nils-herrmann/clean_scirepeval huggingface dataset:
On Formal and Cognitive Semantics for Semantic Computing

Semantics is the meaning of symbols, notations, concepts, functions, and behaviors, as well as their relations that can be deduced onto a set of predefined entities and/or known concepts. Semantic computing is an emerging computational methodology that models and implements computational structures and behaviors at semantic or knowledge level beyond that of symbolic data. In semantic computing, formal semantics can be classified into the categories of to be, to have, and to do semantics. This paper presents a comprehensive survey of formal and cognitive semantics for semantic computing in the fields of computational linguistics, software science, computational intelligence, cognitive computing, and denotational mathematics. A set of novel formal semantics, such as deductive semantics, concept-algebra-based semantics, and visual semantics, is introduced that forms a theoretical and cognitive foundation for semantic computing. Applications of formal semantics in semantic computing are presented in case studies on semantic cognition of natural languages, semantic analyses of computing behaviors, behavioral semantics of human cognitive processes, and visual semantic algebra for image and visual object manipulations.
Congestion Control in Wireless Communication Network Using Fuzzy Logic and Machine Learning Techniques

Nowadays wireless networks are the most popular way of communication. For example, internet services in companies, cafes, e-markets and in homes. Therefore, it must be protected against the spiteful users who try to harm the privacy, genuineness and privacy of it. Also there is need of traffic control of information sent over these wireless networks. In this research paper, a technique for controlling the congestion over the wireless networks is shown and to implement it, fuzzy logic and machine learning tools are used. Some of the parameters which are necessary to be considered for congestion control decision mechanism are:Transmission energy, queue size, distance from receiver, transmission rate, cost assigned. On evaluating these parameters using fuzzy logic, a desired output for congestion control can be determined and its efficiency is evaluated using machine learning tools.
Sleep duration and school performance in Korean adolescents

In 2012, 63 688 adolescents participated in the 8th Korea Youth Risk Behavior Web-Based Survey (KYRBWS-VIII) project. The relationship between sleep duration and school performance was evaluated by multivariate logistic regression analysis after adjustment for covariate variables, including body mass index, age, parental education, economic status, mental stress, smoking, alcohol consumption, breakfast patterns, and three measures of physical activity.
AdOtsu: An adaptive and parameterless generalization of Otsu's method for document image binarization

Adaptive binarization methods play a central role in document image processing. In this work, an adaptive and parameterless generalization of Otsu’s method is presented. The adaptiveness is obtained by combining grid-based modeling and the estimated background map. The parameterless behavior is achieved by automatically estimating the document parameters, such as the average stroke width and the average line height. The proposed method is extended using a multiscale framework, and has been applied on various datasets, including the DIBCO’09 dataset, with promising results.
Deep Reasoning with Multi-scale Context for Salient Object Detection

To detect and segment salient objects accurately, existing methods are usually devoted to designing complex network architectures to fuse powerful features from the backbone networks. However, they put much less efforts on the saliency inference module and only use a few fully convolutional layers to perform saliency reasoning from the fused features. However, should feature fusion strategies receive much attention but saliency reasoning be ignored a lot? In this paper, we find that weakness of the saliency reasoning unit limits salient object detection performance, and claim that saliency reasoning after multi-scale convolutional features fusion is critical. To verify our findings, we first extract multi-scale features with a fully convolutional network, and then directly reason from these comprehensive features using a deep yet light-weighted network, modified from ShuffleNet [45], to fast and precisely predict salient objects. Such simple design is shown to be capable of reasoning from multi-scale saliency features as well as giving superior saliency detection performance with less computation cost. Experimental results show that our simple framework outperforms the best existing method with 2.3% and 3.6% promotion for F-measure scores, 2.8% reduction for MAE score on PASCAL-S, DUT-OMRON and SOD datasets respectively.
Nonisolated High Gain DC–DC Converter for DC Microgrids

DC microgrids are popular due to the integration of renewable energy sources such as solar photovoltaics and fuel cells. Owing to the low output voltage of these dc power generators, high efficient high gain dc–dc converters are in need to connect the dc microgrid. In this paper, a nonisolated high gain dc–dc converter is proposed without using the voltage multiplier cell and/or hybrid switched-capacitor technique. The proposed topology utilizes two nonisolated inductors that are connected in series/parallel during discharging/charging mode. The operation of switches with two different duty ratios is the main advantage of the converter to achieve high voltage gain without using extreme duty ratio. The steady-state analysis of the proposed converter using two different duty ratios is discussed in detail. In addition, a 100 W, 20/200 V prototype circuit of the high gain dc–dc converter is developed, and the performance is validated using experimental results.
Computer puppetry: An importance-based approach

Computer puppetry maps the movements of a performer to an animated character in real-time. In this article, we provide a comprehensive solution to the problem of transferring the observations of the motion capture sensors to an animated character whose size and proportion may be different from the performer's. Our goal is to map as many of the important aspects of the motion to the target character as possible, while meeting the online, real-time demands of computer puppetry. We adopt a Kalman filter scheme that addresses motion capture noise issues in this setting. We provide the notion of dynamic importance of an end-effector that allows us to determine what aspects of the performance must be kept in the resulting motion. We introduce a novel inverse kinematics solver that realizes these important aspects within tight real-time constraints. Our approach is demonstrated by its application to broadcast television performances.
MC-HOG Correlation Tracking with Saliency Proposal

Designing effective feature and handling the model drift problem are two important aspects for online visual tracking. For feature representation, gradient and color features are most widely used, but how to effectively combine them for visual tracking is still an open problem. In this paper, we propose a rich feature descriptor, MC-HOG, by leveraging rich gradient information across multiple color channels or spaces. Then MC-HOG features are embedded into the correlation tracking framework to estimate the state of the target. For handling the model drift problem caused by occlusion or distracter, we propose saliency proposals as prior information to provide candidates and reduce background interference. In addition to saliency proposals, a ranking strategy is proposed to determine the importance of these proposals by exploiting the learnt appearance filter, historical preserved object samples and the distracting proposals. In this way, the proposed approach could effectively explore the color-gradient characteristics and alleviate the model drift problem. Extensive evaluations performed on the benchmark dataset show the superiority of the proposed method.
Skin color as post-colonial hierarchy: a global strategy for conflict resolution.

The post-colonial hierarchy is a critical dynamic of global coexistence. Power is associated with those sovereignties characterized by light-skinned populations. Those characterized by dark skin are denigrated and assumed less qualified to negotiate global issues as equals. Although political objectives are expected to stimulate conflict, skin color is directly correlated with the present world order. Moreover, most post-colonial sovereignties are heterogeneous in one way or another and yet do not engage in destructive conflict. From a global perspective, conflict resolution will require post-colonial sovereignties--particularly those of relative light skin--to forfeit their self-serving denigration of others. Strategies for conflict resolution should ignore skin color and incorporate measures designed to improve problem solving, moral reasoning, and the general etiquette skills of those engaged in any negotiation process.
Ml.lib: robust, cross-platform, open-source machine learning for max and pure data

This paper documents the development of ml.lib: a set of opensource tools designed for employing a wide range of machine learning techniques within two popular real-time programming environments, namely Max and Pure Data. ml.lib is a crossplatform, lightweight wrapper around Nick Gillian’s Gesture Recognition Toolkit, a C++ library that includes a wide range of data processing and machine learning techniques. ml.lib adapts these techniques for real-time use within popular dataflow IDEs, allowing instrument designers and performers to integrate robust learning, classification and mapping approaches within their existing workflows. ml.lib has been carefully designed to allow users to experiment with and incorporate machine learning techniques within an interactive arts context with minimal prior knowledge. A simple, logical and consistent, scalable interface has been provided across over sixteen externals in order to maximize learnability and discoverability. A focus on portability and maintainability has enabled ml.lib to support a range of computing architectures—including ARM— and operating systems such as Mac OS, GNU/Linux and Windows, making it the most comprehensive machine learning implementation available for Max and Pure Data.
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm

The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limitation-no spatial information is taken into account. This causes the FM model to work only on well-defined images with low levels of noise; unfortunately, this is often not the the case due to artifacts such as partial volume effect and bias field distortion. Under these conditions, FM model-based methods produce unreliable results. Here, the authors propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown that the FM model is a degenerate version of the HMRF model. The advantage of the HMRF model derives from the way in which the spatial information is encoded through the mutual influences of neighboring sites. Although MRF modeling has been employed in MR image segmentation by other researchers, most reported methods are limited to using MRF as a general prior in an FM model-based approach. To fit the HMRF model, an EM algorithm is used. The authors show that by incorporating both the HMRF model and the EM algorithm into a HMRF-EM framework, an accurate and robust segmentation can be achieved. More importantly, the HMRF-EM framework can easily be combined with other techniques. As an example, the authors show how the bias field correction algorithm of Guillemaud and Brady (1997) can be incorporated into this framework to achieve a three-dimensional fully automated approach for brain MR image segmentation.
SOK: (State of) The Art of War: Offensive Techniques in Binary Analysis

Finding and exploiting vulnerabilities in binary code is a challenging task. The lack of high-level, semantically rich information about data structures and control constructs makes the analysis of program properties harder to scale. However, the importance of binary analysis is on the rise. In many situations binary analysis is the only possible way to prove (or disprove) properties about the code that is actually executed. In this paper, we present a binary analysis framework that implements a number of analysis techniques that have been proposed in the past. We present a systematized implementation of these techniques, which allows other researchers to compose them and develop new approaches. In addition, the implementation of these techniques in a unifying framework allows for the direct comparison of these apporaches and the identification of their advantages and disadvantages. The evaluation included in this paper is performed using a recent dataset created by DARPA for evaluating the effectiveness of binary vulnerability analysis techniques. Our framework has been open-sourced and is available to the security community.
Preventing software architecture erosion through static architecture conformance checking

Software architecture erosion is a problem faced by many organizations in the software industry. It happens when `as-implemented' architecture does not conform to the `as-intended' architecture, which results in low quality, complex, hard to maintain software. Architecture conformance checking refers to assessing the conformity of the implemented architecture to the intended architecture and can provide a strategy for detecting software architecture erosion and thereby prevent its negative consequences. When considering the current state-of-the-art of software architecture research and popular industry practices on architectural erosion, it obviously appears that such solution strategy is much needed to address the ever increasing demands for large scale complex software systems. In this paper an analysis of existing static architecture conformance checking is undertaken. Extending previously conducted research, we are in the process of developing a static architecture conformance checking tool for Java, based on GRASP ADL as a mean to overcome the challenges of software architecture erosion. Early design/implementation details of this tool are also presented.
Developing an Isolated Word Recognition System in MATLAB

The Development Workflow There are two major stages within isolated word recognition: a training stage and a testing stage. Training involves “teaching” the system by building its dictionary, an acoustic model for each word that the system needs to recognize. In our example, the dictionary comprises the digits ‘zero’ to ‘nine’. In the testing stage we use acoustic models of these digits to recognize isolated words using a classification algorithm. The development workflow consists of three steps: Speech acquisition • Speech analysis • User interface development •
Exploratory Analysis of the Factors Affecting Consumer Choice in E-Commerce: Conjoint Analysis

According to previous studies of online consumer behaviour, three factors are the most influential on purchasing behavior brand, colour and position of the product on the screen. However, a simultaneous influence of these three factors on the consumer decision making process has not been investigated previously. In this particular work we aim to execute a comprehensive study of the influence of these three factors. In order to answer our main research questions, we conducted an experiment with 96 different combinations of the three attributes, and using statistical analysis, such as conjoint analysis, t-test analysis and Kendall analysis we identified that the most influential factor to the online consumer decision making process is brand, the second most important attribute is the colour, which was estimated half as important as brand, and the least important attribute is the position on the screen. Additionally, we identified the main differences regarding consumers stated and revealed preferences regarding these three attributes.
Direct storage hybrid (DSH) inverter: A new concept of intelligent hybrid inverter

This paper proposes a new configuration of a single-phase hybrid inverter with an integrated battery energy storage, which is suitable for residential households to maximize local consumption of solar energy and thus reduce dependency on grid support. The hybrid inverter is called Direct Storage Hybrid (DSH) Inverter. A transformer-less topology such as HERIC, operating at low frequency to generate a three-level rectangular output voltage, is adopted to connect a photovoltaic (PV) panel to the load and/or the grid. A series active filter is employed to compensate the high harmonic components from the rectangular voltage and provide a sinusoidal voltage. A bidirectional dc/dc converter connects the battery to the PV panel to control the battery state of charge (SoC) and optimize the PV panel operation during both off-grid and grid-connected modes. The DSH inverter can let the battery bypass the dc/dc converter and connect directly to the inverter stage, leading to a significant improvement in throughput efficiency in battery utilization. This paper discusses the operation and loss analysis of the DSH inverter in off-grid mode.
Wiki-LDA: A Mixed-Method Approach for Effective Interest Mining on Twitter Data

Learning analytics (LA) and Educational data mining (EDM) have emerged as promising technologyenhanced learning (TEL) research areas in recent years. Both areas deal with the development of methods that harness educational data sets to support the learning process. A key area of application for LA and EDM is learner modelling. Learner modelling enables to achieve adaptive and personalized learning environments, which are able to take into account the heterogeneous needs of learners and provide them with tailored learning experience suited for their unique needs. As learning is increasingly happening in open and distributed environments beyond the classroom and access to information in these environments is mostly interest-driven, learner interests need to constitute an important learner feature to be modeled. In this paper, we focus on the interest dimension of a learner model and present Wiki-LDA as a novel method to effectively mine user’s interests in Twitter. We apply a mixed-method approach that combines Latent Dirichlet Allocation (LDA), text mining APIs, and wikipedia categories. Wiki-LDA has proven effective at the task of interest mining and classification on Twitter data, outperforming standard LDA.
Design and development of low-cost EMG amplifier for assistive technology

The importance of EMG signal is very widespread in many areas of biomedical application due to its distinct output characteristics. However, the small amplitude and noise vulnerability nature of EMG signal necessitates amplification and filtering through properly designed electronic circuitry. So far, a suitable design of an EMG amplifier with an optimum signal-to-noise ratio (SNR), and biosafety feature is a challenging issue. Besides, the high cost of a medical grade bioelectric amplifier also limits the open source approach to clinical research and development. Our work delineates a simple, affordable, but efficient method to design an EMG amplifier with a clinical safety feature of optical isolation compared to other standard bio amplifiers. In addition, the research also focuses on the development of an efficient signal processing algorithm for the post processing of the bio-signals which can be easily implemented into any embedded system dedicated for human-computer interaction.
Net!works European Technology Platform Expert Working Group on Smart Cities Applications and Requirements Executive Summary List of Contributors Contributors Company/institute Emcanta, Es Alcatel-lucent/bell Labs, De Gowex, Es List of Acronyms 3d Three Dimensional Api Application Programming Interfa

Smart Cities gained importance as a means of making ICT enabled services and applications available to the citizens, companies and authorities that are part of a city's system. It aims at increasing citizens' quality of life, and improving the efficiency and quality of the services provided by governing entities and businesses. This perspective requires an integrated vision of a city and of its infrastructures, in all its components. A Smart City can be taken according to six characteristics: All these domains raise new challenges in security and privacy, since users implicitly expect systems to be secure and privacy-preserving. One of the critical elements is which role(s) the city will take up as an actor within an increasingly complex value network. New players enter the market, actors shift their business strategies, roles change, different types of platforms emerge and vie for market dominance, technological developments create new threats and opportunities, etc. An element related to the trend of platformisation is cloud computing, which is increasingly helping the private sector to reduce cost, increase efficiency, and work smarter. One particular challenge relates to open data business models. Activities necessary for Public Sector Information provision can be identified. The development of efficient and effective e-government is a prerequisite. Transnational authentication systems for citizens and businesses, agreed frameworks for data privacy, and the sharing and collection of individual and business data, are key. Smart Cities need to be able to integrate themselves into national, regional and international infrastructures. Although the implementation aspects depend strongly on the authorities of these infrastructures, European wide recommendations and directives will definitely contribute to accelerate the deployment of Smart Cities. Health, inclusion and assisted living will play an essential role, since the demand for related services is rising, because ageing is changing disease composition. Requirements address a number of technologies, beyond the ones related to mobile and fixed networks. An integrated perspective on healthcare solutions for the near-to long-term can be foreseen, bridging a direct gap in between the health area and the technological development of communications (radio and network components). The needs for mobility in urban areas result into a number of problems, such as traffic congestion and energy consumption, which can be alleviated by exploiting Intelligent Transportation Systems and further adoption of vehicle-to-vehicle and vehicle-to-infrastructure communication networks. The information being managed in this area can be relevant to other domains, which increases its potential. An effective deployment …
Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems

Bayesian neural networks (BNNs) with latent variables are probabilistic models which can automatically identify complex stochastic patterns in the data. We study in these models a decomposition of predictive uncertainty into its epistemic and aleatoric components. We show how such a decomposition arises naturally in a Bayesian active learning scenario and develop a new objective for reliable reinforcement learning (RL) with an epistemic and aleatoric risk element. Our experiments illustrate the usefulness of the resulting decomposition in active learning and reliable RL.