title: PRISM: platform for remote sensing using smartphones

abstract: To realize the potential of opportunistic and participatory sensing using mobile smartphones, a key challenge is ensuring the ease of developing and deploying such applications, without the need for the application writer to reinvent the wheel each time. To this end, we present a Platform for Remote Sensing using Smartphones (PRISM) that balances the interconnected goals of generality, security, and scalability. PRISM allows application writers to package their applications as executable binaries, which offers efficiency and also the flexibility of reusing existing code modules. PRISM then pushes the application out automatically to an appropriate set of phones based on a specified set of predicates. This push model enables timely and scalable application deployment while still ensuring a good degree of privacy. To safely execute untrusted applications on the smartphones, while allowing them controlled access to sensitive sensor data, we augment standard software sandboxing with several PRISM-specific elements like resource metering and forced amnesia. We present three applications built on our implementation of PRISM on Windows Mobile: citizen journalist, party thermometer, and road bump monitor. These applications vary in the set of sensors they use and in their mode of operation (depending on human input vs. automatic). We report on our experience from a small-scale deployment of these applications. We also present a large-scale simulation-based analysis of the scalability of PRISM's push model.


title: Comparative analysis of evolving software systems using the Gini coefficient

abstract: Software metrics offer us the promise of distilling useful information from vast amounts of software in order to track development progress, to gain insights into the nature of the software, and to identify potential problems. Unfortunately, however, many software metrics exhibit highly skewed, non-Gaussian distributions. As a consequence, usual ways of interpreting these metrics — for example, in terms of “average” values — can be highly misleading. Many metrics, it turns out, are distributed like wealth — with high concentrations of values in selected locations. We propose to analyze software metrics using the Gini coefficient, a higherorder statistic widely used in economics to study the distribution of wealth. Our approach allows us not only to observe changes in software systems efficiently, but also to assess project risks and monitor the development process itself. We apply the Gini coefficient to numerous metrics over a range of software projects, and we show that many metrics not only display remarkably high Gini values, but that these values are remarkably consistent as a project evolves over time.


Visual Perception of Texture


title: In vitro antimicrobial activities against cariogenic streptococci and their antioxidant capacities: A comparative study of green tea versus different herbs

abstract: Abstract The antimicrobial activity against cariogenic bacteria, total antioxidant capacity and phenolic constituents of methanolic extracts from 11 herbs were investigated and compared with those of green tea ( Camellia sinensis ). Among the 12 tested herbs, eight herbal extracts could inhibit the growth of Streptococcus sanguinis . Jasmine, jiaogulan, and lemongrass were the most potent, with minimum inhibitory concentrations (MIC) of 1 mg/ml, while green tea was less effective, with a MIC of 4 mg/ml. Among them, only rosemary could inhibit the growth of S. mutans at a MIC of 4 mg/ml. Total antioxidant capacities of herbal extracts were analyzed by three different assays, including 2,2-diphenyl-1-picrylhydrazyl (DPPH·) radical scavenging activity, trolox equivalent antioxidant capacity (TEAC) and oxygen radical absorbance capacity (ORAC). Regardless of the assays used, green tea exhibited the highest antioxidant capacity, followed by osmanthus. Wide variations in total phenolics and total flavonoids of herbal tea extracts were observed. Chlorogenic acid was detected in high amount in honeysuckle and duzhong. These data suggest that rosemary is a potent inhibitor of oral streptococci, and green tea and osmanthus may be effective potential sources of natural antioxidants.


title: Planning, Inference and Pragmatics in Sequential Language Games

abstract: We study sequential language games in which two players, each with private information, communicate to achieve a common goal. In such games, a successful player must (i) infer the partner's private information from the partner's messages, (ii) generate messages that are most likely to help with the goal, and (iii) reason pragmatically about the partner's strategy. We propose a model that captures all three characteristics and demonstrate their importance in capturing human behavior on a new goal-oriented dataset we collected using crowdsourcing.


title: Look at Boundary: A Boundary-Aware Face Alignment Algorithm

abstract: We present a novel boundary-aware face alignment algorithm by utilising boundary lines as the geometric structure of a human face to help facial landmark localisation. Unlike the conventional heatmap based method and regression based method, our approach derives face landmarks from boundary lines which remove the ambiguities in the landmark definition. Three questions are explored and answered by this work: 1. Why using boundary? 2. How to use boundary? 3. What is the relationship between boundary estimation and landmarks localisation? Our boundary-aware face alignment algorithm achieves 3.49% mean error on 300-W Fullset, which outperforms state-of-the-art methods by a large margin. Our method can also easily integrate information from other datasets. By utilising boundary information of 300-W dataset, our method achieves 3.92% mean error with 0.39% failure rate on COFW dataset, and 1.25% mean error on AFLW-Full dataset. Moreover, we propose a new dataset WFLW to unify training and testing across different factors, including poses, expressions, illuminations, makeups, occlusions, and blurriness. Dataset and model are publicly available at https://wywu.github.io/projects/LAB/LAB.html


title: Accurate Equivalent-Circuit Descriptions of Thin Glide-Symmetric Corrugated Metasurfaces

abstract: Thin artificial surfaces that act as high frequency bandgap structures have been recently studied for the design of gap waveguides, hard surfaces, and planar lenses. Here, we propose a circuit-based method to analyze glide-symmetric corrugated metasurfaces that are embedded in a thin parallel plate waveguide. Our closed-form solution is based on rigorous analytical derivations. It achieves remarkable agreement with full-wave solvers, even when the waveguide thickness is extremely thin. In contrast, classical homogenization approaches are shown to be inaccurate for thin waveguides due to the interaction of higher order Floquet modes between the surfaces. Numerical results validate our theoretical analysis and show the utility of the proposed method.


title: RoboCupSimData: A RoboCup soccer research dataset

abstract: RoboCup is an international scientific robot competition in which teams of multiple robots compete against each other. Its different leagues provide many sources of robotics data, that can be used for further analysis and application of machine learning. This paper describes a large dataset from games of some of the top teams (from 2016 and 2017) in RoboCup Soccer Simulation League (2D), where teams of 11 robots (agents) compete against each other. Overall, we used 10 different teams to play each other, resulting in 45 unique pairings. For each pairing, we ran 25 matches (of 10mins), leading to 1125 matches or more than 180 hours of game play. The generated CSV files are 17GB of data (zipped), or 229GB (unzipped). The dataset is unique in the sense that it contains both the ground truth data (global, complete, noise-free information of all objects on the field), as well as the noisy, local and incomplete percepts of each robot. These data are made available as CSV files, as well as in the original soccer simulator formats.


title: Impact of bibliometrics upon the science system: Inadvertent consequences?

abstract: Ranking of research institutions by bibliometric methods is an improper tool for research performance evaluation, even at the level of large institutions. The problem, however, is not the ranking as such. The indicators used for ranking are often not advanced enough, and this situation is part of the broader problem of the application of insufficiently developed bibliometric indicators used by persons who do not have clear competence and experience in the field of quantitative studies of science. After a brief overview of the basic elements of bibliometric analysis, I discuss the major technical and methodological problems in the application of publication and citation data in the context of evaluation. Then I contend that the core of the problem lies not necessarily at the side of the data producer. Quite often persons responsible for research performance evaluation, for instance scientists themselves in their role as head of institutions and departments, science administrators at the government level and other policy makers show an attitude that encourages 'quick and dirty' bibliometric analyses whereas better quality is available. Finally, the necessary conditions for a successful application of advanced bibliometric indicators as support tool for peer review are discussed.


title: Automation of Prosthetic Upper Limbs for Transhumeral Amputees Using Switch-controlled Motors

abstract: The issues of research required in the field of bio medical engineering and externally-powered prostheses are attracting attention of regulatory bodies and the common people in various parts of the globe. Today, 90 percent of prostheses used are conventional body powered cable-controlled ones which are very uncomfortable to the amputees as fairly large amount of forces and excursions have to be generated by the amputee. Additionally, its amount of rotation is limited. Alternatively, prosthetic limbs driven using electrical motors might deliver added functionality and improved control, accompanied by better cosmesis, however,it could be bulky and costly. Presently existing proposals usually require fewer bodily response and need additional upkeep than the cable operated prosthetic limbs. Due to the motives mentioned, proposal for mechanization of body-powered prostheses, with ease of maintenance and cost in mind, is presented in this paper. The prosthetic upper limb which is being automated is for Transhumeral type of amputees that is amputated from above elbow. The study consists of two main portions: one is lifting mechanism of the limb and the other is gripping mechanism for the hand using switch controls, which is the most cost effective and optimized solution, rather than using complex and expensive myoelectric control signals.


title: Interoperability of multiple autonomous databases

abstract: Database systems were a solution to the problem of shared access to heterogeneous files created by multiple autonomous applications in a centralized environment. To make data usage easier, the files were replaced by a globally integrated database. To a large extent, the idea was successful, and many databases are now accessible through local and long-haul networks. Unavoidably, users now need shared access to multiple autonomous databases. The question is what the corresponding methodology should be. Should one reapply the database approach to create globally integrated distributed database systems or should a new approach be introduced? We argue for a new approach to solving such data management system problems, called multidatabase or federated systems. These systems make databases interoperable, that is, usable without a globally integrated schema. They preserve the autonomy of each database yet support shared access. Systems of this type will be of major importance in the future. This paper first discusses why this is the case. Then, it presents methodologies for their design. It further shows that major commerical relational database systems are evolving toward multidatabase systems. The paper discusses their capabilities and limitations, presents and discusses a set of prototypes, and, finally, presents some current research issues.


SEMANTIC INDEXING FOR VISUAL RECOGNITION OF BUILDINGS


3 D segmentation of mandible from multisectional CT scans by convolutional neural networks


title: An Efficient and Privacy-Preserving Biometric Identification Scheme in Cloud Computing

abstract: Biometric identification has become increasingly popular in recent years. With the development of cloud computing, database owners are motivated to outsource the large size of biometric data and identification tasks to the cloud to get rid of the expensive storage and computation costs, which, however, brings potential threats to users’ privacy. In this paper, we propose an efficient and privacy-preserving biometric identification outsourcing scheme. Specifically, the biometric To execute a biometric identification, the database owner encrypts the query data and submits it to the cloud. The cloud performs identification operations over the encrypted database and returns the result to the database owner. A thorough security analysis indicates that the proposed scheme is secure even if attackers can forge identification requests and collude with the cloud. Compared with previous protocols, experimental results show that the proposed scheme achieves a better performance in both preparation and identification procedures.


title: Cross Site Scripting: An Overview

abstract: This paper describes the security attacks and specially focuses on Cross Site Scripting attacks. It further also discusses types and several counter measures. The major problem faced by the web application is the parameter manipulation, through which the attackers are aiming to access the database. Generally web applications maintain same structure and value. In that, required information is being accessed by the identical variables and keywords through web parameters. Parameter manipulation is the major issue in the web application used by the attacker to manipulate the parameter being sent by the browser and executed by the server. These vulnerabilities occur after the string gets returned to the user's web browser by a susceptible web application. Therefore, to prevent XSS vulnerabilities, it is obligatory to prepare preventative measures to protect the parsing processing in the web browser so that there is no influence even from the effect of the string prepared by the


Fake currency detection using image processing


title: mD3DOCKxb: An Ultra-Scalable CPU-MIC Coordinated Virtual Screening Framework

abstract: Molecular docking is an important method in computational drug discovery. In large-scale virtual screening, millions of small drug-like molecules (chemical compounds) are compared against a designated target protein (receptor). Depending on the utilized docking algorithm for screening, this can take several weeks on conventional HPC systems. However, for certain applications including large-scale screening tasks for newly emerging infectious diseases such high runtimes can be highly prohibitive. In this paper, we investigate how the massively parallel neo-heterogeneous architecture of Tianhe-2 Supercomputer consisting of thousands of nodes comprising CPUs and MIC coprocessors that can efficiently be used for virtual screening tasks. Our proposed approach is based on a coordinated parallel framework called mD3DOCKxb in which CPUs collaborate with MICs to achieve high hardware utilization. mD3DOCKxb comprises a novel efficient communication engine for dynamic task scheduling and load balancing between nodes in order to reduce communication and I/O latency. This results in a highly scalable implementation with parallel efficiency of over 84% (strong scaling) when executing on 8,000 Tianhe-2 nodes comprising 192,000 CPU cores and 1,368,000 MIC cores.


title: Training hidden markov models with multiple observations-a combinatorial method

abstract: Hidden Markov models (HMM) are stochastic models capable of statistical learning and classification. They have been applied in speech recognition and handwriting recognition because of their great adaptability and versatility in handling sequential signals. On the other hand, as these models have a complex structure and also because the involved data sets usually contain uncertainty, it is difficult to analyze the multiple observation training problem without certain assumptions. For many years researchers have used the training equations of Levinson (1983) in speech and handwriting applications, simply assuming that all observations are independent of each other. This paper presents a formal treatment of HMM multiple observation training without imposing the above assumption. In this treatment, the multiple observation probability is expressed as a combination of individual observation probabilities without losing generality. This combinatorial method gives one more freedom in making different dependence-independence assumptions. By generalizing Baum's auxiliary function into this framework and building up an associated objective function using the Lagrange multiplier method, it is proven that the derived training equations guarantee the maximization of the objective function. Furthermore, we show that Levinson's training equations can be easily derived as a special case in this treatment.


title: A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks

abstract: Much effort has been devoted to evaluate whether multi-task learning can be leveraged to learn rich representations that can be used in various Natural Language Processing (NLP) down-stream applications. However, there is still a lack of understanding of the settings in which multi-task learning has a significant effect. In this work, we introduce a hierarchical model trained in a multi-task learning setup on a set of carefully selected semantic tasks. The model is trained in a hierarchical fashion to introduce an inductive bias by supervising a set of low level tasks at the bottom layers of the model and more complex tasks at the top layers of the model. This model achieves state-of-the-art results on a number of tasks, namely Named Entity Recognition, Entity Mention Detection and Relation Extraction without hand-engineered features or external NLP tools like syntactic parsers. The hierarchical training supervision induces a set of shared semantic representations at lower layers of the model. We show that as we move from the bottom to the top layers of the model, the hidden states of the layers tend to represent more complex semantic information.


title: Shoulder Strength Imbalances as Injury Risk in Handball

abstract: This study was conducted to analyze whether internal (IR) and external (ER) rotator shoulder muscles weakness and/or imbalance collected through a preseason assessment could be predictors of subsequent shoulder injury during a season in handball players. In preseason, 16 female elite handball players (HPG) and 14 healthy female nonathletes (CG) underwent isokinetic IR and ER strength test with use of a Con-Trex® dynamometer in a seated position with 45° shoulder abduction in scapular plane, at 60, 120 and 240°/s in concentric and at 60°/s in eccentric, for both sides. An imbalanced muscular strength profile was determined using -statistically selected cut-offs from CG values. For HPG, all newly incurred shoulder injuries were reported during the season. There were significant differences between HPG and CG only for dominant eccentric IR strength, ER/IR ratio at 240°/s and for IRecc/ERcon ratio. In HPG, IR and ER strength was higher, and ER/IR ratios lower for dominant than for nondominant side. The relative risk was 2.57 (95%CI: 1.60-3.54; P<0.05) if handball players had an imbalanced muscular strength profile. In youth female handball players IR and ER muscle strength increases on the dominant side without ER/IR imbalances; and higher injury risk was associated with imbalanced muscular strength profile.


title: Predicting Perceived Brand Personality with Social Media

abstract: Brand personality has been shown to affect a variety of user behaviors such as individual preferences and social interactions. Despite intensive research efforts in human personality assessment, little is known about brand personality and its relationship with social media. Leveraging the theory in marketing, we analyze how brand personality associates with its contributing factors embodied in social media. Based on the analysis of over 10K survey responses and a large corpus of social media data from 219 brands, we quantify the relative importance of factors driving brand personality. The brand personality model developed with social media data achieves predicted R 2 values as high as 0.67. We conclude by illustrating how modeling brand personality can help users find brands suiting their personal characteristics and help companies manage brand perceptions.


title: Parallel Programming Paradigms and Frameworks in Big Data Era

abstract: With Cloud Computing emerging as a promising new approach for ad-hoc parallel data processing, major companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. We have entered the Era of Big Data. The explosion and profusion of available data in a wide range of application domains rise up new challenges and opportunities in a plethora of disciplines--ranging from science and engineering to biology and business. One major challenge is how to take advantage of the unprecedented scale of data--typically of heterogeneous nature--in order to acquire further insights and knowledge for improving the quality of the offered services. To exploit this new resource, we need to scale up and scale out both our infrastructures and standard techniques. Our society is already data-rich, but the question remains whether or not we have the conceptual tools to handle it. In this paper we discuss and analyze opportunities and challenges for efficient parallel data processing. Big Data is the next frontier for innovation, competition, and productivity, and many solutions continue to appear, partly supported by the considerable enthusiasm around the MapReduce paradigm for large-scale data analysis. We review various parallel and distributed programming paradigms, analyzing how they fit into the Big Data era, and present modern emerging paradigms and frameworks. To better support practitioners interesting in this domain, we end with an analysis of on-going research challenges towards the truly fourth generation data-intensive science.


title: Real-time video stabilization without phantom movements for micro aerial vehicles

abstract: In recent times, micro aerial vehicles (MAVs) are becoming popular for several applications as rescue, surveillance, mapping, etc. Undesired motion between consecutive frames is a problem in a video recorded by MAVs. There are different approaches, applied in video post-processing, to solve this issue. However, there are only few algorithms able to be applied in real time. An additional and critical problem is the presence of false movements in the stabilized video. In this paper, we present a new approach of video stabilization which can be used in real time without generating false movements. Our proposal uses a combination of a low-pass filter and control action information to estimate the motion intention.


title: Business Process Modelling in Production Logistics: Complementary Use of BPMN and UML

abstract: This paper investigates the suitability and sufficincy of BPMN language in business processmodelling. Production logistics system is modeled at the highest domain level using case study in carcompo ...


title: Feature identification: a novel approach and a case study

abstract: Feature identification is a well-known technique to identify subsets of a program source code activated when exercising a functionality. Several approaches have been proposed to identify features. We present an approach to feature identification and comparison for large object-oriented multi-threaded programs using both static and dynamic data. We use processor emulation, knowledge filtering, and probabilistic ranking to overcome the difficulties of collecting dynamic data, i.e., imprecision and noise. We use model transformations to compare and to visualise identified features. We compare our approach with a naive approach and a concept analysis-based approach using a case study on a real-life large object-oriented multi-threaded program, Mozilla, to show the advantages of our approach. We also use the case study to compare processor emulation with statistical profiling.


title: Privacy-Preserving Keyword Search Over Encrypted Data in Cloud Computing

abstract: Search over encrypted data is a technique of great interest in the cloud computing era, because many believe that sensitive data has to be encrypted before outsourcing to the cloud servers in order to ensure user data privacy. Devising an efficient and secure search scheme over encrypted data involves techniques from multiple domains – information retrieval for index representation, algorithms for search efficiency, and proper design of cryptographic protocols to ensure the security and privacy of the overall system. This chapter provides a basic introduction to the problem definition, system model, and reviews the state-of-the-art mechanisms for implementing privacy-preserving keyword search over encrypted data. We also present one integrated solution, which hopefully offer more insights into this important problem.


title: Initial Conditions and Moment Restrictions in Dynamic Panel Data Models

abstract: In this paper we consider estimation of the autoregressive error components model. When the autoregressive parameter is moderately large and the number of time series observations is moderately small, the usual Generalised Methods of Moments (GMM) estimator obtained after first differencing has been found to be poorly behaved. Here we consider alternative linear estimators that are designed to improve the properties of the standard first-differenced GMM estimator. We consider two approaches to estimation. The first approach extends the model by adding the observed initial values as an extra regressor. This allows consistent estimates to be obtained by error-components GLS. This estimator is shown to be equivalent to the optimal GMM estimator for the normal homoskedastic error components model. The second approach considers a mild restriction on the initial condition process under which lagged differences in the dependent variable can be used to construct linear moment conditions in the levels equations. The complete set of moment conditions can then be exploited by a linear GMM estimator in a system of first-differenced and levels equations, rendering the non-linear moment conditions redundant for estimation. This estimator is strictly more efficient than non-linear GMM when the additional restriction is valid. Monte Carlo simulations are reported which demonstrate the dramatic improvement in performance of the proposed estimators compared to the usual first-differenced GMM estimator, especially for high values of the autoregressive parameter.


title: Iterative and incremental development of component-based software architectures

abstract: While the notion of components has had a major positive impact on the way software architectures are conceptualized and represented, they have had relatively little impact on the processes and procedures used to develop software systems. In terms of software development processes, use case-driven iterative and incremental development has become the predominant paradigm, which at best ignores components and at worse is even antagonistic to them. However, use-case driven, I&I development (as popularized by agile methods) and component-based development have opposite strengths and weaknesses. The former's techniques for risk mitigation and prioritization greatly reduce the risks associated with software engineering, but often give rise to suboptimal architectures that emerge in a semi-ad hoc fashion over time. In contrast, the latter gives rise to robust, optimized architectures, but to date has poor process support. In principle, therefore, there is a lot to be gained by fundamentally aligning the core principles of component-based and I&I development into a single, unified development approach. In this position paper we discuss the key issues involved in attaining such a synergy and suggest some core ideas for merging the principles of component-based and I&I development.


title: Automatic optimization of thread-coarsening for graphics processors

abstract: OpenCL has been designed to achieve functional portability across multi-core devices from different vendors. However, the lack of a single cross-target optimizing compiler severely limits performance portability of OpenCL programs. Programmers need to manually tune applications for each specific device, preventing effective portability. We target a compiler transformation specific for data-parallel languages: thread-coarsening and show it can improve performance across different GPU devices. We then address the problem of selecting the best value for the coarsening factor parameter, i.e., deciding how many threads to merge together. We experimentally show that this is a hard problem to solve: good configurations are difficult to find and naive coarsening in fact leads to substantial slowdowns. We propose a solution based on a machine-learning model that predicts the best coarsening factor using kernel-function static features. The model automatically specializes to the different architectures considered. We evaluate our approach on 17 benchmarks on four devices: two Nvidia GPUs and two different generations of AMD GPUs. Using our technique, we achieve speedups between 1.11× and 1.33× on average.


title: Dynamic Subarrays for Hybrid Precoding in Wideband mmWave MIMO Systems

abstract: Hybrid analog/digital precoding architectures can address the trade-off between achievable spectral efficiency and power consumption in large-scale MIMO systems. This makes it a promising candidate for millimeter wave systems, which require deploying large antenna arrays at both the transmitter and receiver to guarantee sufficient received signal power. Most prior work on hybrid precoding focused on narrowband channels and assumed fully-connected hybrid architectures. MmWave systems, though, are expected to be wideband with frequency selectivity. In this paper, a closed-form solution for fully-connected OFDM-based hybrid analog/digital precoding is developed for frequency selective mmWave systems. This solution is then extended to partially-connected but fixed architectures in which each RF chain is connected to a specific subset of the antennas. The derived solutions give insights into how the hybrid subarray structures should be designed. Based on them, a novel technique that dynamically constructs the hybrid subarrays based on the long-term channel characteristics is developed. Simulation results show that the proposed hybrid precoding solutions achieve spectral efficiencies close to that obtained with fully-digital architectures in wideband mmWave channels. Further, the results indicate that the developed dynamic subarray solution outperforms the fixed hybrid subarray structures in various system and channel conditions.


title: Efficient Estimation of Spatially Varying Subsurface Scattering Parameters for Relighting

abstract: We present an image-based technique to efficiently acquire spatially varying subsurface reflectance properties of a human face. The estimated properties can be used directly to render faces with spatially varying scattering, or can be used to estimate a robust average across the face. We demonstrate our technique with renderings of peoples’ faces under novel, spatially-varying illumination and provide comparisons with current techniques. Our captured data consists of images of the face from a single viewpoint under two small sets of projected images. The first set, a sequence of phase-shifted periodic stripe patterns, provides a per-pixel profile of how light scatters from adjacent locations. The second set of structured light patterns is used to obtain face geometry. We subtract the minimum of each profile to remove the contribution of interreflected light from the rest of the face, and then match the observed reflectance profiles to scattering properties predicted by a scattering model using a lookup table. From these properties we can generate images of the subsurface reflectance of the face under any incident illumination, including local lighting. The rendered images exhibit realistic subsurface transport, including light bleeding across shadow edges. Our method works more than an order of magnitude faster than current techniques for capturing subsurface scattering information, and makes it possible for the first time to capture these properties over an entire face.


title: IoT based solid waste management system: A conceptual approach with an architectural solution as a smart city application

abstract: The Internet of Things (IoT) is constantly evolving and is giving unique solutions to the everyday problems faced by man. “Smart City” is one such implementation aimed at improving the lifestyle of human beings. One of the major hurdles in most cities is its solid waste management, and effective management of the solid waste produced becomes an integral part of a smart city. This paper aims at providing an IoT based architectural solution to tackle the problems faced by the present solid waste management system. By providing a complete IoT based system, the process of tracking, collecting, and managing the solid waste can be easily automated and monitored efficiently. By taking the example of the solid waste management crisis of Bengaluru city, India, we have come up with the overall system architecture and protocol stack to give a IoT based solution to improve the reliability and efficiency of the system. By making use of sensors, we collect data from the garbage bins and send them to a gateway using LoRa technology. The data from various garbage bins are collected by the gateway and sent to the cloud over the Internet using the MQTT (Message Queue Telemetry Transport) protocol. The main advantage of the proposed system is the use of LoRa technology for data communication which enables long distance data transmission along with low power consumption as compared to Wi-Fi, Bluetooth or Zigbee.


title: Solving the Sparsity Problem in Recommender Systems Using Association Retrieval

abstract: Recommender systems are being widely applied in many fields, such as e-commerce etc, to provide products, services and information to potential customers. Collaborative filtering as the most successful approach, which recommends contents to the current customers mainly is based on the past transactions and feedback of the similar customer. However, it is difficult to distinguish the similar interests between customers because the sparsity problem is caused by the insufficient number of the transactions and feedback data, which confined the usability of the collaborative filtering. This paper proposed the direct similarity and the indirect similarity between users, and computed the similarity matrix through the relative distance between the user’s rating; using association retrieval technology to explore the transitive associations based on the user’s feedback data, realized a new collaborative filtering approach to alleviate the sparsity problem and improved the quality of the recommendation. In the end, we implemented experiment based on Movielens data set, the experiment results indicated that the proposed approach can effectively alleviate the sparsity problem, have good coverage rate and recommendation quality.


title: Functional electrical stimulation in rehabilitation engineering: a survey

abstract: Functional electrical stimulation (FES) is used widely in rehabilitation to restore motor functions for paralyzed patients. This paper makes a comprehensive review on current situation of FES. The content includes stimulation interface, applications, FES control, challenges and prospect of FES. Especially, combination FES with electromyography (EMG) and brain computer interface (BCI) is surveyed.


title: Pragmatic OAM with polarization multiplexing transmission for future 5G ultra-high capacity radio

abstract: A new orbital angular momentum (OAM) mode and polarization multiplexing transmission system in the millimeter-wave range is proposed for future Ultra high capacity backhaul/fronthaul radio. Our theoretical analysis reveals that all the OAM modes can be used with polarization transmission while maintaining high degree of orthogonality. Furthermore, we show that links in the order of 100m can be achieved using the OAM lower-order modes in the millimeter-wave bands. A combination of an antenna array and a Butler matrix can encode and decode multiple OAM modes. We achieved 7-m transmission with two-modes multiplexing with a prototype of our system at 5.2 GHz. This translates to over 100m when applied to the mmwave bands. The experimental results agree well with the theoretical analysis, and are verifies our estimation of available link distance.


3D Parallel Unstructured Mesh Generation


title: Tensor Ring Decomposition

abstract: Tensor networks have in recent years emerged as the powerful tools for solving the large-scale optimization problems. One of the most popular tensor network is tensor train (TT) decomposition that acts as the building blocks for the complicated tensor networks. However, the TT decomposition highly depends on permutations of tensor dimensions, due to its strictly sequential multilinear products over latent cores, which leads to difficulties in finding the optimal TT representation. In this paper, we introduce a fundamental tensor decomposition model to represent a large dimensional tensor by a circular multilinear products over a sequence of low dimensional cores, which can be graphically interpreted as a cyclic interconnection of 3rd-order tensors, and thus termed as tensor ring (TR) decomposition. The key advantage of TR model is the circular dimensional permutation invariance which is gained by employing the trace operation and treating the latent cores equivalently. TR model can be viewed as a linear combination of TT decompositions, thus obtaining the powerful and generalized representation abilities. For optimization of latent cores, we present four different algorithms based on the sequential SVDs, ALS scheme, and block-wise ALS techniques. Furthermore, the mathematical properties of TR model are investigated, which shows that the basic multilinear algebra can be performed efficiently by using TR representaions and the classical tensor decompositions can be conveniently transformed into the TR representation. Finally, the experiments on both synthetic signals and real-world datasets were conducted to evaluate the performance of different algorithms.


title: Probabilistic routing in intermittently connected networks

abstract: We consider the problem of routing in intermittently connected networks. In such networks there is no guarantee that a fully connected path between source and destination exist at any time, rendering traditional routing protocols unable to deliver messages between hosts. We propose a probabilistic routing protocol for such networks.


title: Centroidal dynamics of a humanoid robot

abstract: The center of mass (CoM) of a humanoid robot occupies a special place in its dynamics. As the location of its effective total mass, and consequently, the point of resultant action of gravity, the CoM is also the point where the robot's aggregate linear momentum and angular momentum are naturally defined. The overarching purpose of this paper is to refocus our attention to centroidal dynamics: the dynamics of a humanoid robot projected at its CoM. In this paper we specifically study the properties, structure and computation schemes for the centroidal momentum matrix (CMM), which projects the generalized velocities of a humanoid robot to its spatial centroidal momentum. Through a transformation diagram we graphically show the relationship between this matrix and the well-known joint-space inertia matrix. We also introduce the new concept of "average spatial velocity" of the humanoid that encompasses both linear and angular components and results in a novel decomposition of the kinetic energy. Further, we develop a very efficient $$O(N)$$ O ( N ) algorithm, expressed in a compact form using spatial notation, for computing the CMM, centroidal momentum, centroidal inertia, and average spatial velocity. Finally, as a practical use of centroidal dynamics we show that a momentum-based balance controller that directly employs the CMM can significantly reduce unnecessary trunk bending during balance maintenance against external disturbance.


title: Context-aware Sentiment Word Identification: sentiword2vec

abstract: Traditional sentiment analysis often uses sentiment dictionary to extract sentiment information in text and classify documents. However, emerging informal words and phrases in user generated content call for analysis aware to the context. Usually, they have special meanings in a particular context. Because of its great performance in representing inter-word relation, we use sentiment word vectors to identify the special words. Based on the distributed language model word2vec, in this paper we represent a novel method about sentiment representation of word under particular context, to be detailed, to identify the words with abnormal sentiment polarity in long answers. Result shows the improved model shows better performance in representing the words with special meaning, while keep doing well in representing special idiomatic pattern. Finally, we will discuss the meaning of vectors representing in the field of sentiment, which may be different from general object-based conditions.


title: Timing Attacks on Implementations of Diffie-Hellman, RSA, DSS, and Other Systems

abstract: By carefully measuring the amount of time required tm perform private key operalions, attackers may be able to find fixed Diffie-Hellman exponents, factor RSA keys, and break other cryptosystems. Against, a valnerable system, the attack is computationally inexpensive and often requires only known ciphertext. Actual systems are potentially at risk, including cryptographic tokens, network-based cryptosystems, and other applications where attackers can make reasonably accurate timing measurements. Techniques for preventing the attack for RSA and Diffie-Hellman are presented. Some cryptosystems will need to be revised to protect against the attack, and new protocols and algorithms may need to incorporate measures to prevenl timing attacks.


Extrinsic Calibration of Camera Networks Using a Sphere


title: People on drugs: credibility of user statements in health communities

abstract: Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information.


title: Concept Drift Detection Through Resampling

abstract: Detecting changes in data-streams is an important part of enhancing learning quality in dynamic environments. We devise a procedure for detecting concept drifts in data-streams that relies on analyzing the empirical loss of learning algorithms. Our method is based on obtaining statistics from the loss distribution by reusing the data multiple times via resampling. We present theoretical guarantees for the proposed procedure based on the stability of the underlying learning algorithms. Experimental results show that the method has high recall and precision, and performs well in the presence of noise.


title: Data-driven chance constrained stochastic program

abstract: In this paper, we study data-driven chance constrained stochastic programs, or more specifically, stochastic programs with distributionally robust chance constraints (DCCs) in a data-driven setting to provide robust solutions for the classical chance constrained stochastic program facing ambiguous probability distributions of random parameters. We consider a family of density-based confidence sets based on a general $$\phi $$ź-divergence measure, and formulate DCC from the perspective of robust feasibility by allowing the ambiguous distribution to run adversely within its confidence set. We derive an equivalent reformulation for DCC and show that it is equivalent to a classical chance constraint with a perturbed risk level. We also show how to evaluate the perturbed risk level by using a bisection line search algorithm for general $$\phi $$ź-divergence measures. In several special cases, our results can be strengthened such that we can derive closed-form expressions for the perturbed risk levels. In addition, we show that the conservatism of DCC vanishes as the size of historical data goes to infinity. Furthermore, we analyze the relationship between the conservatism of DCC and the size of historical data, which can help indicate the value of data. Finally, we conduct extensive computational experiments to test the performance of the proposed DCC model and compare various $$\phi $$ź-divergence measures based on a capacitated lot-sizing problem with a quality-of-service requirement.


title: A dual band printed dipole antenna with spiral structure for WLAN application

abstract: In this letter, the design procedure and electrical performance of a dual band (2.4/5.8GHz) printed dipole antenna using spiral structure are proposed and investigated. For the first time, a dual band printed dipole antenna with spiral configuration is proposed. In addition, a matching method by adjusting the transmission line width, and a new bandwidth broadening method varying the distance between the top and bottom spirals are reported. The operating frequencies of the proposed antenna are 2.4GHz and 5.8GHz which cover WLAN system. The proposed antenna achieves a good matching using tapered transmission lines for the top and bottom spirals. The desired resonant frequencies are obtained by adjusting the number of turns of the spirals. The bandwidth is optimized by varying the distance between the top and bottom spirals. A relative position of the bottom spiral plays an important role in achieving a bandwidth in terms of 10-dB return loss.


title: Hop-by-Hop Message Authenticationand Source Privacy in WirelessSensor Networks

abstract: Message authentication is one of the most effective ways to thwart unauthorized and corrupted messages from being forwarded in wireless sensor networks (WSNs). For this reason, many message authentication schemes have been developed, based on either symmetric-key cryptosystems or public-key cryptosystems. Most of them, however, have the limitations of high computational and communication overhead in addition to lack of scalability and resilience to node compromise attacks. To address these issues, a polynomial-based scheme was recently introduced. However, this scheme and its extensions all have the weakness of a built-in threshold determined by the degree of the polynomial: when the number of messages transmitted is larger than this threshold, the adversary can fully recover the polynomial. In this paper, we propose a scalable authentication scheme based on elliptic curve cryptography (ECC). While enabling intermediate nodes authentication, our proposed scheme allows any node to transmit an unlimited number of messages without suffering the threshold problem. In addition, our scheme can also provide message source privacy. Both theoretical analysis and simulation results demonstrate that our proposed scheme is more efficient than the polynomial-based approach in terms of computational and communication overhead under comparable security levels while providing message source privacy.


title: Spectre Attacks: Exploiting Speculative Execution

abstract: Modern processors use branch prediction and speculative execution to maximize performance. For example, if the destination of a branch depends on a memory value that is in the process of being read, CPUs will try to guess the destination and attempt to execute ahead. When the memory value finally arrives, the CPU either discards or commits the speculative computation. Speculative logic is unfaithful in how it executes, can access the victim's memory and registers, and can perform operations with measurable side effects. Spectre attacks involve inducing a victim to speculatively perform operations that would not occur during correct program execution and which leak the victim's confidential information via a side channel to the adversary. This paper describes practical attacks that combine methodology from side channel attacks, fault attacks, and return-oriented programming that can read arbitrary memory from the victim's process. More broadly, the paper shows that speculative execution implementations violate the security assumptions underpinning numerous software security mechanisms, including operating system process separation, containerization, just-in-time (JIT) compilation, and countermeasures to cache timing and side-channel attacks. These attacks represent a serious threat to actual systems since vulnerable speculative execution capabilities are found in microprocessors from Intel, AMD, and ARM that are used in billions of devices. While makeshift processor-specific countermeasures are possible in some cases, sound solutions will require fixes to processor designs as well as updates to instruction set architectures (ISAs) to give hardware architects and software developers a common understanding as to what computation state CPU implementations are (and are not) permitted to leak.


title: A Survey of Homomorphic Encryption for Nonspecialists

abstract: Processing encrypted signals requires special properties of the underlying encryption scheme. A possible choice is the use of homomorphic encryption. In this paper, we propose a selection of the most important available solutions, discussing their properties and limitations.


title: Data exchange toward PLC programming and virtual commissioning: Is AutomationML an appropriate data exchange format?

abstract: Lossless data exchange is one of the recent investigated paths for engineering process improvement with respect to engineering process quality and efficiency. AutomationML is one data exchange format in a broad range of modern XML based data formats developed for the improvement of engineering processes of manufacturing systems. In this paper the applicability of AutomationML is proven for transferring PLC programming and virtual commissioning relevant information within the engineering process.


TED Talks as an Emergent Genre


An analysis of acceptance policies for blockchain transactions


title: Depth-Based Hand Pose Estimation: Data, Methods, and Challenges

abstract: Hand pose estimation has matured rapidly in recent years. The introduction of commodity depth sensors and a multitude of practical applications have spurred new advances. We provide an extensive analysis of the state-of-the-art, focusing on hand pose estimation from a single depth frame. To do so, we have implemented a considerable number of systems, and will release all software and evaluation code. We summarize important conclusions here: (1) Pose estimation appears roughly solved for scenes with isolated hands. However, methods still struggle to analyze cluttered scenes where hands may be interacting with nearby objects and surfaces. To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes. (2) Many methods evaluate themselves with disparate criteria, making comparisons difficult. We define a consistent evaluation criteria, rigorously motivated by human experiments. (3) We introduce a simple nearest-neighbor baseline that outperforms most existing systems. This implies that most systems do not generalize beyond their training sets. This also reinforces the under-appreciated point that training data is as important as the model itself. We conclude with directions for future progress.


title: Learning to Accept New Classes without Training

abstract: Classic supervised learning makes the closed-world assumption, meaning that classes seen in testing must have been seen in training. However, in the dynamic world, new or unseen class examples may appear constantly. A model working in such an environment must be able to reject unseen classes (not seen or used in training). If enough data is collected for the unseen classes, the system should incrementally learn to accept/classify them. This learning paradigm is called open-world learning (OWL). Existing OWL methods all need some form of re-training to accept or include the new classes in the overall model. In this paper, we propose a meta-learning approach to the problem. Its key novelty is that it only needs to train a meta-classifier, which can then continually accept new classes when they have enough labeled data for the meta-classifier to use, and also detect/reject future unseen classes. No re-training of the meta-classifier or a new overall classifier covering all old and new classes is needed. In testing, the method only uses the examples of the seen classes (including the newly added classes) on-the-fly for classification and rejection. Experimental results demonstrate the effectiveness of the new approach.


A Quick Gbest Guided Artificial Bee Colony Algorithm for Stock Market Prices Prediction


title: Cost Effective Surface Disruption Detection System for Paved and Unpaved Roads

abstract: Roads are exposed to road surface disruptions (RSD) because of erosion, poor water drainage, rain, and soil quality. A delay in maintenance results in severe road damage that blocks the traffic for several days. Up to now, RSD detection on unpaved roads is done manually or reported by drivers. Conversely, different techniques have already been proposed for paved roads, such as vibration, image, and laser scanning. Unfortunately, the methods proposed for paved roads are not directly applicable for unpaved roads due to constraints and properties of RSD and pothole on unpaved roads. Therefore, this paper proposes a novel and low-cost method of detecting RSD based on ultrasonic sensors. The suggested model uses, as input data, relative distances collected by ultrasonic sensor beams, compute the approximate potholes and bumps on surfaces and outputs 2-D surface state map. This innovative and applicable approach has been tested on unpaved and paved roads and showed an accuracy of 94% regarding pothole characteristics (size, surface, and depth) on both paved and unpaved roads. A pothole detection rate of 62% was achieved on the paved road. Furthermore, the implemented algorithm is adaptable through a number of thresholds along with the desirable surface-sensor distance, the required RSD, the sensor’s velocity, and the distance between measurements. The results showed that the system could detect RSD and give valuable information which can help the maintenance team to plan for repair.


Nasal Duplication Combined with Cleft Lip and Palate: Surgical Correction and Long-Term Follow-Up


title: Image Clustering Using Local Discriminant Models and Global Integration

abstract: In this paper, we propose a new image clustering algorithm, referred to as clustering using local discriminant models and global integration (LDMGI). To deal with the data points sampled from a nonlinear manifold, for each data point, we construct a local clique comprising this data point and its neighboring data points. Inspired by the Fisher criterion, we use a local discriminant model for each local clique to evaluate the clustering performance of samples within the local clique. To obtain the clustering result, we further propose a unified objective function to globally integrate the local models of all the local cliques. With the unified objective function, spectral relaxation and spectral rotation are used to obtain the binary cluster indicator matrix for all the samples. We show that LDMGI shares a similar objective function with the spectral clustering (SC) algorithms, e.g., normalized cut (NCut). In contrast to NCut in which the Laplacian matrix is directly calculated based upon a Gaussian function, a new Laplacian matrix is learnt in LDMGI by exploiting both manifold structure and local discriminant information. We also prove that K-means and discriminative K-means (DisKmeans) are both special cases of LDMGI. Extensive experiments on several benchmark image datasets demonstrate the effectiveness of LDMGI. We observe in the experiments that LDMGI is more robust to algorithmic parameter, when compared with NCut. Thus, LDMGI is more appealing for the real image clustering applications in which the ground truth is generally not available for tuning algorithmic parameters.


Sparse Sampling for Real-time Ray Tracing


title: Testing the vulnerability and scar models of self-esteem and depressive symptoms from adolescence to middle adulthood and across generations.

abstract: The vulnerability model states that low self-esteem functions as a predictor for the development of depressive symptoms whereas the scar model assumes that these symptoms leave scars in individuals resulting in lower self-esteem. Both models have received empirical support, however, they have only been tested within individuals and not across generations (i.e., between family members). Thus, we tested the scope of these competing models by (a) investigating whether the effects hold from adolescence to middle adulthood (long-term vulnerability and scar effects), (b) whether the effects hold across generations (intergenerational vulnerability and scar effects), and (c) whether intergenerational effects are mediated by parental self-esteem and depressive symptoms and parent–child discord. We used longitudinal data from adolescence to middle adulthood (N = 1,359) and from Generation 1 adolescents (G1) to Generation 2 adolescents (G2) (N = 572 parent–child pairs). Results from latent cross-lagged regression analyses demonstrated that both adolescent self-esteem and depressive symptoms were prospectively related to adult self-esteem and depressive symptoms 3 decades later. That is, both the vulnerability and scar models are valid over decades with stronger effects for the vulnerability model. Across generations, we found a substantial direct transmission effect from G1 to G2 adolescent depressive symptoms but no evidence for the proposed intergenerational vulnerability and scar effect or for any of the proposed mediating mechanisms.


title: Things of Interest Recommendation by Leveraging Heterogeneous Relations in the Internet of Things

abstract: The emerging Internet of Things (IoT) bridges the gap between the physical and the digital worlds, which enables a deeper understanding of user preferences and behaviors. The rich interactions and relations between users and things call for effective and efficient recommendation approaches to better meet users’ interests and needs. In this article, we focus on the problem of things recommendation in IoT, which is important for many applications such as e-Commerce and health care. We discuss the new properties of recommending things of interest in IoT, and propose a unified probabilistic factor based framework by fusing relations across heterogeneous entities of IoT, for example, user-thing relations, user-user relations, and thing-thing relations, to make more accurate recommendations. Specifically, we develop a hypergraph to model things’ spatiotemporal correlations, on top of which implicit things correlations can be generated. We have built an IoT testbed to validate our approach and the experimental results demonstrate its feasibility and effectiveness.


title: Foglight: Visible Light-Enabled Indoor Localization System for Low-Power IoT Devices

abstract: Advances in small and low power electronics have created new opportunities for the Internet of Things (IoT), leading to an explosion of physical objects being connected to the Internet. However, there still lacks an indoor localization solution that can answer the needs of various location-based IoT applications with desired simplicity, robustness, accuracy, and responsiveness. We introduce Foglight, a visible light enabled indoor localization system for IoT devices that relies on unique spatial encoding produced when mechanical mirrors inside a projector are flipped based on gray-coded binary images. Foglight employs simple off-the-shelf light sensors that can be easily coupled with existing IoT devices—such as thermometers, gas meters, or light switches—making their location discoverable. Our sensor unit is computation efficient; it can perform high-accuracy localization with minimum signal processing overhead, allowing any low-power IoT device on which it rests to be able to locate itself. Additionally, results from our evaluation reveal that Foglight can locate a target device with an average accuracy of 1.7 mm and average refresh rate of 84 Hz with minimal latency, 31.46 ms on Wi-Fi and 23.2 ms on serial communication. Two example applications are developed to demonstrate possible scenarios as proof of concept. We also discuss limitations, how they could be overcome, and propose next steps.


title: Deep discriminative manifold learning

abstract: This paper presents a new non-linear dimensionality reduction with stochastic neighbor embedding. A deep neural network is developed for discriminative manifold learning where the class information in transformed low-dimensional space is preserved. Importantly, the objective function for deep manifold learning is formed as the Kullback-Leibler divergence between the probability measures of the labeled samples in high-dimensional and low-dimensional spaces. Different from conventional methods, the derived objective does not require the empirically-tuned parameter. This objective is optimized to attractive those samples from the same class to be close together and simultaneously impose those samples from different classes to be far apart. In the experiments on image and audio tasks, we illustrate the effectiveness of the proposed discriminative manifold learning in terms of visualization and classification performance.


title: A planar Coaxial Collinear antenna with rectangular coaxial strip

abstract: A novel planar Coaxial Collinear (COCO) antenna with rectangular coaxial strip is presented. The planar COCO antenna is fed by microstrip and comprises several radiation sections of rectangular coaxial strips which are cross-linked one by one. A prototype has been designed and fabricated to cover the band of 2.4-2.48 GHz. The simulated and measured results show that the fractional -10 dB bandwidth of the prototype is 24.5%. With this band, the prototype possesses an omni-directional radiation pattern and achieves a gain of over 4 dBi in the direction of θ = 50°. The proposed antenna has characteristics of planar shape, compact size, wideband and easy fabrication. It is suitable for base station application of WiFi, RFID et al.


title: Comparison of Auditory Brainstem Response Results in Normal-Hearing Patients With and Without Tinnitus

abstract: Objective To evaluate electrophysiologically the auditory nerve and the auditory brainstem function of patients with tinnitus and normal-hearing thresholds using the auditory brainstem response (ABR). Design Case-control study. Setting Ambulatory section of the Department of Otolaryngology, Hospital de Base de Brasilia. Patients Thirty-seven individuals with tinnitus and 38 without tinnitus, with ages ranging from 20 to 45 years and pure-tone thresholds of 25 dB or better at frequencies between 500 and 8000 Hz. Main Outcome Measures We compared the latencies of waves I, III, and V; the interpeak intervals I-III, III-V, and I-V; the interaural latency difference (wave V); and the V/I amplitude ratio between the 2 groups. Results Among the 37 patients in the study group, abnormal results were found in 16 (43%) in at least 1 of the 8 parameters evaluated. When we analyzed the latencies, although the values were on average in the normal range used in the present study, the tinnitus group presented a significant prolongation of the latencies of waves I, III, and V when compared with the control group. Furthermore, we found the interpeak I-III, III-V, and I-V values to be within the normal limits, but the interpeak III-V value was significantly ( P = .003) enlarged in the study group compared with the control group. The V/I amplitude ratio found in the tinnitus group was within normal limits; however, a significant ( P = .004) difference was found when the 2 groups were compared. The averages of the interaural latency difference (wave V) did not show significant differences in relation to the control group. Conclusions We conclude that, although the averages obtained in several analyzed parameters were within normal limits, the ABR results from the patients with and without tinnitus and normal hearing are different, suggesting that ABR might contribute to the workup of these patients. Our data show that there are changes in the central pathways in the study group. The meaning of these changes must be further investigated.


title: DeepTox: Toxicity Prediction using Deep Learning

abstract: The Tox21 Data Challenge has been the largest effort of the scientific community to compare computational methods for toxicity prediction. This challenge comprised 12,000 environmental chemicals and drugs which were measured for 12 different toxic effects by specifically designed assays. We participated in this challenge to assess the performance of Deep Learning in computational toxicity prediction. Deep Learning has already revolutionized image processing, speech recognition, and language understanding but has not yet been applied to computational toxicity. Deep Learning is founded on novel algorithms and architectures for artificial neural networks together with the recent availability of very fast computers and massive datasets. It discovers multiple levels of distributed representations of the input, with higher levels representing more abstract concepts. We hypothesized that the construction of a hierarchy of chemical features gives Deep Learning the edge over other toxicity prediction methods. Furthermore, Deep Learning naturally enables multi-task learning, that is, learning of all toxic effects in one neural network and thereby learning of highly informative chemical features. In order to utilize Deep Learning for toxicity prediction, we have developed the DeepTox pipeline. First, DeepTox normalizes the chemical representations of the compounds. Then it computes a large number of chemical descriptors that are used as input to machine learning methods. In its next step, DeepTox trains models, evaluates them, and combines the best of them to ensembles. Finally, DeepTox predicts the toxicity of new compounds. In the Tox21 Data Challenge, DeepTox had the highest performance of all computational methods winning the grand challenge, the nuclear receptor panel, the stress response panel, and six single assays (teams ``Bioinf@JKU''). We found that Deep Learning excelled in toxicity prediction and outperformed many other computational approaches like naive Bayes, support vector machines, and random forests.


title: BValidating the independent components of neuroimaging time series via clustering and visualization

abstract: Recently, independent component analysis (ICA) has been widely used in the analysis of brain imaging data. An important problem with most ICA algorithms is, however, that they are stochastic; that is, their results may be somewhat different in different runs of the algorithm. Thus, the outputs of a single run of an ICA algorithm should be interpreted with some reserve, and further analysis of the algorithmic reliability of the components is needed. Moreover, as with any statistical method, the results are affected by the random sampling of the data, and some analysis of the statistical significance or reliability should be done as well. Here we present a method for assessing both the algorithmic and statistical reliability of estimated independent components. The method is based on running the ICA algorithm many times with slightly different conditions and visualizing the clustering structure of the obtained components in the signal space. In experiments with magnetoencephalographic (MEG) and functional magnetic resonance imaging (fMRI) data, the method was able to show that expected components are reliable; furthermore, it pointed out components whose interpretation was not obvious but whose reliability should incite the experimenter to investigate the underlying technical or physical phenomena. The method is implemented in a software package called Icasso.


title: Color Constancy by GANs: An Experimental Survey

abstract: In this paper, we formulate the color constancy task as an image-to-image translation problem using GANs. By conducting a large set of experiments on different datasets, an experimental survey is provided on the use of different types of GANs to solve for color constancy i.e. CC-GANs (Color Constancy GANs). Based on the experimental review, recommendations are given for the design of CC-GAN architectures based on different criteria, circumstances and datasets.


title: A New Feature Pyramid Network For Road Scene Segmentation

abstract: Road scene segmentation is of great significance in intelligent transportation system for different applications such as autonomous driving and semantic map building. Despite great progress in this field with the deep learning methods, there are still many difficulties such as robust segmentation of small objects and same type of objects with different sizes in different scenes. In this paper, we propose a new pyramid architecture for scene segmentation, which is a top-down architecture with lateral connections for multi-scale semantic feature maps building, and sufficiently incorporate the momentous global scenery prior. Besides, we also propose a novel training method, which combines the re-sampling, pixel-wise cost learning and transfer learning together, to deal with the imbalance problem. Experimental results on KITTI and Cityscapes dataset demonstrate effectiveness of the proposed method.


title: Low frequency analysis of resting-state fMRI

abstract: Resting state functional magnetic resonance imaging (rsfMRI) is a relatively new and an powerful method for evaluating regional interactions that occur when a participant is not performing an explicit task. Because of sensitiveness to time delay and length of time courses, respectively, traditional methods of correlation and coherence based on region of interest are not very suitable for rsfMRI. In this paper, we analyzed low frequency property of rsfMRI data. Firstly, we calculated the low frequency of resting-state Blood-Oxygenation-Level-Dependent(BOLD) signal on every voxel using Welch method, based on which we analyzed the statistical distribution cross subjects and voxels. The low frequency feature is proved by a series of experiments to be more robust and consistent comparing with coorelation in time domain and coherency in frequency domain. We found that the property of low frequency statistical distribution is dominant on narrow band, which is around 0.0137HZ.


title: DDoS attacks on the internet of things and their prevention methods

abstract: The Internet of Things (IoT) vulnerabilities provides an ideal target for botnets, making them a major contributor in the increased number of Distributed Denial of Service (DDoS) attacks. The increase in DDoS attacks has made it important to address the consequences it implies on the IoT industry being one of the major causes. The aim of this paper is to provide an analysis of the attempts to prevent DDoS attacks, mainly at a network level. The sensibility of these solutions is extracted from their impact in resolving IoT vulnerabilities. It is evident from this review that there is no perfect solution yet for IoT security, this field still has many opportunities for research and development.


title: A New Control Strategy for Power Quality Improvement Using MPPT from Hybrid PV-Wind Connected to the Grid

abstract: In this paper, robust and simple control strategy is presented for power quality improvement of a grid connected renewable energy based the maximum power point tracking (MPPT) from solar photovoltaic (PV) and wind based permanent magnet synchronous generator (PMSG) sources. A combination of MPPT control and active power control algorithms are applied to two level inverters for interfacing between PV-PMSG energy sources and the grid. For MPPT, dc-dc boost and AC-DC converters are used for PV system and wind turbine, respectively. Both these renewable sources are connected at the voltage source converter (VSC) to feed the grid. VSC is controlled for PQ improvement and AC grid voltage regulation. The developed control algorithms for a grid connected VSC based on modified synchronous reference frame (SRF), its use for desired power quality, minimize the total harmonic distortion (THD) and quick dc-link voltage regulation. The performance, effectiveness and the robustness of the hybrid system are validated using Matlab/Simulink. The proposed composite controller ensures rapid and desired control action without any adjustment during varying climate conditions and load variations.


title: Energy Efficient Sink Placement in Wireless Sensor Networks by Brain Storm Optimization Algorithm

abstract: Wireless sensor networks represent one of the most promising technologies whose use has significantly increased in the past years. They are used in various applications such as health care monitoring, surveillance and monitoring in agriculture, industrial monitoring, habitat and underwater monitoring, etc. Deployment of the wireless sensor networks introduces number of hard optimization problems. Placement of the elements such as sensors, gateways, sinks and base stations, depend on different conditions and constraints such as signal propagation, distance, energy preservation, reliability. In this paper, we propose a method based on brain storm optimization algorithm for placing multiple sinks in a network consisting of regular sensors and gateways with higher battery capacity. Regular sensor nodes are statically organized in clusters around gateways considering not only distance but also energy efficiency. Gateways communicate with sinks for which optimal positions need to be determined. Location problems are in general difficult and in this case requirement of minimizing the energy additionally complicates the problem. The simulation results report estimation of the network life time. Obtained results have shown that our proposed method outperformed particle swarm optimization based method from literature in terms of mentioned metrics.


title: To appear in the Internatonal Handbook on Information Technology in Education Comparative Analysis of Policies for ICT in Education

abstract: National policies and programs can be an important tool for the realization of ICTs promise in education and some of their major components are the focus of this chapter. The chapter presents a framework of alternative rationales and program components that can be used by researchers and policymakers to analyze, formulate, revise, and compare national ICT efforts. The framework consists of four alternative policy rationales-or “strategic” policy positions-and five components of ICT programs, or “operational” policies. Strategic and operational policies of various countries are used to illustrate these rationales and components. The chapter concludes with recommendations that countries can use when formulating or updating their educational ICT plans.


title: Parenting and childhood anxiety: theory, empirical findings, and future directions

abstract: Theories of anxiety development suggest that parental acceptance, control, and modeling of anxious behaviors are associated with children's manifestations of anxiety. This paper reviews research published in the past decade on the relation between parenting and childhood anxiety. Observed parental control during parent-child interactions was consistently linked with shyness and child anxiety disorders across studies. Mixed support for the role of parental acceptance and modeling of anxious behaviors was found in observational studies. However, there was little evidence supporting the contention that self-reported parenting style was related to children's trait anxiety. Because of limitations associated with past research, inferences about the direction of effects linking parenting and child anxiety cannot be made. A conceptual framework based on recent models of anxiety development (e.g., Vasey & Dadds, 2001) is presented to aid in the interpretation of extant research findings and to provide suggestions for future research and theory development. Improved methodological designs are proposed, including the use of repeated-measure and experimental designs for examining the direction of effects.


title: Acute symptomatic calcific discitis in adults: a case report and review of literature

abstract: Symptomatic calcific discitis has been reported in the paediatric population but is a rare entity in adults with only eight cases reported in the English literature. We present a case of adult calcific discitis presenting with acute onset back pain. Radiographs and CT demonstrated central T11-T12 disc calcification with diffuse marrow oedema on subsequent MRI. The patient was referred to our spinal oncology unit due to the extensive marrow oedema as a possible underlying primary bone tumour. Review of the CT confirmed an end-plate defect with herniated calcific nucleus pulposus with no underlying bone lesion. Features were in keeping with acute calcific discitis. The patient was treated symptomatically and made an uneventful recovery. We discuss the characteristic imaging features seen on radiograph, CT and MRI and review the current literature. Calcific discitis is a self-limiting pathology requiring symptomatic management only. Radiologists need to be aware of this rare entity as it can occur in adults and may be mistaken for a more sinister pathology such as infective discitis or a bone tumour and lead to further unnecessary imaging or invasive procedures.


title: Classification methods in EEG based motor imagery BCI systems

abstract: The structure and functioning of the human brain has always attracted the attention of scientists. In the past 10-20 years, one of the popular areas of study related to the human brain is the Brain Computer Interface (BCI) systems. Different brain monitoring techniques such as EEG, fNIRS, MEG can be used to observe human brain activities in BCI systems. In addition to the differences in monitoring methods in BCI systems, the focus of system operation may also change. In some BCI systems, it may be preferable to focus on Motor Imagery in another system, while the focus is on the P300 signals in EEG data. This requires a differentiation between the systems during the operational phases of the BCI system. One of the most significant points of system differentiation is the classification stage of the BCI system. In this study, a compilation of classification methods commonly used in Brain Computer Interface systems based on EEG based Motor Imagery will be carried out. This study will provide researchers with information about the methods that can be used in their own systems for classification in Brain Computer Interface systems based on EEG based Motor Imagery.


title: Is There a Future for AI Without Representation?

abstract: This paper investigates the prospects of Rodney Brooks' proposal for AI without representation. It turns out that the supposedly characteristic features of "new AI" (embodiment, situatedness, absence of reasoning, and absence of representation) are all present in conventional systems: "New AI" is just like old AI. Brooks proposal boils down to the architectural rejection of central control in intelligent agents--Which, however, turns out to be crucial. Some of more recent cognitive science suggests that we might do well to dispose of the image of intelligent agents as central representation processors. If this paradigm shift is achieved, Brooks' proposal for cognition without representation appears promising for full-blown intelligent agents--Though not for conscious agents.


title: Deringing cartoons by image analogies

abstract: In this article, we propose a novel method to reduce ringing artifacts in BDCT-encoded cartoon images using image analogies. The quantization procedure of BDCT compression (such as JPEG and MPEG) introduces annoying visual artifacts. Our main focus is on the removal of ringing artifacts that is seldom addressed by existing methods. In the proposed method, the contaminated image is modeled as a Markov random field (MRF). We “learn” the behavior of contamination by extracting massive numbers of artifact patterns from a training set, and organizing them using tree-structured vector quantization (TSVQ). Instead of postfiltering the input contaminated image, we synthesize an artifact-reduced image. Our method is noniterative and hence, can remove artifacts within a very short period of time. We show that substantial improvement is achieved using the proposed method in terms of visual quality and statistics.


title: DDSC: A Density Differentiated Spatial Clustering Technique

abstract: Finding clusters with widely differing sizes, shapes and densities in presence of noise and outliers is a challenging job. The DBSCAN is a versatile clustering algorithm that can find clusters with differing sizes and shapes in databases containing noise and outliers. But it cannot find clusters based on difference in densities. We extend the DBSCAN algorithm so that it can also detect clusters that differ in densities. Local densities within a cluster are reasonably homogeneous. Adjacent regions are separated into different clusters if there is significant change in densities. Thus the algorithm attempts to find density based natural clusters that may not be separated by any sparse region. Computational complexity of the algorithm is O(n log n).


title: A Fade Level-Based Spatial Model for Radio Tomographic Imaging

abstract: RSS-based device-free localization (DFL) monitors changes in the received signal strength (RSS) measured by a network of static wireless nodes to locate and track people without requiring them to carry or wear any electronic device. Current models assume that the spatial impact area , i.e., the area in which a person affects a link's RSS, has constant size. This paper shows that the spatial impact area varies considerably for each link. Data from extensive experiments are used to derive a spatial weight model that is a function of the fade level , i.e., a measure of whether a link is experiencing destructive or constructive multipath interference, and of the sign of RSS change. In addition, a measurement model is proposed which calculates for each RSS measurement the probability of a person being located inside the derived spatial impact area. An online radio tomographic imaging (RTI) system is described which uses channel diversity and the presented models. Experiments in an open indoor environment, in a typical one-bedroom apartment and in a through-wall scenario are conducted to determine the performance of the proposed system. We demonstrate that the new system is capable of localizing and tracking a person with high accuracy ( \(\boldsymbol {\leq 0.30}\) m) in all the environments, without the need to change the model parameters.


title: What is a moment? Transient synchrony as a collective mechanism for spatiotemporal integration

abstract: A previous paper described a network of simple integrate-and-fire neurons that contained output neurons selective for specific spa- tiotemporal patterns of inputs; only experimental results were described. We now present the principles behind the operation of this network and discuss how these principles point to a general class of computational operations that can be carried out easily and naturally by networks of spiking neurons. Transient synchrony of the action potentials of a group of neurons is used to signal ''recognition'' of a space-time pattern across the inputs of those neurons. Appropriate synaptic coupling produces synchrony when the inputs to these neurons are nearly equal, leaving the neurons unsynchronized or only weakly synchronized for other input cir- cumstances. When the input to this system comes from timed past events represented by decaying delay activity, the pattern of synaptic connections can be set such that synchronization occurs only for selected spatiotemporal patterns. We show how the recognition is invariant to uniform time warp and uniform inten- sity change of the input events. The fundamental recognition event is a transient collective synchronization, representing ''many neurons now agree,'' an event that is then detected easily by a cell with a small time constant. If such synchronization is used in neurobiological computation, its hallmark will be a brief burst of gamma-band electroencephalogram noise when and where such a recognition event or decision occurs.


title: Exploiting topic modeling to boost metagenomic reads binning

abstract: Background ::: With the rapid development of high-throughput technologies, researchers can sequence the whole metagenome of a microbial community sampled directly from the environment. The assignment of these metagenomic reads into different species or taxonomical classes is a vital step for metagenomic analysis, which is referred to as binning of metagenomic data.


title: The Use of Hebbian Cell Assemblies for Nonlinear Computation

abstract: When learning a complex task our nervous system self-organizes large groups of neurons into coherent dynamic activity patterns. During this, a network with multiple, simultaneously active, and computationally powerful cell assemblies is created. How such ordered structures are formed while preserving a rich diversity of neural dynamics needed for computation is still unknown. Here we show that the combination of synaptic plasticity with the slower process of synaptic scaling achieves (i) the formation of cell assemblies and (ii) enhances the diversity of neural dynamics facilitating the learning of complex calculations. Due to synaptic scaling the dynamics of different cell assemblies do not interfere with each other. As a consequence, this type of self-organization allows executing a difficult, six degrees of freedom, manipulation task with a robot where assemblies need to learn computing complex non-linear transforms and – for execution – must cooperate with each other without interference. This mechanism, thus, permits the self-organization of computationally powerful sub-structures in dynamic networks for behavior control.


title: The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches

abstract: Deep learning has demonstrated tremendous success in variety of application domains in the past few years. This new field of machine learning has been growing rapidly and applied in most of the application domains with some new modalities of applications, which helps to open new opportunity. There are different methods have been proposed on different category of learning approaches, which includes supervised, semi-supervised and un-supervised learning. The experimental results show state-of-the-art performance of deep learning over traditional machine learning approaches in the field of Image Processing, Computer Vision, Speech Recognition, Machine Translation, Art, Medical imaging, Medical information processing, Robotics and control, Bio-informatics, Natural Language Processing (NLP), Cyber security, and many more. This report presents a brief survey on development of DL approaches, including Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) including Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). In addition, we have included recent development of proposed advanced variant DL techniques based on the mentioned DL approaches. Furthermore, DL approaches have explored and evaluated in different application domains are also included in this survey. We have also comprised recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys have published on Deep Learning in Neural Networks [1, 38] and a survey on RL [234]. However, those papers have not discussed the individual advanced techniques for training large scale deep learning models and the recently developed method of generative models [1].


title: A Roadmap to Fully Homomorphic Elections: Stronger Security, Better Verifiability

abstract: After the trials of remote internet voting for local elections in 2011 and parliamentary elections in 2013, a number of local referendums has renewed interest in internet voting in Norway.


title: IoT architectural concerns: a systematic review

abstract: There is increasing interest in studying and applying Internet of Things (IoT) within the overall context of digital-physical ecosystems. Most recently, much has been published on the benefits and applications of IoT. The main question is: what are the key IoT architectural concerns, which must be addressed to effectively develop and implement an IoT architecture? There is a need to systematically review and synthesize the literature on IoT architectural challenges or concerns. Using the SLR approach and applying customised search criteria derived from the research question, 22 relevant studies were identified and reviewed in this paper. The data from these papers were extracted to identify the IoT architectural challenges and relevant solutions. These results were organised into to 9 major challenge and 7 solution categories. The results of this research will serve as a resource for practitioners and researchers for the effective adoption, and setting future research priorities and directions in this emerging area of IoT architecture.


title: Major mesh-related complications following hernia repair

abstract: Mesh material affects complications following hernia repair. Medical device reports on the use of surgical mesh for hernia repair were reviewed from the Food and Drug Administration’s (FDA) Manufacturer User Facility Device Experience Database from January 1996 to September 2004. We analyzed 252 adverse event reports related to the use of surgical mesh for hernia repair. Adverse events included infection (42%, 107 reports), mechanical failure (18%, 46), pain (9%, 23), reaction (8%, 20), intestinal complications (7%, 18), adhesions (6%, 14), seroma (4%, 9), erosion (2%, 6), and other (4%, 9). Compared to all other mesh types, Sepra/polypropylene mesh had more mechanical failures (80 vs 14%, p < 0.05), biomaterial mesh had more reactions (57 vs 7%, p < 0.05), polytetrafluoroethylene (PTFE)/polypropylene mesh had more intestinal complications (14 vs 7%, p < 0.05), and PTFE mesh tended to have more infections (75 vs 41% all other, p = 0.07). Death occurred in 2% (5). We conclude that specific mesh materials are related to specific complications.


title: Robotic technology in surgery: Past, present, and future

abstract: It has been nearly 20 years since the first appearance of robotics in the operating room. In that time, much progress has been made in integrating robotic technologies with surgical instrumentation, as evidenced by the many thousands of successful robot-assisted cases. However, to build on past success and to fully leverage the potential of surgical robotics in the future, it is essential to maximize a shared understanding and communication among surgeons, engineers, entrepreneurs, and healthcare administrators. This article provides an introduction to medical robotic technologies, develops a possible taxonomy, reviews the evolution of a surgical robot, and discusses future prospects for innovation. Robotic surgery has demonstrated some clear benefits. It remains to be seen where these benefits will outweigh the associated costs over the long term. In the future, surgical robots should be smaller, less expensive, easier to operate, and should seamlessly integrate emerging technologies from a number of different fields. Such advances will enable continued progress in surgical instrumentation and, ultimately, surgical care.


A Survey of Display Device Properties and Visual Acuity for Visualization


title: Understanding the tolerance of the industrial yeast Saccharomyces cerevisiae against a major class of toxic aldehyde compounds

abstract: Development of the next-generation biocatalyst is vital for fermentation-based industrial applications and a sustainable bio-based economy. Overcoming the major class of toxic compounds associated with lignocellulose-to-biofuels conversion is one of the significant challenges for new strain development. A significant number of investigations have been made to understand mechanisms of the tolerance for industrial yeast. It is humbling to learn how complicated the cell's response to the toxic chemicals is and how little we have known about yeast tolerance in the universe of the living cell. This study updates our current knowledge on the tolerance of industrial yeast against aldehyde inhibitory compounds at cellular, molecular and the genomic levels. It is comprehensive yet specific based on reproducible evidence and cross confirmed findings from different investigations using varied experimental approaches. This research approaches a rational foundation toward a more comprehensive understanding on the yeast tolerance. Discussions and perspectives are also proposed for continued exploring the puzzle of the yeast tolerance to aid the next-generation biocatalyst development.


title: XSEDE: Accelerating Scientific Discovery

abstract: Computing in science and engineering is now ubiquitous: digital technologies underpin, accelerate, and enable new, even transformational, research in all domains. Access to an array of integrated and well-supported high-end digital services is critical for the advancement of knowledge. Driven by community needs, the Extreme Science and Engineering Discovery Environment (XSEDE) project substantially enhances the productivity of a growing community of scholars, researchers, and engineers (collectively referred to as "scientists"' throughout this article) through access to advanced digital services that support open research. XSEDE's integrated, comprehensive suite of advanced digital services federates with other high-end facilities and with campus-based resources, serving as the foundation for a national e-science infrastructure ecosystem. XSEDE's e-science infrastructure has tremendous potential for enabling new advancements in research and education. XSEDE's vision is a world of digitally enabled scholars, researchers, and engineers participating in multidisciplinary collaborations to tackle society's grand challenges.


title: Patterns and Predictors of Service Use Among Women Who Have Separated from an Abusive Partner

abstract: Using baseline data from a survey of 309 Canadian women recently separated from an abusive partner, we investigated patterns of access to health, social, legal, and violence-specific services and whether abuse history and social and health variables predict service use. We compared rates of service use to population rates, and used logistic regression to identify determinants of use. Service use rates were substantially higher than population estimates in every category, particularly in general and mental health sectors. Although women were confident in their ability to access services, they reported substantial unmet need, difficulty accessing services, and multiple barriers. The strongest unique predictors of use varied across service type. Health variables (high disability chronic pain, symptoms of depression and PTSD), low income, and mothering were the most consistent predictors. Service providers and policy makers must account for social location, abuse history, and health status of Intimate Violence (IPV) survivors. Strategies to enhance access to primary health care services, and to create a system of more integrated, accessible services, are required.


title: Suffering, Selfish, Slackers? Myths and Reality About Emerging Adults

abstract: Many myths about adolescence have been re- futed by research, but similar myths have grown up in recent years around emerging adulthood. This essay ad- dresses three of those myths: the claim that they suffer from a normative "crisis"; the accusations that they are "self- ish"; and their alleged reluctance to "grow up" and become adults. For each issue, evidence is presented showing that the myths exaggerate or falsify the true experience of emerging adults.