nils-herrmann/clean_scirepeval huggingface dataset:
Twitter sentiment analysis using hybrid cuckoo search method

Sentiment analysis is one of the prominent fields of data mining that deals with the identification and analysis of sentimental contents generally available at social media. Twitter is one of such social medias used by many users about some topics in the form of tweets. These tweets can be analyzed to find the viewpoints and sentiments of the users by using clustering-based methods. However, due to the subjective nature of the Twitter datasets, metaheuristic-based clustering methods outperforms the traditional methods for sentiment analysis. Therefore, this paper proposes a novel metaheuristic method (CSK) which is based on K-means and cuckoo search. The proposed method has been used to find the optimum cluster-heads from the sentimental contents of Twitter dataset. The efficacy of proposed method has been tested on different Twitter datasets and compared with particle swarm optimization, differential evolution, cuckoo search, improved cuckoo search, gauss-based cuckoo search, and two n-grams methods. Experimental results and statistical analysis validate that the proposed method outperforms the existing methods. The proposed method has theoretical implications for the future research to analyze the data generated through social networks/medias. This method has also very generalized practical implications for designing a system that can provide conclusive reviews on any social issues.
Fine-grained attention for image caption generation

Despite the progress, generating natural language descriptions for images is still a challenging task. Most state-of-the-art methods for solving this problem apply existing deep convolutional neural network (CNN) models to extract a visual representation of the entire image, based on which the parallel structures between images and sentences are exploited using recurrent neural networks. However, there is an inherent drawback that their models may attend to a partial view of a visual element or a conglomeration of several concepts. In this paper, we present a fine-grained attention based model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation. The model contains three sub-networks: a deep recurrent neural network for sentences, a deep convolutional network for images, and a region proposal network for nearly cost-free region proposals. Our model is able to automatically learn to fix its gaze on salient region proposals. The process of generating the next word, given the previously generated ones, is aligned with this visual perception experience. We validate the effectiveness of the proposed model on three benchmark datasets (Flickr 8K, Flickr 30K and MS COCO). The experimental results confirm the effectiveness of the proposed system.
Wikipedia in the eyes of its beholders: A systematic review of scholarly research on Wikipedia readers and readership

Hundreds of scholarly studies have investigated various aspects of the immensely popular Wikipedia. Although a number of literature reviews have provided overviews of this vast body of research, none of them has specifically focused on the readers of Wikipedia and issues concerning its readership. In this systematic literature review, we review 99 studies to synthesize current knowledge regarding the readership of Wikipedia and also provide an analysis of research methods employed. The scholarly research has found that Wikipedia is popular not only for lighter topics such as entertainment, but also for more serious topics such as health information and legal background. Scholars, librarians and students are common users of Wikipedia, and it provides a unique opportunity for educating students in digital
Remaining useful life estimation - A review on the statistical data driven approaches

0377-2217/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.ejor.2010.11.018 ⇑ Corresponding authors at: Salford Business School M5 4WT, UK.  2954124; fax: +44 0161 2 010 62794461;  (D.-H. Zhou). E-mail addresses: W.Wang@salford.ac.uk (W. Wan (D.-H. Zhou). Remaining useful life (RUL) is the useful life left on an asset at a particular time of operation. Its estimation is central to condition based maintenance and prognostics and health management. RUL is typically random and unknown, and as such it must be estimated from available sources of information such as the information obtained in condition and health monitoring. The research on how to best estimate the RUL has gained popularity recently due to the rapid advances in condition and health monitoring techniques. However, due to its complicated relationship with observable health information, there is no such best approach which can be used universally to achieve the best estimate. As such this paper reviews the recent modeling developments for estimating the RUL. The review is centred on statistical data driven approaches which rely only on available past observed data and statistical models. The approaches are classified into two broad types of models, that is, models that rely on directly observed state information of the asset, and those do not. We systematically review the models and approaches reported in the literature and finally highlight future research challenges.
Risk assessment and the prevention of radicalization from nonviolence into terrorism.

This article considers the challenges associated with completing risk assessments in countering violent extremism. In particular, it is concerned with risk assessment of those who come to the attention of government and nongovernment organizations as being potentially on a trajectory toward terrorism and where there is an obligation to consider the potential future risk that they may pose. Risk assessment in this context is fraught with difficulty, primarily due to the variable nature of terrorism, the low base-rate problem, and the dearth of strong evidence on relevant risk and resilience factors. Statistically, this will lead to poor predictive value. Ethically, it can lead to the labeling of an individual who is not on a trajectory toward violence as being "at risk" of engaging in terrorism and the imposing of unnecessary risk management actions. The article argues that actuarial approaches to risk assessment in this context cannot work. However, it further argues that approaches that help assessors to process and synthesize information in a structured way are of value and are in line with good practice in the broader field of violence risk assessment.
Advancing Software-Defined Networks: A Survey

Having gained momentum from its promise of centralized control over distributed network architectures at bargain costs, software-defined Networking (SDN) is an ever-increasing topic of research. SDN offers a simplified means to dynamically control multiple simple switches via a single controller program, which contrasts with current network infrastructures where individual network operators manage network devices individually. Already, SDN has realized some extraordinary use cases outside of academia with companies, such as Google, AT&T, Microsoft, and many others. However, SDN still presents many research and operational challenges for government, industry, and campus networks. Because of these challenges, many SDN solutions have developed in an ad hoc manner that are not easily adopted by other organizations. Hence, this paper seeks to identify some of the many challenges where new and current researchers can still contribute to the advancement of SDN and further hasten its broadening adoption by network operators.
CREDIT RISK SCORING MODELS

Credit scoring models play a fundamental role in the risk management practice at most banks. They are used to quantify credit risk at counterparty or transaction level in the different phases of the credit cycle (e.g. application, behavioural, collection models). The credit score empowers users to make quick decisions or even to automate decisions and this is extremely desirable when banks are dealing with large volumes of clients and relatively small margin of profits at individual transaction level (i.e. consumer lending, but increasingly also small business lending). In this article, we analyze the history and new developments related to credit scoring models. We find that with the new Basel Capital Accord, credit scoring models have been remotivated and given unprecedented significance. Banks, in particular, and most financial institutions worldwide, have either recently developed or modified existing internal credit risk models to conform with the new rules and best practices recently updated in the market. Moreover, we analyze the key steps of the credit scoring model’s lifecycle (i.e. assessment, implementation, validation) highlighting the main requirement imposed by Basel II. We conclude that banks that are going to implement the most advanced approach to calculate their capital requirements under Basel II will need to increase their attention and consideration of credit scoring models in the next future.
An active compliance controller for quadruped trotting

In this paper, we present the idea that very simple control schemes can lead to complex running gaits. To show this, a novel control framework is proposed for dynamic quadruped trotting, consisting of a toe trajectory planning part and an active compliance part driving each leg. While keeping the controller's structure simple, the notion of the system virtual stiffness is introduced as a single control parameter tuned to drive a quadruped through a complete dynamic trotting scenario, with acceleration from stance, constant speed locomotion, and deceleration. The method can serve as a tool for generating robust running locomotion at a wide range of speeds, or as a basis for other high level controllers. The presented ideas are evaluated through simulation experiments with a 2d dynamic model of four three-segment legs.
THEORIES OF COUNSELING AND PSYCHOTHERAPY : A Multicultural Perspective , 6 / e © 2007

The pages of this Sample Chapter may have slight variations in final published form. H istorically, we talk of first-force psychodynamic, second-force cognitive-behavioral, and third-force existential-humanistic counseling and therapy theories. Counseling and psychotherapy really began with Freud and psychoanalysis. James Watson and, later, B. F. Skinner challenged Freud's emphasis on the unconscious and focused on observable behavior. Carl Rogers, with his person-centered counseling, revolutionized the helping professions by focusing on the importance of nurturing a caring therapist-client relationship in the helping process. All three approaches are still alive and well in the fields of counseling and psychology, as discussed in Chapters 5 through 10. As you reflect on the new knowledge and skills you exercised by reading the preceding chapters and completing the competency-building activities in those chapters, hopefully you part three 319 will see that you have gained a more sophisticated foundational understanding of the three traditional theoretical forces that have shaped the fields of counseling and therapy over the past one hundred years. Efforts in this book have been intended to bring your attention to both the strengths and limitations of psychodynamic, cognitive-behavioral, and existential-humanistic perspectives. With these perspectives in mind, the following chapters examine the fourth major theoretical force that has emerged in the mental health professions over the past 40 years: the multicultural-feminist-social justice counseling world-view. The perspectives of the fourth force challenge you to learn new competencies you will need to acquire to work effectively, respectfully, and ethically in a culturally diverse 21st-century society. Part Three begins by discussing the rise of the feminist counseling and therapy perspective (Chapter 11) and multicultural counseling and therapy (MCT) theories (Chapter 12). To assist you in synthesizing much of the information contained in all of the preceding chapters, Chapter 13 presents a comprehensive and integrative helping theory referred to as developmental counseling and therapy (DCT). Chapter 14 offers a comprehensive examination of family counseling and therapy theories to further extend your knowledge of ways that mental health practitioners can assist entire families in realizing new and untapped dimensions of their collective well-being. Finally Chapter 15 provides guidelines to help you develop your own approach to counseling and therapy that complements a growing awareness of your own values, biases, preferences, and relational compe-tencies as a mental health professional. Throughout, competency-building activities offer you opportunities to continue to exercise new skills associated with the different theories discussed in Part Three. …
A Survey of Maneuvering Target Tracking — Part IV : Decision-Based Methods

This is the fourth part of a series of papers that provide a comprehensive survey of techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. Part I  and Part II [2] deal with target motion models. Part III  covers the measurement models and the associated techniques. This part surveys tracking techniques that are based on decisions regarding target maneuver. Three classes of techniques are identified and described: equivalent noise, input detection and estimation, and switching model. Maneuver detection methods are also included.
Recurrent Convolutional Neural Network for Relation Classification

Relation classification is an important semantic processing task in the field of natural language processing (NLP). In this paper, we present a novel model, Structure Regularized Bidirectional Recurrent Convolutional Neural Network(SRBRCNN), to classify the relation of two entities in a sentence, and the new dataset of Chinese Sanwen for named entity recognition and relation classification. Some state-of-theart systems concentrate on modeling the shortest dependency path (SDP) between two entities leveraging convolutional or recurrent neural networks. We further explore how to make full use of the dependency relations information in the SDP and how to improve the model by the method of structure regularization. We propose a structure regularized model to learn relation representations along the SDP extracted from the forest formed by the structure regularized dependency tree, which benefits reducing the complexity of the whole model and helps improve the F1 score by 10.3. Experimental results show that our method outperforms the state-of-the-art approaches on the Chinese Sanwen task and performs as well on the SemEval-2010 Task 8 dataset.
Rejection sensitivity and the rejection-hostility link in romantic relationships.

Rejection sensitivity is the disposition to anxiously expect, readily perceive, and intensely react to rejection. In response to perceived social exclusion, highly rejection sensitive people react with increased hostile feelings toward others and are more likely to show reactive aggression than less rejection sensitive people in the same situation. This paper summarizes work on rejection sensitivity that has provided evidence for the link between anxious expectations of rejection and hostility after rejection. We review evidence that rejection sensitivity functions as a defensive motivational system. Thus, we link rejection sensitivity to attentional and perceptual processes that underlie the processing of social information. A range of experimental and diary studies shows that perceiving rejection triggers hostility and aggressive behavior in rejection sensitive people. We review studies that show that this hostility and reactive aggression can perpetuate a vicious cycle by eliciting rejection from those who rejection sensitive people value most. Finally, we summarize recent work suggesting that this cycle can be interrupted with generalized self-regulatory skills and the experience of positive, supportive relationships.
Designing Effective Serious Games: Opportunities and Challenges for Research

Serious Games represent an acknowledged potential for instruction, because they are able to strongly motivate learners. They can also provide immersive environments where advanced users can practice knowledge and skills, also exploiting multimodal interaction. They can combine the effectiveness of computer processing and data storage, with high levels of attractiveness. Our work has investigated the state of the art research on SGs, starting from the cognitive aspects, that are necessary in order to root technological development and applications in sound theoretical foundations. The paper discusses some key aspects about SG design and exploitation: choice of components-off-the-shelf or from-scratch design, tools and methodologies for development or adaptation, intelligent tutoring, virtual coaches and affective learning, living worlds, game mechanics, Human-Computer Interaction. While several SGs have been developed, still the literature stresses a lack of significant, extensive user tests. Further research is necessary to investigate in greater detail the real effectiveness of the various types of SGs. The paper proposes several research questions that range from requirements elicitation to design and from deployment to use and evaluation to be answered in order to avoid technology pushing and drive technological research according to the requirements of the end-users and stakeholders. We believe that deepening the analysis about these issues is key to strengthen the foundations of SG research, for which we identify four major directions: definition of metrics and learning progress evaluation tools; methodologies and tools for designing games from various topics and for various users; computing and communication architectures; technologies that can enhance the overall system performance.
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences. However, modeling complex distributions over graphs and then efficiently sampling from these distributions is challenging due to the non-unique, high-dimensional nature of graphs and the complex, non-local dependencies that exist between edges in a given graph. Here we propose GraphRNN, a deep autoregressive model that addresses the above challenges and approximates any distribution of graphs with minimal assumptions about their structure. GraphRNN learns to generate graphs by training on a representative set of graphs and decomposes the graph generation process into a sequence of node and edge formations, conditioned on the graph structure generated so far. In order to quantitatively evaluate the performance of GraphRNN, we introduce a benchmark suite of datasets, baselines and novel evaluation metrics based on Maximum Mean Discrepancy, which measure distances between sets of graphs. Our experiments show that GraphRNN significantly outperforms all baselines, learning to generate diverse graphs that match the structural characteristics of a target set, while also scaling to graphs 50× larger than previous deep models.
Adaptive Sampling Towards Fast Graph Representation Learning

Graph Convolutional Networks (GCNs) have become a crucial tool on learning representations of graph vertices. The main challenge of adapting GCNs on largescale graphs is the scalability issue that it incurs heavy cost both in computation and memory due to the uncontrollable neighborhood expansion across layers. In this paper, we accelerate the training of GCNs through developing an adaptive layer-wise sampling method. By constructing the network layer by layer in a top-down passway, we sample the lower layer conditioned on the top one, where the sampled neighborhoods are shared by different parent nodes and the over expansion is avoided owing to the fixed-size sampling. More importantly, the proposed sampler is adaptive and applicable for explicit variance reduction, which in turn enhances the training of our method. Furthermore, we propose a novel and economical approach to promote the message passing over distant nodes by applying skip connections. Intensive experiments on several benchmarks verify the effectiveness of our method regarding the classification accuracy while enjoying faster convergence speed.
Single Image Dehazing by Multi-Scale Fusion

Haze is an atmospheric phenomenon that significantly degrades the visibility of outdoor scenes. This is mainly due to the atmosphere particles that absorb and scatter the light. This paper introduces a novel single image approach that enhances the visibility of such degraded images. Our method is a fusion-based strategy that derives from two original hazy image inputs by applying a white balance and a contrast enhancing procedure. To blend effectively the information of the derived inputs to preserve the regions with good visibility, we filter their important features by computing three measures (weight maps): luminance, chromaticity, and saliency. To minimize artifacts introduced by the weight maps, our approach is designed in a multiscale fashion, using a Laplacian pyramid representation. We are the first to demonstrate the utility and effectiveness of a fusion-based technique for dehazing based on a single degraded image. The method performs in a per-pixel fashion, which is straightforward to implement. The experimental results demonstrate that the method yields results comparative to and even better than the more complex state-of-the-art techniques, having the advantage of being appropriate for real-time applications.
Improving trace accuracy through data-driven configuration and composition of tracing features

Software traceability is a sought-after, yet often elusive quality in large software-intensive systems primarily because the cost and effort of tracing can be overwhelming. State-of-the art solutions address this problem through utilizing trace retrieval techniques to automate the process of creating and maintaining trace links. However, there is no simple one- size-fits all solution to trace retrieval. As this paper will show, finding the right combination of tracing techniques can lead to significant improvements in the quality of generated links. We present a novel approach to trace retrieval in which the underlying infrastructure is configured at runtime to optimize trace quality. We utilize a machine-learning approach to search for the best configuration given an initial training set of validated trace links, a set of available tracing techniques specified in a feature model, and an architecture capable of instantiating all valid configurations of features. We evaluate our approach through a series of experiments using project data from the transportation, healthcare, and space exploration domains, and discuss its implementation in an industrial environment. Finally, we show how our approach can create a robust baseline against which new tracing techniques can be evaluated.