nils-herrmann/clean_scirepeval huggingface dataset:
Material innovation for MOL, BEOL, and 3D integration

Ti barrier showed a good adhesion, limited growth of Co silicide, and a low contact resistivity of the order of 10<sup>−9</sup> Ωcm<sup>2</sup> on both n+ and p+ Si. For BEOL, a CVD-MnOx layer could be formed conformally in high-aspect ratio contact holes. The ALD-MnOx layer of 1.2 nm thick showed a good diffusion barrier property at 400 °C. For 3D integration, TSV of 10 μm diameter and 80 μm depth could be filled with low resistivity sintered Cu paste without voids.
The Neuroscience Literacy of Trainee Teachers

There is concern about the prevalence of neuromyths in education, but little is known about how teachers think about the brain and how this may influence their practice.  To further understanding of how teachers, at entry to the profession, think about brain development and function. Sample: 158 graduate trainee teachers at the end of their one-year course.  Preliminary semi-structured interviews contributed to the development of a suitable survey instrument. Participants were then surveyed during one of their final lectures.  Trainees’ ideas reflected misconceptions in public circulation and notions promoted by popular brain-based educational programmes. Most of the trainee teachers in our survey did not accept, or were unsure, about whether mental activity derives from biological brain function. Trainee teachers place equal importance on home environment and education as determinants of educational outcome, with genetics a significant but smaller influence than either. A follow up survey with a new cohort of trainees confirmed that constructs about development are linked to a sense of agency, with beliefs in strong genetic influence associated with stronger notions of biologically-defined limits on pupil achievement.  In the absence of formal training, trainee teachers acquire their own ideas about brain function, many of which are potentially detrimental to their practice as teachers. Neuroscience Literacy   3
Business Process Management: A Survey

Business Process Management (BPM) includes methods, techniques, and tools to support the design, enactment, management, and analysis of operational business processes. It can be considered as an extension of classical Workflow Management (WFM) systems and approaches. Although the practical relevance of BPM is undisputed, a clear definition of BPM and related acronyms such as BAM, BPA, and STP are missing. Moreover, a clear scientific foundation is missing. In this paper, we try to demystify the acronyms in this domain, describe the state-of-the-art technology, and argue that BPM could benefit from formal methods/languages (cf. Petri nets, process algebras, etc.).
Routing questions to appropriate answerers in community question answering services

Community Question Answering (CQA) service provides a platform for increasing number of users to ask and answer for their own needs but unanswered questions still exist within a fixed period. To address this, the paper aims to route questions to the right answerers who have a top rank in accordance of their previous answering performance. In order to rank the answerers, we propose a framework called Question Routing (QR) which consists of four phases: (1) performance profiling, (2) expertise estimation, (3) availability estimation, and (4) answerer ranking. Applying the framework, we conduct experiments with Yahoo! Answers dataset and the results demonstrate that on average each of 1,713 testing questions obtains at least one answer if it is routed to the top 20 ranked answerers.
Efficient mesh denoising via robust normal filtering and alternate vertex updating

The most challenging problem in mesh denoising is to distinguish features from noise. Based on the robust guided normal estimation and alternate vertex updating strategy, we investigate a new feature-preserving mesh denoising method. To accurately capture local structures around features, we propose a corner-aware neighborhood (CAN) scheme. By combining both overall normal distribution of all faces in a CAN and individual normal influence of the interested face, we give a new consistency measuring method, which greatly improves the reliability of the estimated guided normals. As the noise level lowers, we take as guidance the previous filtered normals, which coincides with the emerging rolling guidance idea. In the vertex updating process, we classify vertices according to filtered normals at each iteration and reposition vertices of distinct types alternately with individual regularization constraints. Experiments on a variety of synthetic and real data indicate that our method adapts to various noise, both Gaussian and impulsive, no matter in the normal direction or in a random direction, with few triangles flipped.
A Self-Calibrating Palmer Drought Severity

The Palmer Drought Severity Index (PDSI) has been used for more than 30 years to quantify the long-term drought conditions for a given location and time. However, a common critique of the PDSI is that the behavior of the index at various locations is inconsistent, making spatial comparisons of PDSI values difficult, if not meaningless. A self-calibrating Palmer Drought Severity Index (SC-PDSI) is presented and evaluated. The SC-PDSI automatically calibrates the behavior of the index at any location by replacing empirical constants in the index computation with dynamically calculated values. An evaluation of the SC-PDSI at 761 sites within Nebraska, Kansas, Colorado, Wyoming, Montana, North Dakota, and South Dakota, as well as at all 344 climate divisions shows that it is more spatially comparable than the PDSI, and reports extreme wet and dry conditions with frequencies that would be expected for rare conditions.
Effect of Prayer and “ OM ” Meditation in EnhancingGalvanic Skin Response

The research was conducted with the purpose to study the effect of prayer and meditation on galvanic skin response (GSR). It was hypothesized that there was a significant positive effect of prayer and meditation (Om chanting) on galvanic skin response (GSR). The sample consisted of 20 normal, healthy female participants through purposive sampling. The age group of the sample was 18 to 24 years (Mean= 18.7, SD= 1.55). Gender was female and minimum education was graduation. The daily practice time of prayer and meditation session was 30 minutes for one month. PrePost data were recorded before and after intervention of prayer and meditation session by using single group pre-post research design. Recordings of galvanic skin response (GSR) were made on a computerized polygraph (Model Physiopac, PP 4, Medicaid Systems, Chandigarh, India) test. The results revealed a significant increase in GSR values as an effect of prayer and meditation which suggested the psychophysiological relaxation. Practicing prayer and meditation increases the galvanic skin response and hence decreases the stress level of the individual.
A Hybrid Data Mining Approach for Intrusion Detection on Imbalanced NSL-KDD Dataset

Intrusion detection systems aim to detect malicious viruses from computer and network traffic, which is not possible using common firewall. Most intrusion detection systems are developed based on machine learning techniques. Since datasets which used in intrusion detection are imbalanced, in the previous methods, the accuracy of detecting two attack classes, R2L and U2R, is lower than that of the normal and other attack classes. In order to overcome this issue, this study employs a hybrid approach. This hybrid approach is a combination of synthetic minority oversampling technique (SMOTE) and cluster center and nearest neighbor (CANN). Important features are selected using leave one out method (LOO). Moreover, this study employs NSL KDD dataset. Results indicate that the proposed method improves the accuracy of detecting U2R and R2L attacks in comparison to the baseline paper by 94% and 50%, respectively. intrusion detection system; feature selection; imbalanced dataset; SMOTE; NSL KDD
Updatable Block-Level Message-Locked Encryption

Deduplication is a widely used technique for reducing storage space of cloud service providers. Yet, it is unclear how to support deduplication of encrypted data securely until the study of Bellareetal on message-locked encryption (Eurocrypt 2013). Since then, there are many improvements such as strengthening its security, reducing client storage, etc. While updating a (shared) file is common, there is little attention on how to efficiently update large encrypted files in a remote storage with deduplication. To modify even a single bit, existing solutions require the trivial and expensive way of downloading and decrypting the large ciphertext.
 We initiate the study of updatable block-level message-locked encryption. We propose a provably secure construction that is efficiently updatable with O(log|F|) computational cost, where |F| is the file size. It also supports proof-of-ownership, a nice feature which protects storage providers from being abused as a free content distribution network.
Visual Analysis of Large Graphs

The analysis of large graphs plays a prominent role in various fields of research and is relevant in many important application areas. Effective visual analysis of graphs requires appropriate visual presentations in combination with respective user interaction facilities and algorithmic graph analysis methods. How to design appropriate graph analysis systems depends on many factors, including the type of graph describing the data, the analytical task at hand, and the applicability of graph analysis methods. The most recent surveys of graph visualization and navigation techniques were presented by Herman et al. [HMM00] and Diaz [DPS02]. The first work surveyed the main techniques for visualization of hierarchies and graphs in general that had been introduced until 2000. The second work concentrated on graph layouts introduced until 2002. Recently, new techniques have been developed covering a broader range of graph types, such as time-varying graphs. Also, in accordance with ever growing amounts of graph-structured data becoming available, the inclusion of algorithmic graph analysis and interaction techniques becomes increasingly important. In this State-of-the-Art Report, we survey available techniques for the visual analysis of large graphs. Our review firstly considers graph visualization techniques according to the type of graphs supported. The visualization techniques form the basis for the presentation of interaction approaches suitable for visual graph exploration. As an important component of visual graph analysis, we discuss various graph algorithmic aspects useful for the different stages of the visual graph analysis process.
Capacitive angular-position sensor with electrically floating conductive rotor and measurement redundancy

This paper presents a capacitive angular-position sensor with a contactless electrically floating conductive rotor and an interface electronic circuit that is designed to maximize the performance/cost ratio. The sensor includes two separate and independent measurement sections that sense the same angle and provide redundancy in critical applications. The electronic interface is based on a relaxation oscillator that, for each of the two sections, measures an appropriate quantity that relates capacitance ratio to angular position and provides a dc output voltage that varies ratiometrically with respect to the supply voltage. The sensor was built in a version with /spl plusmn/11/spl deg/ measuring range for each section. Experimental tests showed a linearity better that 1% of the span.
Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples

In recent years, deep learning has shown performance breakthroughs in many prominent problems. However, this comes with a major concern: deep networks have been found to be vulnerable to adversarial examples. Adversarial examples are modified inputs designed to cause the model to err. In the domains of images and speech, the modifications are untraceable by humans, but affect the model’s output. In binary files, small modifications to the byte-code often lead to significant changes in its functionality/validity, therefore generating adversarial examples is not straightforward. We introduce a novel approach for generating adversarial examples for discrete input sets, such as binaries. We modify malicious binaries by injecting a small sequence of bytes to the binary file. The modified files are detected as benign, while preserving their malicious functionality. We applied this approach to an end-to-end convolutional neural malware detector and present a high evasion rate. Moreover, we showed that our payload is transferable within different positions of the same file and across different files and file families.
Algorithms for nonnegative matrix and tensor factorizations: a unified view based on block coordinate descent framework

We review algorithms developed for nonnegative matrix factorization (NMF) and nonnegative tensor factorization (NTF) from a unified view based on the block coordinate descent (BCD) framework. NMF and NTF are low-rank approximation methods for matrices and tensors in which the low-rank factors are constrained to have only nonnegative elements. The nonnegativity constraints have been shown to enable natural interpretations and allow better solutions in numerous applications including text analysis, computer vision, and bioinformatics. However, the computation of NMF and NTF remains challenging and expensive due the constraints. Numerous algorithmic approaches have been proposed to efficiently compute NMF and NTF. We provide simplified explanations of several successful NMF and NTF algorithms based on the BCD framework in constrained non-linear optimization. The BCD framework readily explains the theoretical convergence properties of several efficient NMF and NTF algorithms, which are consistent with experimental observations reported in literature. We also discuss algorithms that do not fit in the BCD framework contrasting them from those based on the BCD framework. With insights acquired from the unified perspective, we also propose efficient algorithms for updating NMF when there is a small change in the reduced dimension or in the data. The effectiveness of the proposed updating algorithms are validated experimentally with synthetic and real-world data sets.
Design of a high fill factor permanent magnet integrated starter generator with compressed stator windings

This paper focuses on the improved thermal performance of electrical machines by increasing the conductor fill factor. A comparative design study of integrated starter generator (ISG) considering different pole/slot combinations and magnet topologies is used as a case study to fix slot dimensions. Compressed stator windings coupled with segmented stator structure are introduced to reduce copper losses of the optimized ISG. By performing quasi-static explicit dynamic simulations, deformation of insulation has been investigated regarding mechanical properties of the magnet wire enamels. It is noted that magnet wires at certain cross-sectional area can be compressed up to 0.72 fill factor without observing insulation failure. Thermal conductivity enhancement of the stator windings at improved slot fill factor is demonstrated by performing steady state and transient finite element thermal simulations.
Gait transition based on CPG modulation for quadruped locomotion

Quadruped robots are required to implement a fast transition between gaits in situations such as an abruptly increase in speed, an unexpected obstacle to stride over. Considering these situations, we propose a novel approach to achieve fast gait transition within a step cycle period of locomotion. This approach relies on the central pattern generators (CPGs) to produce rhythmic control signals, along with a CPG modulation to harmonize the limb phases. Therefore, smooth and fast gait transition can be obtained. We implement our approach on a simulated quadruped model. Simulation results demonstrate the adequacy of the approach to execute smooth and fast gait transition within a step cycle period. In addition, we analyze the ground reaction force (GRF), the pitch angle and the roll angle of the quadruped model to evaluate the proposed gait transition approach.
EvalVid - A Framework for Video Transmission and Quality Evaluation

With EvalVid we present a complete framework and tool-set for evaluation of the quality of video transmitted over a real or simu lated communication network. Besides measuring QoS parameters of the underlyin g network, like loss rates, delays, and jitter, we support also a subjective vide o quality evaluation of the received video based on the frame-by-frame PSNR calcula tion. The tool-set has a modular construction, making it possible to exchange b oth the network and the codec. We present here its application for MPEG-4 as exam ple. EvalVid is targeted for researchers who want to evaluate their network designs or setups in terms of user perceived video quality. The tool-set is publi cly available [11].
Molecular and Morphological Characterization of Aphelenchoides fuchsi sp. n. (Nematoda: Aphelenchoididae) Isolated from Pinus eldarica in Western Iran.

Aphelenchoides fuchsi sp. n. is described and illustrated from bark and wood samples of a weakened Mondell pine in Kermanshah Province, western Iran. The new species has body length of 332 to 400 µm (females) and 365 to 395 µm (males). Lip region set off from body contour. The cuticle is weakly annulated, and there are four lines in the lateral field. The stylet is 8 to 10 μm long and has small basal swellings. The excretory pore is located ca one body diam. posterior to metacorpus valve or 51 to 62 μm from the head. The postuterine sac well developed (60-90 µm). Spicules are relatively short (15-16 μm in dorsal limb) with apex and rostrum rounded, well developed, and the end of the dorsal limb clearly curved ventrad like a hook. The male tail has usual three pairs of caudal papillae (2+2+2) and a well-developed mucro. The female tail is conical, terminating in a complicated step-like projection, usually with many tiny nodular protuberances. The new species belongs to the Group 2 sensu Shahina, category of Aphelenchoides species. Phylogenetic analysis based on small subunit (SSU) and partial large subunit (LSU) sequences of rRNA supported the morphological results.
Taming App Delivery via Streaming

The number of apps downloaded for smart devices has surpassed 80 billion, with trends suggesting continued substantial increases. The resulting volume of app installs and updates puts pressure on the existing appdelivery infrastructure due to interactions of end user devices with app stores via the Internet that involve app stores’ data center, content delivery network’s (CDNs) points of presence and the Internet Service Provider (ISP) pipes. This paper presents ‘AppFlux’ – a novel app streaming approach to app delivery which reduces the load app delivery poses on the app infrastructure, and relieves users from having to deal with unnecessary updates potentially saving bytes on their costly data plan. By leveraging the emerging ‘edge cloud’ tier of the Internet, the AppFlux approach provides ‘just-in-time’ delivery of apps and their updates while (i) reducing the traffic due to app installs and updates seen in the last mile of Internet by up to 70%, (ii) facilitating twice as fast app delivery compared to CDNs and (iii) App streaming can potentially lead to 20% improvements in the app load times on devices. With its implementation for the Android app ecosystem, AppFlux achieves this in a completely invisible manner i.e., without requiring any changes by app developers or any explicit actions by end users.
Using ontology-based semantic similarity to facilitate the article screening process for systematic reviews

Systematic Reviews (SRs) are utilized to summarize evidence from high quality studies and are considered the preferred source of evidence-based practice (EBP). However, conducting SRs can be time and labor intensive due to the high cost of article screening. In previous studies, we demonstrated utilizing established (lexical) article relationships to facilitate the identification of relevant articles in an efficient and effective manner. Here we propose to enhance article relationships with background semantic knowledge derived from Unified Medical Language System (UMLS) concepts and ontologies.



We developed a pipelined semantic concepts representation process to represent articles from an SR into an optimized and enriched semantic space of UMLS concepts. Throughout the process, we leveraged concepts and concept relations encoded in biomedical ontologies (SNOMED-CT and MeSH) within the UMLS framework to prompt concept features of each article. Article relationships (similarities) were established and represented as a semantic article network, which was readily applied to assist with the article screening process. We incorporated the concept of active learning to simulate an interactive article recommendation process, and evaluated the performance on 15 completed SRs. We used work saved over sampling at 95% recall (WSS95) as the performance measure.



We compared the WSS95 performance of our ontology-based semantic approach to existing lexical feature approaches and corpus-based semantic approaches, and found that we had better WSS95 in most SRs. We also had the highest average WSS95 of 43.81% and the highest total WSS95 of 657.18%.



We demonstrated using ontology-based semantics to facilitate the identification of relevant articles for SRs. Effective concepts and concept relations derived from UMLS ontologies can be utilized to establish article semantic relationships. Our approach provided a promising performance and can easily apply to any SR topics in the biomedical domain with generalizability.
Genetic epidemiology of major depression: review and meta-analysis.

The authors conducted a meta-analysis of relevant data from primary studies of the genetic epidemiology of major depression.



The authors searched MEDLINE and the reference lists of previous review articles to identify relevant primary studies. On the basis of a review of family, adoption, and twin studies that met specific inclusion criteria, the authors derived quantitative summary statistics.



Five family studies met the inclusion criteria. The odds ratios for proband (subjects with major depression or comparison subjects) versus first-degree relative status (affected or unaffected with major depression) were homogeneous across the five studies (Mantel-Haenszel odds ratio=2.84, 95% CI=2.31-3.49). No adoption study met the inclusion criteria, but the results of two of the three reports were consistent with genetic influences on liability to major depression. Five twin studies met the inclusion criteria, and their statistical summation suggested that familial aggregation was due to additive genetic effects (point estimate of heritability of liability=37%, 95% CI=31%-42%), with a minimal contribution of environmental effects common to siblings (point estimate=0%, 95% CI=0%-5%), and substantial individual-specific environmental effects/measurement error (point estimate=63%, 95% CI=58%-67%). The literature suggests that recurrence best predicts the familial aggregation of major depression.



Major depression is a familial disorder, and its familiality mostly or entirely results from genetic influences. Environmental influences specific to an individual are also etiologically significant. Major depression is a complex disorder that does not result from either genetic or environmental influences alone but rather from both. These findings are notably consistent across samples and methods and are likely to be generally applicable.