack of consensus or unsatisfactory decisions between patients and family members, within interprofessional teams, or among patients, patients’ families, and critical care teams. Often at stake are each person’s central ethical values, obligations, and commitments. When confronted with these challenges, many clinicians experience moral distress in response to threats or violations of their integrity.1-5 Moral distress ensues when clinicians recognize ethical conflicts and their responsibility to respond to them but are unable to translate their moral choices into ethically grounded action that preserves integrity.5 Although controversies persist regarding the definition and contours of moral distress, the literature is replete with data that documents the pervasive experience of moral distress by critical care clinicians and the profound human costs that accompany it.6-14 Spiraling rates of burnout, turnover, and shortages of critical care clinicians and diminished employee engagement threaten the quality and safety of patient care and the overall stability of the health care system.15-22 Moral distress is not likely to be extinguished, and given the complexities of the health care system, will continue to escalate in the future. Why is it that some people are able to navigate ethical dilemmas and moral distress without the deep sense of despair and hopelessness that others experience? What individual qualities and capacities do they possess? What is it that supports them to find meaning in situations that appear senseless? How do they transform their experiences into growth-producing transformations? These are some of the questions to which answers are needed in order to more fully understand how clinicians, particularly critical care clinicians, address moral distress. So far, strategies to address moral distress focused on building skills in ethical decision making, conflict resolution, interdisciplinary collaboration, system reforms, mediation, and ethics consultation have been only partial solutions.23-26 Much of what has been tried has been done on a small scale and has not sufficiently shifted the incidence or consequences of moral distress.27-30 Moreover, while clinicians resonate with data that verifies the existence of moral distress and its negative consequences, the accompanying narrative of disempowerment, despair, and hopelessness may have inadvertently


title: Predicting the Evolution of Scientific Output

abstract: Various efforts have been made to quantify scientific impact and identify the mechanisms that influence its future evolution. The first step is the identification of what constitutes scholarly impact and how it is measured. In this direction, various approaches focus on future citation count or h-index prediction at author or publication level, on fitting the distribution of citation accumulation or accurately identifying award winners, upcoming hot research topics or academic rising stars. A plethora of features have been contemplated as possible influential factors and assorted machine-learning methodologies have been adopted to ensure timely and accurate estimations. Here, we provide an overview of the field challenges, as well as a taxonomy of the existing approaches to identify the open issues that are yet to be addressed.


title: Private Analysis of Infinite Data Streams via Retroactive Grouping

abstract: With the rapid advances in hardware technology, data streams are being generated daily in large volumes, enabling a wide range of real-time analytical tasks. Yet data streams from many sources are inherently sensitive, and thus providing continuous privacy protection in data streams has been a growing demand. In this paper, we consider the problem of private analysis of infinite data streams under differential privacy. We propose a novel data stream sanitization framework that periodically releases histograms summarizing the event distributions over sliding windows to support diverse data analysis tasks. Our framework consists of two modules, a sampling-based change monitoring module and a continuous histogram publication module. The monitoring module features an adaptive Bernoulli sampling process to accurately track the evolution of a data stream. We for the first time conduct error analysis of sampling under differential privacy, which allows to select the best sampling rate. The publication module features three different publishing strategies, including a novel technique called retroactive grouping to enjoy reduced noise. We provide theoretical analysis of the utility, privacy and complexity of our framework. Extensive experiments over real datasets demonstrate that our solution substantially outperforms the state-of-the-art competitors.


title: GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data

abstract: Object Transfiguration replaces an object in an image with another object from a second image. For example it can perform tasks like "putting exactly those eyeglasses from image A on the nose of the person in image B". Usage of exemplar images allows more precise specification of desired modifications and improves the diversity of conditional image generation. However, previous methods that rely on feature space operations, require paired data and/or appearance models for training or disentangling objects from background. In this work, we propose a model that can learn object transfiguration from two unpaired sets of images: one set containing images that "have" that kind of object, and the other set being the opposite, with the mild constraint that the objects be located approximately at the same place. For example, the training data can be one set of reference face images that have eyeglasses, and another set of images that have not, both of which spatially aligned by face landmarks. Despite the weak 0/1 labels, our model can learn an "eyeglasses" subspace that contain multiple representatives of different types of glasses. Consequently, we can perform fine-grained control of generated images, like swapping the glasses in two images by swapping the projected components in the "eyeglasses" subspace, to create novel images of people wearing eyeglasses. ::: Overall, our deterministic generative model learns disentangled attribute subspaces from weakly labeled data by adversarial training. Experiments on CelebA and Multi-PIE datasets validate the effectiveness of the proposed model on real world data, in generating images with specified eyeglasses, smiling, hair styles, and lighting conditions etc. The code is available online.


title: Automating the construction of supply chain key performance indicators

abstract: Purpose – This paper aims to automate the supply chain operations reference (SCOR) model as an enabler for process‐oriented supply chain business intelligence.Design/methodology/approach – The hypothesis is the following: SCOR model automation is possible using data that is directly extracted from integrated enterprise systems. To test the hypothesis, an alignment product that allows the SCOR model to be automated with information that is directly extracted from the Oracle E‐Business Suite was developed.Findings – In order to achieve the full benefits from the SCOR model, effective business process management and the SCOR key performance indicators (KPIs) must be implemented and used. Unless data collection to support KPI construction is automated, it is difficult to institutionalize the SCOR model as a measurement and benchmarking framework. We have demonstrated that automated support for KPIs is feasible and achievable.Research limitations/implications – The E‐Business Suite is a single enterprise solut...


title: Electronic governance theory

abstract: For any research field to advance it has to have its own 'core' theory and a set of reference disciplines it draws on and contributes to. Thus far it is clear that E-governance research draws primarily on theory and methods developed in the Management Information Systems (MIS) discipline. MIS itself draws on fields such as Computer Science, Economics, Sociology, etc. The theory and methods that any e-governance researcher will rely on will be determined by the problems being addressed and the context in which the problems arise. This tutorial workshop presents an overview of theory used in e-governance research. It then presents an introduction to two richly developed theories in MIS namely - Structuration and Actor-Network theory. The presentations highlight the main ideas of these theories with a view to prompting their increased incorporation in e-governance research. E-governance issues for developing countries is also presented, as these constitute a growing area of research. The workshop features three papers that will form the basis for a discussion on state-of-the-art in e-governance theory.


title: Recognizing Surgical Activities with Recurrent Neural Networks

abstract: We apply recurrent neural networks to the task of recognizing surgical activities from robot kinematics. Prior work in this area focuses on recognizing short, low-level activities, or gestures, and has been based on variants of hidden Markov models and conditional random fields. In contrast, we work on recognizing both gestures and longer, higher-level activites, or maneuvers, and we model the mapping from kinematics to gestures/maneuvers with recurrent neural networks. To our knowledge, we are the first to apply recurrent neural networks to this task. Using a single model and a single set of hyperparameters, we match state-of-the-art performance for gesture recognition and advance state-of-the-art performance for maneuver recognition, in terms of both accuracy and edit distance. Code is available at this https URL .


title: Computing visual attention from scene depth

abstract: Visual attention is the ability to rapidly detect the interesting parts of a given scene. Inspired by biological vision, the principle of visual attention is used with a similar goal in computer vision. Several previous works deal with the computation of visual attention from images provided by standard video cameras, but little attention has been devoted so far to scene depth as source for visual attention. The investigation presented in this paper aims at an extension of the visual attention model to the scene depth component. The first part of the paper is devoted to the integration of depth in the computational model built around conspicuity and saliency maps. The second part is devoted to experimental work in which results of visual attention, obtained form the extended model and for various 3D scenes, are presented. The results speak for the usefulness of the enhanced computational model.


title: Ideomotor design: Using common coding theory to derive novel video game interactions

abstract: Recent experiments show video games have a range of positive cognitive effects, such as improvement in attention, spatial cognition and mental rotation, and also overcoming of cognitive disabilities such as fear of flying. Further, game environments are now being used to generate scientific discoveries, and bring about novel phenomenological effects, such as out-of-body experiences. These advances provide interesting interaction design possibilities for video games. However, since the cognitive mechanisms underlying these experimental effects are unknown, it is difficult to systematically derive novel systems and interaction designs based on these results. We review the emerging cognitive mechanism known as common coding (which proposes a common neural representation connecting execution, perception and imagination of movements), and outline how this mechanism could provide an integrated account of the cognitive effects of video games. We then illustrate, using two ongoing projects, how novel video game interaction designs could be derived by extending common coding theory.


title: A Permutation-based Model for Crowd Labeling: Optimal Estimation and Robustness

abstract: The aggregation and denoising of crowd-labeled data is a task that has gained increased significance with the advent of crowdsourcing platforms and massive datasets. In this paper, we propose a permutation-based model for crowd labeled data that is a significant generalization of the common Dawid-Skene model, and introduce a new error metric by which to compare different estimators. Working in a high-dimensional non-asymptotic framework that allows both the number of workers and tasks to scale, we derive minimax rates of convergence for the permutation-based model that are optimal (up to logarithmic factors). We show that the permutation-based model offers significant robustness in estimation due to its richness, while surprisingly incurring only a small additional statistical penalty as compared to the Dawid-Skene model. We then design a computationally-efficient method, called the OBI-WAN estimator, that is optimal over a class intermediate between the permutation-based and the Dawid-Skene models (up to logarithmic factors), and also simultaneously achieves non-trivial guarantees over the entire permutation-based model class. Finally, we conduct synthetic simulations and experiments on real-world crowdsourcing data, and these corroborate our theoretical findings.


A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate


Discrete roles and bifurcation of PTEN signaling and mTORC1-mediated anabolic metabolism underlie IL-7–driven B lymphopoiesis


title: Achieving Privacy in the Adversarial Multi-Armed Bandit

abstract: In this paper, we improve the previously best known regret bound to achieve $\epsilon$-differential privacy in oblivious adversarial bandits from $\mathcal{O}{(T^{2/3}/\epsilon)}$ to $\mathcal{O}{(\sqrt{T} \ln T /\epsilon)}$. This is achieved by combining a Laplace Mechanism with EXP3. We show that though EXP3 is already differentially private, it leaks a linear amount of information in $T$. However, we can improve this privacy by relying on its intrinsic exponential mechanism for selecting actions. This allows us to reach $\mathcal{O}{(\sqrt{\ln T})}$-DP, with a regret of $\mathcal{O}{(T^{2/3})}$ that holds against an adaptive adversary, an improvement from the best known of $\mathcal{O}{(T^{3/4})}$. This is done by using an algorithm that run EXP3 in a mini-batch loop. Finally, we run experiments that clearly demonstrate the validity of our theoretical analysis.


title: Hand Body Language Gesture Recognition Based on Signals From Specialized Glove and Machine Learning Algorithms

abstract: The man–machine interface (MMI) is one of the most exciting areas of contemporary research. To make the MMI as convenient for a human as possible, it is desirable that efficient algorithms for recognizing body language are developed. This paper presents a system for quick and effective recognition of gestures of hand body language, based on data from a specialized glove equipped with ten sensors. In the experiment, 10 people performed 22 hand body language gestures. Each of the 22 gestures was executed 10 times. Collected data were preprocessed in multiple ways and three machine learning algorithms were designed based on classifiers (probabilistic neural network, support vector machine, and k-nearest neighbors algorithm) trained and tested by a tenfold cross-validation technique. The best designed classifiers gained effectiveness of gesture recognition at $\kappa$ = 98.24% with a very short time of testing, below 1 ms. The experiments confirm that efficient and quick recognition of hand body language is possible.


title: Multimodal assistive technologies for depression diagnosis and monitoring

abstract: Depression is a severe mental health disorder with high societal costs. Current clinical practice depends almost exclusively on self-report and clinical opinion, risking a range of subjective biases. The long-term goal of our research is to develop assistive technologies to support clinicians and sufferers in the diagnosis and monitoring of treatment progress in a timely and easily accessible format. In the first phase, we aim to develop a diagnostic aid using affective sensing approaches. This paper describes the progress to date and proposes a novel multimodal framework comprising of audio-video fusion for depression diagnosis. We exploit the proposition that the auditory and visual human communication complement each other, which is well-known in auditory-visual speech processing; we investigate this hypothesis for depression analysis. For the video data analysis, intra-facial muscle movements and the movements of the head and shoulders are analysed by computing spatio-temporal interest points. In addition, various audio features (fundamental frequency f0, loudness, intensity and mel-frequency cepstral coefficients) are computed. Next, a bag of visual features and a bag of audio features are generated separately. In this study, we compare fusion methods at feature level, score level and decision level. Experiments are performed on an age and gender matched clinical dataset of 30 patients and 30 healthy controls. The results from the multimodal experiments show the proposed framework’s effectiveness in depression analysis.


title: High-gain 60-GHz patch antenna array using GaN MMIC technology

abstract: In this paper, a high-gain 2×2 antenna array at 60 GHz using GaN MMIC process technology is presented. For gain enhancement, a modified patch antenna element with two parasitic strips on each non-radiation edge and a parasitic patch on one of the radiation edges is proposed. This antenna element achieves a gain of 3.42 dBi at 60 GHz, about 2 dBi higher than a conventional patch antenna element using the same process technology. The 2×2 antenna array consisting of four modified patch antenna element exhibits a 10-dB impedance bandwidth of 10.1% and a peak gain of 5.7 dBi at 60 GHz.


title: Control ability of spin coating planarization of resist filmand optimal control of developers

abstract: The issue of how to transfer a pattern onto a wafer during photolithography is very important. Normally, the resist is treated as a pattern-transferring medium. Such a medium should have a very smooth surface to reduce the focus error. In this experiment, spin coating is used. The velocity of the center differs from that of the outer edges of a rotating disk, so a perfectly smooth surface cannot be obtained. Therefore, resist temperature, cooling temperature, heating temperature, cup temperature, cup humidity and exhaust pressure were controlled to eliminate this imperfection to yield an acceptable error. A lower cooling temperature yields a thicker center, such that the surface of the wafer protrudes at the center. A lower cooling temperature also corresponds to a thicker center, with the same effect. The cup temperature was set to the cooling temperature so that thickness distribution would be the same as. A higher heating temperature yields a thinner wafer. Higher humidity yields a thinner wafer.


title: How much do word embeddings encode about syntax?

abstract: Do continuous word embeddings encode any useful information for constituency parsing? We isolate three ways in which word embeddings might augment a stateof-the-art statistical parser: by connecting out-of-vocabulary words to known ones, by encouraging common behavior among related in-vocabulary words, and by directly providing features for the lexicon. We test each of these hypotheses with a targeted change to a state-of-the-art baseline. Despite small gains on extremely small supervised training sets, we find that extra information from embeddings appears to make little or no difference to a parser with adequate training data. Our results support an overall hypothesis that word embeddings import syntactic information that is ultimately redundant with distinctions learned from treebanks in other ways.


title: Are Foreign IT Workers Cheaper? U.S. Visa Policies and Compensation of Information Technology Professionals

abstract: The use of H-1B and other work visas to hire foreign information technology (IT) professionals in the United States has attracted significant controversy and policy debates. On one hand, hiring high-skill foreign IT professionals on work visas can be advantageous for U.S. firms and the overall economy. On the other hand, high-skill immigration can adversely impact the wages of foreign and American IT professionals. This study uses data on skills and compensation of more than 50,000 IT professionals in the United States over the period 2000--2005 to study patterns in compensation of foreign and American IT professionals to inform these debates. Contrary to the popular belief that foreign workers are a cheap source of labor for U.S. firms, we find that after controlling for their human capital attributes, foreign IT professionals (those without U.S. citizenship and those with H-1B or other work visas) earn a salary premium when compared with IT professionals with U.S. citizenship. The salary premiums for non-U.S. citizens and for those on work visas fluctuate in response to supply shocks created by the annual caps on new H-1B visas. Setting lower and fully utilized annual caps results in higher salary premiums for non-U.S. citizens and those with work visas. We discuss implications of this study for crafting informed visa-and immigration-related policies by the U.S. government, for staffing practices of firms, and for human capital investments by IT professionals.


title: Activity recognition and abnormality detection with the switching hidden semi-Markov model

abstract: This paper addresses the problem of learning and recognizing human activities of daily living (ADL), which is an important research issue in building a pervasive and smart environment. In dealing with ADL, we argue that it is beneficial to exploit both the inherent hierarchical organization of the activities and their typical duration. To this end, we introduce the switching hidden semi-markov model (S-HSMM), a two-layered extension of the hidden semi-Markov model (HSMM) for the modeling task. Activities are modeled in the S-HSMM in two ways: the bottom layer represents atomic activities and their duration using HSMMs; the top layer represents a sequence of high-level activities where each high-level activity is made of a sequence of atomic activities. We consider two methods for modeling duration: the classic explicit duration model using multinomial distribution, and the novel use of the discrete Coxian distribution. In addition, we propose an effective scheme to detect abnormality without the need for training on abnormal data. Experimental results show that the S-HSMM performs better than existing models including the flat HSMM and the hierarchical hidden Markov model in both classification and abnormality detection tasks, alleviating the need for presegmented training data. Furthermore, our discrete Coxian duration model yields better computation time and generalization error than the classic explicit duration model.


title: Clearing algorithms for barter exchange markets: enabling nationwide kidney exchanges

abstract: In barter-exchange markets, agents seek to swap their items with one another, in order to improve their own utilities. These swaps consist of cycles of agents, with each agent receiving the item of the next agent in the cycle. We focus mainly on the upcoming national kidney-exchange market, where patients with kidney disease can obtain compatible donors by swapping their own willing but incompatible donors. With over 70,000 patients already waiting for a cadaver kidney in the US, this market is seen as the only ethical way to significantly reduce the 4,000 deaths per year attributed to kidney diseas. The clearing problem involves finding a social welfare maximizing exchange when the maximum length of a cycle is fixed. Long cycles are forbidden, since, for incentive reasons, all transplants in a cycle must be performed simultaneously. Also, in barter-exchanges generally, more agents are affected if one drops out of a longer cycle. We prove that the clearing problem with this cycle-length constraint is NP-hard. Solving it exactly is one of the main challenges in establishing a national kidney exchange. We present the first algorithm capable of clearing these markets on a nationwide scale. The key is incremental problem formulation. We adapt two paradigms for the task: constraint generation and column generation. For each, we develop techniques that dramatically improve both runtime and memory usage. We conclude that column generation scales drastically better than constraint generation. Our algorithm also supports several generalizations, as demanded by real-world kidney exchanges. Our algorithm replaced CPLEX as the clearing algorithm of the Alliance for Paired Donation, one of the leading kidney exchanges. The match runs are conducted every two weeks and transplants based on our optimizations have already been conducted.


title: Behavioral Analysis of Docker Swarm Under DoS/ DDoS Attack

abstract: In recent time, a lot of talks is going about Docker an open source application containerization technology for building, shipping, and running applications. Docker when compared to other technologies has the ability to simplify the things that would previously require a lot of efforts. Due to this simplification, the time and cost of deploying and managing services have reduced tremendously. When we further talk about the Docker, one of its prime features which come to mind is the concept of Docker Swarm, a native clustering solution for Docker. This swarm clustering concept has over shattered the old and out-dated computer clustering techniques. Now, when we look into this paper, it totally talks about Docker Swarm from building a swarm cluster to managing the tasks. At last, this paper shows the behavioral analysis of Docker Swarm under DoS/ DDoS attack. This analysis has been done at two levels first at the node level and second at the cluster level and based on the analysis conclusion have been drawn.


title: PointConv: Deep Convolutional Networks on 3D Point Clouds

abstract: Unlike images which are represented in regular dense grids, 3D point clouds are irregular and unordered, hence applying convolution on them can be difficult. In this paper, we extend the dynamic filter to a new convolution operation, named PointConv. PointConv can be applied on point clouds to build deep convolutional networks. We treat convolution kernels as nonlinear functions of the local coordinates of 3D points comprised of weight and density functions. With respect to a given point, the weight functions are learned with multi-layer perceptron networks and the density functions through kernel density estimation. A novel reformulation is proposed for efficiently computing the weight functions, which allowed us to dramatically scale up the network and significantly improve its performance. The learned convolution kernel can be used to compute translation-invariant and permutation-invariant convolution on any point set in the 3D space. Besides, PointConv can also be used as deconvolution operators to propagate features from a subsampled point cloud back to its original resolution. Experiments on ModelNet40, ShapeNet, and ScanNet show that deep convolutional neural networks built on PointConv are able to achieve state-of-the-art on challenging semantic segmentation benchmarks on 3D point clouds. Besides, our experiments converting CIFAR-10 into a point cloud showed that networks built on PointConv can match the performance of convolutional networks in 2D images of a similar structure.


title: Smart Greybox Fuzzing

abstract: Coverage-based greybox fuzzing (CGF) is one of the most successful methods for automated vulnerability detection. Given a seed file (as a sequence of bits), CGF randomly flips, deletes or bits to generate new files. CGF iteratively constructs (and fuzzes) a seed corpus by retaining those generated files which enhance coverage. However, random bitflips are unlikely to produce valid files (or valid chunks in files), for applications processing complex file formats. In this work, we introduce smart greybox fuzzing (SGF) which leverages a high-level structural representation of the seed file to generate new files. We define innovative mutation operators that work on the virtual file structure rather than on the bit level which allows SGF to explore completely new input domains while maintaining file validity. We introduce a novel validity-based power schedule that enables SGF to spend more time generating files that are more likely to pass the parsing stage of the program, which can expose vulnerabilities much deeper in the processing logic. Our evaluation demonstrates the effectiveness of SGF. On several libraries that parse structurally complex files, our tool AFLSmart explores substantially more paths (up to 200%) and exposes more vulnerabilities than baseline AFL. Our tool AFLSmart has discovered 42 zero-day vulnerabilities in widely-used, well-tested tools and libraries; so far 17 CVEs were assigned.


Optimal Design of --() Filters


title: A Hand-Centric Classification of Human and Robot Dexterous Manipulation

abstract: This work contributes to the development of a common framework for the discussion and analysis of dexterous manipulation across the human and robotic domains. An overview of previous work is first provided along with an analysis of the tradeoffs between arm and hand dexterity. A hand-centric and motion-centric manipulation classification is then presented and applied in four different ways. It is first discussed how the taxonomy can be used to identify a manipulation strategy. Then, applications for robot hand analysis and engineering design are explained. Finally, the classification is applied to three activities of daily living (ADLs) to distinguish the patterns of dexterous manipulation involved in each task. The same analysis method could be used to predict problem ADLs for various impairments or to produce a representative benchmark set of ADL tasks. Overall, the classification scheme proposed creates a descriptive framework that can be used to effectively describe hand movements during manipulation in a variety of contexts and might be combined with existing object centric or other taxonomies to provide a complete description of a specific manipulation task.


L-SR1: A SECOND ORDER OPTIMIZATION METHOD


Orientation Power-LowOn the Accuracy Improvement of 1 IMU and MARG Sensor Arrays Usings Filter


title: Maximum Power Point Tracking (MPPT) of Partially Shaded Photovoltaic Cells: A Technical Review

abstract: The world’s energy supply has been under a tremendous pressure due to the rapid depletion of fossil resources, energy security and reliability, environmental sustainability and the ever-increasing modern living sophistication. The issue of persistent hikes in oil prices, climate threats and soaring energy demand has diverted the global interest to exploiting and investing in renewable types of energy (RE), solar energy in particular. A photovoltaic (PV) system is easy to install, has no moving parts, is almost free of maintenance, has maximum reliability, reduced vulnerability to power loss and is expandable. Regardless of these advantages, PV energy costs considerably higher than fossil fuels. This is due to its lower efficiency and higher costs. An expansive study has been carried out for PV fabrication, but it is equally important to extract the maximum power by enhancing the maximum power point tracking (MPPT) capability. The review covers the detailed partial shading condition (PSC) modelling and the various MPPT techniques and algorithms employed. It describes how to extract the maximum output power under continuous varying irradiation conditions in large PV arrays to make the technology commercially viable.


title: Coil Design and Shielding Methods for a Magnetic Resonant Wireless Power Transfer System This paper presents the basic principles of WPT based on magnetic field resonance with parametric effects , and discusses electromagnetic field noise from WPT , and related shielding and cancellation methodologi

abstract: In this paper, we introduce the basic principles of wireless power transfer using magnetic field resonance and describe techniques for the design of a resonant magnetic coil, the formation of a magnetic field distribution, and electromagnetic field (EMF) noise suppression methods. The experimental results of wireless power transfer systems in consumer electronics applications are discussed in terms of issues related to their efficiency and EMF noise. Furthermore, we present a passive shielding method and a magnetic field cancellation method using a reactive resonant current loop and the utilization of these methods in an online electric vehicle (OLEV) system, in which an OLEV green transportation bus system absorbs wireless power from power cables underneath the road surface with only a minimal battery capacity.


title: Deep Deterministic Policy Gradient for Urban Traffic Light Control

abstract: Traffic light timing optimization is still an active line of research despite the wealth of scientific literature on the topic, and the problem remains unsolved for any non-toy scenario. One of the key issues with traffic light optimization is the large scale of the input information that is available for the controlling agent, namely all the traffic data that is continually sampled by the traffic detectors that cover the urban network. This issue has in the past forced researchers to focus on agents that work on localized parts of the traffic network, typically on individual intersections, and to coordinate every individual agent in a multi-agent setup. In order to overcome the large scale of the available state information, we propose to rely on the ability of deep Learning approaches to handle large input spaces, in the form of Deep Deterministic Policy Gradient (DDPG) algorithm. We performed several experiments with a range of models, from the very simple one (one intersection) to the more complex one (a big city section).


title: Deep BLSTM neural networks for unconstrained continuous handwritten text recognition

abstract: Recently, two different trends in neural network-based machine learning could be observed. The first one are the introduction of Bidirectional Long Short-Term Memory (BLSTM) neural networks (NN) which made sequences with long-distant dependencies amenable for neural network-based processing. The second one are deep learning techniques, which greatly increased the performance of neural networks, by making use of many hidden layers. In this paper, we propose to combine these two ideas for the task of unconstrained handwriting recognition. Extensive experimental evaluation on the IAM database demonstrate an increase of the recognition performance when using deep learning approaches over commonly used BLSTM neural networks, as well as insight into how different types of hidden layers affect the recognition accuracy.


title: How conserved are the conserved 16S-rRNA regions?

abstract: The 16S rRNA gene has been used as master key for studying prokaryotic diversity in almost every environment. Despite the claim of several researchers to have the best universal primers, the reality is that no primer has been demonstrated to be truly universal. This suggests that conserved regions of the gene may not be as conserved as expected. The aim of this study was to evaluate the conservation degree of the so-called conserved regions flanking the hypervariable regions of the 16S rRNA gene. Data contained in SILVA database (release 123) were used for the study. Primers reported as matches of each conserved region were assembled to form contigs; sequences sizing 12 nucleotides (12-mers) were extracted from these contigs and searched into the entire set of SILVA sequences. Frequency analysis shown that extreme regions, 1 and 10, registered the lowest frequencies. 12-mer frequencies revealed segments of contigs that were not as conserved as expected (≤90%). Fragments corresponding to the primer contigs 3, 4, 5b and 6a were recovered from all sequences in SILVA database. Nucleotide frequency analysis in each consensus demonstrated that only a small fraction of these so-called conserved regions is truly conserved in non-redundant sequences. It could be concluded that conserved regions of the 16S rRNA gene exhibit considerable variation that has to be considered when using this gene as biomarker.


title: How to Avoid the Dark Side of Gamification: Ten Business Scenarios and Their Unintended Consequences

abstract: The problems that may arise from gamification have been largely ignored by researchers and practitioners alike. At the same time, use of gamification in recruitment, onboarding, training, and performance management are on the rise in organizations as businesses turn toward technology to meet their objectives. This chapter investigates drawbacks of using elements of games in each of these applications through a series of scenarios describing different gamified interventions. For each scenario, a discussion follows regarding potential problems with the intervention, how psychological science may explain this, how these errors can be avoided, as well as future directions for gamification research. Employee motivation is noted as a critical concern in gamification, and classic theories of motivation are utilized to help explain why some interventions may fail to motivate desired behavior. For training design, a popular area for gamification, practitioners are urged to consider the intended training outcomes before designing a training program with gaming elements.


title: Zero current detection technique for fast transient response in buck DC-DC converters

abstract: A load dependent zero-current detector is proposed in this paper for speeding up the transient response when load current changes from heavy to light loads. The fast transient control signal determines how long the reversed inductor current according to sudden load variations. At the beginning of load variation from heavy to light loads, the sensed voltage compared with higher voltage to discharge the overshoot output voltage for achieving fast transient response. Besides, for an adaptive reversed current period, the fast transient mechanism is turned off since the output voltage is rapidly regulated back to the acceptable level. Simulation results demonstrate that the ZCD circuit permits the reverse current flowing back into n-type power MOSFET at the beginning of load variations. The settling time is decreased to about 35 mus when load current suddenly changes from 500mA to 10 mA.


title: Sparse subspace clustering

abstract: We propose a method based on sparse representation (SR) to cluster data drawn from multiple low-dimensional linear or affine subspaces embedded in a high-dimensional space. Our method is based on the fact that each point in a union of subspaces has a SR with respect to a dictionary formed by all other data points. In general, finding such a SR is NP hard. Our key contribution is to show that, under mild assumptions, the SR can be obtained `exactly' by using l1 optimization. The segmentation of the data is obtained by applying spectral clustering to a similarity matrix built from this SR. Our method can handle noise, outliers as well as missing data. We apply our subspace clustering algorithm to the problem of segmenting multiple motions in video. Experiments on 167 video sequences show that our approach significantly outperforms state-of-the-art methods.


title: RefineNet: Refining Object Detectors for Autonomous Driving

abstract: Highly accurate, camera-based object detection is an essential component of autonomous navigation and assistive technologies. In particular, for on-road applications, localization quality of objects in the image plane is important for accurate distance estimation, safe trajectory prediction, and motion planning. In this paper, we mathematically formulate and study a strategy for improving object localization with a deep convolutional neural network. An iterative region-of-interest pooling framework is proposed for predicting increasingly tight object boxes and addressing limitations in current state-of-the-art deep detection models. The method is shown to significantly improve the performance on a variety of datasets, scene settings, and camera perspectives, producing high-quality object boxes at a minor additional computational expense. Specifically, the architecture achieves impressive gains in performance (up to 6% improvement in detection accuracy) at fast run-time speed (0.22 s per frame on $1242 \times 375$ sized images). The iterative refinement is shown to impact subsequent vision tasks, such as object tracking in the image plane and in ground plane.


Colour Stability of Composite Resins : A General Overview


title: A survey of nature inspired optimization algorithms applied to cooperative strategies in robot soccer

abstract: Nature inspired optimization algorithms are known for their inherent ability to solve complex problems where multiple agents interact to perform a task at hand. This paper presents a survey of how these optimization algorithms have been applied in determining cooperative strategies for a team of soccer playing agents. The survey discusses the contributions made by researchers and makes an effort to map nature inspired optimization algorithms with the type of cooperative strategy evolved. A categorization of cooperative study is also presented specifically in the domain of RoboCup Soccer Simulation League where agents cooperate in a virtual environment to develop a strategy without any issue of physical wear and tear. This study would serve as a starting point for teams participating in RoboCup competitions to enhance their strategy utilizing ideas from nature inspired algorithms.


title: Personality development through natural language

abstract: Ego level is a broad construct that summarizes individual differences in personality development ::: 1 ::: . We examine ego level as it is represented in natural language, using a composite sample of four datasets comprising nearly 44,000 responses. We find support for a developmental sequence in the structure of correlations between ego levels, in analyses of Linguistic Inquiry and Word Count (LIWC) categories ::: 2 ::: and in an examination of the individual words that are characteristic of each level. The LIWC analyses reveal increasing complexity and, to some extent, increasing breadth of perspective with higher levels of development. The characteristic language of each ego level suggests, for example, a shift from consummatory to appetitive desires at the lowest stages, a dawning of doubt at the Self-aware stage, the centrality of achievement motivation at the Conscientious stage, an increase in mutuality and intellectual growth at the Individualistic stage and some renegotiation of life goals and reflection on identity at the highest levels of development. Continuing empirical analysis of ego level and language will provide a deeper understanding of ego development, its relationship with other models of personality and individual differences, and its utility in characterizing people, texts and the cultural contexts that produce them.


title: Moving Object Localization Based on UHF RFID Phase and Laser Clustering

abstract: RFID (Radio Frequency Identification) offers a way to identify objects without any contact. However, positioning accuracy is limited since RFID neither provides distance nor bearing information about the tag. This paper proposes a new and innovative approach for the localization of moving object using a particle filter by incorporating RFID phase and laser-based clustering from 2d laser range data. First of all, we calculate phase-based velocity of the moving object based on RFID phase difference. Meanwhile, we separate laser range data into different clusters, and compute the distance-based velocity and moving direction of these clusters. We then compute and analyze the similarity between two velocities, and select K clusters having the best similarity score. We predict the particles according to the velocity and moving direction of laser clusters. Finally, we update the weights of the particles based on K clusters and achieve the localization of moving objects. The feasibility of this approach is validated on a Scitos G5 service robot and the results prove that we have successfully achieved a localization accuracy up to 0.25 m.


title: Systems Thinking: Critical Thinking Skills for the 1990s and Beyond

abstract: The problems we face at all levels in the world today resist unilateral solutions. While the web of interdependencies tightens, our capacity for thinking in terms of dynamic interdependencies has not kept pace. As the gap between the nature of our problems and the ability to understand them grows, we face increasing perils on a multitude of fronts. Systems thinking and one of its subsets-system dynamics-are important for developing effective strategies to close this gap. Unfortunately, system dynamicists and systems thinkers have not effectively taught their framework, skills, and technologies to others. The door has not been opened wide enough to let others share our insights with respect to the workings of closed-loop systems. To transfer this understanding on a broad scale, we need a clearer view of its nature and of the education system into which it must be transferred. This article casts some light on what we have to bestow and on the education system that is to receive our bounty. Its intended audience is both system thinkers and educators, and the hope is to help eradicate the distinction between the two.


title: Frequency resources sharing in FH radio networks

abstract: FH emissions are currently used in many wireless communication systems, where military communication is a special area of their application. The use of such emissions reduces the likelihood of detection and interception of transmission and increases their resistance to jamming. The larger is the set of carrier frequencies, the more FH emission is dispersed and harder to detect and disrupt. Currently used frequency allocation algorithm does not allow sharing of available frequencies in the same area. For this reason, the number of assigned frequencies for individual network drops significantly, reducing all the advantages of FH emissions. In this paper an effective way to use frequency resources to increase the capacity of the transmission channel assuming a certain number of radio networks or the certain number of available carrier frequencies is proposed. The basic assumption is the possibility of simultaneous operation of many FH radio networks in the same territorial area, on a common frequency set. At a certain probability of collision, the dependence of the number of radio networks which worked simultaneously, from the cardinality of a common set of carrier frequencies and the influence of the number of frequencies on this probability is considered. The results of the analysis of the number of collisions between different FH networks working simultaneously in the same area with an identical set of carrier frequencies are also presented. Relevant mathematical formulas are proposed for different collision scenarios that can be used in the frequency assignment process. The results are verified in practice with the use of VHF radios.


title: A novel classification method for paper-reviewer recommendation

abstract: Reviewer recommendation problem in the research field usually refers to invite experts to comment on the quality of papers, proposals, etc. How to effectively and accurately recommend reviewers for the submitted papers and proposals is a meaningful and still tough task. At present, many unsupervised recommendation methods have been researched to solve this task. In this paper, a novel classification method named Word Mover’s Distance–Constructive Covering Algorithm (WMD–CCA, for short) is proposed to solve the reviewer recommendation problem as a classification issue. A submission or a reviewer is described by some tags, such as keywords, research interests, and so on. First, the submission or the reviewer is represented as some vectors by a word embedding method. That is to say, each tag describing a submission or a reviewer is represented as a vector. Second, the Word Mover’s Distance (WMD, for short) method is used to measure the minimum distances between submissions and reviewers. Actually, the papers usually have research field information, and utilizing them well might improve the reviewer recommendation accuracy. So finally, the reviewer recommendation task is transformed into a classification problem which is solved by a supervised learning method- Constructive Covering Algorithm (CCA, for short). Comparative experiments are conducted with 4 public datasets and a synthetic dataset from Baidu Scholar, which show that the proposed method WMD–CCA effectively solves the reviewer recommendation task as a classification issue and improves the recommendation accuracy.


title: System Identification and Estimation Framework for Pivotal Automotive Battery Management System Characteristics

abstract: The battery management system (BMS) is an integral part of an automobile. It protects the battery from damage, predicts battery life, and maintains the battery in an operational condition. The BMS performs these tasks by integrating one or more of the functions, such as protecting the cell, thermal management, controlling the charge-discharge, determining the state of charge (SOC), state of health (SOH), and remaining useful life (RUL) of the battery, cell balancing, data acquisition, communication with on-board and off-board modules, as well as monitoring and storing historical data. In this paper, we propose a BMS that estimates the critical characteristics of the battery (such as SOC, SOH, and RUL) using a data-driven approach. Our estimation procedure is based on a modified Randles circuit model consisting of resistors, a capacitor, the Warburg impedance for electrochemical impedance spectroscopy test data, and a lumped parameter model for hybrid pulse power characterization test data. The resistors in a Randles circuit model usually characterize the self-discharge and internal resistance of the battery, the capacitor generally represents the charge stored in the battery, and the Warburg impedance represents the diffusion phenomenon. The Randles circuit parameters are estimated using a frequency-selective nonlinear least squares estimation technique, while the lumped parameter model parameters are estimated by the prediction error minimization method. We investigate the use of support vector machines (SVMs) to predict the capacity fade and power fade, which characterize the SOH of a battery, as well as estimate the SOC of the battery. An alternate procedure for estimating the power fade and energy fade from low-current Hybrid Pulse Power characterization (L-HPPC) test data using the lumped parameter battery model has been proposed. Predictions of RUL of the battery are obtained by support vector regression of the power fade and capacity fade estimates. Survival function estimates for reliability analysis of the battery are obtained using a hidden Markov model (HMM) trained using time-dependent estimates of capacity fade and power fade as observations. The proposed framework provides a systematic way for estimating relevant battery characteristics with a high-degree of accuracy.


title: 3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation

abstract: This paper presents a fully automated atlas-based pancreas segmentation method from CT volumes utilizing 3D fully convolutional network (FCN) feature-based pancreas localization. Segmentation of the pancreas is difficult because it has larger inter-patient spatial variations than other organs. Previous pancreas segmentation methods failed to deal with such variations. We propose a fully automated pancreas segmentation method that contains novel localization and segmentation. Since the pancreas neighbors many other organs, its position and size are strongly related to the positions of the surrounding organs. We estimate the position and the size of the pancreas (localization) from global features by regression forests. As global features, we use intensity differences and 3D FCN deep learned features, which include automatically extracted essential features for segmentation. We chose 3D FCN features from a trained 3D U-Net, which is trained to perform multi-organ segmentation. The global features include both the pancreas and surrounding organ information. After localization, a patient-specific probabilistic atlas-based pancreas segmentation is performed. In evaluation results with 146 CT volumes, we achieved 60.6% of the Jaccard index and 73.9% of the Dice overlap.


title: Automated Static Analysis of Unit Test Code

abstract: Automated unit tests are an essential software quality assurance measure that is widely used in practice. In many projects, thus, large volumes of test code have co-evolved with the production code throughout development. Like any other code, test code too may contain faults, affecting the effectiveness, reliability and usefulness of the tests. Furthermore, throughout the software system's ongoing development and maintenance phase, the test code too has to be constantly adapted and maintained. To support detecting problems in test code and improving its quality, we implemented 42 static checks for analyzing JUnit tests. These checks encompass best practices for writing unit tests, common issues observed in using xUnit frameworks, and our experiences collected from several years of providing trainings and reviews of test code for industry and in teaching. The checks can be run using the open source analysis tool PMD. In addition to a description of the implemented checks and their rationale, we demonstrate the applicability of using static analysis for test code by analyzing the unit tests of the open source project JFreeChart.


title: Fall detection and activity monitoring system using dynamic time warping for elderly and disabled people

abstract: We present a system that both tracks human movements and detects falling in elderly and disable people. We applied Dynamic Time Warping (DTW) to recognize human activities in daily living. Seven different movements, stand, sit, walk, run, stand-to-sit, sit-to-stand and lyings were considered and recorded as reference database signals. Our system consists of two parts: transmitter and receiver. A transmitter part is a device mounted at the user's waist with a size of a pager case measuring 90x40x20 mm. A sensor used in this device is a 3-axial accelerometer (Hitachi H48C). The signals from the accelerometer are transmitted wirelessly to a personal computer in receiver part using Zigbee Pro 2.4GHz. DTW is used to match the signals from different behaviors online with the databases and classify the data to a known activity. Falls are detected with a rule-based approach, in which the signal values are over thresholds following by the lying activity. Thresholds are computed from the minimum and maximum value in each axis of acceleration in the reference databases. The experiment shows 98.6 percent accuracy in recognizing these behaviors and in detecting fall.


title: ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion--Part II: shoulder, elbow, wrist and hand.

abstract: In this communication, the Standardization and Terminology Committee (STC) of the International Society of Biomechanics proposes a definition of a joint coordinate system (JCS) for the shoulder, elbow, wrist, and hand. For each joint, a standard for the local axis system in each articulating segment or bone is generated. These axes then standardize the JCS. The STC is publishing these recommendations so as to encourage their use, to stimulate feedback and discussion, and to facilitate further revisions. Adopting these standards will lead to better communication among researchers and clinicians.


title: Reliability and topology based network design using pattern mining guided genetic algorithm

abstract: Genetic algorithm guided with pattern mining for designing optimal network is proposed.Networks considered have different link and node failure rates.Association rules between networks of good and bad performances are devised.Mined patterns guide the genetic algorithm during mutation & crossover operations.Proposed method is effective in designing optimal network. This research proposes a new reliable network design methodology that is based on a pattern mining guided genetic algorithm (GA). The proposed method can be applied for a variety of applications including telecommunication, ad hoc, and power systems. In these networks, failures in certain parts of a network make it necessary for other parts to tolerate a higher traffic load in order to maintain adequate network connections. In addition, path changes due to dynamic routing of traffic can cause a time delay of communications in the network. To understand and reduce the connection failures costs, vigorous investigations are required to select the best design option under budget constraints. Given that many options for network topology and reliability allocation exist, a GA guided with pattern mining is proposed as an effective optimization method to design reliable network while considering link and node failures. Experimental designs under various assumptions have concluded that the guided GA approach is effective in identifying a network solution within a short period of time.


title: The pallial basal ganglia pathway modulates the behaviorally driven gene expression of the motor pathway.

abstract: The discrete neural network for songbird vocal communication provides an effective system to study neural mechanisms of learned motor behaviors in vertebrates. This system consists of two pathways--a vocal motor pathway used to produce learned vocalizations and a vocal pallial basal ganglia loop used to learn and modify the vocalizations. However, it is not clear how the loop exerts control over the motor pathway. To study the mechanism, we used expression of the neural activity-induced gene ZENK (or egr-1), which shows singing-regulated expression in a social context-dependent manner: high levels in both pathways when singing undirected and low levels in the lateral part of the loop and in the robust nucleus of the arcopallium (RA) of the motor pathway when singing directed to another animal. Here, we show that there are two parallel interactive parts within the pallial basal ganglia loop, lateral and medial, which modulate singing-driven ZENK expression of the motor pathway nuclei RA and HVC, respectively. Within the loop, the striatal and pallial nuclei appear to have opposing roles; the striatal vocal nucleus lateral AreaX is required for high ZENK expression in its downstream nuclei, particularly during undirected singing, while the pallial vocal lateral magnocellular nucleus of the anterior nidopallium is required for lower expression, particularly during directed singing. These results suggest a dynamic molecular interaction between the basal ganglia pathway and the motor pathway during production of a learned motor behavior.


title: Studying the BER performance, power- and bandwidth- efficiency for FSO communication systems under various modulation schemes

abstract: In this paper, we are interested in more efficient modulation techniques that achieve a good Bit Error Rate (BER) performance, bandwidth and power efficiencies for Free Space Optics (FSO) Communication Systems. The theoretical expressions of the BER versus the Signal-to-Noise Ratio (SNR), the bandwidth efficiency, and the normalized power requirements for NRZ-OOK, RZ-OOK, PPM, PAM, DPSK, BPSK, and QPSK modulation schemes for FSO communication systems are introduced, and a comparison between these modulation techniques is presented. The comparative study of the PPM and PAM schemes is also discussed and showed that for similar SNR, PPM offered improved BER performance. In addition, the BER performance under BPSK modulation is better compared to OOK, RZ-OOK, PAM, 2-PPM, 4-PPM, DPSK, and QPSK modulation techniques. And for FSO communication systems, although the power efficiency is inferior to PPM, OOK is more commonly used due to its efficient bandwidth usage, but PAM scheme is the bandwidth efficient modulation scheme in this research for more than two bits of information sent, while the PPM scheme is the power efficient modulation scheme for more than two bits of information sent.


title: Path integration and the neural basis of the 'cognitive map'

abstract: The hippocampal formation can encode relative spatial location, without reference to external cues, by the integration of linear and angular self-motion (path integration). Theoretical studies, in conjunction with recent empirical discoveries, suggest that the medial entorhinal cortex (MEC) might perform some of the essential underlying computations by means of a unique, periodic synaptic matrix that could be self-organized in early development through a simple, symmetry-breaking operation. The scale at which space is represented increases systematically along the dorsoventral axis in both the hippocampus and the MEC, apparently because of systematic variation in the gain of a movement-speed signal. Convergence of spatially periodic input at multiple scales, from so-called grid cells in the entorhinal cortex, might result in non-periodic spatial firing patterns (place fields) in the hippocampus.