nils-herrmann/clean_scirepeval huggingface dataset:
Sentiment-aware stock market prediction: A deep learning method

Stock market prediction has attracted much attention from academia as well as business. However, it is a challenging research topic, in which many advanced computational methods have been proposed, but not yet attained a desirable and reliable performance. This study proposes a new method for stock market prediction, which adopts the Long Short-Term Memory (LSTM) neural network and incorporates investor sentiment and market factors to improve forecasting performance. By extracting investor sentiment from forum posts using Naïve Bayes, this paper makes it possible to analyze the irrational component of stock price. Our empirical study on CSI300 index proves that our prediction method provides better prediction performance. It gives a prediction accuracy of 87.86%, outperforming other benchmark models by at least 6%. Furthermore, our empirical study reveals evidence that helps to better understand investor sentiment and stock behaviors. Finally, this work shows the potential of deep learning financial time series in the presence of strong noises.
Distant supervision for relation extraction without labeled data

Modern models of relation extraction for tasks like ACE are based on supervised learning of relations from small hand-labeled corpora. We investigate an alternative paradigm that does not require labeled corpora, avoiding the domain dependence of ACEstyle algorithms, and allowing the use of corpora of any size. Our experiments use Freebase, a large semantic database of several thousand relations, to provide distant supervision. For each pair of entities that appears in some Freebase relation, we find all sentences containing those entities in a large unlabeled corpus and extract textual features to train a relation classifier. Our algorithm combines the advantages of supervised IE (combining 400,000 noisy pattern features in a probabilistic classifier) and unsupervised IE (extracting large numbers of relations from large corpora of any domain). Our model is able to extract 10,000 instances of 102 relations at a precision of 67.6%. We also analyze feature performance, showing that syntactic parse features are particularly helpful for relations that are ambiguous or lexically distant in their expression.
- 1-Corruption , Public Investment , and Growth

Up to the time when a huge corruption scandal, popularly labeled tangentopoli”(bribe city), brought down the political establishment that had ruled Italy for several decades, that country had reported one of the largest shares of capital spending in GDP among the OECD countries. After the scandal broke out and several prominent individuals were sent to jail, or even committed suicide, capital spending fell sharply. The fall seems to have been caused by a reduction in the number of capital projects being undertaken and, perhaps more importantly, by a sharp fall in the costs of the projects still undertaken. Information released by Transparency International (TI) reports that, within the space of two or three years, in the city of Milan, the city where the scandal broke out in the first place, the cost of city rail links fell by 52 percent, the cost of one kilometer of subway fell by 57 percent, and the budget for the new airport terminal was reduced by 59 percent to reflect the lower construction costs. Although one must be aware of the logical fallacy of post hoc, ergo propter hoc, the connection between the two events is too strong to be attributed to a coincidence. In fact, this paper takes the view that it could not have been a coincidence.
Exploring the credibility of online celebrities' Instagram profiles in influencing the purchase decisions of young female users

The growth of Instagram continues, with the majority of its users being young women. This study investigates the impact of Instagram upon source credibility, consumer buying intention and social identification with different types of celebrities. In-depth interviews were conducted with 18 female Instagram users aged 18-30 to determine the extent to which Instagram influences their buying behaviour. The research findings show that celebrities on Instagram are influential in the purchase behaviour of young female users. However, non-traditional celebrities such as bloggers, YouTube personalities and ‘Instafamous’ profiles are more powerful, as participants regard them as more credible and are able to relate to these, rather than more traditional, celebrities. Female users are perceptively aware and prefer to follow Instagram profiles that intentionally portray positive images and provide encouraging reviews.
Strategies for Competitive Advantage in Electronic Commerce

Despite rapid and sustained development of electronic commerce, many companies doing e-business are still in the investment and brand-building phase and have yet to show a profit. However, as e-businesses shift their focus from building a customer base to increasing revenue growth and profitability, they should re-evaluate their current business strategies, if any, and develop strategies that provide a clear path to profitability. This study uses McCarthy’s four marketing mix model and Porter’s five competitive forces model to identify strategies for Internet companies that respond to the five competitive forces and thereby achieve a competitive advantage. The study provides significant new insights into the development and implementation of e-business strategies that contribute to increased profit.
One deep music representation to rule them all? A comparative analysis of different representation learning strategies

Inspired by the success of deploying deep learning in the fields of Computer Vision and Natural Language Processing, this learning paradigm has also found its way into the field of Music Information Retrieval. In order to benefit from deep learning in an effective, but also efficient manner, deep transfer learning has become a common approach. In this approach, it is possible to reuse the output of a pre-trained neural network as the basis for a new learning task. The underlying hypothesis is that if the initial and new learning tasks show commonalities and are applied to the same type of input data (e.g., music audio), the generated deep representation of the data is also informative for the new task. Since, however, most of the networks used to generate deep representations are trained using a single initial learning source, their representation is unlikely to be informative for all possible future tasks. In this paper, we present the results of our investigation of what are the most important factors to generate deep representations for the data and learning tasks in the music domain. We conducted this investigation via an extensive empirical study that involves multiple learning sources, as well as multiple deep learning architectures with varying levels of information sharing between sources, in order to learn music representations. We then validate these representations considering multiple target datasets for evaluation. The results of our experiments yield several insights into how to approach the design of methods for learning widely deployable deep data representations in the music domain.
A Systematic DNN Weight Pruning Framework Using Alternating Direction Method of Multipliers

Weight pruning methods for deep neural networks (DNNs) have been investigated recently, but prior work in this area is mainly heuristic, iterative pruning, thereby lacking guarantees on the weight reduction ratio and convergence time. To mitigate these limitations, we present a systematic weight pruning framework of DNNs using the alternating direction method of multipliers (ADMM). We first formulate the weight pruning problem of DNNs as a nonconvex optimization problem with combinatorial constraints specifying the sparsity requirements, and then adopt the ADMM framework for systematic weight pruning. By using ADMM, the original nonconvex optimization problem is decomposed into two subproblems that are solved iteratively. One of these subproblems can be solved using stochastic gradient descent, the other can be solved analytically. Besides, our method achieves a fast convergence rate. The weight pruning results are very promising and consistently outperform the prior work. On the LeNet-5 model for the MNIST data set, we achieve 71.2× weight reduction without accuracy loss. On the AlexNet model for the ImageNet data set, we achieve 21× weight reduction without accuracy loss. When we focus on the convolutional layer pruning for computation reductions, we can reduce the total computation by five times compared with the prior work (achieving a total of 13.4× weight reduction in convolutional layers). Our models and codes are released at https://github.com/KaiqiZhang/admm-pruning.
Neural Style Transfer: A Review

The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). Since then, NST has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention and a variety of approaches are proposed to either improve or extend the original NST algorithm. In this paper, we aim to provide a comprehensive overview of the current progress towards NST. We first propose a taxonomy of current algorithms in the field of NST. Then, we present several evaluation methods and compare different NST algorithms both qualitatively and quantitatively. The review concludes with a discussion of various applications of NST and open problems for future research. A list of papers discussed in this review, corresponding codes, pre-trained models and more comparison results are publicly available at: https://github.com/ycjing/Neural-Style-Transfer-Papers.
A State of the Art Survey of Fraud Detection Technology

With the introduction of IT to conduct business we accepted the loss of a human control step. For this reason, the introduction of new IT systems was accompanied by the development of the authorization concept. But since, in reality, there is no such thing as 100 per cent security; auditors are commissioned to examine all transactions for misconduct. Since the data exists in digital form already, it makes sense to use computer-based processes to analyse it. Such processes allow the auditor to carry out extensive checks within an acceptable timeframe and with reasonable effort. Once the algorithm has been defined, it only takes sufficient computing power to evaluate larger quantities of data. This contribution presents the state of the art for IT-based data analysis processes that can be used to identify fraudulent activities.
Job Recommendation from Semantic Similarity of LinkedIn Users' Skills

Until recently job seeking has been a tricky, tedious and time consuming process, because people looking for a new position had to collect information from many different sources. Job recommendation systems have been proposed in order to automate and simplify this task, also increasing its effectiveness. However, current approaches rely on scarce manually collected data that often do not completely reveal people skills. Our work aims to find out relationships between jobs and people skills making use of data from LinkedIn users’ public profiles. Semantic associations arise by applying Latent Semantic Analysis (LSA). We use the mined semantics to obtain a hierarchical clustering of job positions and to build a job recommendation system. The outcome proves the effectiveness of our method in recommending job positions. Anyway, we argue that our approach is definitely general, because the extracted semantics could be worthy not only for job recommendation systems but also for recruiting systems. Furthermore, we point out that both the hierarchical clustering and the recommendation system do not require parameters to be tuned.
Dynamic Resource Allocation Algorithm for Container-Based Service Computing

Cloud computing and virtualization technologies play important roles in modern service-oriented computing paradigm. More conventional services are being migrated to virtualized computing environments to achieve flexible deployment and high availability. We introduce a schedule algorithm based on fuzzy inference system (FIS), for global container resource allocation by evaluating nodes' statuses using FIS. We present the approaches to build containerized test environment and validates the effectiveness of the resource allocation policies by running sample use cases. Experiment results show that the presented infrastructure and schema derive optimal resource configurations and significantly improves the performance of the cluster.
Integrating Natural Language Processing and Software Engineering

This paper tries to put various ways in which Natural Language Processing (NLP) and Software Engineering (SE) can be seen as inter-disciplinary research areas. We survey the current literature, with the aim of assessing use of Software Engineering and Natural Language Processing tools in the researches undertaken. An assessment of how various phases of SDLC can employ NLP techniques is presented. The paper also provides the justification of the use of text for automating or combining both these areas. A short research direction while undertaking multidisciplinary research is also provided.
Scenarios for Multistage Stochastic Programs

A major issue in any application of multistage stochastic programming is the representation of the underlying random data process. We discuss the case when enough data paths can be generated according to an accepted parametric or nonparametric stochastic model. No assumptions on convexity with respect to the random parameters are required. We emphasize the notion of representative scenarios (or a representative scenario tree) relative to the problem being modeled.
Advances in dental veneers: materials, applications, and techniques

Laminate veneers are a conservative treatment of unaesthetic anterior teeth. The continued development of dental ceramics offers clinicians many options for creating highly aesthetic and functional porcelain veneers. This evolution of materials, ceramics, and adhesive systems permits improvement of the aesthetic of the smile and the self-esteem of the patient. Clinicians should understand the latest ceramic materials in order to be able to recommend them and their applications and techniques, and to ensure the success of the clinical case. The current literature was reviewed to search for the most important parameters determining the long-term success, correct application, and clinical limitations of porcelain veneers.
WORKING PAPER SERIES Strategic Human Resource Management and Performance

The connection between human resources and performance in firms in the private sector is well documented. What is less clear is whether the move towards managerialism that has taken place within the Australian public sector during the last twenty years has brought with it some of the features of the relationships between Human Resource Management (HRM) and performance experienced within the private sector. The research begins with a review of the literature. In particular the conceptual thinking surrounding the connection between HRM and performance within private sector organisations is explored. Issues of concern are the direction of the relationship between HRM and performance and definitional questions as to the nature and level of HRM to be investigated and the measurement of performance. These conceptual issues are also debated within the context of a public sector and particularly the Australian environment. An outcome of this task is the specification of a set of appropriate parameters for a study of these linkages within Australian public sector organizations. Short Description The paper discusses the significance of strategic human resource management in relation to performance.
A global analysis of protected area management effectiveness.

We compiled details of over 8000 assessments of protected area management effectiveness across the world and developed a method for analyzing results across diverse assessment methodologies and indicators. Data was compiled and analyzed for over 4000 of these sites. Management of these protected areas varied from weak to effective, with about 40% showing major deficiencies. About 14% of the surveyed areas showed significant deficiencies across many management effectiveness indicators and hence lacked basic requirements to operate effectively. Strongest management factors recorded on average related to establishment of protected areas (legal establishment, design, legislation and boundary marking) and to effectiveness of governance; while the weakest aspects of management included community benefit programs, resourcing (funding reliability and adequacy, staff numbers and facility and equipment maintenance) and management effectiveness evaluation. Estimations of management outcomes, including both environmental values conservation and impact on communities, were positive. We conclude that in spite of inadequate funding and management process, there are indications that protected areas are contributing to biodiversity conservation and community well-being.
Phrase embedding learning based on external and internal context with compositionality constraint

Different methods are proposed to learn phrase embedding, which can be mainly divided into two strands. The first strand is based on the distributional hypothesis to treat a phrase as one non-divisible unit and to learn phrase embedding based on its external context similar to learn word embedding. However, distributional methods cannot make use of the information embedded in component words and they also face data spareness problem. The second strand is based on the principle of compositionality to infer phrase embedding based on the embedding of its component words. Compositional methods would give erroneous result if a phrase is non-compositional. In this paper, we propose a hybrid method by a linear combination of the distributional component and the compositional component with an individualized phrase compositionality constraint. The phrase compositionality is automatically computed based on the distributional embedding of the phrase and its component words. Evaluation on five phrase level semantic tasks and experiments show that our proposed method has overall best performance. Most importantly, our method is more robust as it is less sensitive to datasets.
Ayahuasca as Antidepressant ? Psychedelics and Styles of Reasoning in

There is a growing interest among scientists and the lay public alike in using the South American psychedelic brew, ayahuasca, to treat psychiatric disorders like depression and anxiety. Such a practice is controversial due to a style of reasoning within conventional psychiatry that sees psychedelic-induced modified states of consciousness as pathological. This article analyzes the academic literature on ayahuasca’s psychological effects to determine how this style of reasoning is shaping formal scientific discourse on ayahuasca’s therapeutic potential as a treatment for depression and anxiety. Findings from these publications suggest that different kinds of experiments are differentially affected by this style of reasoning but can nonetheless indicate some potential therapeutic utility of the ayahuasca-induced modified state of consciousness. The article concludes by suggesting ways in which conventional psychiatry’s dominant style of reasoning about psychedelic modified states of consciousness could be reconsidered. k e yword s : ayahuasca, psychedelic, hallucinogen, psychiatry,
A Review of the Effects of Physical Activity and Exercise on Cognitive and Brain Functions in Older Adults

Studies supporting the notion that physical activity and exercise can help alleviate the negative impact of age on the body and the mind abound. This literature review provides an overview of important findings in this fast growing research domain. Results from cross-sectional, longitudinal, and intervention studies with healthy older adults, frail patients, and persons suffering from mild cognitive impairment and dementia are reviewed and discussed. Together these finding suggest that physical exercise is a promising nonpharmaceutical intervention to prevent age-related cognitive decline and neurodegenerative diseases.
A High-Density, High-Efficiency, Isolated On-Board Vehicle Battery Charger Utilizing Silicon Carbide Power Devices

This paper presents an isolated on-board vehicular battery charger that utilizes silicon carbide (SiC) power devices to achieve high density and high efficiency for application in electric vehicles (EVs) and plug-in hybrid EVs (PHEVs). The proposed level 2 charger has a two-stage architecture where the first stage is a bridgeless boost ac-dc converter and the second stage is a phase-shifted full-bridge isolated dc-dc converter. The operation of both topologies is presented and the specific advantages gained through the use of SiC power devices are discussed. The design of power stage components, the packaging of the multichip power module, and the system-level packaging is presented with a primary focus on system density and a secondary focus on system efficiency. In this work, a hardware prototype is developed and a peak system efficiency of 95% is measured while operating both power stages with a switching frequency of 200 kHz. A maximum output power of 6.1 kW results in a volumetric power density of 5.0 kW/L and a gravimetric power density of 3.8 kW/kg when considering the volume and mass of the system including a case.
An Optimized Cost-Sensitive SVM for Imbalanced Data Learning

Class imbalance is one of the challenging problems for machine learning in many real-world applications. Cost-sensitive learning has attracted significant attention in recent years to solve the problem, but it is difficult to determine the precise misclassification costs in practice. There are also other factors that influence the performance of the classification including the input feature subset and the intrinsic parameters of the classifier. This paper presents an effective wrapper framework incorporating the evaluation measure (AUC and G-mean) into the objective function of cost sensitive SVM directly to improve the performance of classification by simultaneously optimizing the best pair of feature subset, intrinsic parameters and misclassification cost parameters. Experimental results on various standard benchmark datasets and real-world data with different ratios of imbalance show that the proposed method is effective in comparison with commonly used sampling techniques.
EDZNA : EARLIEST ASTRONOMICAL CENTER OF THE MAYA ?

Although the archaeological site of Edzná is one of the more accessible Mayan ruins, being located scarcely 60 km to the southeast of the port-city of Campeche, it has until recently escaped the notice which its true significance would seem to merit. Not only does it appear to have been the earliest major Mayan urban center, dating to the middle of the second century before the Christian era and having served as the focus of perhaps as many as 20, 000 inhabitants, but there is also a growing body of evidence to suggest that it played a key role in the development of Mayan astronomy and calendrics. Among the innovations that seemingly had their origin in Edzná are the Maya's fixing of their New Year's Day, the concept of "year bearers", and what is probably the oldest lunar observatory in the New World.