nils-herrmann/clean_scirepeval huggingface dataset:
Environmental influence in the brain, human welfare and mental health

The developing human brain is shaped by environmental exposures—for better or worse. Many exposures relevant to mental health are genuinely social in nature or believed to have social subcomponents, even those related to more complex societal or area-level influences. The nature of how these social experiences are embedded into the environment may be crucial. Here we review select neuroscience evidence on the neural correlates of adverse and protective social exposures in their environmental context, focusing on human neuroimaging data and supporting cellular and molecular studies in laboratory animals. We also propose the inclusion of innovative methods in social neuroscience research that may provide new and ecologically more valid insight into the social-environmental risk architecture of the human brain.
High-Efficiency Single-Input Multiple-Output DC–DC Converter

The aim of this study is to develop a high-efficiency single-input multiple-output (SIMO) dc-dc converter. The proposed converter can boost the voltage of a low-voltage input power source to a controllable high-voltage dc bus and middle-voltage output terminals. The high-voltage dc bus can take as the main power for a high-voltage dc load or the front terminal of a dc-ac inverter. Moreover, middle-voltage output terminals can supply powers for individual middle-voltage dc loads or for charging auxiliary power sources (e.g., battery modules). In this study, a coupled-inductor-based dc-dc converter scheme utilizes only one power switch with the properties of voltage clamping and soft switching, and the corresponding device specifications are adequately designed. As a result, the objectives of high-efficiency power conversion, high step-up ratio, and various output voltages with different levels can be obtained. Some experimental results via a kilowatt-level prototype are given to verify the effectiveness of the proposed SIMO dc-dc converter in practical applications.
A 51-nW 32.7-kHz CMOS relaxation oscillator with half-period pre-charge compensation scheme for ultra-low power systems

This paper presents a temperature and supply voltage variation-tolerant CMOS relaxation oscillator which is suitable for ultra-low power systems. A low-power low-cost half-period pre-charge compensation scheme is proposed to eliminate influences of the delay of the comparator and the RS latch on the frequency stability of the relaxation oscillator. In a clock period consisting of four working stages including normal charge, discharge, pre-charge and hold stage, the threshold voltage of comparators is adjusted dynamically. A 32.7-kHz relaxation oscillator with the proposed half-period pre-charge compensation scheme is implemented in a TSMC 0.18-μm CMOS process, occupying a silicon area of 0.048 mm2. Simulation results show that the proposed relaxation oscillator consumes 51-nW at room temperature from 0.6-V power supply, and temperature stability of 43.1 ppm/°C from -55 °C to 125 °C and ±0.60% frequency variation with supply voltage from 0.5 V to 1.0 V are achieved.
Unfriending on Facebook: Friend Request and Online/Offline Behavior Analysis

Determine the role of the friend request in unfriending decisions. Find factors in unfriending decisions and find differences in the perception of online and offline behaviors that vary depending on the unfriending decision.  Survey research conducted online. 690 surveys about unfriending were analyzed using exploratory statistical techniques.  The research results show that the initiator of the friend request has more than their expected share of unfriends compared to those who receive the friend request. There are online and offline factors for unfriending decisions; the research identified six constructs to evaluate unfriending decisions. There are 4 components for online behaviors (unimportant/frequent posts, polarizing posts, inappropriate posts and everyday life posts) and 2 offline components (disliked behavior and changes in the relationship). Survey respondents who said they unfriend for online reasons were more likely to agree that the person posted too frequently about unimportant topics, polarizing topics, and inappropriate topics compared to those who unfriended for offline reasons.
Conservative management of thoracolumbar and lumbar spine compression and burst fractures: functional and radiographic outcomes in 136 cases treated by closed reduction and casting

Both fractures of the lumbar spine and at the thoracolumbar junction are quite common. The treatment of these fracture types is discussed controversially. Some authors advocate surgical treatment even in fractures without neurologic compromise while other series report good results after non-operative treatment. Between January 1997 and April 2004, 324 patients with spinal fractures were admitted to our institution. Hundred and thirty-six patients with compression and burst type fractures treated by closed reduction and casting were available for follow-up. Their medical records, radiographs and computer tomography scans were reviewed and their functional status was assessed. 94 male (69.1%) and 42 female (30.9%) patients with a mean age of 48.6 years (range 17–81) at time of injury were included. The thoracolumbar junction (T11-L1) was affected in 104 patients (76.5%). 23.5% had lumbar fractures. All of the burst type fractures with involvement of the posterior column affection were type A3.3. fractures according to the Magerl classification. Significant correction of radiographic parameters was achieved in the early postreduction period (P < 0.0001). Reduction could not be maintained at the final follow-up but still showed slight improvement compared to the initial presentation. Reduction could be maintained better in the thoracolumbar region than in the lumbar spine. Neurologic function was restored in all patients with unilateral radicular pain but only one patient recovered fully after cauda equina-syndrome. Patients after lumbar spine indicated a higher level of pain when compared to patients with fractures at the thoracolumbar junction. Closed reduction and casting is a safe and effective method for treatment of compression and burst type fractures at the thoracolumbar junction and can restore neurologic function in patients with unilateral radicular pain. It is of limited value in lumbar fractures and in burst type fractures with posterior column involvement.
The Application of Unified Theory of Acceptance and Use of Technology ( UTAUT ) for Predicting the Usage of E-Zakat Online System

The utilisation of technology has been widely practiced for the system of payment. It is now being utilised for the payment of zakat. Therefore, this research aims to gauge the awareness of an e-zakat online system in the Selangor and also to examine the extent of utilisation of an online e-zakat among individual zakat payers using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Although the model was reviewed by the prominent researchers, but, it still has a limited resource to explore the usefulness of using e-zakat online system. The primary data collection was carried out through a questionnaire survey. The questionnaire was distributed to the academicians in the Klang Valley. The results provide an intellectual challenge and contribute to knowledge in this area of user perceptions of IT utilisation and provide indispensable evidence of the necessity of improving the awareness and utilization of e-zakat online.
Scala Actors: Unifying thread-based and event-based programming

There is an impedance mismatch between message-passing concurrency and virtual machines, such as the JVM. VMs usually map their threads to heavyweight OS processes. Without a lightweight process abstraction, users are often forced to write parts of concurrent applications in an event-driven style which obscures control flow, and increases the burden on the programmer. In this paper we show how thread-based and event-based programming can be unified under a single actor abstraction. Using advanced abstraction mechanisms of the Scala programming language, we implement our approach on unmodified JVMs. Our programming model integrates well with the threading model of the underlying VM.
Hardware Trojans: Lessons Learned after One Decade of Research

Given the increasing complexity of modern electronics and the cost of fabrication, entities from around the globe have become more heavily involved in all phases of the electronics supply chain. In this environment, hardware Trojans (i.e., malicious modifications or inclusions made by untrusted third parties) pose major security concerns, especially for those integrated circuits (ICs) and systems used in critical applications and cyber infrastructure. While hardware Trojans have been explored significantly in academia over the last decade, there remains room for improvement. In this article, we examine the research on hardware Trojans from the last decade and attempt to capture the lessons learned. A comprehensive adversarial model taxonomy is introduced and used to examine the current state of the art. Then the past countermeasures and publication trends are categorized based on the adversarial model and topic. Through this analysis, we identify what has been covered and the important problems that are underinvestigated. We also identify the most critical lessons for those new to the field and suggest a roadmap for future hardware Trojan research.
Imitation of second language sounds in relation to L2 perception and production

This study reports findings from two experiments on second language learners, comparing their performance in an Imitation task to that in Identification and Read-Aloud tasks. Experiment 1 targeted English speakers' learning of Mandarin tones, while Experiment 2 investigated Korean speakers' learning of English consonants. The results of Experiment 1 showed that the Imitation task was generally easier for English speakers than the Identification and Read-Aloud tasks, suggesting that imitation was performed without some of the skills required by the other two tasks. As for Experiment 2, the Koreans were consistently less accurate in Imitation than in Read-Aloud, while their Imitation was more accurate than their Identification when the L2 sounds have a close counterpart in Korean. The results from both experiments revealed that the accuracy in Imitation was not always constrained by that in the Identification and Read-Aloud tasks. Hence it can be inferred that L2 imitation may not involve all the skills required by the other two tasks and probably bypasses some aspect of phonological encoding. More detailed predictions of the error patterns in the Imitation task based on Perception, Production, and Cascade models were compared. It was found that English speakers' confusion patterns in imitating Mandarin tones correlated to the same degree with the predictions of all three models, which may be because the learners' difficulty in perception and production was largely similar. On the other hand, Korean learners' errors in imitating English consonants were overall more accurately predicted by a Cascade model, suggesting that both perceptual and production imprecision aggravated imitation performance. The comparison of these two experiments corroborates that various factors may affect the relationships between L2 imitation, perception, and production. & 2015
Template-based generation of road networks for virtual city modeling

In modern urban areas, we often find a transportation network that follows a superimposed pattern. In this paper, we propose a novel method to generate a virtual traffic network based on (1) image-derived templates, and (2) a rule-based generating system. Using 2D images as input maps, various road maps with different patterns could be produced. This traffic network generating model adjusts itself intelligently in order to avoid restricted geographical areas or urban developments. The generative model follows closely directions of elevation and connects road ends in ways that allow various types of breakpoints.
Generating Extractive Summaries of Scientific Paradigms

Researchers and scientists increasingly find themselves in the position of having to quickly understand large amounts of technical material. Our goal is to effectively serve this need by using bibliometric text mining and summarization techniques to generate summaries of scientific literature. We show how we can use citations to produce automatically generated, readily consumable, technical extractive summaries. We first propose C-LexRank, a model for summarizing single scientific articles based on citations, which employs community detection and extracts salient information-rich sentences. Next, we further extend our experiments to summarize a set of papers, which cover the same scientific topic. We generate extractive summaries of a set of Question Answering (QA) and Dependency Parsing (DP) papers, their abstracts, and their citation sentences and show that citations have unique information amenable to creating a summary.
Global cancer statistics.

The global burden of cancer continues to increase largely because of the aging and growth of the world population alongside an increasing adoption of cancer-causing behaviors, particularly smoking, in economically developing countries. Based on the GLOBOCAN 2008 estimates, about 12.7 million cancer cases and 7.6 million cancer deaths are estimated to have occurred in 2008; of these, 56% of the cases and 64% of the deaths occurred in the economically developing world. Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death among females, accounting for 23% of the total cancer cases and 14% of the cancer deaths. Lung cancer is the leading cancer site in males, comprising 17% of the total new cancer cases and 23% of the total cancer deaths. Breast cancer is now also the leading cause of cancer death among females in economically developing countries, a shift from the previous decade during which the most common cause of cancer death was cervical cancer. Further, the mortality burden for lung cancer among females in developing countries is as high as the burden for cervical cancer, with each accounting for 11% of the total female cancer deaths. Although overall cancer incidence rates in the developing world are half those seen in the developed world in both sexes, the overall cancer mortality rates are generally similar. Cancer survival tends to be poorer in developing countries, most likely because of a combination of a late stage at diagnosis and limited access to timely and standard treatment. A substantial proportion of the worldwide burden of cancer could be prevented through the application of existing cancer control knowledge and by implementing programs for tobacco control, vaccination (for liver and cervical cancers), and early detection and treatment, as well as public health campaigns promoting physical activity and a healthier dietary intake. Clinicians, public health professionals, and policy makers can play an active role in accelerating the application of such interventions globally.
Multi-Granularity Chinese Word Embedding

This paper considers the problem of learning Chinese word embeddings. In contrast to English, a Chinese word is usually composed of characters, and most of the characters themselves can be further divided into components such as radicals. While characters and radicals contain rich information and are capable of indicating semantic meanings of words, they have not been fully exploited by existing word embedding methods. In this work, we propose multi-granularity embedding (MGE) for Chinese words. The key idea is to make full use of such word-character-radical composition, and enrich word embeddings by further incorporating finer-grained semantics from characters and radicals. Quantitative evaluation demonstrates the superiority of MGE in word similarity computation and analogical reasoning. Qualitative analysis further shows its capability to identify finer-grained semantic meanings of words.
Coin Flipping by Telephone - A Protocol for Solving Impossible Problems

Alice and Bob want to flip a coin by telephone. (They have just divorced, live in different cities, want to decide who gets the car.) Bob would not like to tell Alice HEADS and hear Alice (at the other end of the line) say "Here goes . . . I'm flipping the coin. . . . You lost!"Coin-flipping in the SPECIAL way done here has a serious purpose. Indeed, it should prove an INDISPENSABLE TOOL of the protocol designer. Whenever a protocol requires one of two adversaries, say Alice, to pick a sequence of bits at random, and whenever it serves Alice's interests best NOT to pick her sequence of bits at random, then coin-flipping (Bob flipping coins to Alice) as defined here achieves the desired goal:1. It GUARANTEES to Bob that Alice will pick her sequence of bits at random. Her bit is 1 if Bob flips heads to her, O otherwise.2. It GUARANTEES to Alice that Bob will not know WHAT sequence of bits he flipped to her.Coin-flipping has already proved useful in solving a number of problems once thought impossible: mental poker, certified mail, and exchange of secrets. It will certainly prove a useful tool in solving other problems as well.
Conditional Dynamic Mutual Information-Based Feature Selection

With emergence of new techniques, data in many fields are getting larger and larger, especially in dimensionality aspect. The high dimensionality of data may pose great challenges to traditional learning algorithms. In fact, many of features in large volume of data are redundant and noisy. Their presence not only degrades the performance of learning algorithms, but also confuses end-users in the post-analysis process. Thus, it is necessary to eliminate irrelevant features from data before being fed into learning algorithms. Currently, many endeavors have been attempted in this field and many outstanding feature selection methods have been developed. Among different evaluation criteria, mutual information has also been widely used in feature selection because of its good capability of quantifying uncertainty of features in classification tasks. However, the mutual information estimated on the whole dataset can not exactly represent the correlation between features. To cope with this issue, in this paper we firstly re-estimate mutual information on identified instances dynamically, and then introduce a new feature selection method based on 1194 H. Liu, Y. Mo, J. Zhao conditional mutual information. Performance evaluations on sixteen UCI datasets show that our proposed method achieves comparable performance to other wellestablished feature selection algorithms in most cases.
Hierarchical Control of Hybrid Energy Storage System in DC Microgrids

Hybridization of energy storages (ESs) with different characteristics takes advantages of all ESs. Centralized control with high-/low-pass filter (LPF) for system net power decomposition and ESs' power dispatch is usually implemented in hybrid ES system (HESS). In this paper, hierarchical control of HESS, composed of both centralized and distributed control, is proposed to enhance system reliability. The conventional HESS centralized control is refined with implementations of online iteration, secondary voltage regulation, and autonomous state-of-charge (SoC) recovery. ESs' power references are iteratively generated to maximize the utilization of ES ramp rates and power capacities. Secondary voltage regulation and autonomous SoC recovery are applied to minimize bus voltage deviation and limit slack terminal SoC variation, respectively. In case of communication failure, a novel algorithm for HESS distributed control is proposed to retain system operation. Bus voltage is regarded as the global indicator for system power balance, and droop relationships are imposed for ES control. System net power decomposition and ESs' power dispatch are realized with localized LPFs. SoC recovery in distributed control is implemented by tuning the threshold voltage of a slack terminal. A laboratory-scale dc microgrid is developed to verify the proposed hierarchical control of HESS.
Estimating the Randomness of Domain Names for DGA Bot Callbacks

Bots are some of the most serious threats. Although the administrators must remove bots that reside in their networks, many bots have domain generation algorithms (DGAs) to avoid detection. A DGA is a technique for hiding the callbacks of bots by frequently changing the domain name of a command-and-control server. In this letter, we propose an approach for detecting the callbacks based on the randomness of domain names. Our evaluation indicates high performance, with a recall of 0.9960 and a precision of 0.9029. By enabling one to swiftly address various bots, our approach contributes to dramatically improving network security.
CA-LSTM: Search Task Identification with Context Attention based LSTM

Search task identification aims to understand a user's information needs to improve search quality for applications such as query suggestion, personalized search, and advertisement retrieval. To properly identify the search task within long query sessions, it is important to partition these sessions into segments before further processing. In this paper, we present the first search session segmentation model that uses a long short-term memory (LSTM) network with an attention mechanism. This model considers sequential context knowledge, including temporal information, word and character, and essentially learns which parts of the input query sequence are relevant to the current segmentation task and determines the influence of these parts. This segmentation technique is also combined with an efficient clustering method using a novel query relevance metric for end-to-end search task identification. Using real-world datasets, we demonstrate that our segmentation technique improves task identification accuracy of existing clustering-based algorithms.
Predicting IMDB Movie Ratings Using Social Media

We predict IMDb movie ratings and consider two sets of features: surface and textual features. For the latter, we assume that no social media signal is isolated and use data from multiple channels that are linked to a particular movie, such as tweets from Twitter and comments from YouTube. We extract textual features from each channel to use in our prediction model and we explore whether data from either of these channels can help to extract a better set of textual feature for prediction. Our best performing model is able to rate movies very close to the observed values.
Classifying evolving data streams with partially labeled data

Recently, several approaches have been proposed to deal with the increasingly challenging task of mining conceptdrifting data streams. However, most are based on supervised classification algorithms assuming that true labels are immediately and entirely available in the data streams. Unfortunately, such an assumption is often violated in real-world applications given that it is expensive or because it takes a long time to obtain all true labels. To deal with this problem, we propose in this paper a new semi-supervised approach for handling concept-drifting data streams containing both labeled and unlabeled instances. First, contrary to existing approaches, we monitor three possible kinds of drift: feature, conditional or dual drift. Drift detection is based on a hypothesis test comparing Kullback-Leibler divergence between old and recent data, whose distribution under the null hypothesis of coming from the same distribution is approximated via a bootstrap method. Then, if any drift occurs, a new classifier is learned from the recent data using the EM algorithm; otherwise, the current classifier is left unchanged. Our approach is so general that it can be applied to different classification models. Experimental studies, using the naive Bayes classifier and logistic regression, on both synthetic and real-world data sets demonstrate that our approach performs well.