Reviewerly/exHarmony huggingface dataset:
Predict the Face-Mask-and-Social-Distance Identification by Using Yolo and CNN

Abstract: COVID-19 virus is still a source of concern and hazard in today's world. With such a huge population traveling, manual monitoring of social distance standards is impracticable. Regarding and with a task force and resources that are insufficient to they should be administered There is a requirement for a lightweight, durable, and reliable device. This procedure is automated by a video observation device that operates 24 hours a day, seven days a week. This study offers a detailed and practical solution to the problem. Perform person detection, social distance violation detection, and social distancing violation detection. Using an item, detect faces and classify face masks. Convolution Neural Networks, detection, and grouping (CNN)is a binary classifier that is based on. YOLOv3, Density-fundamentally based spatial bunching of bundles with commotion (DBSCAN), and double are a portion of the instruments that can assist with this. Shot Face Detector (DSFD) and Binary MobileNetV2 Surveillance video assortments were utilized to prepare a classifier. This publication also includes a comparison of various facial types. Face mask detection and classification models. Finally, to make amends for the absence of a dataset within the network, a video dataset labeling technique is presented, couple with a labeled video dataset, which is utilized to evaluate the system. The framework's presentation is estimated as far as precision, F1 score, and gauge time, which should be least for practical use. On the labeled video data set, the device has an accuracy of 91.2 percent and an F1 rating of 90. Seventy-nine percent, with an average forecast time of seven.12 seconds for seventy eight frames of video
Editorial Special Issue on Circuits and Systems for Emerging Computing Paradigms

AS Dennard’s law is coming to an end, on-chip power consumption reduction and throughput improvement due to technology scaling pose serious challenges; workloads of today’s applications (such as AI, big data, and the IoT) have also reached extremely high levels of complex computation. Power dissipation has become the fundamental barrier to scale computing performance across all technology platforms. Computation at nanoscales requires innovative approaches.
Machine Learning Algorithms and Its Applications: A Survey

Abstract: In todays present world lots of microelectronic statistics is created in apiece and every field. The data that we obtain contains valuable info to forecast the future. Owing to the enormous in magnitude, the physical forecasting gives a intricate chore to humans. To overwhelm this problem, the data model is made in such a way so that it can predict the future by the situation with the aid of training data and test datasets. To Pullman the machine or the data model, numerous types of machine learning algorithms (ML) and tools are available. This paper will emphasis on the review of the few machine learning algorithms(ML) and methods used in numerous applications and domains in a detailed manner.
Collection and evaluation of lexical complexity data for Russian language using crowdsourcing

Estimating word complexity with binary or continuous scores is a challenging task that has been studied for several domains and natural languages. Commonly this task is referred to as Complex Word Identification (CWI) or Lexical Complexity Prediction (LCP). Correct evaluation of word complexity can be an important step in many Lexical Simplification pipelines. Earlier works have usually presented methodologies of lexical complexity estimation with several restrictions: hand-crafted features correlated with word complexity, performed feature engineering to describe target words with features such as number of hypernyms, count of consonants, Named Entity tag, and evaluations with carefully selected target audiences. Modern works investigated the use of transforner-based models that afford extracting features from surrounding context as well. However, the majority of papers have been devoted to pipelines for the English language and few translated them to other languages such as German, French, and Spanish. In this paper we present a dataset of lexical complexity in context based on the Russian Synodal Bible collected using a crowdsourcing platform. We describe a methodology for collecting the data using a 5-point Likert scale for annotation, present descriptive statistics and compare results with analogous work for the English language. We evaluate a linear regression model as a baseline for predicting word complexity on handcrafted features, fastText and ELMo embeddings of target words. The result is a corpus consisting of 931 distinct words that used in 3,364 different contexts.
A blind steganography approach for hiding privacy details in images of digital imaging and communications in medicine using QR code

&lt;span&gt;This study aims to hide patient’s privacy details of digital imaging and communications in medicine (DICOM) files using the quick response (QR) code images with the same size using steganographic technique. The proposed method is based on the properties of the discrete cosine transform (DCT) of the DICOM images to embed a QR code image. The proposed method includes two parts: data embedding and extraction process. Moreover, the stego DICOM image could be blindly used to produce the embedded QR code image without the existence of the original DICOM image. The performances of proposed method were evaluated using the metrics of the peak signal to noise ratio (PSNR), the structural similarity index (SSIM), the universal quality index (UQI), the correlation coefficient (R) and the bit error rate (BER) values. The experimental results scored a high PSNR after the embedding process by embedding a QR code image into the DICOM image with the same size.&lt;/span&gt;
Universal clustering algorithm based on an adaptive density gradient

&lt;p&gt;We proposed a universal clustering algorithm by constructing an adaptive density gradient. This algorithm showed no data preference, and performs well on data with arbitrary density, overlap and shape. In comparative experiments, it outperformed other state-of-the-art algorithms on all types of synthetic and real data.&lt;/p&gt;
Certification and Attestation Genuinity Management System

Abstract: CAGMS (Certificate and Attestation Management system) is an easy-to-use management system where any organization or individual can issue or receive digitally verifiable certificate for any type of documents / awards / rewards etc. with ease of complexity of system and technology. The system uses modern techs and high-interactive API which makes fit for all sort of organization
Fuzzy logic based authentication in cognitive radio networks

&lt;p&gt;Security is a critical issue in cognitive radio networks because the cognitive node enters and variably leaves the spectrum, so it is difficult to process communication secretly. We suggested a fuzzy logic-based implicit authentication mechanism to be a solution for the confusion if there were any cognitive node doubts it to be unauthentic, and to improve user privacy in cognitive radio networks. Using a fuzzy logic technique, the proposed scheme computed certification based on proposed feedback. When a cognitive node needs to join the network, it is verified by using fuzzy logic if the node was authenticated or not. Our proposed fuzzy logic's results implicit authentication proved that it was a very successful and applicable scheme on cognitive radio networks, and it will be able to make an effective final decision in the context of incompleteness, ambiguity, and heterogeneity&lt;/p&gt;
A Study on N-Spherical Histogram for Unbounded Tensor Features in Human Action Recognition

Recent years, Dense Trajectory features has a crucial role in extracting implicit features for action recognition. The method encloses motion and appearance descriptors to specify characteristics of each trajectory. Moreover, combining gradient and optical flow field using tensor product has made a strong positive impact on the result as we introduced in our previous work. In this paper, a breakthrough concept of encoding a high dimensional unbound space using spherical coordinate is introduced and imposed to obtain sophisticated spherical tensor features. The experimental result shows that our propose features outperforms other conventional ones and the combination of all feature channels achieves the highest accuracy rate in our selfrecorded dataset.
Art Methods for Capturing an Environment in Video Games

The article presents part of the art methods for visualizing models from the environment captured on a screen in video games. Computer graphics are developed to such a degree in terms of visualization that they perfectly apply the rules of our perspective knowledge, so that they can help us imitate threedimensional objects on those flat surfaces. We will look at essential perspective concepts and how they help depict the environment in video games, as well as the tools that are developed in the virtual space. Artists can use them to show a specific action, emotion and atmosphere connected with the computer game. Such tools are the so-called virtual digital cameras. They use all the rules from the well-known perspective and implement them in the computer for creating the image. We will also look at types of cameras and different settings that game developers use for developing a game. The position and placement in the game level are important for achieving the desired result and how we want the player to look at the picture. There are compositional solutions and a camera angle in different types of video games. So, the best solution can be given regarding the view of the environment in the video game with the action that is happening in it. These are some basic art elements that are implemented in computer technology to create the image in a computer game.
Security Challenges for Digital Transformation in Saudi Arabia

Saudi organizations are increasingly using digital technologies. Despite widespread efforts to benefit from digitalization, few companies can claim to have seen significant results. Organizational employees have been linked to the success or failure of the digitization process in a growing body of studies. Three hypotheses regarding the security of the digital transition have been suggested in this study. These hypotheses include determining how security has evolved because of the increased use of digital systems, as well as determining the best risk management strategies. A range of public and private Saudi entities will get an electronic survey as part of this study. In summary, we have found that as the digital transformation grows, the security risk increases as well, necessitating the implementation of a robust security system and the most effective risk management measures. Additionally, the business must include employees in the development and implementation of the digital transformation to maintain their loyalty. Additionally, all employees must understand and agree on the digital transformation goals, objectives, and plans. Successful business leaders can build a compelling vision, unite, and engage staff to adopt new methods of doing business.
A cognitive linguistic approach to analysis and correction of orthographic errors

In this paper, we apply usage-based linguistic analysis to systematize the inventory of orthographic errors observed in the writing of non-native users of Russian. The data comes from a longitudinal corpus (560K tokens) of non-native academic writing. Traditional spellcheckers mark errors and suggest corrections, but do not attempt to model why errors are made. Our approach makes it possible to recognize not only the errors themselves, but also the conceptual causes of these errors, which lie in misunderstandings of Russian phonotactics and morphophonology and the way they are represented by orthographic conventions. With this linguistically-based system in place, we can propose targeted grammar explanations that improve users’ command of Russian morphophonology rather than merely correcting errors. Based on errors attested in the non-native academic writing corpus, we introduce a taxonomy of errors, organized by pedagogical domains. Then, on the basis of this taxonomy, we create a set of mal-rules to expand an existing finite-state analyzer of Russian. The resulting morphological analyzer tags wordforms that fit our taxonomy with specific error tags. For each error tag, we also develop an accompanying grammar explanation to help users understand why and how to correct the diagnosed errors. Using our augmented analyzer, we build a webapp to allow users to type or paste a text and receive detailed feedback and correction on common Russian morphophonological and orthographic errors.
Run and Frequency quotas for q-binary trials

We study the distributions of waiting times in variations of the $q$-sooner and later waiting time problem. One variation imposes length and frequency quotas on the runs of successes and failures. Another case considers binary trials for which the probability of ones is geometrically varying. We also study the distributions of Longest run under the same variations. The main theorems are sooner and later waiting time problem and the joint distribution of the length of the longest success and longest failure runs when a run and frequency quotas imposed on runs of successes and failure. In the present work, we consider a sequence of independent binary $(0-1)$ trials with not necessarily identical distributed with probability of ones varying according to a geometric rule. Exact formulae for the distributions obtained by means of enumerative combinatorics.
Security Model and Algorithms in Federate learning under IoT Critical Infrastructure

Abstract in today's digital world, IoT has a vital and very important role in offering and exchanging information services throughout the internet. it is new technologies and applications that have a deep effect on every domain of our daily life such as education, social, healthcare, and so on. IoT deals with many different sensing objects and machines that interact with each other to do their activities, IoT collects and exchanges data from all sensing objects and machines. Stores the collected data in structured or unstructured format managed through cloud platform services. Due to IoT have classical centralized learning algorithms, it affects directly by computational power and time, most importantly security and privacy issues respect with to user data. So for overcoming these challenges, federated learning is a favorable solution that enables on-object machine learning without migrating end-user private data to the central point (cloud), as well as Artificial Intelligent (AI) benefits to domains with sensitive data, only learning model update will be transferred between end-devices and the central server. By using the federated learning model the scope of the decentralized approach will be boost by having a huge number of clients for collaborating training, this FL makes vulnerable again various security threats and risks. In this paper, we analyze the overall vulnerabilities being exploited by malicious attackers which impact system security and privacy violation, common security threats and attacks in FL to distributed machine learning solutions and defensive techniques to enhance security/privacy vulnerabilities such as security models and algorithms to secure information in a decentralized system, network security as well as access control for availability, integrity, and confidentiality ( AIC), end device are required to protect and secured from unauthorized access.
Implementation of authenticated encryption with associated data grain-128aead algorithm on stm32f400 processor family

An embedded device is becoming popular in daily life thanks to their low power consumption and multiprocessing capability. In particular, the security of embedded devices has been a big issue of concern to academic and industrial communities. This study aims at the Grain 128-AEAD authenticated encryption with associated data algorithm embedded on low-power and resource-constrained devices. This stream cipher belongs to the Grain family developed from the Grain-128a algorithm, and it has the advantage of not only providing security, but also adding authenticity to the associated data to ensure the authenticity, integrity and confidentiality of the data. It is also considered suitable for IoT (Internet of Thing) platforms and embedded device applications with limited resources and low power consumption. In this study, the algorithm was implemented on STM32 processor family. The resulting code size is only 832 bytes, and the total execution time for a 128-byte input block of Grain-128AEAD algorithm (Encryption and Decryption) takes 30 µs, which is better than previous implementations on various hardware platforms. The compiled file size is only 54kB, which makes the algorithm fit embedded applications.
Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One

Autoregressive generative models are commonly used, especially for those tasks involving sequential data. They have, however, been plagued by a slew of inherent flaws due to the intrinsic characteristics of chain-style conditional modeling (e.g., exposure bias or lack of long-range coherence), severely limiting their ability to model distributions properly. In this paper, we propose a unique method termed E-ARM for training autoregressive generative models that takes advantage of a well-designed energy-based learning objective. By leveraging the extra degree of freedom of the softmax operation, we are allowed to make the autoregressive model itself be an energy-based model for measuring the likelihood of input without introducing any extra parameters. Furthermore, we show that E-ARM can be trained efficiently and is capable of alleviating the exposure bias problem and increase temporal coherence for autoregressive generative models. Extensive empirical results, covering benchmarks like language modeling, neural machine translation, and image generation, demonstrate the effectiveness of the proposed approach.
Open Set Classification of Untranscribed Handwritten Documents

Huge amounts of digital page images of important manuscripts are preserved in archives worldwide. The amounts are so large that it is generally unfeasible for archivists to adequately tag most of the documents with the required metadata so as to low proper organization of the archives and effective exploration by scholars and the general public. The class or ``typology'' of a document is perhaps the most important tag to be included in the metadata. The technical problem is one of automatic classification of documents, each consisting of a set of untranscribed handwritten text images, by the textual contents of the images. The approach considered is based on ``probabilistic indexing'', a relatively novel technology which allows to effectively represent the intrinsic word-level uncertainty exhibited by handwritten text images. We assess the performance of this approach on a large collection of complex notarial manuscripts from the Spanish Archivo Host\'orico Provincial de C\'adiz, with promising results.
Probing Causes of Hallucinations in Neural Machine Translations

Hallucination, one kind of pathological translations that bothers Neural Machine Translation, has recently drawn much attention. In simple terms, hallucinated translations are fluent sentences but barely related to source inputs. Arguably, it remains an open problem how hallucination occurs. In this paper, we propose to use probing methods to investigate the causes of hallucinations from the perspective of model architecture, aiming to avoid such problems in future architecture designs. By conducting experiments over various NMT datasets, we find that hallucination is often accompanied by the deficient encoder, especially embeddings, and vulnerable cross-attentions, while, interestingly, cross-attention mitigates some errors caused by the encoder.
Speech or Text to Indian Sign Language Convertor

Abstract: Sign language display is the software of converting the text/speech to animated sign language to meet the demands of the special needs population. The goal of this is to make the communication A more comfortable and to improve both health and productivity .As technology has improved, the usage of computer systems has increased, which means we can implement better ways to meet the needs that can impact the health of special needs population . So, this project could help people improve their communication. Thus, they improve their mental health .With the help of Blender, Python, SQL, and NLP, We have tried to design a process to detect text and subsequently the live speech , which can converted to animated sign language in real time. For the entire process, I used Blender for animation and video processing and SQL and NLP to determine the different words and text to by training the dataset and for text to animation conversion .Therefore, this project could to meet the needs of many people .The number of different countries use different sign language even same countries that are multilingual use a different system which meets the social and cultural standards of their countries. As such there is a wide disparity in many countries for digital sign language But, Software such as our could be helpful to bridge the gap between such discrepancies and be useful not just for acting as a tool for communication but also as tool use to teach . In conclusion , it is very cost effective and widely applicable
Trusting in the Sciences Requires Explainability [From the Editor]

The July issue of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Signal Processing Magazine</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SPM</i> ) is a special issue focused on “Explainability in Data Science: Interpretability, Reproducibility, and Replicability.” With increased enthusiasm for machine learning, it is a very timely topic, and I invite every IEEE Signal Processing Society (SPS) member to read these very instructive papers.
Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution Shift

Recently, Miller et al. showed that a model's in-distribution (ID) accuracy has a strong linear correlation with its out-of-distribution (OOD) accuracy on several OOD benchmarks -- a phenomenon they dubbed ''accuracy-on-the-line''. While a useful tool for model selection (i.e., the model most likely to perform the best OOD is the one with highest ID accuracy), this fact does not help estimate the actual OOD performance of models without access to a labeled OOD validation set. In this paper, we show a similar but surprising phenomenon also holds for the agreement between pairs of neural network classifiers: whenever accuracy-on-the-line holds, we observe that the OOD agreement between the predictions of any two pairs of neural networks (with potentially different architectures) also observes a strong linear correlation with their ID agreement. Furthermore, we observe that the slope and bias of OOD vs ID agreement closely matches that of OOD vs ID accuracy. This phenomenon, which we call agreement-on-the-line, has important practical applications: without any labeled data, we can predict the OOD accuracy of classifiers}, since OOD agreement can be estimated with just unlabeled data. Our prediction algorithm outperforms previous methods both in shifts where agreement-on-the-line holds and, surprisingly, when accuracy is not on the line. This phenomenon also provides new insights into deep neural networks: unlike accuracy-on-the-line, agreement-on-the-line appears to only hold for neural network classifiers.