nils-herrmann/clean_scirepeval huggingface dataset:
Time series forecast in non-stationary environment with occurence of economic bubbles Bitcoin Price prediction

Being capable to foresee the future of a given financial asset as an investor, may lead to significant economic profits. Therefore, stock market prediction is a field that has been extensively developed by numerous researchers and companies. Recently, however, a new branch of financial assets has emerged, namely cryptocurrencies. As a representative of these tokens, we chose the largest and most popular cryptocurrency, called Bitcoin [40]. Its value is characterized by with non-stationary behaviour [63] and occurrence of speculative bubbles, which cause a rapid explosion of the price, followed by a major crisis and market panic [63]. Currently, most of the research community does not take these issues into account, while predicting its price, which may lead to wrong conclusions or unstable results. Therefore, in this thesis, we take a step back and reconsider how does the environment influence model’s performance and how to use this knowledge to implement more accurate forecast in the future. Moreover, by designing an appropriate methodology and employing semantic features from online text sources, such as Twitter, Reddit and online news portals, we attempt to build a robust prediction system that offers stable performance regardless of the market fluctuations. Executed experiments prove that non-stationarity negatively influences the results, causing the deterioration of model’s performance over time. Furthermore, it appears that there may be certain properties of economic bubbles that facilitate more efficient prediction, as well as some predictors have an ability to successfully forecast the beginning of a market crisis. However, these findings are based on individual observations, which need to be confirmed by further research. In addition, by designing an appropriate methodology, we prevented performance deterioration, caused by price signal non-stationarity. Although, the semantic features based on online sources did not boost the robustness of the system significantly, combined with the suitable system’s design, they lead to improvement in the overall performance of the predictor.
The Role of Natural Polyphenols in the Prevention and Treatment of Cervical Cancer—An Overview

Cervical cancer represents the second leading cause of death for women worldwide. The importance of the diet and its impact on specific types of neoplasia has been highlighted, focusing again interest in the analysis of dietary phytochemicals. Polyphenols have shown a wide range of cellular effects: they may prevent carcinogens from reaching the targeted sites, support detoxification of reactive molecules, improve the elimination of transformed cells, increase the immune surveillance and the most important factor is that they can influence tumor suppressors and inhibit cellular proliferation, interfering in this way with the steps of carcinogenesis. From the studies reviewed in this paper, it is clear that certain dietary polyphenols hold great potential in the prevention and therapy of cervical cancer, because they interfere in carcinogenesis (in the initiation, development and progression) by modulating the critical processes of cellular proliferation, differentiation, apoptosis, angiogenesis and metastasis. Specifically, polyphenols inhibit the proliferation of HPV cells, through induction of apoptosis, growth arrest, inhibition of DNA synthesis and modulation of signal transduction pathways. The effects of combinations of polyphenols with chemotherapy and radiotherapy used in the treatment of cervical cancer showed results in the resistance of cervical tumor cells to chemo- and radiotherapy, one of the main problems in the treatment of cervical neoplasia that can lead to failure of the treatment because of the decreased efficiency of the therapy.
Artificial Intelligence Approaches to Music Composition

Artificial Intelligence (AI) techniques have been successfully applied to a wide range of problems that perform problem solving such as diagnosis, decision making and optimization problems. However, any AI algorithm applied to a creative problem requires some mechanism to substitute for the creative spark found in the human, as the computer has no creative capacity. Randomness, as supplied by a random number generator, cannot be the sole mechanism to bring about a creative composition. This paper examines three AI approaches applied to music composition. Specifically, the paper introduces MAGMA, a knowledge-based system that uses three different AI algorithms to generate music. Sample songs generated by MAGMA are compared.
Design Thinking implemented in Software Engineering Tools Proposing and Applying the Design Thinking Transformation Framework

Thinking has collected theories and best-practices to foster creativity and innovation in group processes. This is in particular valuable for sketchy and complex problems. Other disciplines can learn from this body-of-behaviors and values to tackle their complex problems. In this paper, using four Design Thinking qualities, we propose a framework to identify the level of Design Thinkingness in existing analytical software engineering tools: Q1) Iterative Creation Cycles, Q2) Human Integration in Design, Q3) Suitability for Heterogeneity, and Q4) Media Accessibility. We believe that our framework can also be used to transform tools in various engineering areas to support abductive and divergent thinking processes. We argue, based on insights gained from the successful transformation of classical business process modeling into tangible business process modeling. This was achieved by incorporating rapid prototyping, human integration, knowledge base heterogeneity and the media-models theory. The latter is given special attention as it allows us to break free from the limiting factors of the exiting analytic tools.
Industrial Internet: A Survey on the Enabling Technologies, Applications, and Challenges

This paper provides an overview of the Industrial Internet with the emphasis on the architecture, enabling technologies, applications, and existing challenges. The Industrial Internet is enabled by recent rising sensing, communication, cloud computing, and big data analytic technologies, and has been receiving much attention in the industrial section due to its potential for smarter and more efficient industrial productions. With the merge of intelligent devices, intelligent systems, and intelligent decisioning with the latest information technologies, the Industrial Internet will enhance the productivity, reduce cost and wastes through the entire industrial economy. This paper starts by investigating the brief history of the Industrial Internet. We then present the 5C architecture that is widely adopted to characterize the Industrial Internet systems. Then, we investigate the enabling technologies of each layer that cover from industrial networking, industrial intelligent sensing, cloud computing, big data, smart control, and security management. This provides the foundations for those who are interested in understanding the essence and key enablers of the Industrial Internet. Moreover, we discuss the application domains that are gradually transformed by the Industrial Internet technologies, including energy, health care, manufacturing, public section, and transportation. Finally, we present the current technological challenges in developing Industrial Internet systems to illustrate open research questions that need to be addressed to fully realize the potential of future Industrial Internet systems.
Blockchain Design for Trusted Decentralized IoT Networks

The use of Blockchain in the Internet of Things (IoT) promises to provide an avenue of decentralized, fault-resistant management and data immutability. The wide range of research on blockchain, focused on how to improve upon its drawbacks of latency and resource consumption, demonstrates that the blockchain design has flexibility. Almost every aspect of blockchain can be tailored to fit the requirements of a desired application. However, the wide range of options in configuring blockchains presents challenges in the adoption of blockchains to secure IoT. In this paper, we present the opportunities and challenges of implementing blockchains and present a use case of integrating blockchain into an IoT framework for securing sensor data acquisition.
(Short Paper) A Wild Velvet Fork Appears! Inclusive Blockchain Protocol Changes in Practice

The loosely defined terms hard fork and soft fork have established themselves as descriptors of different classes of upgrade mechanisms for the underlying consensus rules of (proof-of-work) blockchains. Recently, a novel approach termed velvet fork, which expands upon the concept of a soft fork, was outlined in . Specifically, velvet forks intend to avoid the possibility of disagreement by a change of rules through rendering modifications to the protocol backward compatible and inclusive to legacy blocks. We present an overview and definitions of these different upgrade mechanisms and outline their relationships. Hereby, we expose examples where velvet forks or similar constructions are already actively employed in Bitcoin and other cryptocurrencies. Furthermore, we expand upon the concept of velvet forks by proposing possible applications and discuss potentially arising security implications.
Study of Performance Measurement Practices in Supply Chain Management

Supply chain management creates value for companies, customers and stakeholders interacting throughout a supply chain. The strategic dimension of supply chains makes it paramount that their performances are measured. In today’s performance evaluation processes, companies tend to refer to several models that will differ in terms of corporate organization, the distribution of responsibilities and supply chain maturity. The present article analyzes various models used to assess supply chains by highlighting their specific characteristics and applicability in different contexts. It also offers an analytical grid breaking these models down into seven layers. This grid will help managers evolve towards a model that is more suitable for their needs.
Link Prediction in Online Social Networks Using Group Information

Users of online social networks voluntarily participate in different user groups or communities. Researches suggest the presence of strong local community structure in these social networks, i.e., users tend to meet other people via mutual friendship. Recently, different approaches have considered communities structure information for increasing the link prediction accuracy. Nevertheless, these approaches consider that users belong to just one community. In this paper, we propose three measures for the link prediction task which take into account all different communities that users belong to. We perform experiments for both unsupervised and supervised link prediction strategies. The evaluation method considers the links imbalance problem. Results show that our proposals outperform state-of-the-art unsupervised link prediction measures and help to improve the link prediction task approached as a supervised strategy.
The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum of tissues.

A parametric model was developed to describe the variation of dielectric properties of tissues as a function of frequency. The experimental spectrum from 10 Hz to 100 GHz was modelled with four dispersion regions. The development of the model was based on recently acquired data, complemented by data surveyed from the literature. The purpose is to enable the prediction of dielectric data that are in line with those contained in the vast body of literature on the subject. The analysis was carried out on a Microsoft Excel spreadsheet. Parameters are given for 17 tissue types.
Rehabilitation , etiology , and self-regulation : The comprehensive good lives model of treatment for sexual offenders

In this paper, we draw upon two sets of theoretical resources to develop a comprehensive theory of sexual offender rehabilitation named the Good Lives Model-Comprehensive (GLM-C). The original Good Lives Model (GLM-O) forms the overarching values and principles guiding clinical practice in the GLM-C. In addition, the latest sexual offender theory (i.e., the Integrated Theory of Sexual Offending; ITSO) provides a clear etiological grounding for these principles. The result is a more substantial and improved rehabilitation model that is able to conceptually link latest etiological theory with clinical practice. Analysis of the GLM-C reveals that it also has the theoretical resources to secure currently used self-regulatory treatment practice within a meaningful structure. D 2005
Text mining and semantics: a systematic mapping study

As text semantics has an important role in text meaning, the term semantics has been seen in a vast sort of text mining studies. However, there is a lack of studies that integrate the different research branches and summarize the developed works. This paper reports a systematic mapping about semantics-concerned text mining studies. This systematic mapping study followed a well-defined protocol. Its results were based on 1693 studies, selected among 3984 studies identified in five digital libraries. The produced mapping gives a general summary of the subject, points some areas that lacks the development of primary or secondary studies, and can be a guide for researchers working with semantics-concerned text mining. It demonstrates that, although several studies have been developed, the processing of semantic aspects in text mining remains an open research problem.
Lactic acid bacteria as probiotics.

A number of Lactobacillus species, Bifidobacterium sp, Saccharomyces boulardii, and some other microbes have been proposed as and are used as probiotic strains, i.e. live microorganisms as food supplement in order to benefit health. The health claims range from rather vague as regulation of bowel activity and increasing of well-being to more specific, such as exerting antagonistic effect on the gastroenteric pathogens Clostridium difficile, Campylobacter jejuni, Helicobacter pylori and rotavirus, neutralising food mutagens produced in colon, shifting the immune response towards a Th2 response, and thereby alleviating allergic reactions, and lowering serum cholesterol (Tannock, 2002). Unfortunately, most publications are case reports, uncontrolled studies in humans, or reports of animal or in vitro studies. Whether or not the probiotic strains employed shall be of human origin is a matter of debate but this is not a matter of concern, as long as the strains can be shown to survive the transport in the human gastrointestinal (GI) tract and to colonise the human large intestine. This includes survival in the stressful environment of the stomach - acidic pH and bile - with induction of new genes encoding a number of stress proteins. Since the availability of antioxidants decreases rostrally in the GI tract production of antioxidants by colonic bacteria provides a beneficial effect in scavenging free radicals. LAB strains commonly produce antimicrobial substance(s) with activity against the homologous strain, but LAB strains also often produce microbicidal substances with effect against gastric and intestinal pathogens and other microbes, or compete for cell surface and mucin binding sites. This could be the mechanism behind reports that some probiotic strains inhibit or decrease translocation of bacteria from the gut to the liver. A protective effect against cancer development can be ascribed to binding of mutagens by intestinal bacteria, reduction of the enzymes beta-glucuronidase and beta-glucosidase, and deconjugation of bile acids, or merely by enhancing the immune system of the host. The latter has attracted considerable interest, and LAB have been tested in several clinical trials in allergic diseases. Characteristics ascribed to a probiotic strain are in general strain specific, and individual strains have to be tested for each property. Survival of strains during production, packing and storage of a viable cell mass has to be tested and declared.
Combining user and database perspective for solving keyword queries over relational databases

Over the last decade, keyword search over relational data has attracted considerable attention. A possible approach to face this issue is to transform keyword queries into one or more SQL queries to be executed by the relational DBMS. Finding these queries is a challenging task since the information they represent may be modeled across different elements where the data of interest is stored, but also to find out how these elements are interconnected. All the approaches that have been proposed so far provide a monolithic solution. In this work, we, instead, divide the problem into three steps: the first one, driven by the user's point of view, takes into account what the user has in mind when formulating keyword queries, the second one, driven by the database perspective, considers how the data is represented in the database schema. Finally, the third step combines these two processes. We present the theory behind our approach, and its implementation into a system called QUEST (QUEry generator for STructured sources), which has been deeply tested to show the efficiency and effectiveness of our approach. Furthermore, we report on the outcomes of a number of experimental results that we
Opportunities and Challenges of Tunnel FETs

Sustaining of Moore's Law over the next decade will require not only continued scaling of the physical dimensions of transistors but also performance improvement and aggressive reduction in power consumption. Heterojunction Tunnel FET (TFET) has emerged as promising transistor candidate for supply voltage scaling down to sub-0.5V due to the possibility of sub-kT/q switching without compromising on-current (ION). Recently, n-type III-V HTFET with reasonable on-current and sub-kT/q switching at supply voltage of 0.5V have been experimentally demonstrated. However, steep switching performance of III-V HTFET till date has been limited to range of drain current (IDS) spanning over less than a decade. In this work, we will present progress on complimentary Tunnel FETs and analyze primary roadblocks in the path towards achieving steep switching performance in III-V HTFET.
Lung nodule detection in CT images using neuro fuzzy classifier

Automated lung cancer detection using computer aided diagnosis (CAD) is an important area in clinical applications. As the manual nodule detection is very time consuming and costly so computerized systems can be helpful for this purpose. In this paper, we propose a computerized system for lung nodule detection in CT scan images. The automated system consists of two stages i.e. lung segmentation and enhancement, feature extraction and classification. The segmentation process will result in separating lung tissue from rest of the image, and only the lung tissues under examination are considered as candidate regions for detecting malignant nodules in lung portion. A feature vector for possible abnormal regions is calculated and regions are classified using neuro fuzzy classifier. It is a fully automatic system that does not require any manual intervention and experimental results show the validity of our system.
A pilot study on wheat grass juice for its phytochemical , nutritional and therapeutic potential on chronic diseases

Triticum aestivum (Wheat grass juice) has high concentrations of chlorophyll, amino acids, minerals, vitamins, and enzymes. Fresh juice has been shown to possess anti-cancer activity, anti-ulcer activity, anti-inflammatory, antioxidant activity, anti-arthritic activity, and blood building activity in Thalassemia. It has been argued that wheat grass helps blood flow, digestion, and general detoxification of the body due to the presence of biologically active compounds and minerals in it and due to its antioxidant potential which is derived from its high content of bioflavonoids such as apigenin, quercitin, luteoline. Furthermore, indole compounds, amely choline, which known for antioxidants and also possess chelating property for iron overload disorders. The presence of 70% chlorophyll, which is almost chemically identical to haemoglobin. The only difference is that the central element in chlorophyll is magnesium and in hemoglobin it is iron. In wheat grass makes it more useful in various clinical conditions involving hemoglobin deficiency and other chronic disorders ultimately considered as green blood.
R3: Reinforced Ranker-Reader for Open-Domain Question Answering

In recent years researchers have achieved considerable success applying neural network methods to question answering (QA). These approaches have achieved state of the art results in simplified closed-domain settings such as the SQuAD (Rajpurkar et al. 2016) dataset, which provides a preselected passage, from which the answer to a given question may be extracted. More recently, researchers have begun to tackle open-domain QA, in which the model is given a question and access to a large corpus (e.g., wikipedia) instead of a pre-selected passage (Chen et al. 2017a). This setting is more complex as it requires large-scale search for relevant passages by an information retrieval component, combined with a reading comprehension model that “reads” the passages to generate an answer to the question. Performance in this setting lags well behind closed-domain performance. In this paper, we present a novel open-domain QA system called Reinforced Ranker-Reader (R), based on two algorithmic innovations. First, we propose a new pipeline for open-domain QA with a Ranker component, which learns to rank retrieved passages in terms of likelihood of extracting the ground-truth answer to a given question. Second, we propose a novel method that jointly trains the Ranker along with an answer-extraction Reader model, based on reinforcement learning. We report extensive experimental results showing that our method significantly improves on the state of the art for multiple open-domain QA datasets.
Integrating forensic anthropology into Disaster Victim Identification.

This paper will provide mass fatality emergency planners, police, medical examiners, coroners and other Disaster Victim Identification (DVI) personnel ways to integrate forensic anthropologists into DVI operations and demonstrate how anthropological contributions have improved DVI projects. In mass disaster situations, anthropologists have traditionally been limited to developing biological profiles from skeletal remains. Over the past decade, however, anthropologists' involvement in DVI has extended well beyond this traditional role as they have taken on increasingly diverse tasks and responsibilities. Anthropological involvement in DVI operations is often dictated by an incident's specific characteristics, particularly events involving extensive fragmentation, commingling, or other forms of compromised remains. This paper will provide examples from recent DVI incidents to illustrate the operational utility of anthropologists in the DVI context. The points where it is most beneficial to integrate anthropologists into the DVI process include: (1) during recovery at the disaster scene; (2) at the triage station as remains are brought into the mortuary; and (3) in conducting the reconciliation process. Particular attention will be paid to quality control and quality assurance measures anthropologists have developed and implemented for DVI projects. Overall, this paper will explain how anthropological expertise can be used to increase accuracy in DVI while reducing the project's cost and duration.