Reviewerly/exHarmony huggingface dataset:
Real-time Telugu Sign Language Translator with Computer Vision

Abstract: Sign language is the basic communication method among hearing disabled and speech disabled people. To express themselves, they require an interpreter or motion sensing devices who/which converts sign language in a few of the standard languages. However, there is no system for those who speak in the Telugu language and hence they are forced to speak in the national language over the regional language of their culture along with the same issues of cumbersome hardware or need for an interpreter. This paper proposes a system that detects hand gestures and signs from a real-time video stream that is processed with the help of computer vision and classified with object detection YOLOv3 algorithm. Additionally, the labels are mapped to corresponding Telugu text. The style of learning is transfer learning, unlike conventional CNNs, RNNs or traditional Machine Learning models. It involves applying a pre-trained model onto a completely new problem to solve the related problem statement and adapts to the new problem’s requirements efficiently. This requires lesser training effort in terms of dataset size and greater accuracy. It is the first system developed as a sign language translator for Telugu script. It has given the best results as compared to the existing systems. The system is trained on 52 Telugu letters, 10 numbers and 8 frequently used Telugu words.
Sistem Informasi Penjadwalan Latihan dan Kunjungan Penyuluh Pertanian (SIJALUTANI)

The rapid development of information technology can affect all aspects of daily life. One example is information systems. An information system is very important because it can make it easier for us to get the information we need. This is an abstract version of the Indonesian language. The Department of Food Security and Agriculture of Bojonegoro Regency is one of the agencies that needs to implement an information system related to scheduling agricultural extension workers because there is a problem where the scheduling of training and visits of agricultural instructors is done manually. Making an information system is a solution to the problems experienced. The information system that will be created by the author is named SIJALUTANI, where the name is an abbreviation of the Information System for Scheduling Training and Agricultural Extension Visits. The model applied to this information system is using the Waterfall Model.
Questionnaire Validity and Reliability of Intuitive Eating Behaviour towards Obesity: Behavioural perspective

Introduction: Intuitive eating involves consuming food based on internal hunger and satiety cues. This study aims to establish a validated and reliable questionnaire. Methods: The questionnaire items' difficulty, uncertainty, and inappropriateness were conducted. The validity is analysed using the Content Validity Index and Lawshe's Content Validity Ratio, estimated by inter-item reliability (Cronbach's Alpha) and test-retest reliability (Intraclass Correlation Coefficient). Results: The CVIs correlation coefficient is 0.89 – 0.99. The Cronbach's Alpha coefficient value is 0.66 – 0.68. Conclusion: This study showed that the questionnaire has good validity and reliability towards obesity from an intuitive eating behaviour perspective. Keywords: intuitive eating, obesity, validity, reliability eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under the responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians), and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7i21.3678
Theoretical and methodological aspects of assessing the security of cloud it components according to the criteria of existing standards.

Computing of clouds provide the admittance to the collection of computational possessions like storage of network and services. These possessions can be resealed and prompt with the minor efforts of the management because they have a scalable and dynamic environment. As a service the cloud computing offers the computing infrastructure, development of the stage as well as software and web applications like the model of pay as you go for the customers. Services are measured as the infrastructures as services (IAAS), Platform as a Service (PAAS), and the service software (SAAS) in diligence. In the research, we deliberate the Assessing the Security of Cloud It Components according to the criterion of obtainable principles. Methodology. In the advancement of the cloud computing metrics hierarchy, we covenant with the cloud computing management security with the GQM methodology. The main intention of the hierarchy proposed is to develop index for the security which entirely explained the security level in an estimate computing cloud environment. On the stride of the index for the security, we compute the index for the security to compute the allocation index. This allocation index will help in the management of the priorities set with a strong security prejudice. In this, we elucidate the slant for computingcloud management by use of security as a standard. Scientific review. The most enviable prerequisite of the computing of the cloud is to shun waste the underused possessions and mount the point of the response rate in the shortage of resources. In the latest literature in the administration field prioritization provision of the resources, we noticed that algorithm is preferred for the energy proficient management of the computing cloud environments. The metrics for the security use as instrument to provide the status about the information of security. Conclusion. In this editorial, we projected a management method for the computingcloud by using the criteria for the security. We accessible the 2 approaches to resource management. These strategies pact with the granularity and scalability in the computing cloud system. The security index covey the level of the security in the cloud computing environment in the modeled with metrics hierarchy. The biggest gain of using this approach is that it ropes the hierarchical decomposition that makes the model more scalable and disseminated.
Components of viruses and antivirus software in modern information security.

In fact, in today's conditions of continuous innovative development of technologies, in the field of programming, computer networks and the Internet, the dark side of progress continues to grow at great rates: virus software. As a result, in this article we will try to focus on all aspects of the fight against viruses: prevention of infection, methods of detecting malicious programs, their destruction, as well as elimination of consequences. To do this, we conducted research and study of the main scientific sources on this topic and statistical data. As a result, this article focuses on different types of viruses and analyzes the methods of their classification, detection and destruction.
Assessing the Impact of Horizontal Integration on Competition in Iran’s Banking Sector: The Panzar-Rosse Approach

Assessing the Impact of Horizontal Integration on Competition in Iran’s Banking Sector: The Panzar-Rosse Approach
The Effect of Financial development and Trade liberalization on Eeconomic growth In Upper Middle Income Countries

The Effect of Financial development and Trade liberalization on Eeconomic growth In Upper Middle Income Countries
Comparative analysis of the effects of monetary and government financial expenditures shocks in the context of fractional and full reserve banking in Iran: DSGE approach

Comparative analysis of the effects of monetary and government financial expenditures shocks in the context of fractional and full reserve banking in Iran: DSGE approach
Minimum Cost Adaptive Submodular Cover

We consider the problem of minimum cost cover of adaptive-submodular functions, and provide a 4(ln Q+1)-approximation algorithm, where Q is the goal value. This bound is nearly the best possible as the problem does not admit any approximation ratio better than ln Q (unless P=NP). Our result is the first O(ln Q)-approximation algorithm for this problem. Previously, O(ln Q) approximation algorithms were only known assuming either independent items or unit-cost items. Furthermore, our result easily extends to the setting where one wants to simultaneously cover multiple adaptive-submodular functions: we obtain the first approximation algorithm for this generalization.
An Overview of Metrics for Evaluating the Quality of Graph Partitioners

Nowadays, many applications that involve big data can be modelled as graphs.In many cases these graphs may be too large to be loaded and processed on a single commodity computer.This necessitated the development of frameworks that allow processing large graphs by distributing the graph among nodes in a cluster.To process a graph using these frameworks, first, the graph is partitioned into smaller components called subgraphs or partitions, and then assign these smaller subgraphs to different nodes for parallel processing.Depending on the type of processing (example, computing pagerank, counting number of triangles etc.), there will be some communication between nodes during the execution, this communication affects execution time.Therefore, graph partitioning is an important step in distributed graph processing.Being able to determine the quality of a partition prior to processing is important as this will allow us to predict the execution time before the actual processing.A number of metrics for evaluating the quality of a graph partitions exist, but studies show that these metrics may not serve as accurate predictors in many cases.In this work, we reviewed published papers about graph partitioning and we were able to identify and defined more metrics in order to have a catalogue of these metrics.
Better Pseudo-label: Joint Domain-aware Label and Dual-classifier for Semi-supervised Domain Generalization

With the goal of directly generalizing trained model to unseen target domains, domain generalization (DG), a newly proposed learning paradigm, has attracted considerable attention. Previous DG models usually require a sufficient quantity of annotated samples from observed source domains during training. In this paper, we relax this requirement about full annotation and investigate semi-supervised domain generalization (SSDG) where only one source domain is fully annotated along with the other domains totally unlabeled in the training process. With the challenges of tackling the domain gap between observed source domains and predicting unseen target domains, we propose a novel deep framework via joint domain-aware labels and dual-classifier to produce high-quality pseudo-labels. Concretely, to predict accurate pseudo-labels under domain shift, a domain-aware pseudo-labeling module is developed. Also, considering inconsistent goals between generalization and pseudo-labeling: former prevents overfitting on all source domains while latter might overfit the unlabeled source domains for high accuracy, we employ a dual-classifier to independently perform pseudo-labeling and domain generalization in the training process. When accurate pseudo-labels are generated for unlabeled source domains, the domain mixup operation is applied to augment new domains between labeled and unlabeled domains, which is beneficial for boosting the generalization capability of the model. Extensive results on publicly available DG benchmark datasets show the efficacy of our proposed SSDG method.
Improving End-to-End Contextual Speech Recognition with Fine-Grained Contextual Knowledge Selection

Nowadays, most methods in end-to-end contextual speech recognition bias the recognition process towards contextual knowledge. Since all-neural contextual biasing methods rely on phrase-level contextual modeling and attention-based relevance modeling, they may encounter confusion between similar context-specific phrases, which hurts predictions at the token level. In this work, we focus on mitigating confusion problems with fine-grained contextual knowledge selection (FineCoS). In FineCoS, we introduce fine-grained knowledge to reduce the uncertainty of token predictions. Specifically, we first apply phrase selection to narrow the range of phrase candidates, and then conduct token attention on the tokens in the selected phrase candidates. Moreover, we re-normalize the attention weights of most relevant phrases in inference to obtain more focused phrase-level contextual representations, and inject position information to better discriminate phrases or tokens. On LibriSpeech and an in-house 160,000-hour dataset, we explore the proposed methods based on a controllable all-neural biasing method, collaborative decoding (ColDec). The proposed methods provide at most 6.1% relative word error rate reduction on LibriSpeech and 16.4% relative character error rate reduction on the in-house dataset over ColDec.
Fast detection of specific fragments against a set of sequences

We design alignment-free techniques for comparing a sequence or word, called a target, against a set of words, called a reference. A target-specific factor of a target $T$ against a reference $R$ is a factor $w$ of a word in $T$ which is not a factor of a word of $R$ and such that any proper factor of $w$ is a factor of a word of $R$. We first address the computation of the set of target-specific factors of a target $T$ against a reference $R$, where $T$ and $R$ are finite sets of sequences. The result is the construction of an automaton accepting the set of all considered target-specific factors. The construction algorithm runs in linear time according to the size of $T\cup R$. The second result consists of the design of an algorithm to compute all the occurrences in a single sequence $T$ of its target-specific factors against a reference $R$. The algorithm runs in real-time on the target sequence, independently of the number of occurrences of target-specific factors.
Fortified Test Functions for Global Optimization and the Power of Multiple Runs

Some popular functions used to test global optimization algorithms have multiple local optima, all with the same value. That is all local optima are also global optima. This paper suggests that such functions are easily fortified by adding a localized bump at the location of one of the optima, making the functions more difficult to optimize due to the multiple competing local optima. This process is illustrated here for the Branin-Hoo function, which has three global optima. We use the popular Python SciPy differential evolution (DE) optimizer for the illustration. DE also allows the use of the gradient-based BFGS local optimizer for final convergence. By making a large number of replicate runs we establish the probability of reaching a global optimum with the original and fortified Branin-Hoo. With the original function we find 100% probability of success with a moderate number of function evaluations. With the fortified version, the probability of getting trapped in a non-global optimum could be made small only with a much larger number of function evaluations. However, since the probability of ending up at the global optimum is usually 1/3 or more, it may be beneficial to perform multiple inexpensive optimizations rather than one expensive optimization. Then the probability of one of them hitting the global optimum can be made high. We found that for the most challenging global optimum, multiple runs reduced substantially the extra cost for the fortified function compared to the original Branin-Hoo.
Deep Learning for Fitness

We present Fitness tutor, an application for maintaining correct posture during workout exercises or doing yoga. Current work on fitness focuses on suggesting food supplements, accessing workouts, workout wearables does a great job in improving the fitness. Meanwhile, the current situation is making difficult to monitor workouts by trainee. Inspired by healthcare innovations like robotic surgery, we design a novel application Fitness tutor which can guide the workouts using pose estimation. Pose estimation can be deployed on the reference image for gathering data and guide the user with the data. This allow Fitness tutor to guide the workouts (both exercise and yoga) in remote conditions with a single reference posture as image. We use posenet model in tensorflow with p5js for developing skeleton. Fitness tutor is an application of pose estimation model in bringing a realtime teaching experience in fitness. Our experiments shows that it can leverage potential of pose estimation models by providing guidance in realtime.
Center Loss Regularization for Continual Learning

The ability to learn different tasks sequentially is essential to the development of artificial intelligence. In general, neural networks lack this capability, the major obstacle being catastrophic forgetting. It occurs when the incrementally available information from non-stationary data distributions is continually acquired, disrupting what the model has already learned. Our approach remembers old tasks by projecting the representations of new tasks close to that of old tasks while keeping the decision boundaries unchanged. We employ the center loss as a regularization penalty that enforces new tasks' features to have the same class centers as old tasks and makes the features highly discriminative. This, in turn, leads to the least forgetting of already learned information. This method is easy to implement, requires minimal computational and memory overhead, and allows the neural network to maintain high performance across many sequentially encountered tasks. We also demonstrate that using the center loss in conjunction with the memory replay outperforms other replay-based strategies. Along with standard MNIST variants for continual learning, we apply our method to continual domain adaptation scenarios with the Digits and PACS datasets. We demonstrate that our approach is scalable, effective, and gives competitive performance compared to state-of-the-art continual learning methods.
Sims: a text-based information system using symmetric and asymmetric algorithm

Information security is today’s everybody’s concern. Technology keeps on increasing however the issue of information theft are getting higher. The confidentiality, integrity and availability of information are being compromised and always at risk though information security are keeps on improving. This study aimed to build an information security using a text-based platform to store the data and preserved its confidentiality, integrity and availability. The study will focus on the security of information and its confidentiality, integrity, and availability. An information system was developed using Java. Once the user started to process the data, the system will automatically create a text file where data inputted from the system will be are stored. The system will generate text-based formats per user. Another text file pass dot(.) txt whew passwords are stored. The data or information are stored in different text files. The transaction data and passwords are encypted using asymmetric algorithms. Each user account should have a private key to be able to access the data. Once the user is log in to the system, user will use a public key to able to access the data. Moreover, the text files where the passwords are kept and the text files where data are stored will be encrypted using symmetric algorithm which uses a public key to all users. The researcher used descriptive research design in the project. Descriptive research is being used by the proponents to describe the assessment of the respondents in the security of an information.
A Chaos Auto-Associative Model with Chebyshev Activation Function

In this work, we shall put forward a novel chaos memory retrieval model with a Chebyshev-type activation function as an artificial chaos neuron. According to certain numerical analyses of the present association model with autocorrelation connection matrix between neurons, the dependence of memory retrieval properties on the initial Hamming distance between the input pattern and a target pattern to be retrieved among the embedded patterns will be presented to examine the retrieval abilities, i.e. the memory capacity of the associative memory.
Towards Preserving Server-Side Privacy of On-Device Models

Machine learning-based predictions are popular in many applications including healthcare, recommender systems and finance. More recently, the development of low-end edge hardware (e.g., Apple’s Neural Engine and Intel’s Movidius VPU) has provided a path for the proliferation of machine learning on the edge with on-device modeling. Modeling on the device reduces latency and helps maintain the user’s privacy. However, on-device modeling can leak private server-side information. In this work, we investigate on-device machine learning models that are used to provide a service and propose novel privacy attacks that can leak sensitive proprietary information of the service provider. We demonstrate that different adversaries can easily exploit such models to maximize their profit and accomplish content theft. Motivated by the need to preserve both client and server privacy, we present preliminary ideas on thwarting such attacks.
Statistical and Topological Properties of Gaussian Smoothed Sliced Probability Divergences

Gaussian smoothed sliced Wasserstein distance has been recently introduced for comparing probability distributions, while preserving privacy on the data. It has been shown, in applications such as domain adaptation, to provide performances similar to its non-private (non-smoothed) counterpart. However, the computational and statistical properties of such a metric is not yet been well-established. In this paper, we analyze the theoretical properties of this distance as well as those of generalized versions denoted as Gaussian smoothed sliced divergences. We show that smoothing and slicing preserve the metric property and the weak topology. We also provide results on the sample complexity of such divergences. Since, the privacy level depends on the amount of Gaussian smoothing, we analyze the impact of this parameter on the divergence. We support our theoretical findings with empirical studies of Gaussian smoothed and sliced version of Wassertein distance, Sinkhorn divergence and maximum mean discrepancy (MMD). In the context of privacy-preserving domain adaptation, we confirm that those Gaussian smoothed sliced Wasserstein and MMD divergences perform very well while ensuring data privacy.
A reformulation of collision avoidance algorithm based on artificial potential fields for fixed-wing UAVs in a dynamic environment

As mini UAVs become increasingly useful in the civilian work domain, the need for a method for them to operate safely in a cluttered environment is growing, especially for fixed-wing UAVs as they are incapable of following the stop-decide-execute methodology. This paper presents preliminary research to design a reactive collision avoidance algorithm based on the improved definition of the repulsive forces used in the Artificial potential field algorithms to allow feasible and safe navigation of fixed-wing UAVs in cluttered, dynamic environments. We present simulation results of the improved definition in multiple scenarios, and we have also discussed possible future studies to improve upon these results.