nils-herrmann/clean_scirepeval huggingface dataset:
Chemopreventive and Therapeutic Effects of Edible Berries: A Focus on Colon Cancer Prevention and Treatment

Colon cancer is one of the most prevalent diseases across the world. Numerous epidemiological studies indicate that diets rich in fruit, such as berries, provide significant health benefits against several types of cancer, including colon cancer. The anticancer activities of berries are attributed to their high content of phytochemicals and to their relevant antioxidant properties. In vitro and in vivo studies have demonstrated that berries and their bioactive components exert therapeutic and preventive effects against colon cancer by the suppression of inflammation, oxidative stress, proliferation and angiogenesis, through the modulation of multiple signaling pathways such as NF-κB, Wnt/β-catenin, PI3K/AKT/PKB/mTOR, and ERK/MAPK. Based on the exciting outcomes of preclinical studies, a few berries have advanced to the clinical phase. A limited number of human studies have shown that consumption of berries can prevent colorectal cancer, especially in patients at high risk (familial adenopolyposis or aberrant crypt foci, and inflammatory bowel diseases). In this review, we aim to highlight the findings of berries and their bioactive compounds in colon cancer from in vitro and in vivo studies, both on animals and humans. Thus, this review could be a useful step towards the next phase of berry research in colon cancer.
Semantic Word Clusters Using Signed Spectral Clustering

Vector space representations of words capture many aspects of word similarity, but such methods tend to produce vector spaces in which antonyms (as well as synonyms) are close to each other. For spectral clustering using such word embeddings, words are points in a vector space where synonyms are linked with positive weights, while antonyms are linked with negative weights. We present a new signed spectral normalized graph cut algorithm, signed clustering, that overlays existing thesauri upon distributionally derived vector representations of words, so that antonym relationships between word pairs are represented by negative weights. Our signed clustering algorithm produces clusters of words that simultaneously capture distributional and synonym relations. By using randomized spectral decomposition (Halko et al., 2011) and sparse matrices, our method is both fast and scalable. We validate our clusters using datasets containing human judgments of word pair similarities and show the benefit of using our word clusters for sentiment prediction.
Enhancing Entity Linking by Combining NER Models

Numerous entity linking systems are addressing the entity recognition problem by using off-the-shelf NER systems. It is, however, a difficult task to select which specific model to use for these systems, since it requires to judge the level of similarity between the datasets which have been used to train models and the dataset at hand to be processed in which we aim to properly recognize entities. In this paper, we present the newest version of ADEL, our adaptive entity recognition and linking framework, where we experiment with an hybrid approach mixing a model combination method to improve the recognition level and to increase the efficiency of the linking step by applying a filter over the types. We obtain promising results when performing a 4-fold cross validation experiment on the OKE 2016 challenge training dataset. We also demonstrate that we achieve better results that in our previous participation on the OKE 2015 test set. We finally report the results of ADEL on the OKE 2016 test set and we present an error analysis highlighting the main difficulties of this challenge.
When everything new is well-forgotten old: Vygotsky/Luria insights in the development of executive functions.

The concept of "extra-cortical organization of higher mental functions" proposed by Lev Vygotsky and expanded by Alexander Luria extends cultural-historical psychology regarding the interplay of natural and cultural factors in the development of the human mind. Using the example of self-regulation, the authors explore the evolution of this idea from its origins to recent findings on the neuropsychological trajectories of the development of executive functions. Empirical data derived from the Tools of the Mind project are used to discuss the idea of using classroom intervention to study the development of self-regulation in early childhood.
A Comparative Study on Brain Tumor Analysis Using Image Mining Techniques

The growth of abnormal cells in the brain is called as Brain Tumor which affects the normal functionalities of the brain. It is one among the major leading cause for the human death. The causes for the brain tumors are unpredictable, because there are more than 120 types of brain tumors. Earlier diagnosis can save the life of a person. National Brain Tumor Society has given a survey that 31.7% in male and 34.4% in female are affected by brain tumor. American brain tumor association has given a survey that, more or less 70,000 people will be diagnosed in this year. Therefore, many researchers have proposed new system for the diagnosis of the brain tumor. We analyzed those research and finally summarized this paper with some conclusion about the efficiently and accuracy of those proposed systems.  ―Image mining, Preprocessing, Feature extraction, Image classification, Segmentation‖
Artificial Intelligence-Assisted Online Social Therapy for Youth Mental Health

Benefits from mental health early interventions may not be sustained over time, and longer-term intervention programs may be required to maintain early clinical gains. However, due to the high intensity of face-to-face early intervention treatments, this may not be feasible. Adjunctive internet-based interventions specifically designed for youth may provide a cost-effective and engaging alternative to prevent loss of intervention benefits. However, until now online interventions have relied on human moderators to deliver therapeutic content. More sophisticated models responsive to user data are critical to inform tailored online therapy. Thus, integration of user experience with a sophisticated and cutting-edge technology to deliver content is necessary to redefine online interventions in youth mental health. This paper discusses the development of the moderated online social therapy (MOST) web application, which provides an interactive social media-based platform for recovery in mental health. We provide an overview of the system's main features and discus our current work regarding the incorporation of advanced computational and artificial intelligence methods to enhance user engagement and improve the discovery and delivery of therapy content.  Our case study is the ongoing Horyzons site (5-year randomized controlled trial for youth recovering from early psychosis), which is powered by MOST. We outline the motivation underlying the project and the web application's foundational features and interface. We discuss system innovations, including the incorporation of pertinent usage patterns as well as identifying certain limitations of the system. This leads to our current motivations and focus on using computational and artificial intelligence methods to enhance user engagement, and to further improve the system with novel mechanisms for the delivery of therapy content to users. In particular, we cover our usage of natural language analysis and chatbot technologies as strategies to tailor interventions and scale up the system.  To date, the innovative MOST system has demonstrated viability in a series of clinical research trials. Given the data-driven opportunities afforded by the software system, observed usage patterns, and the aim to deploy it on a greater scale, an important next step in its evolution is the incorporation of advanced and automated content delivery mechanisms.
Automatic Text Summarization

Summarization is the art of abstracting key content from one or more information sources . Summarization includes text summarization, image summarization, and video summarization. Text summarization is one of application of natural language processing and is becoming more popular for information condensation [1].Information is accessible in great quantity for every topic on internet assembly the key information in the form of summary would benefit a number of users. Automatic text summarization system generates a summary, i.e. it contains short length text which comprises all the key information of the document. Summary can be generated through extractive as well as abstractive methods.
A Distortion Rectification Method for Distorted QR Code Images Based on Self-Adapting Structural Element

With the rapid development of Internet of Things and communication technologies, QR code images are widely used in the embedded automatic identification field. However, previous works for the location of QR code have not considered how to extract the vertexes accurately in geometric distorted QR code images. In this paper, utilizing the characteristics of QR code, we propose an effective mathematical morphology method based on self-adapting structural element to obtain the distorted vertexes precisely and revise the distortion. Our method evaluates two data sets. Compared with previous works, the experiments indicate that our method can achieve higher recognition rate and be more adaptive for geometric distorted QR code images. Thus, the proposed method provides valuable application for embedded system and mobile terminal device.
Virtual Reality for Research in Social Neuroscience

The emergence of social neuroscience has significantly advanced our understanding of the relationship that exists between social processes and their neurobiological underpinnings. Social neuroscience research often involves the use of simple and static stimuli lacking many of the potentially important aspects of real world activities and social interactions. Whilst this research has merit, there is a growing interest in the presentation of dynamic stimuli in a manner that allows researchers to assess the integrative processes carried out by perceivers over time. Herein, we discuss the potential of virtual reality for enhancing ecological validity while maintaining experimental control in social neuroscience research. Virtual reality is a technology that allows for the creation of fully interactive, three-dimensional computerized models of social situations that can be fully controlled by the experimenter. Furthermore, the introduction of interactive virtual characters-either driven by a human or by a computer-allows the researcher to test, in a systematic and independent manner, the effects of various social cues. We first introduce key technical features and concepts related to virtual reality. Next, we discuss the potential of this technology for enhancing social neuroscience protocols, drawing on illustrative experiments from the literature.
RCS Reduction of Waveguide Slot Antenna With Metamaterial Absorber

This communication investigates the application of metamaterial absorber (MA) to waveguide slot antenna to reduce its radar cross section (RCS). A novel ultra-thin MA is presented, and its absorbing characteristics and mechanism are analyzed. The PEC ground plane of waveguide slot antenna is covered by this MA. As compared with the slot antenna with a PEC ground plane, the simulation and experiment results demonstrate that the monostatic and bistatic RCS of waveguide slot antenna are reduced significantly, and the performance of antenna is preserved simultaneously.
IGBT History, State-of-the-Art, and Future Prospects

An overview on the history of the development of insulated gate bipolar transistors (IGBTs) as one key component in today’s power electronic systems is given; the state-of-the-art device concepts are explained as well as an detailed outlook about ongoing and foreseeable development steps is shown. All these measures will result on the one hand in ongoing power density and efficiency increase as important contributors for worldwide energy saving and environmental protection efforts. On the other hand, the exciting competition of more maturing Si IGBT technology with the wide bandgap successors of GaN and SiC switches will go on.
Fast and Scalable Expansion of Natural Language Understanding Functionality for Intelligent Agents

Fast expansion of natural language functionality of intelligent virtual agents is critical for achieving engaging and informative interactions. However, developing accurate models for new natural language domains is a time and data intensive process. We propose efficient deep neural network architectures that maximally re-use available resources through transfer learning. Our methods are applied for expanding the understanding capabilities of a popular commercial agent and are evaluated on hundreds of new domains, designed by internal or external developers. We demonstrate that our proposed methods significantly increase accuracy in low resource settings and enable rapid development of accurate models with less data.
Hydrogen Production from Semiconductor-based Photocatalysis via Water Splitting

Hydrogen is the ideal fuel for the future because it is clean, energy efficient, and abundant in nature. While various technologies can be used to generate hydrogen, only some of them can be considered environmentally friendly. Recently, solar hydrogen generated via photocatalytic water splitting has attracted tremendous attention and has been extensively studied because of its great potential for low-cost and clean hydrogen production. This paper gives a comprehensive review of the development of photocatalytic water splitting for generating hydrogen, particularly under visible-light irradiation. The topics covered include an introduction of hydrogen production technologies, a review of photocatalytic water splitting over titania and non-titania based photocatalysts, a discussion of the types of photocatalytic water-splitting approaches, and a conclusion for the current challenges and future prospects of photocatalytic water splitting. Based on the literatures reported here, the development of highly stable visible–light-active photocatalytic materials, and the design of efficient, low-cost photoreactor systems are the key for the advancement of solar-hydrogen production via photocatalytic water splitting in the future.
Ionospheric Scintillation Effects on GPS Receivers during Solar Minimum and Maximum

The ionosphere has practical importance in GPS (Global Positioning System) applications because it influences transionospheric radio wave propagation. Among various phenomena in the ionosphere, ionospheric scintillation is characterized by rapid fluctuation and fading of the received signal intensity due to electron density irregularity inside the ionosphere. Deep signal fading caused by scintillation can lead to loss of lock of the carrier tracking loop in GPS receivers. The aircraft navigation system based on GPS and WAAS (Wide Area Augmentation System) uses code and carrier phase data to create the position estimate. If a significant number of carrier tracking channels lose lock simultaneously, the system cannot provide service until reacquiring a sufficient number of channels. Although scintillation is not frequent in the mid-latitude region, it is a potential hazard in terms of availability and continuity of the service in the equatorial region . This paper investigates scintillation effects on GPS receivers during solar minimum in the equatorial region. Among four different receivers used for data collection in Brazil, this paper primarily focuses on the scintillation effects on a certified WAAS receiver because it is currently used for aircraft navigation. A maximum of two simultaneous loss of phase locks was observed in 0.05% of epochs. With the given satellite geometry, two satellites loss in a small fraction of time was not a critical problem for navigation. This paper also analyzes solar maximum data collected at Ascension Island during the previous solar maximum period. More satellite channels were affected by scintillation compared to solar minimum, but the maximum number of simultaneous loss of lock in a software receiver was the same as the solar minimum case. One of the most important results of this paper is that instances of simultaneous loss of satellite data can be reduced by minimizing reacquisition time of GPS receivers. Although 20 seconds reacquisition time is allowed by WAAS MOPS (Minimum Operational Performance Standards) , this number should be reduced to guarantee enough availability during strong scintillation.
Explainable Recommendation: Theory and Applications

Although personalized recommendation has been investigated for decades, the wide adoption of Latent Factor Models (LFM) has made the explainability of recommendations a critical issue to both the research community and practical application of recommender systems. For example, in many practical systems the algorithm just provides a personalized item recommendation list to the users, without persuasive personalized explanation about why such an item is recommended while another is not. Unexplainable recommendations introduce negative effects to the trustworthiness of recommender systems, and thus affect the effectiveness of recommendation engines. In this work, we investigate explainable recommendation in aspects of data explainability, model explainability, and result explainability, and the main contributions are as follows: 1.  We propose Localized Matrix Factorization (LMF) framework based Bordered Block Diagonal Form (BBDF) matrices, and further applied this technique for parallelized matrix factorization. 2. Model Explainability: We propose Explicit Factor Models (EFM) based on phrase-level sentiment analysis, as well as dynamic user preference modeling based on time series analysis. In this work, we extract product features and user opinions towards different features from large-scale user textual reviews based on phrase-level sentiment analysis techniques, and introduce the EFM approach for explainable model learning and recommendation. 3. Economic Explainability: We propose the Total Surplus Maximization (TSM) framework for personalized recommendation, as well as the model specification in different types of online applications. Based on basic economic concepts, we provide the definitions of utility, cost, and surplus in the application scenario of Web services, and propose the general framework of web total surplus calculation and maximization.
Feel Me Flow: A Review of Control-Flow Integrity Methods for User and Kernel Space

Attackers have evolved classic code-injection attacks, such as those caused by buffer overflows to sophisticated Turing-complete codereuse attacks. Control-Flow Integrity (CFI) is a defence mechanism to eliminate control-flow hijacking attacks caused by common memory errors. CFI relies on static analysis for the creation of a program’s controlflow graph (CFG), then at runtime CFI ensures that the program follows the legitimate path. Thereby, when an attacker tries to execute malicious shellcode, CFI detects an unintended path and aborts execution. CFI heavily relies on static analysis for the accurate generation of the control-flow graph, and its security depends on how strictly the CFG is generated and enforced. This paper reviews the CFI schemes proposed over the last ten years and assesses their security guarantees against advanced exploitation techniques.
Modelling and Analysis of Infilled Frame Structures Under Seismic Loads

In-filled frame structures are commonly used in buildings, even in those located in seismically active regions. Precent codes unfortunately, do not have adequate guidance for treating the modelling, analysis and design of in-filled frame structures. This paper addresses this need and first develops an appropriate technique for modelling the infill-frame interface and then uses it to study the seismic response of in-filled frame structures. Finite element time history analyses under different seismic records have been carried out and the influence of infill strength, openings and soft-storey phenomenon are investigated. Results in terms of tip deflection, fundamental period, inter-storey drift ratio and stresses are presented and they will be useful in the seismic design of in-filled frame structures.
Corruption Traps and Economic Growth

Corruption is closely related to economic growth. This paper considers the prospects for constructing a model of growth in an economy with bureaucrats who collect bribe from entrepreneurs to pursue their bribe income maximization. The bureaucrats have an ability to set their effort level on receiving investment projects, and entrepreneurs must pay a bribe to operate their investment project if the bureaucrats observe them. Under progressive bribe rates along with project's pro tability, corruption can make entrepreneurs choose less productive investment projects to avoid high bribe rate, de ned as a reversion of pro tability. This reversion is possible to lead an economy to be captured in a corruption trap, where its unique steady states depends on its initial conditions, or a corruption collapse, in which a high steady state fails to exist and only a lower steady state survives. A major implication of the analysis is that corruption can explain part of income disparity among developed countries and poor corrupt countries.
MIMO surface wave radar using time staggered FMCW chirp signals

In order to distinguish between different signal paths, full orthogonal waveforms for each transmit antenna element is desirable for the concept of MIMO radar. We investigate time staggered Frequency Modulated Continuous Wave (FMCW) chirp signals with an individual successive temporal delay larger than the maximum round-trip time of the radar signal. This concept is superior compared to those using time division multiplexing or multiple carrier frequencies in terms of unambiguous Doppler and bandwidth requirement, respectively. This is in particular important for long range marine surveillance using high-frequency surface wave radar where the frequency band is very congested and fast boats or helicopters have to be detected.
DesIGN: Design Inspiration from Generative Networks

Can an algorithm create original and compelling fashion designs to serve as an inspirational assistant? To help answer this question, we design and investigate different image generation models associated with different loss functions to boost creativity in fashion generation. The dimensions of our explorations include: (i) different Generative Adversarial Networks architectures that start from noise vectors to generate fashion items, (ii) novel loss functions that encourage creativity, inspired from Sharma-Mittal divergence, a generalized mutual information measure for the widely used relative entropies such as Kullback-Leibler, and (iii) a generation process following the key elements of fashion design (disentangling shape and texture components). A key challenge of this study is the evaluation of generated designs and the retrieval of best ones, hence we put together an evaluation protocol associating automatic metrics and human experimental studies that we hope will help ease future research. We show that our proposed creativity losses yield better overall appreciation than the one employed in  In the end, about 61% of our images are thought to be created by human designers rather than by a computer while also being considered original per our human subject experiments, and our proposed loss scores the highest compared to existing losses in both novelty and likability.  Training generative adversarial models with appropriate losses leads to realistic and creative 512× 512 fashion images.