title: A deep learning approach for predicting the quality of online health expert question-answering services

abstract: Abstract Recently, online health expert question-answering (HQA) services (systems) have attracted more and more health consumers to ask health-related questions everywhere at any time due to the convenience and effectiveness. However, the quality of answers in existing HQA systems varies in different situations. It is significant to provide effective tools to automatically determine the quality of the answers. Two main characteristics in HQA systems raise the difficulties of classification: (1) physicians’ answers in an HQA system are usually written in short text, which yields the data sparsity issue; (2) HQA systems apply the quality control mechanism, which refrains the wisdom of crowd. The important information, such as the best answer and the number of users’ votes, is missing. To tackle these issues, we prepare the first HQA research data set labeled by three medical experts in 90 days and formulate the problem of predicting the quality of answers in the system as a classification task. We not only incorporate the standard textual feature of answers, but also introduce a set of unique non-textual features, i.e., the popular used surface linguistic features and the novel social features, from other modalities. A multimodal deep belief network (DBN)-based learning framework is then proposed to learn the high-level hidden semantic representations of answers from both textual features and non-textual features while the learned joint representation is fed into popular classifiers to determine the quality of answers. Finally, we conduct extensive experiments to demonstrate the effectiveness of including the non-textual features and the proposed multimodal deep learning framework.


title: Dynamic Control of CPU Usage in a Lambda Platform

abstract: Lambda platform is a new concept based on an event-driven server-less computation that empowers application developers to build scalable enterprise software in a virtualized environment without provisioning or managing any physical servers (a server-less solution). In reality, however, devising an effective consolidation method to host multiple Lambda functions into a single machine is challenging. The existing simple resource allocation algorithms, such as the round-robin policy used in many commercial server-less systems, suffer from lack of responsiveness to a sudden surge in the incoming workload. This will result in an unsatisfactory performance degradation that is directly experienced by the end-user of a Lambda application. In this paper, we address the problem of CPU cap management in a Lambda platform for ensuring different QoS enforcement levels in a platform with shared resources, in case of fluctuations and sudden surges in the incoming workload requests. To this end, we present a closed-loop (feedback-based) CPU cap controller, which fulfills the QoS levels enforced by the application owners. The controller adjusts the number of working threads per QoS class and dispatches the outstanding Lambda functions along with the associated events to the most appropriate working thread. The proposed solution reduces the QoS violations by an average of 6.36 times compared to the round-robin policy. It can also maintain the end-to-end response time of applications belonging to the highest priority QoS class close to the target set-point while decreasing the overall response time by up to 52%.


title: Unconstrained face detection based on cascaded Convolutional Neural Networks in surveillance video

abstract: With the popularity of surveillance video, face detection in surveillance video has become a popular and important topic. Face detection in surveillance video plays an important role in many popular applications such as: personal identification, crowd analysis, database establishment, and abnormal event detection. This paper proposes an unconstrained face detection method for surveillance video, which is not influenced by factors such as face location, expression, posture, scale, and lighting conditions. First, the detection area is initially extracted from the video frame using the improved foreground extraction and skin color detection. Next, we then use the multi-scale sliding window and the cascaded Convolutional Neural Network (CNN) designed in this paper to detect faces. This cascaded network consists of two CNN networks: the first network filters out most of the background area while ensuring the running speed of the whole system and the recall rate of the face, while the second network guarantees the accuracy of the overall system. Finally, we set up a database for the experiment which contained samples from the actual surveillance video. The results of our experiment suggest that the proposed method can obtain good results on unconstrained face detection in surveillance video and can also achieve satisfactory detection speed.


title: Three-stage pipeline implementation for SHA2 using data forwarding

abstract: The security hash algorithm 512 (SHA-512), which is used to verify the integrity of a message, involves computation iterations on data. The huge computation delay generated in that iteration limits the entire throughput of the system, and makes it difficult to pipeline the computation. To shorten the computation time in an iteration of the main loop, we used the data forwarding method. Here we introduce an architecture that simultaneously does data computation of an iteration and data movement of the next one. Then the computations are broken into two stages for one operand and three stages for another operand. The implementation occupies 1,520 hardware slices on Xilinx Virtex-4 family FPGA chip, and achieves nearly 2.2 Gbps. Thus, the implementation achieved a better area performance rate (throughput/area) in comparison with the related work.


title: User Profiling Trends, Techniques and Applications

abstract: The Personalization of information has taken recommender systems at a very high level. With personalization these systems can generate user specific recommendations accurately and efficiently. User profiling helps personalization, where information retrieval is done to personalize a scenario which maintains a separate user profile for individual user. The main objective of this paper is to explore this field of personalization in context of user profiling, to help researchers make aware of the user profiling. Various trends, techniques and Applications have been discussed in paper which will fulfill this motto.


title: A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks

abstract: We present a generalization bound for feedforward neural networks in terms of the product of the spectral norm of the layers and the Frobenius norm of the weights. The generalization bound is derived using a PAC-Bayes analysis.


title: Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog

abstract: This paper presents a new model for visual dialog, Recurrent Dual Attention Network (ReDAN), using multi-step reasoning to answer a series of questions about an image. In each question-answering turn of a dialog, ReDAN infers the answer progressively through multiple reasoning steps. In each step of the reasoning process, the semantic representation of the question is updated based on the image and the previous dialog history, and the recurrently-refined representation is used for further reasoning in the subsequent step. On the VisDial v1.0 dataset, the proposed ReDAN model achieves a new state-of-the-art of 64.47% NDCG score. Visualization on the reasoning process further demonstrates that ReDAN can locate context-relevant visual and textual clues via iterative refinement, which can lead to the correct answer step-by-step.


title: AN IMPROVED FRAMEWORK FOR OUTLIER PERIODIC PATTERN DETECTION IN TIME SERIES USING WALMART TRANSACTION DATA

abstract: Periodic pattern detection in time-series is one of the most important data mining task. The periodicity detection of an outlier patterns might be more important than the periodicity of regular, more frequent patterns means periodic pattern .periodic patterns means Patterns which repeat over a period of time. Pattern those which occur unusually or surprisingly called as Outlier Pattern. In this paper ,we present the development of a enhanced spatio-temporal algorithm capable of detecting the periodicity of outlier patterns in a time series using Walmart transaction data and MAD (Median Absolute Deviation) is presented. mean values is used in existing algorithm which is not efficient. We have to use MAD which increases the output of these algorithms and gives more accurate information.


title: Multiresolution signal decomposition schemes. Part 1: Linear and morphological pyramids

abstract: In its original form, the wavelet transform is a linear tool. However, it has been increasingly recognized that nonlinear extensions are possible. A major impulse to the development of nonlinear wavelet transforms has been given by the introduction of the lifting scheme by Sweldens (1995, 1996, 1998). The aim of this paper, which is a sequel to a previous paper devoted exclusively to the pyramid transform, is to present an axiomatic framework encompassing most existing linear and nonlinear wavelet decompositions. Furthermore, it introduces some, thus far unknown, wavelets based on mathematical morphology, such as the morphological Haar wavelet, both in one and two dimensions. A general and flexible approach for the construction of nonlinear (morphological) wavelets is provided by the lifting scheme. This paper briefly discusses one example, the max-lifting scheme, which has the intriguing property that preserves local maxima in a signal over a range of scales, depending on how local or global these maxima are.


Crowdfunding: Tapping the right crowd


title: A novel method to measure steroid hormone concentrations in walrus bone from archaeological, historical, and modern time periods using liquid chromatography/tandem mass spectrometry

abstract: RATIONALE ::: A liquid chromatography/tandem mass spectrometry (LC/MS/MS) method was validated and utilized to measure and analyze four steroid hormones related to stress and reproduction in individual samples from a novel tissue, Pacific walrus (Odobenus rosmarus divergens, herein walrus) bone. This method determines steroid hormone concentrations in the remote walrus population over millennia from archaeological (>200 bp), historical (200-20 bp), and modern (2014-2016) time periods. ::: ::: ::: METHODS ::: Lipids were extracted from walrus bone collected from these periods using methanol before LC/MS/MS analysis. Isotopically labeled internal standards for each target hormone were added to every sample. Analytical and physiological validations were performed. Additionally, a tissue comparison was done among paired walrus bone, serum, and blubber samples. A rapid resolution liquid chromatography system coupled to a QqQ mass spectrometer was used to analyze all samples after derivatization for progesterone, testosterone, cortisol, and estradiol concentrations. Multiple reaction monitoring was used for MS analysis and data were acquired in positive electrospray ionization mode. ::: ::: ::: RESULTS ::: Progesterone, testosterone, cortisol, and estradiol were linear along their respective standard calibration curves based on their R2 values (all > 0.99). Accuracy ranged from 93-111% for all hormones. The recovery of extraction, recovery of hormones without matrix effect, was 92-101%. The overall process efficiency of our method for measuring hormones in walrus bone was 93-112%. Progesterone and testosterone concentrations were not affected by reproductive status among adult females and males, respectively. However, estradiol was different among pregnant and non-pregnant adult females. Overall, steroid hormones reflect a long-term reservoir in cortical bone. This method was also successfully applied to walrus bone as old as 3585 bp. ::: ::: ::: CONCLUSIONS ::: LC/MS/MS analysis of bone tissue (0.2-0.3 g) provides stress and reproductive data from elusive walruses that were alive thousands of years ago. Based on physiological validations, tissue comparison, and published literature, steroid hormone concentrations measured in walrus cortical bone could represent an accumulated average around a 10-20-year time span. By investigating how stress and reproductive physiology may have changed over the past ~3000 years based on bone steroid hormone concentrations, this method will help answer how physiologically resilient walruses are to climate change in the Arctic.


title: An axial flux-focusing magnetically geared motor

abstract: In this paper an axial flux magnetic gear (AFMG) is proposed that has an integrated outer stator. This axial flux magnetically geared motor (AFMGM) is unique in that the stator shares the high-speed rotor with the AFMG, so there is no need for a separate rotor. The high speed and low speed rotors use a flux-focusing typology. The stator is mounted outside the AFMG and this makes the design mechanically less complex. It also enables the stator to be cooled more easily.


title: Learning Causal Structure from Undersampled Time Series

abstract: Even if one can experiment on relevant factors, learning the causal structure of a dynamical system can be quite dicult if the relevant measurement processes occur at a much slower sampling rate than the \true" underlying dynamics. This problem is exacerbated if the degree of mismatch is unknown. This paper gives a formal characterization of this learning problem, and then provides two sets of results. First, we prove a set of theorems characterizing how causal structures change under undersampling. Second, we develop an algorithm for inferring aspects of the causal structure at the \true" timescale from the causal structure learned from the undersampled data. Research on causal learning in dynamical contexts has largely ignored the challenges of undersampling, but this paper provides a framework and foundation for learning causal structure from this type of complex time series data.


title: Revisiting traffic anomaly detection using software defined networking

abstract: Despite their exponential growth, home and small office/home office networks continue to be poorly managed. Consequently, security of hosts in most home networks is easily compromised and these hosts are in turn used for largescale malicious activities without the home users' knowledge. We argue that the advent of Software Defined Networking (SDN) provides a unique opportunity to effectively detect and contain network security problems in home and home office networks. We show how four prominent traffic anomaly detection algorithms can be implemented in an SDN context using Openflow compliant switches and NOX as a controller. Our experiments indicate that these algorithms are significantly more accurate in identifying malicious activities in the home networks as compared to the ISP. Furthermore, the efficiency analysis of our SDN implementations on a programmable home network router indicates that the anomaly detectors can operate at line rates without introducing any performance penalties for the home network traffic.


title: ATTENTIONAL MODULATION OF VISUAL PROCESSING

abstract: ▪ Abstract Single-unit recording studies in the macaque have carefully documented the modulatory effects of attention on the response properties of visual cortical neurons. Attention produces qualitatively different effects on firing rate, depending on whether a stimulus appears alone or accompanied by distracters. Studies of contrast gain control in anesthetized mammals have found parallel patterns of results when the luminance contrast of a stimulus increases. This finding suggests that attention has co-opted the circuits that mediate contrast gain control and that it operates by increasing the effective contrast of the attended stimulus. Consistent with this idea, microstimulation of the frontal eye fields, one of several areas that control the allocation of spatial attention, induces spatially local increases in sensitivity both at the behavioral level and among neurons in area V4, where endogenously generated attention increases contrast sensitivity. Studies in the slice have begun to explain how mod...


title: Optimizing sleep to maximize performance: implications and recommendations for elite athletes

abstract: Despite a growing body of literature demonstrating a positive relationship between sleep and optimal performance, athletes often have low sleep quality and quantity. Insufficient sleep among athletes may be due to scheduling constraints and the low priority of sleep relative to other training demands, as well as a lack of awareness of the role of sleep in optimizing athletic performance. Domains of athletic performance (e.g., speed and endurance), neurocognitive function (e.g., attention and memory), and physical health (e.g., illness and injury risk, and weight maintenance) have all been shown to be negatively affected by insufficient sleep or experimentally modeled sleep restriction. However, healthy adults are notoriously poor at self-assessing the magnitude of the impact of sleep loss, underscoring the need for increased awareness of the importance of sleep among both elite athletes and practitioners managing their care. Strategies to optimize sleep quality and quantity in athletes include approaches for expanding total sleep duration, improving sleep environment, and identifying potential sleep disorders.


SAD : Social Anxiety and Depression Monitoring System for College Students


title: On Space-Time Block Codes from Coordinate Interleaved Orthogonal Designs

abstract: Space-time block codes (STBC) using coordinate interleaved orthogonal designs (CIOD) proposed recently by Khan and Rajan allow single-complex symbol decoding while offering higher data rates than orthogonal STBC. In this paper, we present the equivalent channels of CIOD codes. A new maximum likelihood metric is also derived, which is simpler than the one shown by Khan and Rajan. The exact symbol pair-wise error probability and a tight union bound on symbol error rate are derived. The tight union bound can be used to analyze the performance of CIOD codes with arbitrary constellations. We provide new optimal rotation angles based on minimizing the union bound for various constellations. Furthermore, a new signal design combining signal rotation and power allocation is presented for rectangular quadrature amplitude modulation.


What Is a Sexist Ideology ? Or : Why


title: Corpus and Deep Learning Classifier for Collection of Cyber Threat Indicators in Twitter Stream

abstract: This paper presents a framework for detection and classification of cyber threat indicators in the Twitter stream. Contrary to the bulk of similar proposals that rely on manually-designed heuristics and keywordbased filtering of tweets, our framework provides a data-driven approach for modeling and classification of tweets that are related to cybersecurity events. We present a cascaded Convolutional Neural Network (CNN) architecture, comprised of a binary classifier for detection of cyber-related tweets, and a multi-class model for the classification of cyber-related tweets into multiple types of cyber threats. Furthermore, we present an open-source dataset of 21000 annotated cyber-related tweets to facilitate the validation and further research in this area.


title: An Algorithm for Rendering Generalized Depth of Field Effects Based on Simulated Heat Diffusion

abstract: Depth of field refers to the swath through a 3D scene that is imaged in acceptable focus through an optics system, such as a camera lens. Control over depth of field is an important artistic tool that can be used to emphasize the subject of a photograph. In a real camera, the control over depth of field is limited by the nature of the image formation process and by physical constraints. The depth of field effect has been simulated in computer graphics, but with the same limited control as found in real camera lenses. In this paper, we use diffusion in a non-homogeneous medium to generalize depth of field in computer graphics by enabling the user to independently specify the degree of blur at each point in three-dimensional space. Generalized depth of field provides a novel tool to emphasize an area of interest within a 3D scene, to pick objects out of a crowd, and to render a busy, complex picture more understandable by focusing only on relevant details that may be scattered throughout the scene. Our algorithm operates by blurring a sequence of nonplanar layers that form the scene. Choosing a suitable blur algorithm for the layers is critical; thus, we develop appropriate blur semantics such that the blur algorithm will properly generalize depth of field. We found that diffusion in a non-homogeneous medium is the process that best suits these semantics.


Suja “Methods of Battery Charging with buck Converter using soft-Switching Techniques” Bongfing


title: Hierarchical Clustering Algorithms for Document Datasets

abstract: Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. In particular, clustering algorithms that build meaningful hierarchies out of large document collections are ideal tools for their interactive visualization and exploration as they provide data-views that are consistent, predictable, and at different levels of granularity. This paper focuses on document clustering algorithms that build such hierarchical solutions and (i) presents a comprehensive study of partitional and agglomerative algorithms that use different criterion functions and merging schemes, and (ii) presents a new class of clustering algorithms called constrained agglomerative algorithms, which combine features from both partitional and agglomerative approaches that allows them to reduce the early-stage errors made by agglomerative methods and hence improve the quality of clustering solutions. The experimental evaluation shows that, contrary to the common belief, partitional algorithms always lead to better solutions than agglomerative algorithms; making them ideal for clustering large document collections due to not only their relatively low computational requirements, but also higher clustering quality. Furthermore, the constrained agglomerative methods consistently lead to better solutions than agglomerative methods alone and for many cases they outperform partitional methods, as well.


title: An Analysis of Private Browsing Modes in Modern Browsers

abstract: We study the security and privacy of private browsing modes recently added to all major browsers. We first propose a clean definition of the goals of private browsing and survey its implementation in different browsers. We conduct a measurement study to determine how often it is used and on what categories of sites. Our results suggest that private browsing is used differently from how it is marketed. We then describe an automated technique for testing the security of private browsing modes and report on a few weaknesses found in the Firefox browser. Finally, we show that many popular browser extensions and plugins undermine the security of private browsing. We propose and experiment with a workable policy that lets users safely run extensions in private browsing mode.


title: A CtrA homolog affects swarming motility and encystment in Rhodospirillum centenum

abstract: The α-proteobacterium, Rhodospirillum centenum, has a complex life cycle that allows adaptation to different environments. Transitions between vegetative swim cell and swarmer cell types depend on whether the organism is growing in liquid surroundings or on a solid substrate. Moreover, starvation can induce vegetative cells to differentiate into quiescent cysts. This paper describes the results of our investigation into the role of a putative DNA-binding response regulator that is homologous to CtrA, the cell cycle regulator from Caulobacter crescentus. Deletion of ctrA from the R. centenum genome resulted in a viable strain with impaired swarming motility coupled with an increased tendency to form cysts. Conversely, overexpression of wild type CtrA or a phosphomimetic allele, CtrAD51E, suppressed cyst cell formation, whereas overexpression of a CtrAD51A allele failed to suppress encystment but did prevent swarming motility. Thus, we propose that CtrA participates within a two-component signal transduction pathway that promotes swarming motility while contributing to the suppression of cyst cell formation.


title: RTS games and real-time AI research

abstract: This article 1 motivates AI research in the area of real‐time strategy (RTS) games and describes the current status of the ORTS project whose goals are to implement an RTS game programming environment and to build AI systems that eventually can outperform human experts in this popular and challenging domain.


Design-space exploration in model driven engineering : an initial pattern catalogue


title: Mathematical Analysis of the Pharmacokinetic-Pharmacodynamic (PKPD) Behaviour of Monoclonal Antibodies: Predicting in vivo

abstract: We consider the relationship between the target affinity of a monoclonal antibody and its in vivo potency. The dynamics of the system is described mathematically by a target-mediated drug disposition model. As a measure of potency, we consider the minimum level of the free receptor following a single bolus injection of the ligand into the plasma compartment. From the differential equations, we derive two expressions for this minimum level in terms of the parameters of the problem, one of which is valid over the full range of values of the equilibrium dissociation constant KD and the other which is valid only for a large drug dose or for a small value of KD. Both of these formulae show that the potency achieved by increasing the association constant kon can be very different from the potency achieved by decreasing the dissociation constant koff . In particular, there is a saturation effect when decreasing koff where the increase in potency that can be achieved is limited, whereas there is no such effect when increasing kon. Thus, for certain monoclonal antibodies, an increase in potency may be better achieved by increasing kon than by decreasing koff .


title: ARMI: A risk management incorporation

abstract: Risk management is an essential process in software project management. Effective management of risk significantly helps increase rate of project success as well as reduce potential costs of failures. This paper proposes an innovative card-based game which simulates entire cycle of risk management. During the activity, the participants form an organization called “A Risk Management Incorporation (ARMI)”. They are then given chance to experience, analyze, plan, repeat and evolve their risk management processes. Based on the study result, ARMI helps not only simulate an insightful risk management cycle but also improve the participants' attitude towards this critical project element.


An ensemble prediction approach to weekly Dengue cases forecasting based on climatic and terrain conditions


title: KINECT applications for the physical rehabilitation

abstract: This report presents the results of KINECT applications used in physical rehabilitation tests. Aoyama Gakuin and Kitasato universities collaborated on this project, which is supported by SCOPE. The applications, following standard tests, are for the timed “Up & Go Test”, the timed “10-Meter Walk Test” and for a Joint “Range of Motion” Measurement”; test results are given. The implementation, evaluation and advantages of a proposed “Real-time ROM Measurement” are also given. The proposed KINECT application will be useful for enhancement of KINECT technical capabilities and for further advancements in medical care.


title: Document clustering based on non-negative matrix factorization

abstract: In this paper, we propose a novel document clustering method based on the non-negative factorization of the term-document matrix of the given document corpus. In the latent semantic space derived by the non-negative matrix factorization (NMF), each axis captures the base topic of a particular document cluster, and each document is represented as an additive combination of the base topics. The cluster membership of each document can be easily determined by finding the base topic (the axis) with which the document has the largest projection value. Our experimental evaluations show that the proposed document clustering method surpasses the latent semantic indexing and the spectral clustering methods not only in the easy and reliable derivation of document clustering results, but also in document clustering accuracies.


title: Accelerating the reduction to upper Hessenberg, tridiagonal, and . . .

abstract: We present a Hessenberg reduction (HR) algorithm for hybrid systems of homogeneous multicore with GPU accelerators that can exceed 25x the performance of the corresponding LAPACK algorithm running on current homogeneous multicores. This enormous acceleration is due to proper matching of algorithmic requirements to architectural strengths of the system's hybrid components. The results described in this paper are significant because the HR has not been properly accelerated before on homogeneous multicore architectures, and it plays a significant role in solving non-symmetric eigenvalue problems. Moreover, the ideas from the hybrid HR are used to develop a hybrid tridiagonal reduction algorithm (for symmetric eigenvalue problems) and a bidiagonal reduction algorithm (for singular value decomposition problems). Our approach demonstrates a methodology that streamlines the development of a large and important class of algorithms on modern computer architectures of multicore and GPUs. The new algorithms can be directly used in the software stack that relies on LAPACK.


title: Machine-learning interpolation of population-synthesis simulations to interpret gravitational-wave observations: a case study

abstract: We report on advances to interpret current and future gravitational-wave events in light of astrophysical simulations. A machine-learning emulator is trained on numerical population-synthesis predictions and inserted into a Bayesian hierarchical framework. In this case study, a modest but state-of-the-art suite of simulations of isolated binary stars is interpolated across two event parameters and one population parameter. The validation process of our pipelines highlights how omitting some of the event parameters might cause errors in estimating selection effects, which propagates as systematics to the final population inference. Using LIGO/Virgo data from O1 and O2 we infer that black holes in binaries are most likely to receive natal kicks with one-dimensional velocity dispersion $\sigma$ = 105+44 km/s. Our results showcase potential applications of machine-learning tools in conjunction with population-synthesis simulations and gravitational-wave data.


D-charts: Quasi-developable mesh segmentation


title: Plastics, the environment and human health: current consensus and future trends

abstract: Plastics have transformed everyday life; usage is increasing and annual production is likely to exceed 300 million tonnes by 2010. In this concluding paper to the Theme Issue on Plastics, the Environment and Human Health, we synthesize current understanding of the benefits and concerns surrounding the use of plastics and look to future priorities, challenges and opportunities. It is evident that plastics bring many societal benefits and offer future technological and medical advances. However, concerns about usage and disposal are diverse and include accumulation of waste in landfills and in natural habitats, physical problems for wildlife resulting from ingestion or entanglement in plastic, the leaching of chemicals from plastic products and the potential for plastics to transfer chemicals to wildlife and humans. However, perhaps the most important overriding concern, which is implicit throughout this volume, is that our current usage is not sustainable. Around 4 per cent of world oil production is used as a feedstock to make plastics and a similar amount is used as energy in the process. Yet over a third of current production is used to make items of packaging, which are then rapidly discarded. Given our declining reserves of fossil fuels, and finite capacity for disposal of waste to landfill, this linear use of hydrocarbons, via packaging and other short-lived applications of plastic, is simply not sustainable. There are solutions, including material reduction, design for end-of-life recyclability, increased recycling capacity, development of bio-based feedstocks, strategies to reduce littering, the application of green chemistry life-cycle analyses and revised risk assessment approaches. Such measures will be most effective through the combined actions of the public, industry, scientists and policymakers. There is some urgency, as the quantity of plastics produced in the first 10 years of the current century is likely to approach the quantity produced in the entire century that preceded.


title: Calculation of the Maximum Mechanical Stress on the Rotor of Interior Permanent-Magnet Synchronous Motors

abstract: This paper proposes a novel analytical method to calculate the maximum mechanical stress (MMS) on the rotor of the interior permanent-magnet synchronous motor with V-shaped rotor structure. Based on the traditional equivalent ring method, an improved method, which draws on the principle of equivalent centroid, is proposed to calculate the mechanical stress on the bilateral magnetic bridge. A rotor model with spokes is also built to calculate the mechanical stress on the central magnetic bridge. The stress concentration factor, together with its selection method, is also defined to calculate the MMS on the junctions. Then, the changing trends of the mechanical stress on the bilateral and central bridges are obtained along with the change in the V-shaped angle. The rotor faults of the two IPMSMs are analyzed based on the research results, and the optimized prototype is designed and fabricated. Experimental results of the optimized prototype show that the proposed equivalent methods have great applicability. Finally, the suggestion to design the V-shaped rotor structure is presented to determine the design rapidly and accurately.


title: In Defense of the Triplet Loss for Person Re-Identification

abstract: In the past few years, the field of computer vision has gone through a revolution fueled mainly by the advent of large datasets and the adoption of deep convolutional neural networks for end-to-end learning. The person re-identification subfield is no exception to this. Unfortunately, a prevailing belief in the community seems to be that the triplet loss is inferior to using surrogate losses (classification, verification) followed by a separate metric learning step. We show that, for models trained from scratch as well as pretrained ones, using a variant of the triplet loss to perform end-to-end deep metric learning outperforms most other published methods by a large margin.


title: LFGraph: simple and fast distributed graph analytics

abstract: Distributed graph analytics frameworks must offer low and balanced communication and computation, low preprocessing overhead, low memory footprint, and scalability. We present LFGraph, a fast, scalable, distributed, in-memory graph analytics engine intended primarily for directed graphs. LFGraph is the first system to satisfy all of the above requirements. It does so by relying on cheap hash-based graph partitioning, while making iterations faster by using publish-subscribe information flow along directed edges, fetch-once communication, single-pass computation, and in-neighbor storage. Our analytical and experimental results show that when applied to real-life graphs, LFGraph is faster than the best graph analytics frameworks by factors of 1x--5x when ignoring partitioning time and by 1x--560x when including partitioning time.


title: Deep fusion of heterogeneous sensor data

abstract: Heterogeneous sensor data fusion is a challenging field that has gathered significant interest in recent years. In this paper, we propose a neural network-based multimodal data fusion framework named deep multimodal encoder (DME). Through our new objective function, both the intra- and inter-modal correlations of multimodal sensor data can be better exploited for recovering the missing values, and the shared representation learned can be used directly for prediction tasks. In experiments with real-world sensor data, DME shows remarkable ability for missing data imputation and new modality prediction. Compared with traditional algorithms such as kNN and Sparse-PCA, DME is more expressive, robust, and scalable to large datasets.


title: A piggybacking approach to reduce overhead in sensor network gossiping

abstract: Many wireless sensor network protocols are employing gossip-based message dissemination, where nodes probabilistically forward messages, to reduce message overhead. We are concerned with emerging systems in stationary sensor networks that are multiple-source with each message targeted at every recipient, such as query and code propagation. Default gossip-based approaches tend to treat each stream of messages from different senders independently of the others, overloading each node with message overhead summed from all streams. We apply intelligent scheduling strategies for gossip forwarding, effectively piggybacking streams atop one another, to address this significant message overhead. Our problem formulation introduces a new concept called the "semblance graph" used to schedule gossiping based on streams' gossip periods. Two new heuristic algorithms are proposed to solve the semblance graph problem. The performance of these two heuristics is on average within 3.5% of the optimal solution. Simulations show that the piggybacking strategy reduces the message, bandwidth, and energy overhead while still maintaining the original scalability, reliability and latency of the canonical gossip.


title: A Comparison Of Bus Architectures For Safety-Critical Embedded Systems

abstract: Avionics and control systems for aircraft use distributed, fault-tolerant computer systems to provide safety-critical functions such as flight and engine control. These systems are becoming modular, meaning that they are based on standardized architectures and components, and integrated, meaning that some of the components are shared by different functions—of possibly different criticality levels. The modular architectures that support these functions must provide mechanisms for coordinating the distributed components that provide a single function (e.g., distributing sensor readings and actuator commands appropriately, and assisting replicated components to perform the function in a fault-tolerant manner), while protecting functions from faults in each other. Such an architecture must tolerate hardware faults in its own components and must provide very strong guarantees on the correctness and reliability of its own mechanisms and services. One of the essential services provided by this kind of modular architecture is communication of information from one distributed component to another, so a (physical or logical) communication bus is one of its principal components, and the protocols used for control and communication on the bus are among its principal mechanisms. Consequently, these architectures are often referred to as buses (or databuses), although this term understates their complexity, sophistication, and criticality. The capabilities once found in aircraft buses are becoming available in buses aimed at the automobile market, where the economies of scale ensure low prices. The low price of the automobile buses then renders them attractive to certain aircraft applications—provided they can achieve the safety required. In this report, I describe and compare the architectures of two avionics and two automobile buses in the interest of deducing principles common to all of them, the main differences in their design choices, and the tradeoffs made. The avionics buses considered are the Honeywell SAFEbus (the backplane data bus used in the Boeing 777 Airplane Information Management System) and the NASA SPIDER (an architecture being developed as a demonstrator for certification under the new DO-254 guidelines); the automobile buses considered are the TTTech Time-Triggered Architecture (TTA), recently adopted by Audi for automobile applications, and by Honeywell for avionics and aircraft control functions, and FlexRay, which is being developed by a consortium of BMW, DaimlerChrysler, Motorola, and Philips. I consider these buses from the perspective of their fault hypotheses, mechanisms, services, and assurance.


title: A Cloud Reservation System for Big Data Applications

abstract: Emerging Big Data applications increasingly require resources beyond those available from a single server and may be expressed as a complex workflow of many components and dependency relationships—each component potentially requiring its own specific, and perhaps specialized, resources for its execution. Efficiently supporting this type of Big Data application is a challenging resource management problem for existing cloud environments. In response, we propose a two-stage protocol for solving this resource management problem. We exploit spatial locality in the first stage by dynamically forming rack-level coalitions of servers to execute a workflow component. These coalitions only exist for the duration of the execution of their assigned component and are subsequently disbanded, allowing their resources to take part in future coalitions. The second stage creates a package of these coalitions, designed to support all the components in the complete workflow. To minimize the communication and housekeeping overhead needed to form this package of coalitions , the technique of combinatorial auctions is adapted from market-based resource allocation. This technique has a considerably lower overhead for resource aggregation than the traditional hierarchically organized models. We analyze two strategies for coalition formation: the first, history-based uses information from past auctions to pre-form coalitions in anticipation of predicted demand; the second one is a just-in-time that builds coalitions only when support for specific workflow components is requested.


title: The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic

abstract: Twitter is a free social networking and micro-blogging service that enables its millions of users to send and read each other's “tweets,” or short, 140-character messages. The service has more than 190 million registered users and processes about 55 million tweets per day. Useful information about news and geopolitical events lies embedded in the Twitter stream, which embodies, in the aggregate, Twitter users' perspectives and reactions to current events. By virtue of sheer volume, content embedded in the Twitter stream may be useful for tracking or even forecasting behavior if it can be extracted in an efficient manner. In this study, we examine the use of information embedded in the Twitter stream to (1) track rapidly-evolving public sentiment with respect to H1N1 or swine flu, and (2) track and measure actual disease activity. We also show that Twitter can be used as a measure of public interest or concern about health-related events. Our results show that estimates of influenza-like illness derived from Twitter chatter accurately track reported disease levels.


title: Go with the flow : optimal AUV path planning in coastal environments

abstract: This paper describes a novel optimum path planning strategy for long duration AUV operations in environments with time-varying ocean currents. These currents can exceed the maximum achievable speed of the AUV, as well as temporally expose obstacles. In contrast to most other path planning strategies, paths have to be defined in time as well as space. The solution described here exploits ocean currents to achieve mission goals with minimal energy expenditure, or a tradeoff between mission time and required energy. The proposed algorithm uses a parallel swarm search as a means to reduce the susceptibility to large local minima on the complex cost surface. The performance of the optimisation algorithms is evaluated in simulation and experimentally with the Starbug AUV using a validated ocean model of Brisbane’s Moreton Bay.


title: Passivity Based Control for the “Boost Converter-Inverter-DC Motor” System

abstract: This work presents a regulation control for the “DC/DC Boost converterinverterDC motor” system. Such a control is designed via the Exact Regulation Error Dynamics Passive Output Feedback (EREDPOF). The control validation is carried out through numerical simulations using Matlab-simulink, achieving a satisfactory performance.


title: A novel level set method for image segmentation by combining local and global information

abstract: AbstractA novel level set method integrating local and global statistical information is proposed in this paper. In our method, a new signed pressure force (SPF) function is constructed by two parts. One is the global average intensity of the image, which can accelerate the evolution of the curve when the contour far away from the object boundaries. The other is the intensity average of difference image between the averaging convolution image and the original image, which can guide the evolving curve to catch the boundaries of the objects. In addition, an adaptive weighting function is utilized to adjust the ratio between the global and local terms, which can eliminate the inconvenient selection of weighting parameter. By substituting the new SPF function for the edge stopping function of the geodesic active contour model, we obtain a novel adaptive hybrid segmentation model, which is capable of segmenting the images with intensity inhomogeneity. What is more, in our method, the level set function is init...


title: DESIGN AND ANALYSIS OF LOW POWER PSEUDO DIFFERENTIAL CLASS-AB TELESCOPIC OPERATIONAL AMPLIFIER

abstract: In this paper, the design of a pseudo differential class-AB cascoded telescopic op-amp for a 10-bit pipelined ADC is presented. A pseudo differential class-AB telescopic op-amp is used for the ADC architecture because of its large signal swing and high power efficiency. To further enhance the dc gain of the pseudo differential class-AB cascoded telescopic op-amp, two additional load capacitors are used. This proposed amplifier is designed in 180nm CMOS technology, and the simulation results show rail-to-rail input and output swings. With the modified pseudo differential class-AB cascoded telescopic op-amp the DC gain obtained is 43.41dB. The Common mode Rejection Ratio (CMRR) is 43.4dB, 91.19747° phase margin, 165.4544V/µs slew rate with 8-pF load capacitor and the power consumption is 0.4mW.


title: Haptic Virtual Fixtures for Robot-Assisted Manipulation

abstract: Haptic virtual fixtures are software-generated force and position signals applied to human operators in order to improve the safety, accuracy, and speed of robot-assisted manipulation tasks. Virtual fixtures are effective and intuitive because they capitalize on both the accuracy of robotic systems and the intelligence of human operators. In this paper, we discuss the design, analysis, and implementation of two categories of virtual fixtures: guidance virtual fixtures, which assist the user in moving the manipulator along desired paths or surfaces in the workspace, and forbidden-region virtual fixtures, which prevent the manipulator from entering into forbidden regions of the workspace. Virtual fixtures are analyzed in the context of both cooperative manipulation and telemanipulation systems, considering issues related to stability, passivity, human modeling, and applications.


title: Exceptionally high payload of doxorubicin in hollow gold nanospheres for near-infrared light-triggered drug release.

abstract: We report dual-functional hollow gold nanospheres (HAuNS, approximately 40-nm diameter) capable of mediating both photothermal ablation of cancer cells and drug release upon near-infrared (NIR) light irradiation. As high as 63% DOX by weight ( approximately 1.7 microg DOX/microg Au) could be loaded to polyethylene glycol (PEG)-coated HAuNS since DOX was coated to both the outer and the inner surfaces of HAuNS. Irradiation with NIR laser induced photothermal conversion, which triggered rapid DOX release from DOX-loaded HAuNS. The release of DOX was also pH-dependent, with more DOX released in aqueous solution at lower pH. Significantly greater cell killing was observed when MDA-MB-231 cells incubated with DOX-loaded HAuNS were irradiated with NIR light, attributable to both HAuNS-mediated photothermal ablation and cytotoxicity of released free DOX.


title: Taxi and Ride Sharing : A Dynamic Diala-Ride Problem with Money as an Incentive

abstract: Dynamic taxi and ride sharing problems with Money as an Incentive are studied.Heuristics are proposed and compared with others achieving better results.In the dynamic problem our approach is based on fast computation of minimum paths.Simulations show that users can save up to 30% on shared trips compared to private ones. This paper deals with a combinatorial optimization problem that models situations of both dynamic ride-sharing and taxi-sharing. Passengers who want to share a taxi or a ride, use an app to specify their current location, destination and further information such as the earliest departure time, the latest arrival time and the maximum cost they are willing to pay for the ride. Car owners also specify their origin, destination, the leaving time and the maximum accepted delay. Taxi drivers report their location and the time they will start and end the service. All drivers need to define a price per kilometer. The problem is to compute routes, matching requests to vehicles in such a way that ride-sharing is allowed as long as some restrictions are satisfied, such as: the capacity of the vehicle, maximum trip cost of each passenger and maximum delay. The problem is dynamic since new requests arrive on-line and routes can be modified in order to attend them. To solve this dynamic problem, the day is divided in time periods. For each period, an instance of a static problem is created and solved by a greedy randomized adaptive search procedure (GRASP). Experiments with instances based on real data were made to evaluate the heuristics and the proposed method. In our simulations with taxis, passengers paid, on average, almost 30% less than they would pay on private rides.


title: LILYPOND, A SYSTEM FOR AUTOMATED MUSIC ENGRAVING

abstract: A plastic light-transmitting fiber having a core-sheath structure. A polymer having methacrylimide or N-substituted methacrylimide units is used for the core component. A polymer having a refractive index smaller by at least 1% than that of the polymer of the core component is used for the sheath component. The heat resistance of the light-transmitting fiber is highly improved.


title: The “independent components” of natural scenes are edge filters

abstract: It has previously been suggested that neurons with line and edge selectivities found in primary visual cortex of cats and monkeys form a sparse, distributed representation of natural scenes, and it has been reasoned that such responses should emerge from an unsupervised learning algorithm that attempts to find a factorial code of independent visual features. We show here that a new unsupervised learning algorithm based on information maximization, a nonlinear “infomax” network, when applied to an ensemble of natural scenes produces sets of visual filters that are localized and oriented. Some of these filters are Gabor-like and resemble those produced by the sparseness-maximization network. In addition, the outputs of these filters are as independent as possible, since this infomax network performs Independent Components Analysis or ICA, for sparse (super-gaussian) component distributions. We compare the resulting ICA filters and their associated basis functions, with other decorrelating filters produced by Principal Components Analysis (PCA) and zero-phase whitening filters (ZCA). The ICA filters have more sparsely distributed (kurtotic) outputs on natural scenes. They also resemble the receptive fields of simple cells in visual cortex, which suggests that these neurons form a natural, information-theoretic coordinate system for natural images.


title: Twitter event detection: combining wavelet analysis and topic inference summarization

abstract: Today streaming text mining plays an important role within real-time social media mining. Given the amount and cadence of the data generated by those platforms, classical text mining techniques are not suitable to deal with such new mining challenges. Event detection is no exception, available algorithms rely on text mining techniques applied to pre-known datasets processed with no restrictions about computational complexity and required execution time per document analysis. This work presents a lightweight event detection using wavelet signal analysis of hashtag occurrences in the twitter public stream. It also pro- poses a strategy to describe detected events using a Latent Dirichlet Allocation topic inference model based on Gibbs Sampling. Peak detec- tion using Continuous Wavelet Transformation achieved good results in the identification of abrupt increases on the mentions of specific hash- tags. The combination of this method with the extraction of topics from tweets with hashtag mentions proved to be a viable option to summarize detected twitter events in streaming environments.


Breaking NLP: Using Morphosyntax, Semantics, Pragmatics and World Knowledge to Fool Sentiment Analysis Systems


title: Improved Performance of Half-Bridge Series Resonant Inverter for Induction Heating with Discontinuous Mode Control

abstract: In this paper, a study of the Discontinuous current Mode (DM) control applied to a half-bridge series resonant inverter for domestic induction heating is presented. The proposed control is interesting for this application because it exhibits reduced switching losses and the output power control is linear with the switching frequency. Considering that the load is disconnected from the source during part of the switching period, with DM control. A time-domain analysis method is proposed to obtain the analytical expression of the output power, the capacitor voltage and load current. The validity of the analytical expression is checked through simulation. Finally, an experimental comparison of efficiency between traditional controls and the DM control is performed for the induction heating application. In the low power range the efficiency of DM control is higher than traditional schemes.


title: Nonmetric calibration of camera lens distortion: differential methods and robust estimation

abstract: This paper addresses the problem of calibrating camera lens distortion, which can be significant in medium to wide angle lenses. Our approach is based on the analysis of distorted images of straight lines. We derive new distortion measures that can be optimized using nonlinear search techniques to find the best distortion parameters that straighten these lines. Unlike the other existing approaches, we also provide fast, closed-form solutions to the distortion coefficients. We prove that including both the distortion center and the decentering coefficients in the nonlinear optimization step may lead to instability of the estimation algorithm. Our approach provides a way to get around this, and, at the same time, it reduces the search space of the calibration problem without sacrificing the accuracy and produces more stable and noise-robust results. In addition, while almost all existing nonmetric distortion calibration methods needs user involvement in one form or another, we present a robust approach to distortion calibration based on the least-median-of-squares estimator. Our approach is, thus, able to proceed in a fully automatic manner while being less sensitive to erroneous input data such as image curves that are mistakenly considered projections of three-dimensional linear segments. Experiments to evaluate the performance of this approach on synthetic and real data are reported.


Sequential Model-Based Parameter Optimization: an Experimental Investigation of Automated and Interactive Approaches


title: A tactile distribution sensor which enables stable measurement under high and dynamic stretch

abstract: Recaently, we have been studying various tactile distribution sensors based on Electrical Impedance Tomography (EIT) which is a non-invasive technique to measure the resistance distribution of a conductive material only from a boundary, and needs no wiring inside the sensing area. In this paper, we present a newly developed conductive structure which is pressure sensitive but stretch insensitive and is based on the concept of contact resistance between (1)a network of stretchable wave-like conductive yarns with high resistance and (2)a conductive stretchable sheet with low resistance. Based on this newly developed structure, we have realized a novel tactile distribution sensor which enables stable measurement under dynamic and large stretch from various directions. Stable measurement of pressure distribution under dynamic and complex deformation cases such as pinching and pushing on a balloon surface are demonstrated. The sensor has been originally designed for implementation over interactive robots with soft and highly deformable bodies, but can also be used as novel user interface devices, or ordinary pressure distribution sensors. Some of the most remarkable specifications of the developed tactile sensor are high stretchability up to 140% and toughness under adverse load conditions. The sensor also has a realistic potential of becoming as thin and stretchable as stocking fabric. A goal of this research is to combine this thin sensor with stretch distribution sensors so that richer and more sophisticated tactile interactions can be realized.


Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification


title: Optimizing order fulfillment using design for six sigma and fuzzy logic ∗

abstract: Abstract Supply chain management aims to add value across the supply chain and customer service is now a major strategic issue. Supply chains are complex and subject to variables of forecast, supply, process, and transportation which can lead to problems such as the bull whip effect, product lateness, damaged goods and stock outs. A key issue facing companies today is how to monitor and control performance across the chain. This paper presents Design for Six Sigma (DFSS), which focuses on customer requirements from the onset, as an effective methodology for monitoring and controlling supply chain variables, optimizing supply chain processes and meeting customer’s requirements. By applying DFSS methodology to the key supply chain process of order fulfillment, a customized representation of detailed activities of order fulfillment processes is demonstrated providing key performance indicators. A theoretical transfer function for predicting the performance of the perfect order incorporating fuzzy set theory ...


title: Novel active PID controller employing VDTA

abstract: PID is most widely used in industrial process control. Voltage Differencing Transconductance Amplifier (VDTA) which is recently more popular in analog signal processing applications is discussed and integrated a proportional-integral-derivative (PID) controller. The proposed controller needs minimum number of active and passive components and demonstrates independent/electronic tuning of control parameters. In order to verify the suggested controller and controller's effect on second order step response of a VDTA based circuit is analyzed and presented in section II. The theoretical analysis proposed in this paper is tested through Or-CAD SPICE 9.1 simulations with TSMC 180nm CMOS process parameters.


RSSI based Bluetooth low energy indoor positioning


Activity of bottom animals - comparison of several trap methods


title: An Evolutionary Artificial Neural Networks Approach for Breast Cancer Diagnosis

abstract: This paper presents an evolutionary artificial neural network (EANN) approach based on the pareto-differential evolution (PDE) algorithm augmented with local search for the prediction of breast cancer. The approach is named memetic pareto artificial neural network (MPANN). Artificial neural networks (ANNs) could be used to improve the work of medical practitioners in the diagnosis of breast cancer. Their abilities to approximate nonlinear functions and capture complex relationships in the data are instrumental abilities which could support the medical domain. We compare our results against an evolutionary programming approach and standard backpropagation (BP), and we show experimentally that MPANN has better generalization and much lower computational cost.


title: Hybrid discriminative models for banknote recognition and anti-counterfeit

abstract: Nowadays, advanced technology has played an important task in circulation of anti-counterfeit notes economy. It is essential that requires an efficient solution to detect fake banknotes. This paper proposes an approach for recognition of paper currency based fundamental image processing using deep learning for feature extraction and recognition. Deep neural network techniques have dramatically become the state of the art in image processing. The high capacity of traditional techniques on currency image dataset has been impeded because of varieties of the appearance of the banknotes. This paper focuses recognition face value and anti-counterfeit based on banknote appearance. The proposed method can be applied to recognize many kinds of the denomination or face values as well as the national currencies. The contribution studies a new approach based on sequential deep neural network and data augmentation for improving accuracy. First, the deep neural network is constructed using several inceptions with different parallel convolutional operations which support reducing consuming time. Second, image augmentation of training dataset generates a larger data enough for deep neural network learning. This proposed task is aimed to address the small data problem. It is utilized for enhancing the capabilities of deep learning. Experimental results illustrate that the proposed method is applicable to the real application with enhances performance to 99.97% accuracy rate.


title: Microfluidic 3D cell culture: from tools to tissue models

abstract: The transition from 2D to 3D cell culture techniques is an important step in a trend towards better biomimetic tissue models. Microfluidics allows spatial control over fluids in micrometer-sized channels has become a valuable tool to further increase the physiological relevance of 3D cell culture by enabling spatially controlled co-cultures, perfusion flow and spatial control over of signaling gradients. This paper reviews most important developments in microfluidic 3D culture since 2012. Most efforts were exerted in the field of vasculature, both as a tissue on its own and as part of cancer models. We observe that the focus is shifting from tool building to implementation of specific tissue models. The next big challenge for the field is the full validation of these models and subsequently the implementation of these models in drug development pipelines of the pharmaceutical industry and ultimately in personalized medicine applications.


Integrating Ontologies and Computer Vision for Classification of Objects in Images


title: Fake news detection using naive Bayes classifier

abstract: This paper shows a simple approach for fake news detection using naive Bayes classifier. This approach was implemented as a software system and tested against a data set of Facebook news posts. We achieved classification accuracy of approximately 74% on the test set which is a decent result considering the relative simplicity of the model. This results may be improved in several ways, that are described in the article as well. Received results suggest, that fake news detection problem can be addressed with artificial intelligence methods.


title: Portrait of a narcissist: Manifestations of narcissism in physical

abstract: Narcissism is characterized in part by an acute concern for one's appearance. Despite this fundamental aspect of narcissism, little is known about whether narcissism is manifested in features of physical appearance. Can you tell if someone is narcissistic just by looking at them? Our results indicate that snap judgments of narcissism based on full-body photo- graphs are at least as accurate as snap judgments of any of the big five personality traits. Narcissists are more likely to wear expensive, flashy clothing, have an organized, neat appearance requiring a lot of preparation, and (in females) wear makeup and show cleav- age. Furthermore, observers' judgments correlate with the presence of these cues, suggest- ing that they are drawing on the correct information when making their judgments. Finally, observers' judgments are associated with three of the four facets of narcissism and capture the unique constellation of personality traits typical of narcissists (i.e., high extraversion and low agreeableness). These findings suggest that physical appearance reflects narcissists' personality, preoccupation with good looks, and desire to be the center of attention, and serves as a vehicle with which to promote their status.


title: A bio-inspired tensegrity manipulator with multi-DOF, structurally compliant joints

abstract: Most traditional robotic mechanisms feature inelastic joints that are unable to robustly handle large deformations and off-axis moments. As a result, the applied loads are transferred rigidly throughout the entire structure. The disadvantage of this approach is that the exerted leverage is magnified at each subsequent joint possibly damaging the mechanism. In this paper, we present two lightweight, elastic, bio-inspired tensegrity robotics arms which mitigate this danger while improving their mechanism's functionality. Our solutions feature modular tensegrity structures that function similarly to the human elbow and the human shoulder when connected. Like their biological counterparts, the proposed robotic joints are flexible and comply with unanticipated forces. Both proposed structures have multiple passive degrees of freedom and four active degrees of freedom (two from the shoulder and two from the elbow). The structural advantages demonstrated by the joints in these manipulators illustrate a solution to the fundamental issue of elegantly handling off-axis compliance.


title: Support Vector Machine Histogram: New Analysis and Architecture Design Method of Deep Convolutional Neural Network

abstract: Deep convolutional neural network (DCNN) is a kind of hierarchical neural network models and attracts attention in recent years since it has shown high classification performance. DCNN can acquire the feature representation which is a parameter indicating the feature of the input by learning. However, its internal analysis and the design of the network architecture have many unclear points and it cannot be said that it has been sufficiently elucidated. We propose the novel DCNN analysis method “Support vector machine (SVM) histogram” as a prescription to deal with these problems. This is a method that examines the spatial distribution of DCNN extracted feature representation by using the decision boundary of linear SVM. We show that we can interpret DCNN hierarchical processing using this method. In addition, by using the result of SVM histogram, DCNN architecture design becomes possible. In this study, we designed the architecture of the application to large scale natural image dataset. In the result, we succeeded in showing higher accuracy than the original DCNN.


THE MCKINSEY 7S MODEL FRAMEWORK FOR E-LEARNING SYSTEM READINESS ASSESSMENT


title: On the Computational Economics of Reinforcement Learning

abstract: Abstract Following terminology used in adaptive control, we distinguish between indirect learning methods, which learn explicit models of the dynamic structure of the system to be controlled, and direct learning methods, which do not. We compare an existing indirect method, which uses a conventional dynamic programming algorithm, with a closely related direct reinforcement learning method by applying both methods to an infinite horizon Markov decision problem with unknown state-transition probabilities. The simulations show that although the direct method requires much less space and dramatically less computation per control action, its learning ability in this task is superior to, or compares favorably with, that of the more complex indirect method. Although these results do not address how the methods’ performances compare as problems become more difficult, they suggest that given a fixed amount of computational power available per control action, it may be better to use a direct reinforcement learning method augmented with indirect techniques than to devote all available resources to a computationally costly indirect method. Comprehensive answers to the questions raised by this study depend on many factors making up the economic context of the computation.


title: A comparison of maximum likelihood methods for appearance-based minimalistic SLAM

abstract: This paper compares the performances of several algorithms that address the problem of Simultaneous Localization and Mapping (SLAM) for the case of very small, resource-limited robots. These robots have poor odometry and can typically only carry a single monocular camera. These algorithms do not make the typical SLAM assumption that metric distance/bearing information to landmarks is available. Instead, the robot registers a distinctive sensor "signature", based on its current location, which is used to match robot positions. The performances of a physics-inspired maximum likelihood (ML) estimator, the iterated form of the Extended Kalman Filter (IEKF), and a batch-processed linearized ML estimator are compared under various odometric noise models.


title: Automatic Synthesis of Geometry Problems for an Intelligent Tutoring System

abstract: This paper presents an intelligent tutoring system, GeoTutor, for Euclidean Geometry that is automatically able to synthesize proof problems and their respective solutions given a geometric figure together with a set of properties true of it. GeoTutor can provide personalized practice problems that address student deficiencies in the subject matter.


title: Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting

abstract: This paper proposes a weakly- and self-supervised deep convolutional neural network (WSSDCNN) for content-aware image retargeting. Our network takes a source image and a target aspect ratio, and then directly outputs a retargeted image. Retargeting is performed through a shift map, which is a pixel-wise mapping from the source to the target grid. Our method implicitly learns an attention map, which leads to a content-aware shift map for image retargeting. As a result, discriminative parts in an image are preserved, while background regions are adjusted seamlessly. In the training phase, pairs of an image and its image-level annotation are used to compute content and structure losses. We demonstrate the effectiveness of our proposed method for a retargeting application with insightful analyses.


title: A Likelihood-Based SLIC Superpixel Algorithm for SAR Images Using Generalized Gamma Distribution

abstract: The simple linear iterative clustering (SLIC) method is a recently proposed popular superpixel algorithm. However, this method may generate bad superpixels for synthetic aperture radar (SAR) images due to effects of speckle and the large dynamic range of pixel intensity. In this paper, an improved SLIC algorithm for SAR images is proposed. This algorithm exploits the likelihood information of SAR image pixel clusters. Specifically, a local clustering scheme combining intensity similarity with spatial proximity is proposed. Additionally, for post-processing, a local edge-evolving scheme that combines spatial context and likelihood information is introduced as an alternative to the connected components algorithm. To estimate the likelihood information of SAR image clusters, we incorporated a generalized gamma distribution (GГD). Finally, the superiority of the proposed algorithm was validated using both simulated and real-world SAR images.


Women’s Resistance


Radio-Frequency Identification-Overview


title: Design of a 100watt high-efficiency power amplifier for the 10-500MHz band

abstract: This paper presents a design for a high-efficiency power amplifier with an output power of more than 100W, and an ultra-broad bandwidth from 10 to 500MHz. The amplifier has a 4-way push-pull configuration using Guanella's 1∶1 transmission line transformer. A negative feedback network is adopted to make the power gain flat enough over the operating bandwidth. The implemented power amplifier exhibits a power gain of 29.2±1.8dB from 10 to 500MHz band with its power-added efficiency (PAE) being greater than 43%, and the second-and third-harmonic distortions are below −29dBc and −9.78dBc, respectively, at an output power of 100W over the entire frequency band.


title: Crop Economics and Cropping Pattern Changes

abstract: Mruthyunjaya Praduman Kumar This paper discusses how the crop production strategy followed in the post-green revolution period has led to the narrowing of the base of agricultural production. The authors examine the changes in input use, productivity, cost of production, profitability and employment in crops; identify and explain cropping pattern changes; and suggest ways of controlling the imbalances in the cropping pattern.


title: Decentering and Related Constructs A Critical Review and Metacognitive Processes Model

abstract: The capacity to shift experiential perspective—from within one’s subjective experience onto that experience—is fundamental to being human. Scholars have long theorized that this metacognitive capacity—which we refer to as decentering—may play an important role in mental health. To help illuminate this mental phenomenon and its links to mental health, we critically examine decentering-related constructs and their respective literatures (e.g., self-distanced perspective, cognitive distancing, cognitive defusion). First, we introduce a novel metacognitive processes model of decentering. Specifically, we propose that, to varying degrees, decentering-related constructs reflect a common mental phenomenon subserved by three interrelated metacognitive processes: meta-awareness, disidentification from internal experience, and reduced reactivity to thought content. Second, we examine extant research linking decentering-related constructs and their underlying metacognitive processes to mental health. We conclude by ...


title: Three-dimensional dental model analysis of treatment outcomes for protrusive maxillary dentition: comparison of headgear, miniscrew, and miniplate skeletal anchorage.

abstract: INTRODUCTION ::: The aim of this retrospective study on dental models was to compare the orthodontic outcomes of maxillary dentoalveolar protrusion treated with headgear, miniscrews, or miniplates for maximum anchorage. ::: ::: ::: METHODS ::: The 40 subjects were diagnosed as having either Angle Class II malocclusion or Class I bimaxillary dentoalveolar protrusion. All patients were treated to retract the maxillary dentoalveolar process by using the extraction space of the bilateral maxillary first premolars. They were divided into 3 groups according to the type of anchorage used. Group 1 (n = 16) received traditional anchorage preparation with a transpalatal arch and headgear, group 2 (n = 15) received miniscrews, and group 3 (n = 9) received miniplates for skeletal anchorage. To investigate the movement of the maxillary teeth during dentoalveolar retraction, we used a 3-dimensional (3D) digitizer to assess the positional changes of the maxillary teeth relative to the stable palatal rugose structures on the serial dental models. The 3D coordinates representing pretreatment and posttreatment maxillary dental casts were superimposed to determine the movement of individual teeth from the positional changes of 18 landmarks of the central incisor, canine, second premolar, and first molar. ::: ::: ::: RESULTS ::: Three-dimensional analysis of the maxillary dental models in the buccopalatal, anteroposterior, and vertical directions showed significant differences in tooth movements between the headgear and the mini-implant (miniscrew or miniplate) groups. Both skeletal anchorage groups had greater incisor retraction (6.9 mm for the miniscrew, 7.3 mm for the miniplate) than did the headgear group (5.5 mm). Mesialization of occlusal centroid of the maxillary molar in the skeletal anchorage groups was less than that in the headgear group (1.3 mm for the miniscrew, 1.4 mm for the miniplate, 2.5 mm for the headgear). Tooth movements in the anteroposterior and buccopalatal directions did not reach a statistically significant difference between the miniscrew and miniplate groups, but the maxillary posterior teeth of the subjects receiving miniplates showed greater intrusion than those receiving miniscrews anchorage. ::: ::: ::: CONCLUSIONS ::: This 3D analysis of serial dental models demonstrated that, compared with headgear, skeletal anchorage achieved better results in the treatment of maxillary dentoalveolar protrusion. Significant intrusion of the maxillary posterior teeth was noted in the miniplate group but not in the miniscrew and headgear groups. Greater retraction of the maxillary anterior teeth, less anchorage loss of the maxillary posterior teeth, and the possibility of maxillary molar intrusion all facilitated correction of the Class II malocclusion, especially for patients with a hyperdivergent face.


title: Real-time clickstream data analytics and visualization

abstract: Nowadays web is becoming a main channel for reaching customers and prospects; Clickstream data generated by websites has become another important enterprise data source. As simple as it sounds for recording every click a customer made, so that we can use clickstream data for modelling user behavior, gaining valuable customer insights. Clickstream analysis commonly refers to analyzing click data and website optimization. Such analysis is typically done to extract insights into website visitor behavior especially social-media or e-commerce websites. Also nowadays online-learning became a trend in education system. We can see many online learning portals which are providing live training on various technologies. To identify potential customers or to identify recommendations for existing customers. Clickstream analysis can be used to figure out which geographies and time zones is most of traffic coming from, and which devices, Browsers (such as its name, versions), time spent, Operating Systems, are used to access the websites, which common paths users take before they do something in site. Analysis of clickstream data in real time(streaming) has more value than batch mode(stored). We analyze and visualize online-learning portal's clickstream data on the fly for business intelligence purpose. We constructed a full data pipeline using tools such as Apache Kafka, Apache Spark for streaming and elastic search, Kibana to query and visualize clickstream data respectively.


title: ARCHITECTURAL LAYOUT DESIGN OPTIMIZATION

abstract: This article presents an optimization model of the quantifiable aspects of architectural floorplan layout design, and a companion article presents a method for integrating mathematical optimization and subjective decision making during conceptual design. The model presented here offers a new approach to floorplan layout optimization that takes advantage of the efficiency of gradient-based algorithms, where appropriate, and uses evolutionary algorithms to make discrete decisions and do global search. Automated optimization results are comparable to other methods in this research area, and the new formulation makes it possible to integrate the power of human decision-making into the process.


title: Slotted waveguide antennas for practical radar systems

abstract: This article summarizes recent results on the development, fabrication, and application of slotted waveguide antenna systems for practical radar systems, including Ka-band helicopter collision avoidance and weather radar, Ku-band surveillance and tracking radar, and X-band airborne SAR system. The corresponding design solutions, antenna characteristics, and test results are presented and discussed.


title: Adversarial Attacks on Neural Network Policies

abstract: Machine learning classifiers are known to be vulnerable to inputs maliciously constructed by adversaries to force misclassification. Such adversarial examples have been extensively studied in the context of computer vision applications. In this work, we show adversarial attacks are also effective when targeting neural network policies in reinforcement learning. Specifically, we show existing adversarial example crafting techniques can be used to significantly degrade test-time performance of trained policies. Our threat model considers adversaries capable of introducing small perturbations to the raw input of the policy. We characterize the degree of vulnerability across tasks and training algorithms, for a subclass of adversarial-example attacks in white-box and black-box settings. Regardless of the learned task or training algorithm, we observe a significant drop in performance, even with small adversarial perturbations that do not interfere with human perception. Videos are available at this http URL.


title: A comparison of GPU execution time prediction using machine learning and analytical modeling

abstract: Today, most high-performance computing (HPC) platforms have heterogeneous hardware resources (CPUs, GPUs, storage, etc.) A Graphics Processing Unit (GPU) is a parallel computing coprocessor specialized in accelerating vector operations. The prediction of application execution times over these devices is a great challenge and is essential for efficient job scheduling. There are different approaches to do this, such as analytical modeling and machine learning techniques. Analytic predictive models are useful, but require manual inclusion of interactions between architecture and software, and may not capture the complex interactions in GPU architectures. Machine learning techniques can learn to capture these interactions without manual intervention, but may require large training sets. In this paper, we compare three different machine learning approaches: linear regression, support vector machines and random forests with a BSP-based analytical model, to predict the execution time of GPU applications. As input to the machine learning algorithms, we use profiling information from 9 different applications executed over 9 different GPUs. We show that machine learning approaches provide reasonable predictions for different cases. Although the predictions were inferior to the analytical model, they required no detailed knowledge of application code, hardware characteristics or explicit modeling. Consequently, whenever a database with profile information is available or can be generated, machine learning techniques can be useful for deploying automated on-line performance prediction for scheduling applications on heterogeneous architectures containing GPUs.


title: An extended multidelay filter: fast low-delay algorithms for very high-order adaptive systems

abstract: We propose a novel class of efficient adaptive algorithms in the frequency domain that is tailored to very long adaptive filters and highly autocorrelated input signals as they arise, e.g., in high-quality full-duplex audio applications. The approach exhibits good tracking capabilities of the signal statistics and very low delay. Moreover, it is shown that the low order of computational complexity of the conventional frequency-domain adaptive algorithms can be maintained thanks to efficient realizations. The algorithm allows a tradeoff between the well-known multidelay filter (MDF) and the recursive least-squares (RLS) algorithm. It is also well suited for an efficient generalization to the multichannel case.


title: Interactive relationship discovery via the semantic web

abstract: This paper presents an approach for the interactive discovery of relationships between selected elements via the Semantic Web. It emphasizes the human aspect of relationship discovery by offering sophisticated interaction support. Selected elements are first semi-automatically mapped to unique objects of Semantic Web datasets. These datasets are then crawled for relationships which are presented in detail and overview. Interactive features and visual clues allow for a sophisticated exploration of the found relationships. The general process is described and the RelFinder tool as a concrete implementation and proof-of-concept is presented and evaluated in a user study. The application potentials are illustrated by a scenario that uses the RelFinder and DBpedia to assist a business analyst in decision-making. Main contributions compared to previous and related work are data aggregations on several dimensions, a graph visualization that displays and connects relationships also between more than two given objects, and an advanced implementation that is highly configurable and applicable to arbitrary RDF datasets.


title: Utilizing context in generative bayesian models for linked corpus

abstract: In an interlinked corpus of documents, the context in which a citation appears provides extra information about the cited document. However, associating terms in the context to the cited document remains an open problem. We propose a novel document generation approach that statistically incorporates the context in which a document links to another document. We quantitatively show that the proposed generation scheme explains the linking phenomenon better than previous approaches. The context information along with the actual content of the document provides signicant improvements over the previous approaches for various real world evaluation tasks such as link prediction and log-likelihood estimation on unseen content. The proposed method is more scalable to large collection of documents compared to the previous approaches.


title: Plant Species Identification Based on Neural Network

abstract: Computer-aided plant species identification acts significantly on plant digital museum system and systematic botany, which is the groundwork for research and development of plant. This paper presents a new method for plant species identification using leaf image. It focuses on the stable features extraction of leaf, such as the geometrical features of shape and the texture features of venation. The 2-D moment invariants, Wavelet statistical features are used to extract leaf information. Self-organizing feature map (SOM) neural network has the advantages of simple structure, ordered mapping topology and low complexity of learning. It is suitable for many complex problems such as multi-class pattern recognition, high dimension input vector and large quantity training data. So this paper use SOM neural network to identify the plant species. The experimental results illustrate the effectiveness of this method.


title: Allocation of Virtual Machines in Cloud Data Centers—A Survey of Problem Models and Optimization Algorithms

abstract: Data centers in public, private, and hybrid cloud settings make it possible to provision virtual machines (VMs) with unprecedented flexibility. However, purchasing, operating, and maintaining the underlying physical resources incurs significant monetary costs and environmental impact. Therefore, cloud providers must optimize the use of physical resources by a careful allocation of VMs to hosts, continuously balancing between the conflicting requirements on performance and operational costs. In recent years, several algorithms have been proposed for this important optimization problem. Unfortunately, the proposed approaches are hardly comparable because of subtle differences in the used problem models. This article surveys the used problem formulations and optimization algorithms, highlighting their strengths and limitations, and pointing out areas that need further research.


title: Modified Different Neighbor History Spray and Wait using PROPHET in Delay Tolerant Network

abstract: Delay Tolerant Networks or Disruption Tolerant Networks are intermittent networks that may lack continuous connectivity as most of the time there does not exist a direct link between source and destination for message transmission. In this paper, Modified DNH (Different Neighbor-History) Spray and Wait using PROPHET is proposed. It improves the wait phase of Different Neighbor-History Spray and Wait. This protocol calculates number of message copies to be forwarded based on performance of receiver node in spray phase and in wait phase the waiting node uses probabilistic routing protocol using history of encounters and transitivity for transmission. Simulation results show that this method improves delivery probability and average latency of transmission as compared to Spray and Wait and also DNH-Spray and Wait routing scheme. General Terms Delay Tolerant Network, Opportunistic Network Environment Simulator


title: Frequency of Imagery in Emotional Experience

abstract: 44 subjects each gave phenomenological reports of the imagery present during a recalled situation in which emotion was experienced and in a comparable situation in which no or minimal emotion was e...


title: A 32nm logic technology featuring 2nd-generation high-k + metal-gate transistors, enhanced channel strain and 0.171μm2 SRAM cell size in a 291Mb array

abstract: A 32 nm generation logic technology is described incorporating 2nd-generation high-k + metal-gate technology, 193 nm immersion lithography for critical patterning layers, and enhanced channel strain techniques. The transistors feature 9 Aring EOT high-k gate dielectric, dual band-edge workfunction metal gates, and 4th-generation strained silicon, resulting in the highest drive currents yet reported for NMOS and PMOS. Process yield, performance and reliability are demonstrated on a 291 Mbit SRAM test vehicle, with 0.171 mum2 cell size, containing >1.9 billion transistors.


Superomedial Pedicle Mammaplasty . The Logical Extension Of The Superior Pedicle Technique


title: MACCEPA 2.0: Adjustable compliant actuator with stiffening characteristic for energy efficient hopping

abstract: The MACCEPA (Mechanically Adjustable Compliance and Controllable Equilibrium Position Actuator) is an electric actuator of which the compliance and equilibrium position are fully independently controllable and both are set by a dedicated servomotor. In this paper an improvement of the actuator is proposed where the torque-angle curve and consequently the stiffness-angle curve can be modified by choosing an appropriate shape of a profile disk, which replaces the lever arm of the former design. The actuator has a large joint angle, torque and stiffness range and these properties can be made beneficial for safe human robot interaction and the construction of energy efficient walking, hopping and running robots. The ability to store and release energy is shown by simulations on a 1DOF hopping robot. Its hopping height is much higher compared to a configuration in which the same motor is used in a traditional stiff setup. The stiffness of the actuator has a stiffening characteristic so the leg stiffness resembles more a linear stiffness as found in humans.


title: Review: Application mobility in pervasive computing: A survey

abstract: Pervasive computing applications often need to maintain uninterrupted computing experiences when users move across devices. This advanced feature, recognized as application mobility, brings many challenges to the pervasive computing community. For a better understanding of the challenges and existing approaches to application mobility, this paper surveys related work with a classification and comparison framework established along four dimensions of design concerns in application migration: temporal, spatial, entity and other concerns. Through this survey this paper attempts to provide a systematic reference for developers to leverage off among different migration strategies for seamless application mobility. Moreover, it sheds some light on future work directions.


title: USAAR-WLV: Hypernym Generation with Deep Neural Nets

abstract: This paper describes the USAAR-WLV taxonomy induction system that participated in the Taxonomy Extraction Evaluation task of SemEval-2015. We extend prior work on using vector space word embedding models for hypernym-hyponym extraction by simplifying the means to extract a projection matrix that transforms any hyponym to its hypernym. This is done by making use of function words, which are usually overlooked in vector space approaches to NLP. Our system performs best in the chemical domain and has achieved competitive results in the overall evaluations.


title: Is the routine use of bevacizumab in the treatment of women with advanced or recurrent cancer of the cervix sustainable?

abstract: Background ::: New chemotherapy combinations are being tested for the treatment of women with advanced, persistent or recurrent cervical cancer. We sought to evaluate the cost effectiveness of some newer combination therapies in cervical cancer.


title: Static mass spectrographs of a new type using Euler’s homogeneous electric and magnetic fields. II: Conditions of high-order double focusing for two-cascade schemes

abstract: Static mass spectrometers are masked by more modern and more sophisticated mass spectrometric instruments, but their potential nowhere is almost exhausted. One of the principle advantages of static mass spectrometers is that they are the only type of mass spectrometric instruments capable of operating in a spectrographic mode by measuring the whole range of masses per a single run. However, systems with good spectrographic properties significantly differ from the optical point of view from systems with good spectrometric properties, and the development of such devices leads to completely new optical problems and, accordingly, to novel methods of their solution. In this paper, we consider possibilities related to the application of electrostatic and magnetostatic fields, homogeneous in Euler’s terms, to the development of two-cascade mass spectrographs with double focusing.


title: Reduction of relative centrifugal forces increases growth factor release within solid platelet-rich-fibrin (PRF)-based matrices: a proof of concept of LSCC (low speed centrifugation concept)

abstract: Purpose The present study evaluated the platelet distribution pattern and growth factor release (VEGF, TGF-β1 and EGF) within three PRF (platelet-rich-fibrin) matrices (PRF, A-PRF and A-PRF+) that were prepared using different relative centrifugation forces (RCF) and centrifugation times. Materials and methods immunohistochemistry was conducted to assess the platelet distribution pattern within three PRF matrices. The growth factor release was measured over 10 days using ELISA. Results The VEGF protein content showed the highest release on day 7; A-PRF+ showed a significantly higher rate than A-PRF and PRF. The accumulated release on day 10 was significantly higher in A-PRF+ compared with A-PRF and PRF. TGF-β1 release in A-PRF and A-PRF+ showed significantly higher values on days 7 and 10 compared with PRF. EGF release revealed a maximum at 24 h in all groups. Toward the end of the study, A-PRF+ demonstrated significantly higher EGF release than PRF. The accumulated growth factor releases of TGF-β1 and EGF on day 10 were significantly higher in A-PRF+ and A-PRF than in PRF. Moreover, platelets were located homogenously throughout the matrix in the A-PRF and A-PRF+ groups, whereas platelets in PRF were primarily observed within the lower portion. Discussion the present results show an increase growthfactor release by decreased RCF. However, further studies must be conducted to examine the extent to which enhancing the amount and the rate of released growth factors influence wound healing and biomaterial-based tissue regeneration. Conclusion These outcomes accentuate the fact that with a reduction of RCF according to the previously LSCC (described low speed centrifugation concept), growth factor release can be increased in leukocytes and platelets within the solid PRF matrices.


title: An Analytical Model With 2-D Effects for 4H-SiC Trenched Junction Barrier Schottky Diodes

abstract: This paper presents an analytical model with 2-D effects for 1200 V 4H-silicon carbide trenched junction barrier Schottky (TJBS) diodes. Energy band diagrams of junction barrier Schottky and TJBS diodes in reverse biases are analyzed in detail. An analytical model of potential and electric field distributions with 2-D effects is proposed. Based on the modeling results of the surface electric field, a physical model account- ing for three main mechanisms, namely, thermionic emission, tunneling, and avalanche multiplication is developed for the device reverse I-V characteristics. The models are verified by experimental results. Based on the physical model, optimization of the TJBS parameters including barrier height, junction depth, and junction spacing can be carried out with the target of achieving the lowest device ON-state limiting the reverse leakage current to a reasonable level. As a result, for a given breakdown voltage, an optimum set of parameters can be obtained.