nils-herrmann/clean_scirepeval huggingface dataset:
Ezetimibe - a new approach in hypercholesterolemia management.

Suchy et al.  discuss the benefits of ezetimibe beyond lipid-lowering as well as its effects on atherosclerosis. Some comments may be of interest. Reducing low density lipoprotein cholesterol (LDL-C) is the main indication for ezetimibe [2, 7, 8]. This drug is useful in patients who cannot achieve LDL-C goals on statins or those who are statin intolerant [2, 4, 7, 8]. Meta-analyses suggested that adding ezetimibe decreases LDL-C levels more than statin monotherapy, after doubling the dose of a statin or selecting a more potent statin [2, 7, 8]. It was also suggested that ezetimibe can increase LDL particle size, especially in patients with high triglyceride levels [3, 11]. Ezetimibe may exert other beneficial actions which may or may not be related to its hypolipidemic capacity (e.g., for non-alcoholic fatty liver disease and renoprotection) . Ezetimibe monotherapy reduced the plasma mass and activity of lipoprotein-associated phospholipase A2 (Lp-PLA2), which is considered risk predictor of cardiovascular (CV) events . Furthermore, ezetimibe may lower the levels of oxidative stress markers . In the end, the most important issue is reducing vascular events. In this context, the Study of Heart and Renal Protection (SHARP)  reported that the combination of ezetimibe with simvastatin was associated with reduced risk of CV events compared with placebo after 4.9 years in patients (n = 9,270) who are pre-dialysis or on dialysis. The clinical relevance of these points and those addressed by Suchy et al.  requires to be established by future studies.
Addressing cold start in recommender systems: a semi-supervised co-training algorithm

Cold start is one of the most challenging problems in recommender systems. In this paper we tackle the cold-start problem by proposing a context-aware semi-supervised co-training method named CSEL. Specifically, we use a factorization model to capture fine-grained user-item context. Then, in order to build a model that is able to boost the recommendation performance by leveraging the context, we propose a semi-supervised ensemble learning algorithm. The algorithm constructs different (weak) prediction models using examples with different contexts and then employs the co-training strategy to allow each (weak) prediction model to learn from the other prediction models. The method has several distinguished advantages over the standard recommendation methods for addressing the cold-start problem. First, it defines a fine-grained context that is more accurate for modeling the user-item preference. Second, the method can naturally support supervised learning and semi-supervised learning, which provides a flexible way to incorporate the unlabeled data.
 The proposed algorithms are evaluated on two real-world datasets. The experimental results show that with our method the recommendation accuracy is significantly improved compared to the standard algorithms and the cold-start problem is largely alleviated.
The behavioral regulation in sport questionnaire (BRSQ): instrument development and initial validity evidence.

The purpose of the four studies described in this article was to develop and test a new measure of competitive sport participants' intrinsic motivation, extrinsic motivation, and amotivation (self-determination theory; Deci & Ryan, 1985). The items for the new measure, named the Behavioral Regulation in Sport Questionnaire (BRSQ), were constructed using interviews, expert review, and pilot testing. Analyses supported the internal consistency, test-retest reliability, and factorial validity of the BRSQ scores. Nomological validity evidence was also supportive, as BRSQ subscale scores were correlated in the expected pattern with scores derived from measures of motivational consequences. When directly compared with scores derived from the Sport Motivation Scale (SMS; Pelletier, Fortier, Vallerand, Tuson, & Blais, 1995) and a revised version of that questionnaire (SMS-6; Mallett, Kawabata, Newcombe, Otero-Forero, & Jackson, 2007), BRSQ scores demonstrated equal or superior reliability and factorial validity as well as better nomological validity.
Spark Versus Flink: Understanding Performance in Big Data Analytics Frameworks

Big Data analytics has recently gained increasing popularity as a tool to process large amounts of data on-demand. Spark and Flink are two Apache-hosted data analytics frameworks that facilitate the development of multi-step data pipelines using directly acyclic graph patterns. Making the most out of these frameworks is challenging because efficient executions strongly rely on complex parameter configurations and on an in-depth understanding of the underlying architectural choices. Although extensive research has been devoted to improving and evaluating the performance of such analytics frameworks, most of them benchmark the platforms against Hadoop, as a baseline, a rather unfair comparison considering the fundamentally different design principles. This paper aims to bring some justice in this respect, by directly evaluating the performance of Spark and Flink. Our goal is to identify and explain the impact of the different architectural choices and the parameter configurations on the perceived end-to-end performance. To this end, we develop a methodology for correlating the parameter settings and the operators execution plan with the resource usage. We use this methodology to dissect the performance of Spark and Flink with several representative batch and iterative workloads on up to 100 nodes. Our key finding is that there none of the two framework outperforms the other for all data types, sizes and job patterns. This paper performs a fine characterization of the cases when each framework is superior, and we highlight how this performance correlates to operators, to resource usage and to the specifics of the internal framework design.
An Implementation of Intrusion Detection System Using Genetic Algorithm

Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. So Intrusion Detection Systems have become a needful component in terms of computer and network security. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. So, the quest of betterment continues. In this progression, here we present an Intrusion Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of network intrusions. Parameters and evolution processes for GA are discussed in details and implemented. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of our system we used the KDD99 benchmark dataset and obtained reasonable detection rate.
Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition

Since Chavez proposed the highpass filtering procedure to fuse multispectral and panchromatic images, several fusion methods have been developed based on the same principle: to extract from the panchromatic image spatial detail information to later inject it into the multispectral one. In this paper, we present new fusion alternatives based on the same concept, using the multiresolution wavelet decomposition to execute the detail extraction phase and the intensity-hue-saturation (IHS) and principal component analysis (PCA) procedures to inject the spatial detail of the panchromatic image into the multispectral one. The multiresolution wavelet decomposition has been performed using both decimated and undecimated algorithms and the resulting merged images compared both spectral and spatially. These fusion methods, as well as standard IHS-, PCA-, and wavelet-based methods have been used to merge Systeme Pour l'Observation de la Terre (SPOT) 4 XI and SPOT 4 M images with a ratio 4:1. We have estimated the validity of each fusion method by analyzing, visually and quantitatively, the quality of the resulting fused images. The methodological approaches proposed in this paper result in merged images with improved quality with respect to those obtained by standard IHS, PCA, and standard wavelet-based fusion methods. For both proposed fusion methods, better results are obtained when an undecimated algorithm is used to perform the multiresolution wavelet decomposition.
ClusTile: Toward Minimizing Bandwidth in 360-degree Video Streaming

360-degree video has the potential to transform the video streaming experience by providing a more-immersive environment for users to interact with than standard streaming video. This experience is hampered, however, by high bandwidth requirements resulting from the extra information associated with the 360-degree frames. Because users cannot see this full 360-degree view, but the full view is transmitted in the majority of 360-degree streaming systems, there is much potential to reduce wasted bandwidth in this domain. We propose ClusTile, a tiling approach formulated to select a set of tiles that allows minimal bandwidth needed to be used when streaming 360-degree video over an expected set of views. These tiles are selected by solving a set of integer linear programs (ILPs) independently on clusters of collected user views. The clustering approach reduces computation requirements of the ILPs to practical levels. Tilings computed from ClusTile can save up to 76% bandwidth compared to standard 360-degree streaming and up to 52% bandwidth compared to best-performing fixed tiling schemes.
Architectural support for trust models in decentralized applications

Decentralized applications are composed of distributed entities that directly interact with each other and make local autonomous decisions in the absence of a centralized coordinating authority. Such decentralized applications, where entities can join and leave the system at any time, are particularly susceptible to the attacks of malicious entities. Each entity therefore requires protective measures to safeguard itself against these entities. Trust management solutions serve to provide effective protective measures against such malicious attacks. Trust relationships help an entity model and evaluate its confidence in other entities towards securing itself. Trust management is, thus, both an essential and intrinsic ingredient of decentralized applications. However, research in trust management has not focused on how trust models can be composed into a decentralized architecture. The PACE architectural style, described previously [21], provides structured and detailed guidance on the assimilation of trust models into a decentralized entity's architecture. In this paper, we describe our experiments with incorporating four different reputation-based trust models into a decentralized application using the PACE architectural style. Our observations lead us to conclude that PACE not only provides an effective and easy way to integrate trust management into decentralized applications, but also facilitates reuse while supporting different types of trust models. Additionally, PACE serves as a suitable platform to aid the evaluation and comparison of trust models in a fixed setting towards providing a way to choose an appropriate model for the setting.
A simple algorithm for designing an artificial intelligence based Tic Tac Toe game

This paper proposes a simple technique to develop a game of Tic-Tac-Toe as a two player game, where one player is the computer itself. The algorithm developed uses soft computing techniques and gameplay is programmed using Javascript, HTML and CSS. Algorithm developed helps computer to decide the most appropriate move so as to either win the game or to draw it in the least amount of time.
IoT Smart Home Adoption: The Importance of Proper Level Automation

The word “smart” has been used in various fields and is widely accepted to mean intelligence. Smart home service, one of the representative emerging technologies in the IoT era, has changed house equipment into being more intelligent, remote controllable, and interconnected. However, the intelligence and controllability of a smart home service are contradictory concepts, under certain aspects. In addition, the level of intelligence or controllability of a smart home service that users want may differ according to the user. As potential users of smart home services have diversified in recent years, providing the appropriate functions and features is critical to the diffusion of the service. Thus, this study examines the smart home service features that current users require and empirically evaluates the relationship between the critical factors and the adoption behavior with 216 samples from Korea. The moderating effect of personal characteristics on behavior is also tested. The results of the analysis provide various theoretical and practical implications.
Web-based statistical fact checking of textual documents

User generated content has been growing tremendously in recent years. This content reflects the interests and the diversity of online users. In turn, the diversity among internet users is also reflected in the quality of the content being published online. This increases the need to develop means to gauge the support available for content posted online. In this work, we aim to make use of the web-content to calculate a statistical support score for textual documents. In the proposed algorithm, phrases representing key facts are extracted to construct basic elements of the document. Search is used thereon to validate the support available for these elements online, leading to assigning an overall score for each document. Experimental results have shown a difference between the score distribution of factual news data and false facts data. This indicates that the approach seems to be a promising seed for distinguishing different articles based on the content.
Burnout and psychiatric morbidity among physicians engaged in end-of-life care for cancer patients: a cross-sectional nationwide survey in Japan.

To determine the prevalence of burnout and psychiatric morbidity among physicians engaged in end-of-life care for cancer patients in Japan and to explore associated factors related to end-of-life care.



Questionnaires were mailed to 1436 Japanese clinical oncologists and palliative care physicians with a request to complete the Maslach Burnout Inventory (MBI), the General Health Questionnaire (GHQ-12), and to report on individual factors, including confidence in patient care. High levels of burnout and psychiatric morbidity were identified using cut-off scores of the MBI and GHQ-12.



A total of 697 physicians returned the questionnaires (response rate, 49.6%). Twenty-two percent of the respondents had a high level of emotional exhaustion, 11% had a high level of depersonalization, 62% had a low level of personal accomplishment, and 20% had psychiatric morbidity. Clinical oncologists showed a significantly higher psychiatric morbidity than palliative care physicians. Confidence in having sufficient time to communicate with patients was significantly associated with all the burnout subscales.



A low level of personal accomplishment was relatively high among Japanese physicians compared with previous studies. Insufficient confidence in the psychological care of patients was associated with physician burnout rather than involvement in end-of-life care.
A novel planer transition structure from micro-strip line to waveguide

A planer micro-strip to waveguide transition structure is given in this paper. Coupled patch on the bottom of the substrate is excited by the micro-strip feed-line on the top, which realizes the transformation from the TE10 mode in the waveguide to the quasi-TEM mode on the micro-strip line. Modeling and simulation of the structure are conducted for the design by using electromagnetic simulation software HFSS. The measurement of the manufactured structure exhibits an insertion loss less than 0.5dB within 33GHz~37GHz. Compared to traditional waveguide E-plane probe or H-plane probe transition, this design has properties of low profile and easy integration.
BareDroid: Large-Scale Analysis of Android Apps on Real Devices

To protect Android users, researchers have been analyzing unknown, potentially-malicious applications by using systems based on emulators, such as the Google's Bouncer and Andrubis. Emulators are the go-to choice because of their convenience: they can scale horizontally over multiple hosts, and can be reverted to a known, clean state in a matter of seconds. Emulators, however, are fundamentally different from real devices, and previous research has shown how it is possible to automatically develop heuristics to identify an emulated environment, ranging from simple flag checks and unrealistic sensor input, to fingerprinting the hypervisor's handling of basic blocks of instructions. Aware of this aspect, malware authors are starting to exploit this fundamental weakness to evade current detection systems. Unfortunately, analyzing apps directly on bare metal at scale has been so far unfeasible, because the time to restore a device to a clean snapshot is prohibitive: with the same budget, one can analyze an order of magnitude less apps on a physical device than on an emulator.
 In this paper, we propose BareDroid, a system that makes bare-metal analysis of Android apps feasible by quickly restoring real devices to a clean snapshot. We show how BareDroid is not detected as an emulated analysis environment by emulator-aware malware or by heuristics from prior research, allowing BareDroid to observe more potentially malicious activity generated by apps. Moreover, we provide a cost analysis, which shows that replacing emulators with BareDroid requires a financial investment of less than twice the cost of the servers that would be running the emulators. Finally, we release BareDroid as an open source project, in the hope it can be useful to other researchers to strengthen their analysis systems.
Ethical and Social Aspects of Self-Driving Cars

As an envisaged future of transportation, self-driving cars are being discussed from various perspectives, including social, economical, engineering, computer science, design, and ethics. On the one hand, self-driving cars present new engineering problems that are being gradually successfully solved. On the other hand, social and ethical problems are typically being presented in the form of an idealized unsolvable decision-making problem, the so-called trolley problem, which is grossly misleading. We argue that an applied engineering ethical approach for the development of new technology is what is needed; the approach should be applied, meaning that it should focus on the analysis of complex real-world engineering problems. Software plays a crucial role for the control of self-driving cars; therefore, software engineering solutions should seriously handle ethical and social considerations. In this paper we take a closer look at the regulative instruments, standards, design, and implementations of components, systems, and services and we present practical social and ethical challenges that have to be met, as well as novel expectations for software engineering.
Augmented Human: Augmented Reality and Beyond

Will Augmented Reality (AR) allow us to access digital information, experience others' stories, and thus explore alternate realities? AR has recently attracted attention again due to the rapid advances in related multimedia technologies as well as various glass-type AR display devices. However, in order to widely adopt AR in alternated realities, it is necessary to improve various core technologies and integrate them into an AR platform. Especially, there are several remaining technical challenges such as 1) real-time recognition and tracking of multiple objects while generating an environment map, 2) organic user interface with awareness of user's implicit needs, intentions or emotion as well as explicit requests, 3) immersive multimodal content augmentation and just-in-time information visualization, 4) multimodal interaction and collaboration during augmented telecommunication, etc. In addition, in order to encourage user engagement and enable an AR ecosystem, AR standards should be established that support creating AR content, capturing user experiences, and sharing the captured experiences.
A Single-Stage Single-Switch LED Driver Based on Class-E Converter

This paper proposes a single-stage single-switch light-emitting diode (LED) driver that integrates a buck-boost circuit with a Class-E resonant converter by sharing single-switch device. The buck-boost circuit, working as a power-factor correction (PFC) stage, operates in the discontinuous conduction mode (DCM) to shape the input current. The Class-E converter steps down the voltage to drive the LED. For specific component parameters, the Class-E converter can achieve soft-switching on the power switch and two rectifier diodes, and reduce switch losses at high frequencies. Electrolytic capacitors are used in the proposed converter to achieve a lower cost, but the system reliability decreases. To overcome this disadvantage, film capacitors can be used, but the current ripple increases. Neglecting the cost, multilayered film capacitors are the best option, if higher reliability and lower current ripple are required. The proposed driver features high efficiency (90.8%), and a high power factor (PF) (0.995). In this paper, analytical results and design considerations at 100 kHz are presented, and a 100-W prototype with 110 VAC input was built to validate the theoretical analysis.
On the minimum node degree and connectivity of a wireless multihop network

This paper investigates two fundamental characteristics of a wireless multi -hop network: its minimum node degree and its k--connectivity. Both topology attributes depend on the spatial distribution of the nodes and their transmission range. Using typical modeling assumptions :--- :a random uniform distribution of the nodes and a simple link model :--- :we derive an analytical expression that enables the determination of the required range r0 that creates, for a given node density ρ, an almost surely k--connected network. Equivalently, if the maximum r0 of the nodes is given, we can find out how many nodes are needed to cover a certain area with a k--connected network. We also investigate these questions by various simulations and thereby verify our analytical expressions. Finally, the impact of mobility is discussed.The results of this paper are of practical value for researchers in this area, e.g., if they set the parameters in a network--level simulation of a mobile ad hoc network or if they design a wireless sensor network.
Real time face recognition engine using compact features for electronics key

In this research, a design and implementation of real-time face recognition engine using compact features vector extracted by linear binary pattern (LBP) and zoned discrete cosine transforms (DCT) analysis is proposed for electronics key. The function of LBP is to normalize the lighting variations of the input face image and the function of zoned DCT is to define the local descriptors of the face image. In order to get more compact features vector, the predictive LDA is employed for dimensional reduction. The aims of this research is to develop fast and strong face recognition that can be implemented for electronic key which will be implemented for substituting current security system (PIN and password). In addition, the recognition engine is also designed for real time face recognition which can work on hardware having limited resources such as Intel atom computer, raspberry pi, and android smart phone. The experimental results show that the proposed engine provide high enough recognition rate, and small false rejection rate (FRR) and false acceptance rate (FAR). In addition, the engine needs short processing time.