nils-herrmann/clean_scirepeval huggingface dataset:
TIME SERIES FORECASTING – A MACHINE LEARNING APPROACH

The Stock Market is known for its volatile and unstable nature. A particular stock could be thriving in one period and declining in the next. Stock traders make money from buying equity when they are at their lowest and selling when they are at their highest. The logical question would be: "What Causes Stock Prices To Change?". At the most fundamental level, the answer to this would be the demand and supply. In reality, there are many theories as to why stock prices fluctuate, but there is no generic theory that explains all, simply because not all stocks are identical, and one theory that may apply for today, may not necessarily apply for tomorrow. This paper covers various approaches taken to attempt to predict the stock market without extensive prior knowledge or experience in the subject area, highlighting the advantages and limitations of the different techniques such as regression and classification. We formulate both short term and long term predictions. Through experimentation we achieve 81% accuracy for future trend direction using classification, 0.0117 RMSE for next day price and 0.0613 RMSE for next day change in price using regression techniques. The results obtained in this paper are achieved using only historic prices and technical indicators. Various methods, tools and evaluation techniques will be assessed throughout the course of this paper, the result of this contributes as to which techniques will be selected and enhanced in the final artefact of a stock prediction model. Further work will be conducted utilising deep learning techniques to approach the problem. This paper will serve as a preliminary guide to researchers wishing to expose themselves to this area.
Generating Text through Adversarial Training using Skip-Thought Vectors

In the past few years, various advancements have been made in generative models owing to the formulation of Generative Adversarial Networks (GANs). GANs have been shown to perform exceedingly well on a wide variety of tasks pertaining to image generation and style transfer. In the field of Natural Language Processing, word embeddings such as word2vec and GLoVe are state-of-the-art methods for applying neural network models on textual data. Attempts have been made for utilizing GANs with word embeddings for text generation. This work presents an approach to text generation using SkipThought sentence embeddings in conjunction with GANs based on gradient penalty functions and f-measures. The results of using sentence embeddings with GANs for generating text conditioned on input information are comparable to the approaches where word embeddings are used.
From Complex Event Processing to Simple Event Processing

Many problems in Computer Science can be framed as the computation of queries over sequences, or “streams” of data units called events. The field of Complex Event Processing (CEP) relates to the techniques and tools developed to efficiently process these queries. However, most CEP systems developed so far have concentrated on relatively narrow types of queries, which consist of sliding windows, aggregation functions, and simple sequential patterns computed over events that have a fixed tuple structure. Many of them boast throughput, but in counterpart, they are difficult to setup and cumbersome to extend with user-defined elements. This paper describes a variety of use cases taken from real-world scenarios that present features seldom considered in classical CEP problems. It also provides a broad review of current solutions, that includes tools and techniques going beyond typical surveys on CEP. From a critical analysis of these solutions, design principles for a new type of event stream processing system are exposed. The paper proposes a simple, generic and extensible framework for the processing of event streams of diverse types; it describes in detail a stream processing engine, called BeepBeep, that implements these principles. BeepBeep’s modular architecture, which borrows concepts from many other systems, is complemented with an extensible query language, called eSQL. The end result is an open, versatile, and reasonably efficient query engine that can be used in situations that go beyond the capabilities of existing systems.
Dehazing Using Color-Lines

Photographs of hazy scenes typically suffer having low contrast and offer a limited visibility of the scene. This article describes a new method for single-image dehazing that relies on a generic regularity in natural images where pixels of small image patches typically exhibit a 1D distribution in RGB color space, known as color-lines. We derive a local formation model that explains the color-lines in the context of hazy scenes and use it for recovering the scene transmission based on the lines' offset from the origin. The lack of a dominant color-line inside a patch or its lack of consistency with the formation model allows us to identify and avoid false predictions. Thus, unlike existing approaches that follow their assumptions across the entire image, our algorithm validates its hypotheses and obtains more reliable estimates where possible.
 In addition, we describe a Markov random field model dedicated to producing complete and regularized transmission maps given noisy and scattered estimates. Unlike traditional field models that consist of local coupling, the new model is augmented with long-range connections between pixels of similar attributes. These connections allow our algorithm to properly resolve the transmission in isolated regions where nearby pixels do not offer relevant information.
 An extensive evaluation of our method over different types of images and its comparison to state-of-the-art methods over established benchmark images show a consistent improvement in the accuracy of the estimated scene transmission and recovered haze-free radiances.
A Privacy-Preserving Deep Learning Approach for Face Recognition with Edge Computing

Deep convolutional neural networks (DNNs) have brought significant performance improvements to face recognition. However the training can hardly be carried out on mobile devices because the training of these models requires much computational power. An individual user with the demand of deriving DNN models from her own datasets usually has to outsource the training procedure onto a cloud or edge server. However this outsourcing method violates privacy because it exposes the users’ data to curious service providers. In this paper, we utilize the differentially private mechanism to enable the privacy-preserving edge based training of DNN face recognition models. During the training, DNN is split between the user device and the edge server in a way that both private data and model parameters are protected, with only a small cost of local computations. We show that our mechanism is capable of training models in different scenarios, e.g., from scratch, or through finetuning over existed models.
Pharmacological properties of cannabidiol in the treatment of psychiatric disorders: a critical overview.

Cannabidiol (CBD) represents a new promising drug due to a wide spectrum of pharmacological actions. In order to relate CBD clinical efficacy to its pharmacological mechanisms of action, we performed a bibliographic search on PUBMED about all clinical studies investigating the use of CBD as a treatment of psychiatric symptoms. Findings to date suggest that (a) CBD may exert antipsychotic effects in schizophrenia mainly through facilitation of endocannabinoid signalling and cannabinoid receptor type 1 antagonism; (b) CBD administration may exhibit acute anxiolytic effects in patients with generalised social anxiety disorder through modification of cerebral blood flow in specific brain sites and serotonin 1A receptor agonism; (c) CBD may reduce withdrawal symptoms and cannabis/tobacco dependence through modulation of endocannabinoid, serotoninergic and glutamatergic systems; (d) the preclinical pro-cognitive effects of CBD still lack significant results in psychiatric disorders. In conclusion, current evidences suggest that CBD has the ability to reduce psychotic, anxiety and withdrawal symptoms by means of several hypothesised pharmacological properties. However, further studies should include larger randomised controlled samples and investigate the impact of CBD on biological measures in order to correlate CBD's clinical effects to potential modifications of neurotransmitters signalling and structural and functional cerebral changes.
Research Note: a Study of Direct Selling Perceptions In

(Document  Direct selling as a type of non-store retailing continues to increase internationally and in Australia in its use and popularity. One non-store retailing method, multilevel marketing or network marketing, has recently incurred a degree of consumer suspicion and negative perceptions. A study was developed to investigate consumer perceptions and concerns in New South Wales and Victoria. Consumers were surveyed to determine their perception of direct selling and its relationship to consumer purchasing decisions. Responses indicate consumers had a negative perceptions towards network marketing, while holding a low positive view of direct selling. There appears to be no influence of network marketing on consumer purchase decisions. Direct selling, as a method of non-store retailing, has continued to increase in popularity in Australia and internationally. This study investigated network marketing as a type of direct selling in Australia, by examining consumers' perceptions. The results indicate that Australian consumers were generally negative and suspicious towards network marketing in Australia.
The impact of emotion on perception, attention, memory, and decision-making.

Reason and emotion have long been considered opposing forces. However, recent psychological and neuroscientific research has revealed that emotion and cognition are closely intertwined. Cognitive processing is needed to elicit emotional responses. At the same time, emotional responses modulate and guide cognition to enable adaptive responses to the environment. Emotion determines how we perceive our world, organise our memory, and make important decisions. In this review, we provide an overview of current theorising and research in the Affective Sciences. We describe how psychological theories of emotion conceptualise the interactions of cognitive and emotional processes. We then review recent research investigating how emotion impacts our perception, attention, memory, and decision-making. Drawing on studies with both healthy participants and clinical populations, we illustrate the mechanisms and neural substrates underlying the interactions of cognition and emotion.
Compressed Sensing: Theory and Applications

Compressed sensing is a novel research area, which was introduced in 2006, and since then has already become a key concept in various areas of applied mathematics, computer science, and electrical engineering. It surprisingly predicts that high-dimensional signals, which allow a sparse representation by a suitable basis or, more generally, a frame, can be recovered from what was previously considered highly incomplete linear measurements by using efficient algorithms. This article shall serve as an introduction to and a survey about compressed sensing.
Rational Systems Design for Health Information Systems in Low-Income Countries : An Enterprise Architecture Approach By

Low-income countries with their funding and implementing partners are increasingly recognizing health information systems (HIS) as an essential way to strengthen and support health systems. There is tremendous potential for innovations in information and communication technologies to assist health managers, health workers, and patients. Yet individual technologies and software applications are often developed without specifying how they will interact and communicate with existing and future information systems. Furthermore, they are developed without giving adequate attention to the needs the information system is supposed to address, resulting in software applications that do not effectively meet user needs. There is a lack of documented systematic methodology for gathering and documenting requirements for developing HIS. This article introduces a systematic, architected, and rational approach (SARA) for the design and development of health information systems. SARA, based on an Enterprise Architecture (EA) approach, represents a portfolio of practices, tools, and methods that can be easily and appropriately adapted and applied in the design phase of health information system development. This article will present early efforts to develop this portfolio including lessons learned from applying SARA in Tanzania.
Cross-Organizational Workflow Management Using Blockchain Technology - Towards Applicability, Auditability, and Automation

Bringing Blockchain technology and business process management together, we follow the Design Science Research approach and design, implement, and evaluate a Blockchain prototype for crossorganizational workflow management together with a German bank. For the use case of a documentary letter of credit we describe the status quo of the process, identify areas of improvement, implement a Blockchain solution, and compare both workflows. The prototype illustrates that the process, as of today paper-based and with high manual effort, can be significantly improved. Our research reveals that a tamper-proof process history for improved auditability, automation of manual process steps and the decentralized nature of the system can be major advantages of a Blockchain solution for crossorganizational workflow management. Further, our research provides insights how Blockchain technology can be used for business process management in general.
Hypervisors vs. Lightweight Virtualization: A Performance Comparison

Virtualization of operating systems provides a common way to run different services in the cloud. Recently, the lightweight virtualization technologies claim to offer superior performance. In this paper, we present a detailed performance comparison of traditional hypervisor based virtualization and new lightweight solutions. In our measurements, we use several benchmarks tools in order to understand the strengths, weaknesses, and anomalies introduced by these different platforms in terms of processing, storage, memory and network. Our results show that containers achieve generally better performance when compared with traditional virtual machines and other recent solutions. Albeit containers offer clearly more dense deployment of virtual machines, the performance difference with other technologies is in many cases relatively small.
Putting flesh on the Duality of Structure: the case of IT Consumerization

The use of consumer IT at work, known as "IT Consumerization", is changing the dynamic between employees and IT departments. Employees are empowered and now have greater freedom but also responsibility as to what technologies to use at work and how. At the same time, organizational factors such as rules on technology use still exert considerable influence on employees' actions. Drawing on Structuration Theory, we frame this interaction between organization and employee as one of structure and agency. In the process, we pursue an explorative approach and rely on qualitative data from interviews with public-sector employees. We identify four organizational structures that influence people's behavior with respect to IT Consumerization: policies, equipment, tasks and authority. By spotlighting the mutual influence of these organizational structures and Consumerization-related behavior, we show Giddens's duality of structure in action and demonstrate its relevance to the study of IT Consumerization.
A normative study of family media habits

The present study documents family media habits in six areas: electronic and print Media Use, parental Monitoring of children’s media, parental Consistency regarding rules for children’s media use, parents’ reports of observable Media Effects on their children, parents’ Knowledge about media and media effects, and how much children participate in Alternative Activities to electronic media. A random national sample of 527 parents of 2to 17-year-olds were surveyed. Normative descriptive statistics in each of the six categories are provided. In general, families’ scores in five of the six categories are positively correlated, indicating that families that practice positive media habits in one area tend to practice positive habits in other areas. Many family media habits predict children’s school performance. Furthermore, having TVs in children’s bedrooms is correlated with lower scores in all six areas, as well as with lower school performance. This study provides a normative baseline for future research. D 2002 Elsevier Science Inc. All rights reserved.
Deep Learning for Automated Quality Assessment of Color Fundus Images in Diabetic Retinopathy Screening

192 words]  To develop a computer based method for the automated assessment of image quality in the context of diabetic retinopathy (DR) to guide the photographer.  A deep learning framework was trained to grade the images automatically. A large representative set of 7000 color fundus images were used for the experiment which were obtained from the EyePACS (http://www.eyepacs.com/) that were made available by the California Healthcare Foundation. Three retinal image analysis experts were employed to categorize these images into ‘accept’ and ‘reject’ classes based on the precise definition of image quality in the context of DR. A deep learning framework was trained using 3428 images.  A total of 3572 images were used for the evaluation of the proposed method. The method shows an accuracy of 100% to successfully categorise ‘accept’ and ‘reject’ images.  Image quality is an essential prerequisite for the grading of DR. In this paper we have proposed a deep learning based automated image quality assessment method in the context of DR.
RIOT OS: Towards an OS for the Internet of Things

The Internet of Things (IoT) is characterized by heterogeneous devices. They range from very lightweight sensors powered by 8-bit microcontrollers (MCUs) to devices equipped with more powerful, but energy-efficient 32-bit processors. Neither a traditional operating system (OS) currently running on Internet hosts, nor typical OS for sensor networks are capable to fulfill the diverse requirements of such a wide range of devices. To leverage the IoT, redundant development should be avoided and maintenance costs should be reduced. In this paper we revisit the requirements for an OS in the IoT. We introduce RIOT OS, an OS that explicitly considers devices with minimal resources but eases development across a wide range of devices. RIOT OS allows for standard C and C++ programming, provides multi-threading as well as real-time capabilities, and needs only a minimum of 1.5 kB of RAM.
Agent Oriented Programming

Shoham, Y., Agent-oriented programming, Artificial Intelligence 60 (1993) 51-92. A new computational framework is presented, called agent-oriented programming (AOP), which can be viewed as a specialization of object-oriented programming. The state of an agent consists of components such as beliefs, decisions, capabilities, and obligations; for this reason the state of an agent is called its mental state. The mental state of agents is described formally in an extension of standard epistemic logics: beside temporalizing the knowledge and belief operators, AOP introduces operators for obligation, decision, and capability. Agents are controlled by agent programs, which include primitives for communicating with other agents. In the spirit of speech act theory, each communication primitive is of a certain type: informing, requesting, offering, and so on. This article presents the concept of AOP, discusses the concept of mental state and its formal underpinning, defines a class of agent interpreters, and then describes in detail a specific interpreter that has been implemented.
Techniques , Applications and Challenging Issue in Text Mining

Text mining is a very exciting research area as it tries to discover knowledge from unstructured texts. These texts can be found on a computer desktop, intranets and the internet. The aim of this paper is to give an overview of text mining in the contexts of its techniques, application domains and the most challenging issue. The focus is given on fundamentals methods of text mining which include natural language possessing and information extraction. This paper also gives a short review on domains which have employed text mining. The challenging issue in text mining which is caused by the complexity in a natural language is also addressed in this paper.
DISCRETIZED NEURAL NETWORKS

Neural network quantization has become an important research area due to its great impact on deployment of large models on resource constrained devices. In order to train networks that can be effectively discretized without loss of performance, we introduce a differentiable quantization procedure. Differentiability can be achieved by transforming continuous distributions over the weights and activations of the network to categorical distributions over the quantization grid. These are subsequently relaxed to continuous surrogates that can allow for efficient gradient-based optimization. We further show that stochastic rounding can be seen as a special case of the proposed approach and that under this formulation the quantization grid itself can also be optimized with gradient descent. We experimentally validate the performance of our method on MNIST, CIFAR 10 and Imagenet classification.
Toward a Learning Science for Complex Crowdsourcing Tasks

We explore how crowdworkers can be trained to tackle complex crowdsourcing tasks. We are particularly interested in training novice workers to perform well on solving tasks in situations where the space of strategies is large and workers need to discover and try different strategies to be successful. In a first experiment, we perform a comparison of five different training strategies. For complex web search challenges, we show that providing expert examples is an effective form of training, surpassing other forms of training in nearly all measures of interest. However, such training relies on access to domain expertise, which may be expensive or lacking. Therefore, in a second experiment we study the feasibility of training workers in the absence of domain expertise. We show that having workers validate the work of their peer workers can be even more effective than having them review expert examples if we only present solutions filtered by a threshold length. The results suggest that crowdsourced solutions of peer workers may be harnessed in an automated training pipeline.