nils-herrmann/clean_scirepeval huggingface dataset:
Applying feature selection methods on fMRI data

In neuroscience, the ability to correlate and classify certain activity patterns of the brain to different physical and mental states of the subject is of high importance. Analysis of fMRI data is one of the venues in which this objective is being pursued. However data produced using fMRI technology is highly complex. To this end, machine learning becomes relevant. Prominent hurdles facing fMRI data analysis are their high-dimensionality (thousands of features per instance), low signal-to-noise ratio and interdependency. This motivates the use of feature selection methods in order to consolidate relevant information and discard noise. Many feature selection methods exist but only a few have been applied in the fMRI domain. In this paper we identify positive characteristics of feature selection algorithms that are beneficial when dealing with fMRI datasets. To do this, we evaluate representatives from each of the three main feature selection classes: Filters, wrappers and embedded methods. We have found probabilistic embedded methods to be the most suitable for fMRI data. We would therefore recommend using these (or similar) methods to process data with fMRI-like characteristics.
A familiar face(book): profile elements as signals in an online social network

Using data from a popular online social network site, this paper explores the relationship between profile structure (namely, which fields are completed) and number of friends, giving designers insight into the importance of the profile and how it works to encourage connections and articulated relationships between users. We describe a theoretical framework that draws on aspects of signaling theory, common ground theory, and transaction costs theory to generate an understanding of why certain profile fields may be more predictive of friendship articulation on the site. Using a dataset consisting of 30,773 Facebook profiles, we determine which profile elements are most likely to predict friendship links and discuss the theoretical and design implications of our findings.
Association between vitamin D and risk of colorectal cancer: a systematic review of prospective studies.

To conduct a systematic review of prospective studies assessing the association of vitamin D intake or blood levels of 25-hydroxyvitamin D [25(OH)D] with the risk of colorectal cancer using meta-analysis.



Relevant studies were identified by a search of MEDLINE and EMBASE databases before October 2010 with no restrictions. We included prospective studies that reported relative risk (RR) estimates with 95% CIs for the association between vitamin D intake or blood 25(OH)D levels and the risk of colorectal, colon, or rectal cancer. Approximately 1,000,000 participants from several countries were included in this analysis.



Nine studies on vitamin D intake and nine studies on blood 25(OH)D levels were included in the meta-analysis. The pooled RRs of colorectal cancer for the highest versus lowest categories of vitamin D intake and blood 25(OH)D levels were 0.88 (95% CI, 0.80 to 0.96) and 0.67 (95% CI, 0.54 to 0.80), respectively. There was no heterogeneity among studies of vitamin D intake (P = .19) or among studies of blood 25(OH)D levels (P = .96). A 10 ng/mL increment in blood 25(OH)D level conferred an RR of 0.74 (95% CI, 0.63 to 0.89).



Vitamin D intake and blood 25(OH)D levels were inversely associated with the risk of colorectal cancer in this meta-analysis.
Vaccine Delivery Methods into the Future

Several modes of vaccine delivery have been developed in the last 25 years, which induce strong immune responses in pre-clinical models and in human clinical trials. Some modes of delivery include, adjuvants (aluminum hydroxide, Ribi formulation, QS21), liposomes, nanoparticles, virus like particles, immunostimulatory complexes (ISCOMs), dendrimers, viral vectors, DNA delivery via gene gun, electroporation or Biojector 2000, cell penetrating peptides, dendritic cell receptor targeting, toll-like receptors, chemokine receptors and bacterial toxins. There is an enormous amount of information and vaccine delivery methods available for guiding vaccine and immunotherapeutics development against diseases.
Mean Barrier Height and Richardson Constant for Pd/ZnO Thin Film-Based Schottky Diodes Grown on n-Si Substrates by Thermal Evaporation Method

This letter reports the temperature-dependent electrical parameters of Pd/n-ZnO thin film-based Schottky diodes grown on n-Si substrates by thermal evaporation method. The parameters have been investigated by considering a Gaussian distributed barrier height across the Pd/n-ZnO interface with a standard deviation σ<sub>0</sub> around a mean barrier height qφB,m. As compared with the reported results, the estimated values of the Richardson constant (~19.54Acm<sup>-2</sup>K<sup>-2</sup>) and mean barrier height (~1.41 eV) are much closer to their theoretically predicted values of 32Acm<sup>-2</sup>K<sup>-2</sup> (for m<sub>e</sub>*=0.27 m0) and 1.42 eV (for work function of Pd = 5.12 eV and electron affinity of ZnO = 3.7 eV), respectively.
Gate Enhanced Power UMOSFET With Ultralow On-Resistance

Gate enhanced power UMOSFET (GE-UMOS) is proposed to decrease the specific on -resistance of the device. The key feature of this structure is that the deep trench polysilicon electrode is contacted to the gate electrode, maintaining the breakdown voltage and forming the high electron current density at side n-drift region, thus resulting in a lower on -resistance compared to the superjunction structure and gradient oxide-bypassed (GOB) structure. Furthermore, the performance of GE-UMOS is proved by comparing with the GOB-UMOS structure.
Strategies for detecting fraudulent claims in the automobile insurance industry

Some property and casualty insurers use automated detection systems to help to decide whether or not to investigate claims suspected of fraud. Claim screening systems benefit from the coded experience of previously investigated claims. The embedded detection models typically consist of scoring devices relating fraud indicators to some measure of suspicion of fraud. In practice these scoring models often focus on minimizing the error rate rather than on the cost of (mis)classification. We show that focusing on cost is a profitable approach. We analyse the effects of taking into account information on damages and audit costs early on in the screening process. We discuss several scenarios using real-life data. The findings suggest that with claim amount information available at screening time detection rules can be accommodated to increase expected profits. Our results show the value of cost-sensitive claim fraud screening and provide guidance on how to render this strategy operational.
Blurring Human-Machine Distinctions: Anthropomorphic Appearance in Social Robots as a Threat to Human Distinctiveness

The present research aims at gaining a better insight on the psychological barriers to the introduction of social robots in society at large. Based on social psychological research on intergroup distinctiveness, we suggested that concerns toward this technology are related to how we define and defend our human identity. A threat to distinctiveness hypothesis was advanced. We predicted that too much perceived similarity between social robots and humans triggers concerns about the negative impact of this technology on humans, as a group, and their identity more generally because similarity blurs category boundaries, undermining human uniqueness. Focusing on the appearance of robots, in two studies we tested the validity of this hypothesis. In both studies, participants were presented with pictures of three types of robots that differed in their anthropomorphic appearance varying from no resemblance to humans (mechanical robots), to some body shape resemblance (biped humanoids) to a perfect copy of human body (androids). Androids raised the highest concerns for the potential damage to humans, followed by humanoids and then mechanical robots. In Study 1, we further demonstrated that robot anthropomorphic appearance (and not the attribution of mind and human nature) was responsible for the perceived damage that the robot could cause. In Study 2, we gained a clearer insight in the processes B Maria Paola Paladino mariapaola.paladino@unitn.it Francesco Ferrari francesco.ferrari-1@unitn.it Jolanda Jetten j.jetten@psy.uq.edu.au 1 Department of Psychology and Cognitive Science, University of Trento, Corso Bettini 31, 38068 Rovereto, Italy 2 School of Psychology, The University of Queensland, St Lucia, QLD 4072, Australia underlying this effect by showing that androids were also judged as most threatening to the human–robot distinction and that this perception was responsible for the higher perceived damage to humans. Implications of these findings for social robotics are discussed.
Learning to Represent Words in Context with Multilingual Supervision

We present a neural network architecture based on bidirectional LSTMs to compute representations of words in the sentential contexts. These context-sensitive word representations are suitable for, e.g., distinguishing different word senses and other context-modulated variations in meaning. To learn the parameters of our model, we use cross-lingual supervision, hypothesizing that a good representation of a word in context will be one that is sufficient for selecting the correct translation into a second language. We evaluate the quality of our representations as features in three downstream tasks: prediction of semantic supersenses (which assign nouns and verbs into a few dozen semantic classes), low resource machine translation, and a lexical substitution task, and obtain state-of-the-art results on all of these.
RansomTracer: Exploiting Cyber Deception for Ransomware Tracing

Ransomware is a type of malware that encrypts data or locks a device to extort a ransom. Recently, a variety of high-profile ransomware attacks have been reported, and many ransomware defense systems have been proposed. However, none specializes in resisting untargeted attacks such as those by remote desktop protocol (RDP) attack ransomware. To resolve this problem, this paper proposes a way to combat RDP ransomware attacks by trapping and tracing. It discovers and ensnares the attacker through a network deception environment and uses an auxiliary tracing technology to find the attacker, finally achieving the goal of deterring the ransomware attacker and countering the RDP attack ransomware. Based on cyber deception, an auxiliary ransomware traceable system called RansomTracer is introduced in this paper. RansomTracer collects clues about the attacker by deploying monitors in the deception environment. Then, it automatically extracts and analyzes the traceable clues. Experiments and evaluations show that RansomTracer ensnares the adversary in the deception environment and improves the efficiency of clue analysis significantly. In addition, it is able to recognize the clues that identify the attacker and the screening rate reaches 98.34%.
Different Methods of Long-Term Electric Load Demand Forecasting; A Comprehensive Review

Long-term demand forecasting presents the first step in planning and developing future generation, transmission and distribution facilities. One of the primary tasks of an electric utility accurately predicts load demand requirements at all times, especially for long-term. Based on the outcome of such forecasts, utilities coordinate their resources to meet the forecasted demand using a least-cost plan. In general, resource planning is performed subject to numerous uncertainties. Expert opinion indicates that a major source of uncertainty in planning for future capacity resource needs and operation of existing generation resources is the forecasted load demand. This paper presents an overview of the past and current practice in longterm demand forecasting. It introduces methods, which consists of some traditional methods, neural networks, genetic algorithms, fuzzy rules, support vector machines, wavelet networks and expert systems.
Yeast interactions and wine flavour.

Wine is the product of complex interactions between fungi, yeasts and bacteria that commence in the vineyard and continue throughout the fermentation process until packaging. Although grape cultivar and cultivation provide the foundations of wine flavour, microorganisms, especially yeasts, impact on the subtlety and individuality of the flavour response. Consequently, it is important to identify and understand the ecological interactions that occur between the different microbial groups, species and strains. These interactions encompass yeast-yeast, yeast-filamentous fungi and yeast-bacteria responses. The surface of healthy grapes has a predominance of Aureobasidium pullulans, Metschnikowia, Hanseniaspora (Kloeckera), Cryptococcus and Rhodotorula species depending on stage of maturity. This microflora moderates the growth of spoilage and mycotoxigenic fungi on grapes, the species and strains of yeasts that contribute to alcoholic fermentation, and the bacteria that contribute to malolactic fermentation. Damaged grapes have increased populations of lactic and acetic acid bacteria that impact on yeasts during alcoholic fermentation. Alcoholic fermentation is characterised by the successional growth of various yeast species and strains, where yeast-yeast interactions determine the ecology. Through yeast-bacterial interactions, this ecology can determine progression of the malolactic fermentation, and potential growth of spoilage bacteria in the final product. The mechanisms by which one species/strain impacts on another in grape-wine ecosystems include: production of lytic enzymes, ethanol, sulphur dioxide and killer toxin/bacteriocin like peptides; nutrient depletion including removal of oxygen, and production of carbon dioxide; and release of cell autolytic components. Cell-cell communication through quorum sensing molecules needs investigation.
Broadband suspended plate antenna with concaved center portion

A new suspended plate antenna is presented for the enhancement of impedance bandwidth. The probe-fed plate antenna is suspended above a ground plane and its center portion is concaved to form a "V" shape. The experiment and simulation show that without increase in size the proposed antenna is capable of providing an impedance bandwidth of up to 60% for |S/sub 11/|<-10 dB with an acceptable gain of 8 dBi.
Abstraction for Falsification

ion for Falsification Thomas Ball , Orna Kupferman , and Greta Yorsh 3 1 Microsoft Research, Redmond, WA, USA.  research.microsoft.com/ ∼tball 2 Hebrew University, School of Eng. and Comp. Sci., Jerusalem 91904,   www.cs.huji.ac.il/ ∼orna 3 Tel-Aviv University, School of Comp. Sci., Tel-Aviv 69978, Israel.  www.math.tau.ac.il/ ∼gretay Microsoft Research Technical Report MSR-TR-2005-50 Abstract. Abstraction is traditionally used in the process of verification. There, an abstraction of a concrete system is sound if properties of the abstract system also hold in the conAbstraction is traditionally used in the process of verification. There, an abstraction of a concrete system is sound if properties of the abstract system also hold in the concrete system. Specifically, if an abstract state satisfies a property ψ thenall the concrete states that correspond to a satisfyψ too. Since the ideal goal of proving a system correct involves many obstacles, the primary use of formal methods nowadays is fal ification. There, as intesting, the goal is to detect errors, rather than to prove correctness. In the falsification setting, we can say that an abstraction is sound if errors of the abstract system exist also in the concrete system. Specifically, if an abstract state a violates a propertyψ, thenthere existsa concrete state that corresponds to a and violatesψ too. An abstraction that is sound for falsification need not be sound for verification. This suggests that existing frameworks for abstraction for verification may be too restrictive when used for falsification, and that a new framework is needed in order to take advantage of the weaker definition of soundness in the falsification setting. We present such a framework, show that it is indeed stronger (than other abstraction frameworks designed for verification), demonstrate that it can be made even stronger by parameterizing its transitions by predicates, and describe how it can be used for falsification of branching-time and linear-time temporal properties, as well as for generating testing goals for a concrete system by reasoning about its abstraction.
Impact and Challenges of Requirement Engineering in Agile Methodologies: A Systematic Review

Requirement Engineering is one of important stage in development life cycle. All requirements required for development of product is collected in this phase. A high standard product can be developed by agile methodology in less budget and time. Importance of agile practices have been enhanced since it offers assist cooperation too software engineering. Being basic phase of software engineering, requirement engineering has different processes. The elements of direct correspondence is one of spry way which not at all like to other conventional and traditional approaches .Although a lot of research has been done on agile practices and role of requirement in agile methodologies but still there is need of studies on change manage management ,requirement prioritization, prototyping and nonfunctional requirement in agile methodologies. Aim of this review paper is to present the limitations in presentation of requirement engineering phases in agile practices and what are the issues and challenges that agile person faces in implementation of agile practices. Many research studies from different sources have been reviewed on basis of inclusion and exclusion criteria. Most RE activities has been discussed in review. Evidence helps to prove that how RE process was performed in scrums. Mostly research has been conducted on general agile methodologies, few authors specified RE practices in other methodologies of agile. Finding of this research is the work of researchers that will be beneficial for those who are interested in finding interesting area of research in this field because many techniques of agile (extreme programming, crystal methodology, lean ) requires further study and practical results as clarified by studies. Requirement Engineering; Traditional approaches; Agile methodologies; Challenges in RE; Requirement prioritization; Nonfunctional Requirements; Dynamic system development method; Scrum; Extreme programming
[Self-amputation of the penis treated immediately: Case report and review of the literature].

Self-amputation of the penis treated immediately: case report and review of the literature. Self-amputation of the penis is rare in urological practice. It occurs more often in a context psychotic disease. It can also be secondary to alcohol or drugs abuse. Treatment and care vary according on the severity of the injury, the delay of consultation and the patient's mental state. The authors report a case of self-amputation of the penis in an alcoholic context. The authors analyze the etiological and urological aspects of this trauma.
Acceptance model of a Hospital Information System

The purpose of this study is to develop a model of Hospital Information System (HIS) user acceptance focusing on human, technological, and organizational characteristics for supporting government eHealth programs. This model was then tested to see which hospital type in Indonesia would benefit from the model to resolve problems related to HIS user acceptance.



This study used qualitative and quantitative approaches with case studies at four privately owned hospitals and three government-owned hospitals, which are general hospitals in Indonesia. The respondents involved in this study are low-level and mid-level hospital management officers, doctors, nurses, and administrative staff who work at medical record, inpatient, outpatient, emergency, pharmacy, and information technology units. Data was processed using Structural Equation Modeling (SEM) and AMOS 21.0.



The study concludes that non-technological factors, such as human characteristics (i.e. compatibility, information security expectancy, and self-efficacy), and organizational characteristics (i.e. management support, facilitating conditions, and user involvement) which have level of significance of p<0.05, significantly influenced users' opinions of both the ease of use and the benefits of the HIS. This study found that different factors may affect the acceptance of each user in each type of hospital regarding the use of HIS. Finally, this model is best suited for government-owned hospitals.



Based on the results of this study, hospital management and IT developers should have more understanding on the non-technological factors to better plan for HIS implementation. Support from management is critical to the sustainability of HIS implementation to ensure HIS is easy to use and provides benefits to the users as well as hospitals. Finally, this study could assist hospital management and IT developers, as well as researchers, to understand the obstacles faced by hospitals in implementing HIS.
Adaptive stress testing of airborne collision avoidance systems

This paper presents a scalable method to efficiently search for the most likely state trajectory leading to an event given only a simulator of a system. Our approach uses a reinforcement learning formulation and solves it using Monte Carlo Tree Search (MCTS). The approach places very few requirements on the underlying system, requiring only that the simulator provide some basic controls, the ability to evaluate certain conditions, and a mechanism to control the stochasticity in the system. Access to the system state is not required, allowing the method to support systems with hidden state. The method is applied to stress test a prototype aircraft collision avoidance system to identify trajectories that are likely to lead to near mid-air collisions. We present results for both single and multi-threat encounters and discuss their relevance. Compared with direct Monte Carlo search, this MCTS method performs significantly better both in finding events and in maximizing their likelihood.
AMRoute: Ad Hoc Multicast Routing Protocol

The Adhoc Multicast Routing Protocol (AMRoute) presents a novel approach for robust IP Multicast in mobile adhoc networks by exploiting user-multicast trees and dynamic logical cores. It creates a bidirectional, shared tree for data distribution using only group senders and receivers as tree nodes. Unicast tunnels are used as tree links to connect neighbors on the user-multicast tree. Thus, AMRoute does not need to be supported by network nodes that are not interested/capable of multicast, and group state cost is incurred only by group senders and receivers. Also, the use of tunnels as tree links implies that tree structure does not need to change even in case of a dynamic network topology, which reduces the signaling tra c and packet loss. Thus AMRoute does not need to track network dynamics; the underlying unicast protocol is solely responsible for this function. AMRoute does not require a speci c unicast routing protocol; therefore, it can operate seamlessly over separate domains with di erent unicast protocols. Certain tree nodes are designated by AMRoute as logical cores, and are responsible for initiating and managing the signaling component of AMRoute, such as detection of group members and tree setup. Logical cores di er signi cantly from those in CBT and PIM-SM, since they are not a central point for data distribution and can migrate dynamically among member nodes. Simulation results demonstrate that AMRoute signaling tra c and join latency remain at relatively low levels for typical group sizes. The results also indicate that group members receive a high proportion of data multicast by a sender, even in the case of a dynamic network.