Reviewerly/exHarmony huggingface dataset:
A competence model for training programs in inclusive education

The implementation of inclusion poses fundamentally new requirements for the professional competencies of teachers, which in turn determines the need for an empirically based competence model of a teacher of an inclusive educational organization. A complex of university programs for teacher training specifying the inclusive competencies could be used as an appropriate empirical base of the study. However, this research logic has not yet been implemented and the study was devised to fill this gap. Its purpose was to build a structural and meaningful model of an inclusive teacher based on an analysis of inclusive competencies presented in the teachers’ educational programs of universities. Content analysis was used to study the texts of 59 basic educational programs. Frequency and factor analysis of the matrix of representation of competency indicators in educational programs was also carried out. It was found that the semantic core of the inclusive competencies of the teacher reflects the need to take into account the special psychological and pedagogical status of children in terms of their personal position in learning, the originality of their development, health status and their specific educational needs. The integral inclusive competences of the teacher are the readiness to: organize an inclusive educational process as a whole; organize an individually oriented educational route; individual / collaborative support; and organize psychological and pedagogical support; substantive / instrumental knowledge related to working with students with disabilities. The competence profiles of teachers, set by university educational programs, are not fully consistent with both the existing theoretical models of the inclusive teacher competencies and the conditions for the implementation of inclusive practice. The research results include ideas about five-component structure of teacher’s inclusive competence. The practical significance is in the identification of the essence of the contradictions in the content of the university training of teachers for work in inclusive education, the elimination of which will contribute to improving the quality staffing of the inclusive education system.
Transcoding billions of Unicode characters per second with SIMD instructions

In software, text is often represented using Unicode formats (UTF-8 and UTF-16). We frequently have to convert text from one format to the other, a process called transcoding. Popular transcoding functions are slower than state-of-the-art disks and networks. These transcoding functions make little use of the single-instruction-multiple-data (SIMD) instructions available on commodity processors. By designing transcoding algorithms for SIMD instructions, we multiply the speed of transcoding on current systems (x64 and ARM). To ensure reproducibility, we make our software freely available as an open source library.
Calls for behavioural biometrics as bank fraud soars

Behavioural biometrics – much like physical biometrics – is becoming an increasingly prevalent way for financial institutions worldwide to authenticate and verify their customers. At a time when a staggering 15 billion sets of stolen usernames and passwords – acquired through hundreds of thousands of data breaches – are circulating the dark web 1 , behavioural biometric analysis promises to replace systems relying on personally identifiable information (PII) with a more secure and user-friendly alternative.
Superspreading events of SARS-CoV-2 in Paris: A retrospective analysis of data from the first wave of COVID-19 in 2020

The 2020 COVID-19 pandemic led to a strict lockdown in France from March 17 to May 11, 2020. After the lockdown, the French strategy to mitigate the impact of SARS-CoV-2 relied partly on investigations of all confirmed cases. Monitoring collective settings is particularly important since SARS-CoV-2 seems prone to superspreading events (SSEs). Our study is based on data gathered in Paris from May 11 to December 31, 2020, by the Ile-de-France Regional Health Agency (RHA) to investigate cases occurring in collective and high-risk settings. Specific events in high-risk settings were systematically transmitted to the RHA, and screenings were organized by the facilities, while other settings were reported when three cases were identified within a short period. These settings were more difficult to identify through the surveillance system since no systematic screening was organized by the facility, leaving screenings to rely on the national contact-tracing programme. No official superspreading threshold has been set for SARS-CoV-2. We defined a SSE as an event involving ten cases. We analysed 15,706 events associated with 38,670 cases, representing an average of 2.70 cases per event. Most clusters occurred in educational facilities, workplace environments, social care settings, and healthcare facilities. SSEs represented 3.4% but accounted for 28% of all cases reported. The highest number of SSEs occurred in college settings (12.6%), followed by hospitals and retirement homes. Educational facilities had the lowest number of SSEs, with around 1% in preschools and elementary schools. We observed different SSE rates in each setting. Preschools and primary schools represented the majority of events but experiencing very few SSEs. Colleges were prone to SSEs and were associated with a high number of secondary cases. These findings provide some insights on contact tracing activities and SARS-CoV-2 transmission in different settings.
Once and for All

Videos grow to be one of the largest mediums on the Internet. E-commerce platforms like Alibaba need to process millions of video data across multimedia (e.g., visual, audio, image, and text) and on a variety of tasks (e.g., retrieval, tagging, and summary) every day. In this work, we aim to develop a once and for all pretraining technique for diverse modalities and downstream tasks. To achieve this, we make the following contributions: (1) We propose a self-supervised multi-modal co-training framework. It takes cross-modal pseudo-label consistency as the supervision and can jointly learn representations of multiple modalities. (2) We introduce several novel techniques (e.g., sliding-window subset sampling, coarse-to-fine clustering, fast spatial-temporal convolution and parallel data transmission and processing) to optimize the training process, making billion-scale stable training feasible. (3) We construct a large-scale multi-modal dataset consisting of 1.4 billion videos (~0.5 PB) and train our framework on it. The training takes only 4.6 days on an in-house 256 GPUs cluster, and it simultaneously produces pretrained video, audio, image, motion, and text networks. (4) Finetuning from our pretrained models, we obtain significant performance gains and faster convergence on diverse multimedia tasks at Alibaba. Furthermore, we also validate the learned representation on public datasets. Despite the domain gap between our commodity-centric pretraining and the action-centric evaluation data, we show superior results against state-of-the-arts.
Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing

This article aims to test FOS (first-order statistical) in extracting features of embryonated eggs. This test uses the initial step of image processing to get the best input image in feature extraction. The image processing method starts from the image acquisition process, then improves with image preprocessing and segmentation. Image acquisition in this study uses the concept of egg candling in a dark place captured with a smartphone camera. The acquisition results are improved by image preprocessing using gray scaling, image enhancement (by Histogram Equalization), and segmentation of chicken egg image. The segmentation results were extracted using FOS with five parameters: mean, entropy, variance, skewness, and kurtosis. Based on the calculation of these parameters, it is graphed and shows the difference in patterns between fertile and infertile eggs. However, some eggs have a similar pattern, thus affecting the identification process. The identification process used neural networks by the backpropagation method for training and testing. The training results provide an accuracy value of 100% of all training data; however, 80% of the new test data obtained test results at testing. This test is carried out with 100 data, 50 each for training and test data. Based on the test results, which significantly affect the level of accuracy is the feature extraction method. FOS pattern in detecting the fertility of chicken eggs by BP Neural Network is still categorized as low, so it is necessary to improve methods to get maximum results.
A Trustworthy Learning Technique for Securing Industrial Internet of Things Systems

Since the Industrial Internet of Things (IIoT) networks comprise heterogeneous manufacturing and technological devices and services, discovering advanced cyber threats is an arduous and risk-prone process. Cyber-attack detection techniques have been recently emerged to understand the process of obtaining knowledge about cyber threats to collect evidence. These techniques have broadly employed for identifying malicious events of cyber threats to protect organizations’ assets. The main limitation of these systems is that they are not able to discover and interpret new attack activities. This paper proposes a new adversarial deep learning for discovering adversarial attacks in IIoT networks. Evaluation of correlation reduction has been used as a means of feature selection for reducing the impact of data poisoning attacks on the subsequent deep learning techniques. Feed Forward Deep Neural Networks have been developed using across various parameter permutations, at differing rates of data poisoning, to develop a robust deep learning architecture. The results of the proposed technique have been compared with previously developed deep learning models, proving the increased robustness of the new deep learning architectures across the ToN_IoT datasets.
Design and implementation of monitoring system for paralysis patient using IoT

Internet of things represents a catch line of smart applications. At the same time it plays a leading role in the health care systems, as it provides the connectivity of the distant patients, who are not able to express their concerns. In this modern era there are a lot of technologies that are being used in different healthcare fields for paralyzed people. This paper proposed a model for paralyzed patient by considering Arduino with the integration of gyro sensors for the patient hand and finger movement. Patient can easily be connected with this device by sending his message to caretaker only by moving their hands into respective directions, or they can send their message by moving their fingers and feet. Through this device paralyzed patient will be able to convey their messages to caretakers, so they can assist the patient. This system will enhance the medical care to those patients who are even not able to convey their message.
Noise Standard Deviation Estimation for Additive White Gaussian Noise Corrupted Images using SVD Domain

During denoise an image; noise level estimation is one of the most important key factors. The accurate noise level estimation is needed before processing the image. The prior knowledge of noise level estimation is also used for restoring the image without degradation. In this proposed work, the noise level is estimated by observed singular values on noisy images. The proposed work has two new methods for addressing the main challenges of the noise level estimation.1.The tail magnitude value of the noisy images singular values has high compare with signal image. This aspect is used for estimate the noise level. 2. The visual based Gaussian noise estimation is used for preprocessing the many 2D- signals processing application which enhance the range of this work. The experimental result for this noise level estimation provides reliable and also applicable for real time images/frames and some special images such as cartoon. The proposed work is needed a simple processing unit for implementing in hardware and results are more accurate. It can be used to pre-processing all kinds of real time images.
Neural Networks with Disabilities: An Introduction to Complementary Artificial Intelligence

Machine learning is a good tool to simulate human cognitive skills as it is about mapping perceived information to various labels or action choices, aiming at optimal behavior policies for a human or an artificial agent operating in the environment. Regarding autonomous systems, objects and situations are perceived by some receptors as divided between sensors. Reactions to the input (e.g., actions) are distributed among the particular capability providers or actuators. Cognitive models can be trained as, for example, neural networks. We suggest training such models for cases of potential disabilities. Disability can be either the absence of one or more cognitive sensors or actuators at different levels of cognitive model. We adapt several neural network architectures to simulate various cognitive disabilities. The idea has been triggered by the "coolability" (enhanced capability) paradox, according to which a person with some disability can be more efficient in using other capabilities. Therefore, an autonomous system (human or artificial) pretrained with simulated disabilities will be more efficient when acting in adversarial conditions. We consider these coolabilities as complementary artificial intelligence and argue on the usefulness if this concept for various applications.
Computer Big Data Analysis and Cloud Computing Network Technology

With the continuous development of today’s society, the computer field also quickly occupies an important place. Operation and data processing have significant advantages in the field of computer, and have become the primary part. The development prospect of big data technology is very considerable, based on which many related applications can be developed. But for the role of big data technology, it is not thoroughly discovered. For example, enterprises do not make full use of big data technology to increase revenue, and people do not make full use of big data technology to bring more convenience in daily life. This paper further explores the computer big data technology and cloud computing network technology, in order to better store and make full use of the data.
ACeD: Scalable Data Availability Oracle

A popular method in practice offloads computation and storage in blockchains by relying on committing only hashes of off-chain data into the blockchain. This mechanism is acknowledged to be vulnerable to a stalling attack: the blocks corresponding to the committed hashes may be unavailable at any honest node. The straightforward solution of broadcasting all blocks to the entire network sidesteps this data availability attack, but it is not scalable. In this paper, we propose ACeD, a scalable solution to this data availability problem with O(1) communication efficiency, the first to the best of our knowledge. The key innovation is a new protocol that requires each of the N nodes to receive only O(1/N) of the block, such that the data is guaranteed to be available in a distributed manner in the network. Our solution creatively integrates coding-theoretic designs inside of Merkle tree commitments to guarantee efficient and tamper-proof reconstruction; this solution is distinct from Asynchronous Verifiable Information Dispersal [7] (in guaranteeing efficient proofs of malformed coding) and Coded Merkle Tree [25] (which only provides guarantees for random corruption as opposed to our guarantees for worst-case corruption). We implement ACeD with full functionality in 6000 lines of Rust code, integrate the functionality as a smart contract into Ethereum via a high-performance implementation demonstrating up to 10,000 transactions per second in throughput and 6000x reduction in gas cost on the Ethereum testnet Kovan. Our code is available in [1].
SAIaaS: A Blockchain-Based Solution for Secure Artificial Intelligence as-a-Service

Artificial Intelligence models are crucial elements to support many sectors in the current global economy. Training those models requires 3 main assets: data, machine learning algorithms, and processing capabilities. Given the growing concerns regarding data privacy, algorithm intellectual property, and server security, combining all 3 resources to build a model is challenging. In this paper, we propose a solution allowing providers to share their data and run their algorithms in secured cloud training environments. To provide trust for both clients and asset providers in the system, a blockchain is introduced to support the negotiation, monitoring, and conclusion of model production. Through a preliminary evaluation, we validate the feasibility of the approach and present a road map to a more secure Artificial Intelligence as-a-service.
On classification of finite commutative chain rings

&lt;abstract&gt;&lt;p&gt;Let $ R $ be a finite commutative chain ring with invariants $ p, n, r, k, m. $ It is known that $ R $ is an extension over a Galois ring $ GR(p^n, r) $ by an Eisenstein polynomial of some degree $ k $. If $ p\nmid k, $ the enumeration of such rings is known. However, when $ p\mid k $, relatively little is known about the classification of these rings. The main purpose of this article is to investigate the classification of all finite commutative chain rings with given invariants $ p, n, r, k, m $ up to isomorphism when $ p\mid k. $ Based on the notion of j-diagram initiated by Ayoub, the number of isomorphism classes of finite (complete) chain rings with $ (p-1)\nmid k $ is determined. In addition, we study the case $ (p-1)\mid k, $ and show that the classification is strongly dependent on Eisenstein polynomials not only on $ p, n, r, k, m. $ In this case, we classify finite (incomplete) chain rings under some conditions concerning the Eisenstein polynomials. These results yield immediate corollaries for p-adic fields, coding theory and geometry.&lt;/p&gt;&lt;/abstract&gt;
Design of Effective Lossless Data Compression Technique for Multiple Genomic DNA Sequences

In recent years, a massive amount of genomic DNA sequences are being created which leads to the development of new storing and archiving methods. There is a major challenge to process, store or transmit the huge volume of DNA sequences data. To lessen the number of bits needed to store and transmit data, data compression (DC) techniques are proposed. Recently, DC becomes more popular, and large number of techniques is proposed with applications in several domains. In this paper, a lossless compression technique named Arithmetic coding is employed to compress DNA sequences. In order to validate the performance of the proposed model, the artificial genome dataset is used and the results are investigated interms of different evaluation parameters. Experiments were performed on artificial datasets and the compression performance of Arithmetic coding is compared to Huffman coding, LZW coding, and LZMA techniques. From simulation results, it is clear that the Arithmetic coding achieves significantly better compression with a compression ratio of 0.261 at the bit rate of 2.16 bpc.
Semi-automatic Reply Avatar for VR Training System with Adapted Scenario to Trainee’s Status

For service industry, quality of customer service skills and customer satisfaction are very important topic. However, it is difficult to learn and master this kind of service skill without on-the-job-training (OJT). In this research, we propose a service VR simulator in which user can train man-to-man service by using VR technologies (Fig. 1). For service skill, it is important not only physical behavior and valval skills which can be learn by using manual, but also emotional control and sensing skills. Thus, to training the emotional skill on man-to-man service, we propose and construct preliminary service VR simulator to master emotional skills.Our service VR simulator consist of mental/emotional sensing devices, estimating algorithm and intervention approaches. At first, to sensing mental/emotional state of trainer, we developed vital sensor attached HMD (Fig. 2). By sensing vital signal, such as heart beat and respiration, we can estimate mental/emotional state of trainer during VR training. Next, we also develop mental/emotional intervention which can induce emotion of users. For example, we can alleviate user’s tension with altered auditory feedback. Finally, our proposed system can control difficulty of training according to trainer’s mental state and skills. Combining these mental/emotional sensing and feedback technologies enables effective training of emotional control and expression skills.In addition, by utilizing advantage of the characteristics of VR, our system can be expected to be more effective than training conducted on actual sites. For example, by switching to customer’s viewpoint from trainer’s own viewpoint, trainer can find and understand his/her problem easily. Also, by replaying and rewinding man-to-man service simulation, trainer can try-and-error and find solution during training.
Multilayer Interval Type-2 Fuzzy Controller Design for Hyperchaotic Synchronization

This study presents a multilayer interval type-2 fuzzy controller, which applies to synchronize the hyperchaotic systems. The main contribution of this study is the design of a multilayer structure in the membership function space, which can improve the learning ability and flexibility of the type-2 fuzzy network. Using the proposed multilayer structure, the fuzzy network can reduce the number of rules while ensuring synchronization performance. Particularly, the design of 3-dimensional Gaussian membership functions (3-DGMFs) enhances the ability to against system uncertainties and external disturbances. Moreover, the structure of the proposed network can be autonomously constructed by using the self-evolving algorithm. The parameters of the proposed network are online updated using an adaptive law obtained by the gradient descent method. Finally, numerical simulations on the synchronization of hyperchaotic systems are conducted to validate the efficiency and performance of the proposed method.
A Comparative Study on Velocity Fields, Humidity and Oxygen Concentration in a Front Opening Unified Pod (FOUP) During Purge

Microchip features continue to reduce in size year after year. Oxygen (O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) and moisture present in ambient air can cause product defects and therefore lower production yield. Smaller feature sizes are more sensitive to humidity and oxygen, resulting in a greater potential for defects to arise during wafer processing. Therefore, the level of oxygen and moisture needs to be minimized during integrated circuit (IC) manufacturing. Purge gases like clean dry air (CDA) or nitrogen (N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) are commonly used to reduce the level of oxygen and moisture inside a front opening unified pod (FOUP). In this study, purge cleaning performance was experimentally assessed by streamlines flow analysis, particle image velocimetry (PIV), relative humidity (RH) measurement, and oxygen concentration measurements during purging with practical flow rates of 130, 200 and 300 LPM. The experiments show 200 LPM can be the optimum purge flow rate for a high level of cleaning efficiency and a low level of vibration and instabilities. This purge flow rate can be used to increase product quality through a reduction in defect frequency, while reducing annual energy consumption by the semiconductor manufacturing industry.