Reviewerly/exHarmony huggingface dataset:
Blockchain based Digital Contact Tracing Applications for Pandemic Management (COVID-19): Issues, Challenges, Solutions and Future Directions

The COVID-19 pandemic has caused global disturbance by affecting more than forty-two million people (as of the end of October 2020) Since there is no medication or vaccine available, the only way to combat it is to prevent it from spreading as much as possible Digital contact tracing is an effective technique that can be utilized for this purpose, as it eliminates the manual contact tracing process and would help in identifying and isolating the affected people However, the users are reluctant to share their location and contacts details because of the concerns related to the privacy and security of their personal information which affects its implementation and extensive adoption The blockchain technology has been applied in various domains and has proven to be an effective approach for handling data transactions in secure manner, which makes it an ideal choice for digital contact tracing applications The properties of blockchain such as time stamping and immutability of the data would help in getting the accurate information regarding the trail of the virus in a transparent manner, while cryptography of data assures the integrity of information being provided Furthermore, the anonymity of the user identities alleviates some of the risks related to the privacy and confidentiality of the users In this paper, we provide the users with the detailed discussion on the digital contact tracing mechanism and discusses about the applications developed to combat the COVID-19 pandemic Moreover, we have presented the possible risks, issues and challenges associated with the available contact tracing applications and analyzed how the adoption of blockchain-based decentralized network for handling the application could provide the users with the privacy preserving contact tracing without compromising the performance efficiency
Unitary Branching Programs: Learnability and Lower Bounds

Bounded width branching programs are a formalism that can be used to capture the notion of non-uniform constant-space computation. In this work, we study a generalized version of bounded width branching programs where instructions are defined by unitary matrices of bounded dimension. We introduce a new learning framework for these branching programs that leverages on a combination of local search techniques with gradient descent over Riemannian manifolds. We also show that gapped, read-once branching programs of bounded dimension can be learned with a polynomial number of queries in the presence of a teacher. Finally, we provide explicit near-quadratic size lower-bounds for bounded-dimension unitary branching programs, and exponential size lower-bounds for bounded-dimension read-once gapped unitary branching programs. The first lower bound is proven using a combination Neeiporuk's lower bound technique with classic results from algebraic geometry. The second lower bound is proven within the framework of communication complexity theory.
Blocks and Barriers in Teaching Communication

Didactic communication, as well as general human communication, is subjected to numerous and varied disturbances. An extensive analysis of them and a rigorous systematization of them gives us Dorina Salavastru in one of her works. The author classifies these disturbances as follows: -a. blockages determined by the characteristics of the person engaged in the didactic communication (the teacher on the one hand, the student on the other);-b. blockages determined by the social-value relationships existing between the participants in the didactic communication relationship; -c. blockages determined by the particularities of the field in which the didactic communication is carried out (D. Salavastru, 2004).
An Efficient Improving Cloud Data Storage Security Using Failure Aware Resource Scheduling Algorithm

Cloud computing, provides the delivery of on-demand computing resources, it is displayed as an innovative platform, provides a high-performance computing environment for  the  user.  The need for cloud computing is increasing gradually every day. Cloud computing, data storage security is a big problem since the data in the cloud must  be  sent  via  the  Internet,  data  security  is  a  big  problem.  Integrity, accountability, privacy, access  control, authentication, must be to maintain an important mechanism for data protection, such as approval. To overcome this problem, the proposed Failure Aware Resource scheduling (FARS) to reduce the failure leading and reduced system performance tasks. The proposed method is clients send data using encryption for protecting the highly confidential files because the files or data unauthorized persons cannot be stolen or view the files so the files or data protected then generate the One-Time Password (OTP) for decrypting the files. Nodes with relatively low computing power and a low failure rate can provide better performance and reduced resources. The method is based on failure aware scheduling, especially for users who want to move the collective frequently, which can reduce the discovery time of the cloud service rather than the failed resource planning methods. That the crowdsourcing model is an effective way to solve huge co- tasks.
CS-Based Modulation Recognition of Sparse Multiband Signals Exploiting Cyclic Spectral Density and MLP

Modulation recognition of sparse multiband signals is a key technology for intelligent signal processing. However, the existing method suffers from high sampling rate and poor anti-noise performance. Therefore, compressed sensing and cyclic spectral density are combined to cope with the shortcomings of existing methods followed by the multi-layer perceptron (MLP) to recognize modulation mode of signal. Some simulations are carried out, and the simulation results show that the proposed algorithm is correct and effective.
JOB ANALYSIS OF TEACHER IN INCLUSIVE EDUCATION

One of the main conditions for inclusive education is the professional competence of teachers. The professional competence of the teacher ensures the education, upbringing and development of children with disabilities. In the context of inclusive education, special attention should be paid to the training of teachers to work with children with disabilities. The peculiarity of the organization of educational and correctional work with children with developmental disabilities requires special training of a teacher who provides integrated education. The teacher of an educational institution must know the basics of correctional pedagogy and special psychology. They should also have a clear understanding of the peculiarities of the psychophysical development of children with disabilities, the methods and technologies of organizing the educational and rehabilitation process of such children. People who have not received special education cannot successfully work with children with developmental disabilities.
A Systems Approach to Physical Security in K–12 Schools

School leaders across the United States face the challenge of creating safe and secure environments across their campuses in a way that helps promote the achievement of schools' educational objectives. In this report, Homeland Security Operational Analysis Center researchers define and present a systems approach to school physical security in which protection and mitigation elements work together to provide layered security benefits.
ControlBurn: Feature Selection by Sparse Forests

Tree ensembles distribute feature importance evenly amongst groups of correlated features. The average feature ranking of the correlated group is suppressed, which reduces interpretability and complicates feature selection. In this paper we present ControlBurn, a feature selection algorithm that uses a weighted LASSO-based feature selection method to prune unnecessary features from tree ensembles, just as low-intensity fire reduces overgrown vegetation. Like the linear LASSO, ControlBurn assigns all the feature importance of a correlated group of features to a single feature. Moreover, the algorithm is efficient and only requires a single training iteration to run, unlike iterative wrapper-based feature selection methods. We show that ControlBurn performs substantially better than feature selection methods with comparable computational costs on datasets with correlated features.
Content validity test on moss plant e-book media with Aiken formula

Plantae material consists of three primary materials, namely moss, ferns, and seed plants. Moss plant material is difficult for students to understand. Hence, It is necessary to develop an e-book on moss plants that can provide learning solutions related to moss plant material. This study aims to measure the level of validity, feasibility assessment by High-School Biology Subject Teacher Forum (STF) in Semarang City, and student readability. The research and development method used is the 4-D development model. Several stages in the development research are define, design, develop and disseminate. The development stage includes expert validation, feasibility, and student readability tests. The research subjects are students of class X SMA N 12 Semarang. The data collection instruments are expert validation sheets, assessment sheets, and readability sheets. The analysis technique used is descriptive data analysis. The result of the e-book validity test for the material content, presentation, and language assessment aspects are 96% (very good), 96% (very good), and 94% (very good), respectively. The results of the assessment by learning media experts is 93% (very good). The e-book feasibility test by STF in Semarang is 95% (very good), and the e-book readability test is 90% (strongly agree).
YORÙBÁNET: A DEEP CONVOLUTIONAL NEURAL NETWORK DESIGN FOR YORÙBÁ ALPHABETS RECOGNITION

Numerous works have been proposed and implemented in computerization of various human languages, nevertheless, miniscule effort have also been made so as to put Yorùbá Handwritten Character on the map of Optical Character Recognition. This study presents a novel technique in the development of Yorùbá alphabets recognition system through the use of deep learning. The developed model was implemented on Matlab R2018a environment using the developed framework where 10,500 samples of dataset were for training and 2100 samples were used for testing. The training of the developed model was conducted using 30 Epoch, at 164 iteration per epoch while the total iteration is 4920 iterations. Also, the training period was estimated to 11296 minutes 41 seconds. The model yielded the network accuracy of 100% while the accuracy of the test set is 97.97%, with F1 score of 0.9800, Precision of 0.9803 and Recall value of 0.9797.
EDGE COMPUTING: THE PROMISES IT HOLDS AND MARKET DYNAMICS

The data driven societies pushes for more decentralized management to overcome data storage overhauls and faster transmission spreads in various data streams. As wireless technology is getting more advanced, there is a need to reconsider data hierarchical restructuring through edge computing, to pave the way for better latency, operation efficiency, speed, reliability, and versatility. Already, video and gaming technologies have benefited from edge technologies by streamlining their services delivery as well as boosting their revenue growth. However, lack of standard thwarts unified development and application of edge computing at global level due to defragmented standards. As such, enterprises that have already purged into edge computing such as AWS, Google and Microsoft need to embark on more collaborative approaches to ensure unified standards in edge computing developments.
Red semántica sobre el uso de las tecnologías en educación: estudio en la región de la Orinoquía, Colombia

This study presents the analysis of the ICT use made by teachers at rural areas as a strategy within the teaching process in the Luis Carlos Glan Sarmiento boarding school, located in La Primavera, Colombia. The methodology used was qualitative, non-experimental, transectional, exploratory; the data collection was made through a structured interview answered by 16 teachers. Afterwards, a semantic network was created to ease the understanding of the results, that showed the lack of infrastructure regarding the town’s electric supply, in addition to the lack of physical and technological resources in the school so that teachers were able to use the ICT. Likewise, it was evident that there was a limitation in the staff's training, for they do not have the knowledge needed to use and handle the ICT as part of the teaching process. Finally, a proposal for improvements is raised, aimed at having the support of the Ministry of National Education and establishing an alliance with private companies that back the resources’ adequacy to the school, in order to boost the ICT process as an integral part of teaching.
DATA MINING APPROACHES IN LIFE INSURANCE

Knowledge discovery with inside the monetary corporation were constructed and operated in particular to help choice making the usage of information as a strategically aspect In this document, we inspect the usage of diverse facts mining approaches for information discovery in the coverage commerce. Present spreadsheet is Incapable in exposition such facts features We familiarize exceptional well-known shows for coming across information with inside the shape of affiliation Eccentricity clump, assortment, also Parallel appropriate for facts features moved facts mining approaches, the choice-maker can outline the enlargement of coverage sports to accredit the exceptional forces in current existence coverage region.
Developing Culture-Based Supplementary Reading Material for the Eighth Grade Students  of Junior High School

This study aimed to develop additional material in reading that is based on culture. In this study the subject is the eighth grade students of Junior High School Swasta Cendramata. The main objective in this research identified and reading issue that students faced. On describing this culture-based reading learning material to be developed so that students can have an attraction in reading with culture-based reading materials. In this study, using the development of culture-based reading materials adapted from the character Sugiono (2011) and based on good and correct reading criteria. The description of culture-based reading learning materials for 8 th grade Junior High School Swasta Cendramata is described in 4 aspects, namely testing, content, language use and reading activities. This study uses a validity test which shows that the prototype is evaluated with good quality. The results of the research test were quite satisfying in improving students' reading learning, students enthusiastically showed a sense of curiosity and student activeness in reading culture-based reading materials in understanding how to read properly and correctly with culture-based reading materials. .
THE IMPLEMENTATION OF SUGESTOPEDIA METHOD IN LEARNING WRITING

The purpose of presenting this paper is to provide input to educators in relation to writing learning. This sugestopedia method comes from Bulgaria. This method was first developed by an educator, psychotherapist, and physicist named George Lozanov in 1978. Lozanov believed that relaxation and concentration techniques would help students open their unconscious sources, acquire and master more vocabulary quantities and structures a more solid structure than they think. The sugestopedia strategy is a strategy that has the main principle, namely suggestions that can affect the results of the learning situation, both positively and negatively, by occupying students comfortably, installing background music in the classroom during lessons, increasing individual participation, using posters for member big impressions while highlighting information. Therefore, this method is one solution in learning writing that is considered difficult by some educators. Keywords : sugestopedia
A Secure File Transfer over Virtual Machine Instances using Hybrid Encryption Technique

Cloud Computing is used to share data, services, and resources via a network but this system is vulnerable to cyber-attacks by an unauthorized person denying the user privacy and confidentiality. The exponential growth in Information technology especially in the field of Cloud Computing has seen a rise in security attacks such as Interruption, Interception, Modification, Fabrication making it absolutely necessary to enhance cloud security as well as network security. To tackle the menace of security threats is to make use of the various encryption techniques and to ensure secure transmission of the data to ensure the user of his rights of Privacy, Confidentiality, Integrity, Authentication, and access controls of data. This can be achieved by Cryptographic techniques. In the former implementation, a new virtual instance is created and embedded with all the requested resources and allocated to the user whereas in the later implementation the size of the allotted VM is being altered to account for the extra requested resource or to free the unused resource to increase efficiency. To achieve Secure file transfer between instances Hypervisor tool Virtual Box developed by Oracle Corporation is used. To interface with Hypervisor via Host CLI commands provided by the Virtual Box is used. Thus, developing a model that mimics the cloud environment on small scale using the laptop/desktop which enacts a cloud with a limited resource pool.
Review of Recent Developments in Human Pose Estimation

Pose estimation is a popular computer vision problem that involves identifying and estimating human poses. It has found many use cases in games, animation, virtual trainers, posture analysis, driver assistance systems, etc. There has been significant progress in this field over the years. To cover the recent advances, we have analysed and summarized some of the influential methods to approach the problem of Human body pose detection. These include the traditional Convolution Neural Networks, Normalization, Deep Neural Network or the DeepPose, Adversarial PoseNet, and OpenPose. We start our study with a deep neural network that could yield results comparable to the traditional complicated architectures. Then we discuss how normalization can help in more precise pose estimations and make the relative distribution of joints more compact. We also talk about the estimation of pose using Deep Neural Networks, Part Affinity Fields and multichannel heatmaps.
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts

Least squares estimators, when trained on a few target domain samples, may predict poorly. Supervised domain adaptation aims to improve the predictive accuracy by exploiting additional labeled training samples from a source distribution that is close to the target distribution. Given available data, we investigate novel strategies to synthesize a family of least squares estimator experts that are robust with regard to moment conditions. When these moment conditions are specified using Kullback-Leibler or Wasserstein-type divergences, we can find the robust estimators efficiently using convex optimization. We use the Bernstein online aggregation algorithm on the proposed family of robust experts to generate predictions for the sequential stream of target test samples. Numerical experiments on real data show that the robust strategies may outperform non-robust interpolations of the empirical least squares estimators.
Learning Bounds for Open-Set Learning

Traditional supervised learning aims to train a classifier in the closed-set world, where training and test samples share the same label space. In this paper, we target a more challenging and realistic setting: open-set learning (OSL), where there exist test samples from the classes that are unseen during training. Although researchers have designed many methods from the algorithmic perspectives, there are few methods that provide generalization guarantees on their ability to achieve consistent performance on different training samples drawn from the same distribution. Motivated by the transfer learning and probably approximate correct (PAC) theory, we make a bold attempt to study OSL by proving its generalization error-given training samples with size n, the estimation error will get close to order O_p(1/\sqrt{n}). This is the first study to provide a generalization bound for OSL, which we do by theoretically investigating the risk of the target classifier on unknown classes. According to our theory, a novel algorithm, called auxiliary open-set risk (AOSR) is proposed to address the OSL problem. Experiments verify the efficacy of AOSR. The code is available at this http URL.
Word-level Sign Language Recognition with Multi-stream Neural Networks Focusing on Local Regions.

In recent years, Word-level Sign Language Recognition (WSLR) research has gained popularity in the computer vision community, and thus various approaches have been proposed. Among these approaches, the method using I3D network achieves the highest recognition accuracy on large public datasets for WSLR. However, the method with I3D only utilizes appearance information of the upper body of the signers to recognize sign language words. On the other hand, in WSLR, the information of local regions, such as the hand shape and facial expression, and the positional relationship among the body and both hands are important. Thus in this work, we utilized local region images of both hands and face, along with skeletal information to capture local information and the positions of both hands relative to the body, respectively. In other words, we propose a novel multi-stream WSLR framework, in which a stream with local region images and a stream with skeletal information are introduced by extending I3D network to improve the recognition accuracy of WSLR. From the experimental results on WLASL dataset, it is evident that the proposed method has achieved about 15% improvement in the Top-1 accuracy than the existing conventional methods.
Evaluation of Non-Cooperative DWC with Open-Loop and Closed Loop Simulations

This joint briefing presents open and closed loop metrics about the performance of a reference Detect-and-Avoid algorithm using encounters built from NASA's UAS mission trajectories and Lincoln Lab's uncorrelated encounter model.
Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training

Self-training is a standard approach to semi-supervised learning where the learner's own predictions on unlabeled data are used as supervision during training. In this paper, we reinterpret this label assignment process as an optimal transportation problem between examples and classes, wherein the cost of assigning an example to a class is mediated by the current predictions of the classifier. This formulation facilitates a practical annealing strategy for label assignment and allows for the inclusion of prior knowledge on class proportions via flexible upper bound constraints. The solutions to these assignment problems can be efficiently approximated using Sinkhorn iteration, thus enabling their use in the inner loop of standard stochastic optimization algorithms. We demonstrate the effectiveness of our algorithm on the CIFAR-10, CIFAR-100, and SVHN datasets in comparison with FixMatch, a state-of-the-art self-training algorithm. Our code is available at this https URL.
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