nils-herrmann/clean_scirepeval huggingface dataset:
Blended learning at pre-service teacher education in Turkey: A systematic review

The literature shows an increase in blended learning implementations (N = 74) at faculties of education in Turkey whereas pre-service and in-service teachers’ ICT competencies have been identified as one of the areas where they are in need of professional development. This systematic review was conducted to find out the impact of blended learning on academic achievement and attitudes at teacher education programs in Turkey. 21 articles and 10 theses complying with all pre-determined criteria (i.e., studies having quantitative research design or at least a quantitative aspect conducted at pre-service teacher education programs) included within the scope of this review. With regard to academic achievement, it was synthesized that majority of the studies confirmed its positive impact on attaining course outcomes. Likewise, blended learning environment was revealed to contribute pre-service teachers to develop positive attitudes towards the courses. It was also concluded that face-to-face aspect of the courses was favoured considerably as it enhanced social interaction between peers and teachers. Other benefits of blended learning were listed as providing various materials, receiving prompt feedback, and tracking progress. Slow internet access, connection failure and anxiety in some pre-service teachers on using ICT were reported as obstacles. Regarding the positive results of blended learning and the significance of ICT integration, pre-service teacher education curricula are suggested to be reconstructed by infusing ICT into entire program through blended learning rather than delivering isolated ICT courses which may thus serve for prospective teachers as catalysts to integrate the use of ICT in their own teaching.
A CPW-fed dual band antenna based on metamaterial inspired split ring structure

A compact coplanar waveguide (CPW) fed dual band antenna is designed within a compact size of 21.57 × 25.62 × 1.6 mm3. A prototype antenna consists of an outer closed ring resonator, inner split ring structure, inner most closed ring and modified ground plane. The radiating element is employed to cover −10 dB impedance bandwidth of 110 MHz (2.29–2.4 GHz) and 2210 MHz (3.35–5.56 GHz) with center frequencies of 2.36 GHz and 4.45 GHz, respectively. A CPW-fed line with modified ground plane yields good impedance matching and broad bandwidth of the antenna. The band characteristics of metamaterial-inspired split ring structure as well as negative permeability (μ) are explained in detail. The prototype antenna has consistent Omnidirectional (H-Plane), and dipole (E-Plane) radiation pattern are obtained both resonant frequency, which is significantly useful for Industrial Scientific and Medical (ISM) radio band, Worldwide Interoperability for Microwave Access (WiMAX) and Wireless Local Area Network (WLAN) applications.
Results of Pavlik harness treatment in children with dislocated hips between the age of six and twenty-four months.

We retrospectively studied the outcome of Pavlik harness treatment in late-diagnosed hip dislocation in infants between 6 and 24 months of age (Graf type 3 and 4 or dislocated hips on radiographs) treated in our hospital between 1984 and 2004. The Pavlik harness was progressively applied to improve both flexion and abduction of the dislocated hip. In case of persistent adduction contracture, an abduction splint was added temporarily to improve the abduction.



We included 24 patients (26 hips) between 6 and 24 months of age who presented with a dislocated hip and primarily treated by Pavlik harness in our hospital between 1984 and 2004. The mean age at diagnosis was 9 months (range 6 to 23 mo). The average follow-up was 6 years 6 months (2 to 12 y). Ultrasound images and radiographs were assessed at the time of diagnosis, one year after reposition and at last follow-up.



Twelve of the twenty-six hips (46%) were successfully reduced with Pavlik harness after an average treatment of 14 weeks (4 to 28 wk). One patient (9%) needed a secondary procedure 1 year 9 months after reposition because of residual dysplasia (Pelvis osteotomy). Seventeen of the 26 hips were primary diagnosed by Ultrasound according to the Graf classification. Ten had a Graf type 3 hip and 7 hips were classified as Graf type 4. The success rate was 60% for the type 3 hips and 0% for the type 4 hips. (P=0.035). None of the hips that were reduced with the Pavlik harness developed an avascular necrosis (AVN). Of the hips that failed the Pavlik harness treatment, three hips showed signs of AVN, 1 after closed reposition and 2 after open reposition.



The use of a Pavlik harness in the late-diagnosed hip dislocation type Graf 3 can be a successful treatment option in the older infant. We have noticed few complications in these patients maybe due to progressive and gentle increase of abduction and flexion, with or without temporary use of an abduction splint. The treatment should be abandoned if the hips are not reduced after 6 weeks. None of the Graf 4 hips could be reduced successfully by Pavlik harness. This was significantly different from the success rate for the Graf type 3 hips.



Therapeutic study, clinical case series:
Auf dem Weg zu einer ,,neuen KI“: Verteilte intelligente Systeme

Zusammen mit einigen bemerkenswerten Durchbrüchen wie Watson oder dem Google Car in den vergangenen Monaten stehen wir vor einer neuen Ära in der Künstlichen Intelligenz. Eine besondere Rolle nimmt dabei die ,,Verteilte Künstliche Intelligenz“ ein, Cyber Physical Systems und Internet of Things ,,boomen“. Im Kern handelt es sich bei ersteren um einen Verbund mehrheitlich technischer Subkomponenten, die über eine internetbasierte Dateninfrastruktur miteinander kommunizieren, bei letzterem um die Ausweitung des ,,Beteiligungskonzepts“ des Internets: Teilnehmer sind nicht mehr ausschließlich Menschen, sondern auch ,,Dinge“ – wie etwa die Sensorik eines Autos, Klimadatenstationen, Prozessdatenrechner der Produktionstechnik und andere informationstragende und/oder mit der Umwelt unmittelbar interagierende Systeme.
Update on diseases of chinchillas.

Chinchillas have been successfully maintained in captivity for almost a century. They have only recently been recognized as excellent, long-lived, and robust pets. Most of the literature on diseases of chinchillas comes from farmed chinchillas, whereas reports of pet chinchilla diseases continue to be sparse. This review aims to provide information on current, poorly reported disorders of pet chinchillas, such as penile problems, urolithiasis, periodontal disease, otitis media, cardiac disease, pseudomonadal infections, and giardiasis. This review is intended to serve as a complement to current veterinary literature while providing valuable and clinically relevant information for veterinarians treating chinchillas.
A PAC-Bayesian Tutorial with A Dropout Bound

This tutorial gives a concise overview of existing PAC-Bayesian theory focusing on three generalization bounds. The first is an Occam bound which handles rules with finite precision parameters and which states that generalization loss is near training loss when the number of bits needed to write the rule is small compared to the sample size. The second is a PAC-Bayesian bound providing a generalization guarantee for posterior distributions rather than for individual rules. The PAC-Bayesian bound naturally handles infinite precision rule parameters, L2 regularization, provides a bound for dropout training, and defines a natural notion of a single distinguished PAC-Bayesian posterior distribution. The third bound is a training-variance bound — a kind of bias-variance analysis but with bias replaced by expected training loss. The training-variance bound dominates the other bounds but is more difficult to interpret. It seems to suggest variance reduction methods such as bagging and may ultimately provide a more meaningful analysis of dropouts.
Bayesian Locality Sensitive Hashing for Fast Similarity Search

Given a collection of objects and an associated similarity measure, the all-pairs similarity search problem asks us to find all pairs of objects with similarity greater than a certain user-specified threshold. Locality-sensitive hashing (LSH) based methods have become a very popular approach for this problem. However, most such methods only use LSH for the first phase of similarity search i.e. efficient indexing for candidate generation. In this paper, we presentBayesLSH, a principled Bayesian algorithm for the subsequent phase of similarity search performing candidate pruning and similarity estimation using LSH. A simpler variant, BayesLSHLite, which calculates similarities exactly, is also presented. BayesLSH is able to quickly prune away a large majority of the false positive candidate pairs, leading to significant speedups over baseline approaches. For BayesLSH, we also provide probabilistic guarantees on the quality of the output, both in terms of accuracy and recall. Finally, the quality of BayesLSH’s output can be easily tuned and does not require any manual setting of the number of hashes to use for similarity estimation, unlike standard approaches. For two state-of-the-art candidate generation algorithms, AllPairs  and LSH, BayesLSH enables significant speedups, typically in the range 2x-20x for a wide variety of datasets.
Development of Pallet Recognition System using Kinect Camera

This study proposes a new pallet recognition system using Kinect camera. Depth image of Kinect camera is produced from the infrared ray data of random dot type. This system was applied to an automated guided vehicle(AGV) to recognize the pallet in various conditions. A modularized hardware and software of the pallet recognition system was developed. The performance of the developed pallet recognition system was tested through experiments under various environment, and it show good performance.
Electromagnetic detection of HIV DNA in the blood of AIDS patients treated by antiretroviral therapy

Electromagnetic signals of low frequency have been shown to be durably produced in aqueous dilutions of the Human Imunodeficiency Virus DNA. In vivo, HIV DNA signals are detected only in patients previously treated by antiretroviral therapy and having no detectable viral RNA copies in their blood. We suggest that the treatment of AIDS patients pushes the virus towards a new mode of replication implying only DNA, thus forming a reservoir insensitive to retroviral inhibitors. Implications for new approaches aimed at eradicating HIV infection are discussed.
Cell morphology classification and clutter mitigation in phase-contrast microscopy images using machine learning

We propose using machine learning techniques to analyze the shape of living cells in phase-contrast microscopy images. Large scale studies of cell shape are needed to understand the response of cells to their environment. Manual analysis of thousands of microscopy images, however, is time-consuming and error-prone and necessitates automated tools. We show how a combination of shape-based and appearance-based features of fibroblast cells can be used to classify their morphological state, using the Adaboost algorithm. The classification accuracy of our method approaches the agreement between two expert observers. We also address the important issue of clutter mitigation by developing a machine learning approach to distinguish between clutter and cells in time-lapse microscopy image sequences.
Muscle Strength and Speed Performance in Youth Soccer Players

This study aimed to examine the relationship between maximum leg extension strength and sprinting performance in youth elite male soccer players. Sixty-three youth players (12.5 ± 1.3 years) performed 5 m, flying 15 m and 20 m sprint tests and a zigzag agility test on a grass field using timing gates. Two days later, subjects performed a one-repetition maximum leg extension test (79.3 ± 26.9 kg). Weak to strong correlations were found between leg extension strength and the time to perform 5 m (r = -0.39, p = 0.001), flying 15 m (r = -0.72, p < 0.001) and 20 m (r = -0.67, p < 0.001) sprints; between body mass and 5 m (r = -0.43, p < 0.001), flying 15 m (r = -0.75, p < 0.001), 20 m (r = -0.65, p < 0.001) sprints and agility (r =-0.29, p < 0.001); and between height and 5 m (r = -0.33, p < 0.01) and flying 15 m (r = -0.74, p < 0.001) sprints. Our results show that leg muscle strength and anthropometric variables strongly correlate with sprinting ability. This suggests that anthropometric characteristics should be considered to compare among youth players, and that youth players should undergo strength training to improve running speed.
Cloud Computing: Characteristics and Deployment Approaches

Cloud Computing is a generic term for anything that involves delivering hosted services over the Internet. It is a new paradigm based on a pay-as-you-go approach. Whereas, many large enterprises have embraced the cloud technologies and infrastructures, numerous vendors have developed application, platform and infrastructure services for other organizations to consume. This paper introduces the Cloud Computing concepts, explores the benefits it promises, discusses the inherent issues and challenges and explains, in some detail, the deployment and delivery models for Cloud Computing. The aim is to provide some general information for enterprises wishing to integrate their existing IT processes and systems with Cloud services and infrastructures, which are readily available for their consumption.
Automated Conflict Resolution for Air Traffic Management Using Cooperative Multi-agent Negotiation

The National Airspace System in its current incarnation is nearing its maximum capacity. The Free Flight initiative, which would alter the current system by allowing pilots to select more direct routes to their destinations, has been proposed as a solution to these problems. Allowing pilots to fly anywhere, as opposed to being restricted to planned jetways, greatly complicates the problem of ensuring separation between aircraft. In this paper we propose using cooperative, multi-agent negotiation techniques in order to efficiently and pseudo-optimally resolve air traffic conflicts. Our system makes use of software agents running in each aircraft that negotiate with one another to determine a safe and acceptable solution when a potential air traffic conflict is detected. The agents negotiate using the Monotonic Concession Protocol and communicate using aircraft to aircraft data links. There are many benefits to using such a system to handle the resolution of air traffic conflicts. Automating CD&R will improve safety by reducing the workload of air traffic controllers. Additionally, the robustness of the system is improved, as the decentralization provided by software agents running in each aircraft reduces the dependence on a single ground based system to coordinate all aircraft movements. The pilots, passengers, and carriers benefit as well due to the increased efficiency of the solutions reached by negotiation.
A New Statistical Approach To Chinese Pinyin Input

Chinese input is one of the key challenges for Chinese PC users. This paper proposes a statistical approach to Pinyin-based Chinese input. This approach uses a trigram-based language model and a statistically based segmentation. Also, to deal with real input, it also includes a typing model which enables spelling correction in sentence-based Pinyin input, and a spelling model for English which enables modeless Pinyin input.
TECHNIQUES FOR GLITCH POWER REDUCTION IN CMOS VLSI CIRCUITS

The dynamic power dissipation is the dominant source of power dissipation in CMOS circuits. It is directly related to the number of signal transitions and glitches. The glitches occupy a considerable amount of power of the total power dissipation in CMOS circuits. This paper presents a survey of the different techniques used for decreasing the dynamic power by reduction of glitches. The advantages and limitations of these techniques are also discussed. Glitches, CMOS circuits, low power, path balancing, gate sizing.
Making sense of focus groups.

This paper provides an overview of the contribution of medical education research which has employed focus group methodology to evaluate both undergraduate education and continuing professional development.


PRACTICALITIES AND PROBLEMS
It also examines current debates about the ethics and practicalities involved in conducting focus group research. It gives guidance as to how to go about designing and planning focus group studies, highlighting common misconceptions and pitfalls, emphasising that most problems stem from researchers ignoring the central assumptions which underpin the qualitative research endeavour.


PRESENTING AND DEVELOPING FOCUS GROUP RESEARCH
Particular attention is paid to analysis and presentation of focus group work and the uses to which such information is put. Finally, it speculates about the future of focus group research in general and research in medical education in particular.
Android Malware Detection using Deep Learning on API Method Sequences

Android OS experiences a blazing popularity since the last few years. This predominant platform has established itself not only in the mobile world but also in the Internet of Things (IoT) devices. This popularity, however, comes at the expense of security, as it has become a tempting target of malicious apps. Hence, there is an increasing need for sophisticated, automatic, and portable malware detection solutions. In this paper, we propose MalDozer, an automatic Android malware detection and family attribution framework that relies on sequences classification using deep learning techniques. Starting from the raw sequence of the app’s API method calls, MalDozer automatically extracts and learns the malicious and the benign patterns from the actual samples to detect Android malware. MalDozer can serve as a ubiquitous malware detection system that is not only deployed on servers, but also on mobile and even IoT devices. We evaluate MalDozer on multiple Android malware datasets ranging from 1K to 33K malware apps, and 38K benign apps. The results show that MalDozer can correctly detect malware and attribute them to their actual families with an F1-Score of 96%− 99% and a false positive rate of 0.06%− 2%, under all tested datasets and settings.
Medical Text Processing for SMDA project

Medical care is vital and challenging task as the amount of unstructured and unformalized data has grown dramatically over last decades. The article is dedicated to SMDA project an attempt to build a framework for semantic medicine application for Almazov medical research center, FANW MRC. In this paper we investigate modern approaches to medical textual data processing and analysis, however mentioned approaches do not give a complete background for solving our task. We spot a process as a combination of existing tools as well as our heuristic algorithms, techniques and tools. The paper proposes a new approach to natural language processing and concept extraction applied to medical certificates, doctors’ notes and patients’ diaries. The main purpose of the article is to present a way to solve a particular problem of medical concept extraction and knowledge formalization from an unstructured, lacking in syntax and noisy text.
Vowels Recognition by Modular Arithmetic and Wavelets using Neural Network

Recently, the speech recognition is very attractive for researchers because of the very significant related applications. For this reason, the novel research has been of very importance in the academic community. The aim of this work is to find out a new and appropriate feature extraction method for Arabic language recognition. In the present study, wavelet packet transform (WPT) with modular arithmetic and neural network were investigated for Arabic vowels recognition. The number of repeating the remainder was carried out for a speech signal. 266 coefficients are given to probabilistic neural network (PNN) for classification. The claimed results showed that the proposed method can make an effectual analysis with classification rates may reach 97%. Four published methods were studied for comparison. The proposed modular wavelet packet and neural networks (MWNN) expert system could obtain the best recognition rate.
GPU Enabled Serverless Computing Framework

A new form of cloud computing, serverless computing, is drawing attention as a new way to design micro-services architectures. In a serverless computing environment, services are developed as service functional units. The function development environment of all serverless computing framework at present is CPU based. In this paper, we propose a GPU-supported serverless computing framework that can deploy services faster than existing serverless computing framework using CPU. Our core approach is to integrate the open source serverless computing framework with NVIDIA-Docker and deploy services based on the GPU support container. We have developed an API that connects the open source framework to the NVIDIA-Docker and commands that enable GPU programming. In our experiments, we measured the performance of the framework in various environments. As a result, developers who want to develop services through the framework can deploy high-performance micro services and developers who want to run deep learning programs without a GPU environment can run code on remote GPUs with little performance degradation.
Cuckoo Optimization Algorithm based Image Enhancement

This paper proposes an extension to approach proposed in [13] for image enhancement using a combination of fuzzy logic technique and bio-inspired optimization algorithm. The transformation of the image data from RGB to HSV space has been done without altering HUE information. The image has been categorized into three regions with well tuned membership functions: underexposed, overexposed and mixed region on the basis of two threshold values. Gaussian membership function finds good suitability for fuzzification of overexposed and underexposed regions and mixed region is kept untouched which are further modified by a parametric sigmoid function. To get the quantitative analysis of the image; quality measures like fuzzy contrast, contrast and visual factors have been utilized. An objective function involves entropy and visual factor which is being optimized by bio-inspired optimization algorithm. Here, Cuckoo Optimization Algorithm (COA) has been used for parameter optimization and its results have been compared with the ACO based image enhancement on the scale of visual factor and execution time. COA based image enhancement found better than other approaches. The time taken to enhance the image has also been reduced as compared with latest approaches.