nils-herrmann/clean_scirepeval huggingface dataset:
Queries and concept learning

We consider the problem of using queries to learn an unknown concept. Several types of queries are described and studied: membership, equivalence, subset, superset, disjointness, and exhaustiveness queries. Examples are given of efficient learning methods using various subsets of these queries for formal domains, including the regular languages, restricted classes of context-free languages, the pattern languages, and restricted types of propositional formulas. Some general lower bound techniques are given. Equivalence queries are compared with Valiant's criterion of probably approximately correct identification under random sampling.
Investigation of the TiN Gate Electrode With Tunable Work Function and Its Application for FinFET Fabrication

The titanium nitride (TiN) gate electrode with a tunable work function has successfully been deposited on the sidewalls of upstanding Si-fin channels of FinFETs by using a conventional reactive sputtering. It was found that the work function of the TiN (phi<sub>TiN</sub>) slightly decreases with increasing nitrogen (N<sub>2</sub>) gas flow ratio, R<sub>N</sub>=N<sub>2</sub>/(Ar+N<sub>2</sub>) in the sputtering, from 17% to 100%. The experimental threshold voltage (V<sub>th</sub>) dependence on the R<sub>N</sub> shows that the more R<sub>N</sub> offers the lower V<sub>th</sub> for the TiN gate n-channel FinFETs. The composition analysis of the TiN films with different R<sub>N</sub> showed that the more amount of nitrogen is introduced into the TiN films with increasing R<sub>N</sub>, which suggests that the lowering of phi <sub>TiN</sub> with increasing R<sub>N</sub> should be related to the increase in nitrogen concentration in the TiN film. The desirable V<sub>th</sub> shift from -0.22 to 0.22 V was experimentally confirmed by fabricating n<sup>+</sup> poly-Si and TiN gate n-channel multi-FinFETs without a channel doping. The developed simple technique for the conformal TiN deposition on the sidewalls of Si-fin channels is very attractive to the TiN gate FinFET fabrication
IoT enabled aquatic drone for environmental monitoring

The article describes a monitoring system based on Raspberry Pi platform and a multichannel sensing module associated with water quality and air quality measurement parameters. Thus the temperature, conductivity, relative humidity and gas concentration are measured as was as the underwater acoustic signals. The data is stored on the memory of the drone's computational platform, and synchronized with a remote server database. Advanced data processing algorithms were implemented on the server side. Additionally, a mobile application was developed to be used by people working in the field for data visualization and statistical analysis.
High Vgs MOSFET characteristics with thin gate oxide for PMIC application

We present the characteristics of a newly developed MOSFET which has thin gate oxide but sustains high gate voltage. It consists of MIM capacitor coupled floating gate poly and source/drain junction formed by low voltage well. The characteristics of the MOSFET depend on the choice of capacitance coupling ratio which is determined by the ratio of MIM (Metal-Insulator-Metal) capacitance and gate oxide capacitance. This means that the proper choice of layout can control the device performances; threshold voltage, ID-VD, and ID-VG characteristics. Some of smart power management ICs requires of a low density non-volatile memory for the purpose of PMIC trimming. To meet this purpose, NVM embedding on PMIC solution should be cost effective. This newly proposed high gate voltage MOSFET with thin gate oxide can be an effective solution for the periphery circuits of single poly EEPROM operation.
Arbitrary-Precision Arithmetics on the GPU

The majority of computer applications employ numerical data types with a fixed amount of precision for their computations. Their limited numerical range and precision are sufficient for most use cases. However, for some purposes, such as cryptography or geometrical computations, the required range and precision can become arbitrarily large. Numerical types that can handle such demands have higher memory requirements and are not natively supported by common hardware, which leads to increased computational complexity. In this paper, we examine how basic arithmetic operations on arbitrary-precision integers can be adapted to many-core architectures in the form of graphics processing units, which are widely available as commodity hardware. Apart from a detailed description of our method, we show superior performance characteristics of our implementation in comparison to state-of-the-art CPU libraries for high computational loads.
Multiple description-DASH: Pragmatic video streaming maximizing end-users' quality of experience

Over the past few years, adaptive bitrate streaming protocols, such as DASH, have risen to enhance the End-Users' experience towards video consumption over the Internet. Current methods perform a smoother playback (i.e., fewer re-buffering states) trading off with quality fluctuations due to the client-server link state. The proposed work goes a step further and introduces an innovative lightweight streaming solution by taking advantage of bandwidth aggregation over multiple paths using multiple content sources at the same time. This pragmatic evolving approach outperforms the QoE delivered by current DASH-based or P2P-based solutions and leverages the scope of action made available to the media delivery chain actors. The proposed solution has been implemented and is compared to a DASH-based approach used in most existing VoD use-cases through a first set of experiments. Results demonstrate the strong advantages in terms of quality delivered at the End-User's side and buffer occupancy. Discussion on the necessary trade-offs is also tackled.
A Real-Time Continuous Gesture Recognition System for Sign Language

In this paper, a large vocabulary sign language interpreter is presented with real-time continuous gesture recognition of sign language using a DataGlove . The most critical problem, end-point detection in a stream of gesture input is first solved and then statistical analysis is done according to 4 parameters in a gesture : posture, position, orientation, and motion. We have implemented a prototype system with a lexicon of 250 vocabularies in Taiwanese Sign Language (TWL). This system uses hidden Markov models (HMMs) for 51 fundamental postures, 6 orientations, and 8 motion primitives. In a signerdependent way, a sentence of gestures based on these vocabularies can be continuously recognized in real-time and the average recognition rate is 80.4%.
Detection of Unhealthy Region of plant leaves using Neural Network

A leaf is an organ of vascular plant and is the principal lateral appendage of the stem. Each leaf has a set of features that differentiate it from the other leaves, such as margin and shape. This work proposes a comparison of supervised plant leaves classification using different approaches, based on different representations of these leaves, and the chosen algorithm. Beginning with the representation of leaves, we presented leaves by a fine-scale margin feature histogram, by a Centroid Contour Distance Curve shape signature, or by an interior texture feature histogram in 64 element vector for each one, after we tried different combination among these features to optimize results. We classified the obtained vectors. Then we evaluate the classification using cross validation. The obtained results are very interesting and show the importance of each feature. The classification of plant leaf images with biometric features. Traditionally, the trained taxonomic perform this process by following various tasks. The taxonomic usually classify the plants based on flowering and associative phenomenon. It was found that this process was time consuming and difficult. The biometric features of plants leaf like venation make this classification easy. Leaf biometric feature are analyzed using computer based method like morphological feature analysis and artificial neural network and Naive bayes based classifier. KNN classification model take input as the leaf venation morphological feature and classify them into four different species. The result of this classification based on leaf venation is achieved 96.53% accuracy in the training of the model for classification of leaves provide the 91% accuracy in testing to classify the leaf images.  Artificial neural network, K-NN Classification, Leaf venation pattern, Morphological Features
CryptoRec: Privacy-preserving Recommendation as a Service

Recommender systems rely on large datasets of historical data and entail serious privacy risks. A server offering Recommendation as a Service to a client might leak more information than necessary regarding its recommendation model and dataset. At the same time, the disclosure of the client’s preferences to the server is also a matter of concern. Devising privacypreserving protocols using general cryptographic primitives (e.g., secure multi-party computation or homomorphic encryption), is a typical approach to overcome privacy concerns, but in conjunction with state-of-the-art recommender systems often yields far-from-practical solutions. In this paper, we tackle this problem from the direction of constructing crypto-friendly machine learning algorithms. In particular, we propose CryptoRec, a secure framework for Recommendation as a Service, which encompasses a homomorphic encryption friendly recommender system. This recommendation model possesses two interesting properties: (1) It models useritem interactions in an item-only latent feature space in which personalized user representations are automatically captured by aggregating pre-learned item features. This means that a server with a pre-trained model can provide recommendations for a client whose data is not in its training set. Nevertheless, retraining the model with the client’s data still improves accuracy. (2) It only uses addition and multiplication operations, making the model straightforwardly compatible with homomorphic encryption schemes. We demonstrate the efficiency and accuracy of CryptoRec on three real-world datasets. CryptoRec allows a server with thousands of items to privately answer a prediction query within a few seconds on a single PC, while its prediction accuracy is still competitive with state-of-the-art recommender systems computing over clear data.
Emotion control in collaborative learning situations: do students regulate emotions evoked by social challenges?

During recent decades, self-regulated learning (SRL) has become a major research field. SRL successfully integrates the cognitive and motivational components of learning. Self-regulation is usually seen as an individual process, with the social aspects of regulation conceptualized as one aspect of the context. However, recent research has begun to investigate whether self-regulation processes are complemented by socially shared regulation processes.



The presented study investigated what kind of socio-emotional challenges students experience during collaborative learning and whether the students regulate the emotions evoked during these situations. The interplay of the emotion regulation processes between the individual and the group was also studied.


SAMPLE
The sample for this study was 63 teacher education students who studied in groups of three to five during three collaborative learning tasks.



Students' interpretations of experienced social challenges and their attempts to regulate emotions evoked by these challenges were collected following each task using the Adaptive Instrument for the Regulation of Emotions.



The results indicated that students experienced a variety of social challenges. Students also reported the use of shared regulation in addition to self-regulation. Finally, the results suggested that intrinsic group dynamics are derived from both individual and social elements of collaborative situations.



The findings of the study support the assumption that students can regulate emotions collaboratively as well as individually. The study contributes to our understanding of the social aspects of emotional regulation in collaborative learning contexts.
Patient-Specific Epileptic Seizure Onset Detection Algorithm Based on Spectral Features and IPSONN Classifier

This paper proposes a patient-specific epileptic seizure onset detection algorithm. In this algorithm, spectral features in five frequency bands (Δ, α, ß, θ and γ) is extracted from small frames of seizure and non-seizure EEG signals by applying Discrete Wavelet Transform (DWT) and Discrete Fourier Transform (DFT). These features can create the maximum distinction between two classes. Then a neural network (NN) classifier based on improved particle swarm optimization (IPSO) is used to determine an optimal nonlinear decision boundary. This classifier allows adjusting the parameter of the NN classifier, efficiently. Finally, the performance of algorithm is evaluated based on three measures, sensitivity, specificity and latency. The results indicate that the proposed algorithm obtain a higher sensitivity and smaller latency than other common algorithms. The proposed algorithm can be used as a seizure onset detector to initiate the just-in time therapy methods.
NB-IoT and LoRA connectivity analysis for M2M/IoT smart grids applications

The evolution of the new M2M/IoT paradigm suggests new applications in several fields. However, the unique characteristics of M2M communications pose different challenges in terms of connectivity and capacity with respect to the conventional telecommunications networks. In particular, the harmful locations where IoT devices could be deployed and the massive number of the devices to be connected require a different approach with respect the traditional wireless communication. In this paper, we analyzed two different technologies expressly devoted the M2M communication: the Long Term Evolution (LTE) based solution, NB-IoT (Narrow Band — Internet of Things), and the LPWAN (Low Power Wide Area Network) based solution, LoRa (Low Range). The analysis has been provided taking into account different propagation scenarios and geographical information of the real MNOs (Mobile Network Operators) sites operating in the territory under test. The aim of this work is to check wireless access method of both cellular and proprietary technologies for the novel IoT Smart Grid applications requirements.
Ordering-Based Search: A Simple and Effective Algorithm for Learning Bayesian Networks

One of the basic tasks for Bayesian networks (BNs) is that of learning a network structure from data. The BN-learning problem is NPhard, so the standard solution is heuristic search. Many approaches have been proposed for this task, but only a very small number outperform the baseline of greedy hill-climbing with tabu lists; moreover, many of the proposed algorithms are quite complex and hard to implement. In this paper, we propose a very simple and easy-toimplement method for addressing this task. Our approach is based on the well-known fact that the best network (of bounded in-degree) consistent with a given node ordering can be found very efficiently. We therefore propose a search not over the space of structures, but over the space of orderings, selecting for each ordering the best network consistent with it. This search space is much smaller, makes more global search steps, has a lower branching factor, and avoids costly acyclicity checks. We present results for this algorithm on both synthetic and real data sets, evaluating both the score of the network found and in the running time. We show that orderingbased search outperforms the standard baseline, and is competitive with recent algorithms that are much harder to implement.
A difference in hypothalamic structure between heterosexual and homosexual men.

The anterior hypothalamus of the brain participates in the regulation of male-typical sexual behavior. The volumes of four cell groups in this region [interstitial nuclei of the anterior hypothalamus (INAH) 1, 2, 3, and 4] were measured in postmortem tissue from three subject groups: women, men who were presumed to be heterosexual, and homosexual men. No differences were found between the groups in the volumes of INAH 1, 2, or 4. As has been reported previously, INAH 3 was more than twice as large in the heterosexual men as in the women. It was also, however, more than twice as large in the heterosexual men as in the homosexual men. This finding indicates that INAH is dimorphic with sexual orientation, at least in men, and suggests that sexual orientation has a biological substrate.
Robust real-time underwater digital video streaming using optical communication

We present a real-time video delivery solution based on free-space optical communication for underwater applications. This solution comprises of AquaOptical II, a high-bandwidth wireless optical communication device, and a two-layer digital encoding scheme designed for error-resistant communication of high resolution images. Our system can transmit digital video reliably through a unidirectional underwater channel, with minimal infrastructural overhead. We present empirical evaluation of this system's performance for various system configurations, and demonstrate that it can deliver high quality video at up to 15 Hz, with near-negligible communication latencies of 100 ms. We further characterize the corresponding end-to-end latencies, i.e. from time of image acquisition until time of display, and reveal optimized results of under 200 ms, which facilitates a wide range of applications such as underwater robot tele-operation and interactive remote seabed monitoring.
A review of trisomy X (47,XXX)

Trisomy X is a sex chromosome anomaly with a variable phenotype caused by the presence of an extra X chromosome in females (47,XXX instead of 46,XX). It is the most common female chromosomal abnormality, occurring in approximately 1 in 1,000 female births. As some individuals are only mildly affected or asymptomatic, it is estimated that only 10% of individuals with trisomy X are actually diagnosed. The most common physical features include tall stature, epicanthal folds, hypotonia and clinodactyly. Seizures, renal and genitourinary abnormalities, and premature ovarian failure (POF) can also be associated findings. Children with trisomy X have higher rates of motor and speech delays, with an increased risk of cognitive deficits and learning disabilities in the school-age years. Psychological features including attention deficits, mood disorders (anxiety and depression), and other psychological disorders are also more common than in the general population. Trisomy X most commonly occurs as a result of nondisjunction during meiosis, although postzygotic nondisjunction occurs in approximately 20% of cases. The risk of trisomy X increases with advanced maternal age. The phenotype in trisomy X is hypothesized to result from overexpression of genes that escape X-inactivation, but genotype-phenotype relationships remain to be defined. Diagnosis during the prenatal period by amniocentesis or chorionic villi sampling is common. Indications for postnatal diagnoses most commonly include developmental delays or hypotonia, learning disabilities, emotional or behavioral difficulties, or POF. Differential diagnosis prior to definitive karyotype results includes fragile X, tetrasomy X, pentasomy X, and Turner syndrome mosaicism. Genetic counseling is recommended. Patients diagnosed in the prenatal period should be followed closely for developmental delays so that early intervention therapies can be implemented as needed. School-age children and adolescents benefit from a psychological evaluation with an emphasis on identifying and developing an intervention plan for problems in cognitive/academic skills, language, and/or social-emotional development. Adolescents and adult women presenting with late menarche, menstrual irregularities, or fertility problems should be evaluated for POF. Patients should be referred to support organizations to receive individual and family support. The prognosis is variable, depending on the severity of the manifestations and on the quality and timing of treatment.
E-Government Challenges and the Role of Political Leadership in Indonesia:  The Case of Sragen

In general, developing countries are lagging behind in e-government adoption compared to developed countries. Within Indonesia, there is a huge disparity in e- government implementation between districts. This study presents e-government challenges and the role of political leadership in the rural district of Sragen, one of the leading districts in implementing e-government in the country. The study focuses on the supply-side of e- government, and categorises the challenges in three main areas; management, infrastructure, and human factors. Initiatives taken to deal with these challenges are presented and strong political leadership is found to play an important role.
Parenting cognitions → parenting practices → child adjustment? The standard model.

In a large-scale (N = 317) prospective 8-year longitudinal multiage, multidomain, multivariate, multisource study, we tested a conservative three-term model linking parenting cognitions in toddlerhood to parenting practices in preschool to classroom externalizing behavior in middle childhood, controlling for earlier parenting practices and child externalizing behavior. Mothers who were more knowledgeable, satisfied, and attributed successes in their parenting to themselves when their toddlers were 20 months of age engaged in increased supportive parenting during joint activity tasks 2 years later when their children were 4 years of age, and 6 years after that their 10-year-olds were rated by teachers as having fewer classroom externalizing behavior problems. This developmental cascade of a "standard model" of parenting applied equally to families with girls and boys, and the cascade from parenting attributions to supportive parenting to child externalizing behavior obtained independent of 12 child, parent, and family covariates. Conceptualizing socialization in terms of cascades helps to identify points of effective intervention.
Low-Power High-Throughput BCH Error Correction VLSI Design for Multi-Level Cell NAND Flash Memories

As the reliability is a critical issue for new generation multi-level cell (MLC) flash memories, there is growing call for fast and compact error correction code (ECC) circuit with minimum impact on memory access time and chip area. This paper presents a high-throughput and low-power ECC scheme for MLC NAND flash memories. The BCH encoder and decoder architecture features byte-wise processing and a low complexity key equation solver using a simplified Berlekamp-Massey algorithm. Resource sharing and power reduction techniques are also applied. Synthesized using 0.25-mum CMOS technology in a supply voltage of 2.5 V, the proposed BCH (4148,4096) encoder/decoder achieves byte-wise processing, and it needs an estimated cell area of 0.2 mm2, and an average power of 3.18 mW with 50 MB/s throughput