nils-herrmann/clean_scirepeval huggingface dataset:
Rail defect detection using Gabor filters with texture analysis

Railways is the major means of transport in most of the countries. Rails are the backbone of the track structure and should be protected from defects. Surface defects are irregularities in the rails caused due to the shear stresses between the rails and wheels of the trains. This type of defects should be detected to avoid rail fractures. The objective of this paper is to propose an innovative technique to detect the surface defect on rail heads. In order to identify the defects, it is essential to extract the rails from the background and further enhance the image for thresholding. The proposed method uses Binary Image Based Rail Extraction (BIBRE) algorithm to extract the rails from the background. The extracted rails are enhanced to achieve uniform background with the help of direct enhancement method. The direct enhancement method enhance the image by enhancing the brightness difference between objects and their backgrounds. The enhanced rail image uses Gabor filters to identify the defects from the rails. The Gabor filters maximizes the energy difference between defect and defect less surface. Thresholding is done based on the energy of the defects. From the thresholded image the defects are identified and a message box is generated when there is a presence of defects.
Electronic commerce in China: emerging success stories

Enterprises across the People’s Republic of China (China) seek to gain the benefits of electronic business, but very few of their electronic commerce (e-commerce) initiatives have been successful to date. A recent multiple case study examined some of the exceptions. This paper illustrates the distinctive nature of e-commerce with Chinese characteristics by profiling an online retailer, a traditional B2B intermediary, and an electronic marketplace or marketspace. Their key success factors were found to include the abilities to leverage core capabilities and to overcome institutional deficiencies through relationship building. The prospects for e-commerce in mainland China are related to the development of the rules and infrastructure that are fundamental to a modern market economy.
Optimizing Direct-threaded Code by Selective Inlining

Achieving good performance in bytecoded language interpreters is difficult without sacrificing both simplicity and portability. This is due to the complexity of dynamic translation ("just-in-time compilation") of bytecodes into native code, which is the mechanism employed universally by high-performance interpreters.We demonstrate that a few simple techniques make it possible to create highly-portable dynamic translators that can attain as much as 70% the performance of optimized C for certain numerical computations. Translators based on such techniques can offer respectable performance without sacrificing either the simplicity or portability of much slower "pure" bytecode interpreters.
Boosting the Actor with Dual Critic

This paper proposes a new actor-critic-style algorithm called Dual Actor-Criticor Dual-AC. It is derived in a principled way from the Lagrangian dual form of the Bellman optimality equation, which can be viewed as a two-player game between the actor and a critic-like function, which is named as dual critic. Compared to its actor-critic relatives, Dual-AC has the desired property that the actor and dual critic are updated cooperatively to optimize the same objective function, providing a more transparent way for learning the critic that is directly related to the objective function of the actor. We then provide a concrete algorithm that can effectively solve the minimax optimization problem, using techniques of multi-step bootstrapping, path regularization, and stochastic dual ascent algorithm. We demonstrate that the proposed algorithm achieves the state-of-the-art performances across several benchmarks.
Two decades of internet video streaming: A retrospective view

For over two decades, video streaming over the Internet has received a substantial amount of attention from both academia and industry. Starting from the design of transport protocols for streaming video, research interests have later shifted to the peer-to-peer paradigm of designing streaming protocols at the application layer. More recent research has focused on building more practical and scalable systems, using Dynamic Adaptive Streaming over HTTP. In this article, we provide a retrospective view of the research results over the past two decades, with a focus on peer-to-peer streaming protocols and the effects of cloud computing and social media.
A Virtualized Separation Kernel for Mixed-Criticality Systems

Multi- and many-core processors are becoming increasingly popular in embedded systems. Many of these processors now feature hardware virtualization capabilities, as found on the ARM Cortex A15 and x86 architectures with Intel VT-x or AMD-V support. Hardware virtualization provides a way to partition physical resources, including processor cores, memory, and I/O devices, among guest virtual machines (VMs). Each VM is then able to host tasks of a specific criticality level, as part of a mixed-criticality system with different timing and safety requirements. However, traditional virtual machine systems are inappropriate for mixed-criticality computing. They use hypervisors to schedule separate VMs on physical processor cores. The costs of trapping into hypervisors to multiplex and manage machine physical resources on behalf of separate guests are too expensive for many time-critical tasks. Additionally, traditional hypervisors have memory footprints that are often too large for many embedded computing systems. In this article, we discuss the design of the Quest-V separation kernel, which partitions services of different criticality levels across separate VMs, or sandboxes. Each sandbox encapsulates a subset of machine physical resources that it manages without requiring intervention from a hypervisor. In Quest-V, a hypervisor is only needed to bootstrap the system, recover from certain faults, and establish communication channels between sandboxes. This not only reduces the memory footprint of the most privileged protection domain but also removes it from the control path during normal system operation, thereby heightening security.
A Mechanically Robust Rotor With Transverse Laminations for a Wide-Speed-Range Synchronous Reluctance Traction Motor

From a traction application point of view, proper operation of the synchronous reluctance motor over a wide speed range and mechanical robustness is desired. This paper presents new methods to improve the rotor mechanical integrity and the flux weakening capability at high speed using geometrical and variable ampere-turns concepts. The results from computer-aided analysis and experiment are compared to evaluate the methods. It is shown that, to achieve a proper design at high speed, the magnetic and mechanical performances need to be simultaneously analyzed due to their mutual effect.
Child and adolescent mental disorders: the magnitude of the problem across the globe.

Describe objectively the global gaps in policy, data gathering capacity, and resources to develop and implement services to support child mental health.



Report on the World health Organization (WHO) child and adolescent mental health resources Atlas project. The Atlas project utilized key informants and was supplemented by studies that focused on policy. This report also draws on current epidemiological studies to provide a context for understanding the magnitude of the clinical problem.



Current global epidemiological data consistently reports that up to 20% of children and adolescents suffer from a disabling mental illness; that suicide is the third leading cause of death among adolescents; and that up to 50% of all adult mental disorders have their onset in adolescence. While epidemiological data appears relatively uniform globally, the same is not true for policy and resources for care. The gaps in resources for child mental health can be categorized as follows: economic, manpower, training, services and policy. Key findings from the Atlas project include: lack of program development in low income countries; lack of any policy in low income countries and absent specific comprehensive policy in both low and high income countries; lack of data gathering capacity including that for country-level epidemiology and services outcomes; failure to provide social services in low income countries; lack of a continuum of care; and universal barriers to access. Further, the Atlas findings underscored the need for a critical analysis of the 'burden of disease' as it relates to the context of child and adolescent mental disorders, and the importance of defining the degree of 'impairment' of specific disorders in different cultures.



The recent finding of substantial gaps in resources for child mental health underscores the need for enhanced data gathering, refinement of the economic argument for care, and need for innovative training approaches.
Using Prerequisites to Extract Concept Maps fromTextbooks

We present a framework for constructing a specific type of knowledge graph, a concept map from textbooks. Using Wikipedia, we derive prerequisite relations among these concepts. A traditional approach for concept map extraction consists of two sub-problems: key concept extraction and concept relationship identification. Previous work for the most part had considered these two sub-problems independently. We propose a framework that jointly optimizes these sub-problems and investigates methods that identify concept relationships. Experiments on concept maps that are manually extracted in six educational areas (computer networks, macroeconomics, precalculus, databases, physics, and geometry) show that our model outperforms supervised learning baselines that solve the two sub-problems separately. Moreover, we observe that incorporating textbook information helps with concept map extraction.
Fashion World Map: Understanding Cities Through Streetwear Fashion

Fashion is an integral part of life. Streets as a social center for people's interaction become the most important public stage to showcase the fashion culture of a metropolitan area. In this paper, therefore, we propose a novel framework based on deep neural networks (DNN) for depicting the street fashion of a city by automatically discovering fashion items (e.g., jackets) in a particular look that are most iconic for the city, directly from a large collection of geo-tagged street fashion photos. To obtain a reasonable collection of iconic items, our task is formulated as the prize-collecting Steiner tree (PCST) problem, whereby a visually intuitive summary of the world's iconic street fashion can be created. To the best of our knowledge, this is the first work devoted to investigate the world's fashion landscape in modern times through the visual analytics of big social data. It shows how the visual impression of local fashion cultures across the world can be depicted, modeled, analyzed, compared, and exploited. In the experiments, our approach achieves the best performance (43.19%) on our large collected GSFashion dataset (170K photos), with an average of two times higher than all the other algorithms (FII: 20.13%, AP: 18.76%, DC: 17.90%), in terms of the users' agreement ratio on the discovered iconic fashion items of a city. The potential of our proposed framework for advanced sociological understanding is also demonstrated via practical applications.
Optimistic Bayesian Sampling in Contextual-Bandit Problems

In sequential decision problems in an unknown environment, the decision maker often faces a dilemma over whether to explore to discover more about the environment, or to exploit current knowledge. We address the exploration-exploitation dilemma in a general setting encompassing both standard and contextualised bandit problems. The contextual bandit problem has recently resurfaced in attempts to maximise click-through rates in web based applications, a task with significant commercial interest. In this article we consider an approach of Thompson (1933) which makes use of samples from the posterior distributions for the instantaneous value of each action. We extend the approach by introducing a new algorithm, Optimistic Bayesian Sampling (OBS), in which the probability of playing an action increases with the uncertainty in the estimate of the action value. This results in better directed exploratory behaviour. We prove that, under unrestrictive assumptions, both approaches result in optimal behaviour with respect to the average reward criterion of Yang and Zhu (2002). We implement OBS and measure its performance in simulated Bernoulli bandit and linear regression domains, and also when tested with the task of personalised news article recommendation on a Yahoo! Front Page Today Module data set. We find that OBS performs competitively when compared to recently proposed benchmark algorithms and outperforms Thompson’s method throughout.
Multiple-pronunciation lexical modeling in a speaker independent speech understanding system

One of the sources of difficulty in speech recognition and understanding is the variability due to alternate pronunciations of words. To address the issue we have investigated the use of multiple-pronunciation models (MPMs) in the decoding stage of a speaker-independent speech understanding system. In this paper we address three important issues regarding MPMs: (a) Model construction: How can MPMs be built from available data without human intervention? (b) Model embedding: How should MPM construction interact with the training of the sub-word unit models on which they are based?  Do they help in speech recognition? Automatic, data-driven MPM construction is accomplished using a structural HMM induction algorithm. The resulting MPMs are trained jointlywith a multi-layer perceptron functioningas a phonetic likelihood estimator. The experiments reported here demonstrate that MPMs can significantly improve speech recognition results over standard single pronunciation models.
A detail analysis on intrusion detection datasets

To simulate an efficient Intrusion Detection System (IDS) model, enormous amount of data are required to train and testing the model. To improve the accuracy and efficiency of the model, it is essential to infer the statistical properties from the observable elements of th e dataset. In this work, we have proposed some data preprocessing techniques such as filling the missing values, removing redundant samples, reduce the dimension, selecting most relevant features and finally, normalize the samples. After data preprocessing, we have simulated and tested the dataset by applying various data mining algorithms such as Support Vector Machine (SVM), Decision Tree, K nearest neighbor, K-Mean and Fuzzy C-Mean Clustering which provides better result in less computational time.
18.2 Highly-linear integrated magnetic-free circulator-receiver for full-duplex wireless

A fundamental challenge of full-duplex (FD) wireless  is the implementation of low-cost, small-form-factor, integrated shared-antenna (ANT) interfaces with low loss, low noise, high TX-RX isolation, and large TX power handling. Providing more TX-RX isolation in the ANT interface that is robust to environmental variations lowers the self-interference cancellation (SIC) and dynamic range required in the RF, analog baseband (BB), and digital domains. Reciprocal shared-ANT interfaces, such as electrical-balance duplexers , fundamentally feature at least 3dB loss (practically >4dB). A non-reciprocal active shared-ANT duplexing scheme was demonstrated in , but such active approaches are limited in their maximum supported TX power (−17.3dBm in  limited by RX compression) and noise performance. An integrated FD RX with a magnetic-free non-reciprocal passive circulator was demonstrated in . Despite the circulator's low loss and relatively high linearity, the RX could only handle up to −7dBm TX power due to limited circulator isolation and RX LNA linearity. NF under cancellation was also as high as 10.9dB.
Learning in Nonstationary Environments: A Survey

The prevalence of mobile phones, the internet-of-things technology, and networks of sensors has led to an enormous and ever increasing amount of data that are now more commonly available in a streaming fashion [1]-[5]. Often, it is assumed - either implicitly or explicitly - that the process generating such a stream of data is stationary, that is, the data are drawn from a fixed, albeit unknown probability distribution. In many real-world scenarios, however, such an assumption is simply not true, and the underlying process generating the data stream is characterized by an intrinsic nonstationary (or evolving or drifting) phenomenon. The nonstationarity can be due, for example, to seasonality or periodicity effects, changes in the users' habits or preferences, hardware or software faults affecting a cyber-physical system, thermal drifts or aging effects in sensors. In such nonstationary environments, where the probabilistic properties of the data change over time, a non-adaptive model trained under the false stationarity assumption is bound to become obsolete in time, and perform sub-optimally at best, or fail catastrophically at worst.
Enhancing Person Re-identification in a Self-Trained Subspace

Despite the promising progress made in recent years, person re-identification (re-ID) remains a challenging task due to the complex variations in human appearances from different camera views. For this challenging problem, a large variety of algorithms have been developed in the fully supervised setting, requiring access to a large amount of labeled training data. However, the main bottleneck for fully supervised re-ID is the limited availability of labeled training samples. To address this problem, we propose a self-trained subspace learning paradigm for person re-ID that effectively utilizes both labeled and unlabeled data to learn a discriminative subspace where person images across disjoint camera views can be easily matched. The proposed approach first constructs pseudo-pairwise relationships among unlabeled persons using the k-nearest neighbors algorithm. Then, with the pseudo-pairwise relationships, the unlabeled samples can be easily combined with the labeled samples to learn a discriminative projection by solving an eigenvalue problem. In addition, we refine the pseudo-pairwise relationships iteratively, which further improves learning performance. A multi-kernel embedding strategy is also incorporated into the proposed approach to cope with the non-linearity in a person’s appearance and explore the complementation of multiple kernels. In this way, the performance of person re-ID can be greatly enhanced when training data are insufficient. Experimental results on six widely used datasets demonstrate the effectiveness of our approach, and its performance can be comparable to the reported results of most state-of-the-art fully supervised methods while using much fewer labeled data.
Domains of Concern in Software Architectures and Architecture Description Languages

Software architectures shift the focus of developers from lines-of-code to coarser-grained elements and their interconnection structure. Architecture description languages (ADLs) have been proposed as domain-specific languages for the domain of software architecture. There is still little consensus in the research community on what problems are most important to address in a study of software architecture, what aspects of an architecture should be modeled in an ADL, or even what an ADL is. To shed light on these issues, we provide a framework of architectural domains, or areas of concern in the study of software architectures. We evaluate existing ADLs with respect to the framework and study the relationship between architectural and application domains. One conclusion is that, while the architectural domains perspective enables one to approach architectures and ADLs in a new, more structured manner, further understanding of architectural domains, their tie to application domains, and their specific influence on ADLs is needed.
Improved disk-drive failure warnings

— Improved methods are proposed for disk drive failure prediction. The SMART (Self Monitoring and Reporting Technology) failure prediction system is currently implemented in disk drives. Its purpose is to predict the near-term failure of an individual hard disk drive, and issue a backup warning to prevent data loss. Two experimentally tests of SMART showed only moderate accuracy at low false alarm rates. (A rate of 0.2% of total drives per year implies that 20% of drive returns would be good drives, relative to ≈1% annual failure rate of drives). This requirement for very low false alarm rates is well known in medical diagnostic tests for rare diseases, and methodology used there suggests ways to improve SMART. ACRONYMS ATA Standard drive interface, desktop computers FA Failure analysis of apparently failed drive FAR False alarm rate, 100 times probability value MVRS Multivariate rank sum statistical test NPF Drive failed, but “No problem found” in FA RS Rank sum statistical hypothesis test R Sum of ranks of warning set data Rc Predict fail if R> Rc critical value SCSI Standard drive interface, high-end computers SMART “Self monitoring and reporting technology” WA Failure warning accuracy (probability) Two improved SMART algorithms are proposed here. They use the SMART internal drive attribute measurements in present drives. The present warning algorithm based on maximum error thresholds is replaced by distribution-free statistical hypothesis tests. These improved algorithms are computationally simple enough to be implemented in drive microprocessor firmware code. They require only integer sort operations to put several hundred attribute values in rank order. Some tens of these ranks are added up and the SMART warning is issued if the sum exceeds a prestored limit.  n Number of reference (old) measurements m Number of warning (new) measurements N Total ranked measurements (n+m) p Number of different attributes measured Q(X) Normal probability Pr(x>X) RS Rank sum statistical hypothesis test R Sum of ranks of warning set data Rc Predict fail if R> Rc critical value