Reviewerly/exHarmony huggingface dataset:
Hand Sign Recognition for Medical Field Genesis

Abstract: Inability to talk is taken into account to be true incapacity. folks with this incapacity use totally different modes to speak with others, there are variety of strategies accessible for his or her communication one such common methodology of communication is language. Developing language application for speech impaired folks is vital, as they’ll be ready to communicate simply with even people who don’t perceive language. Our project aims at taking the fundamental step in bridging the communication gap between traditional folks, deaf and dumb folk’s victimization language. the most focus of this work is to form a vision-based system to spot language gestures in real-time. Not solely that, however our project conjointly aims to focus on the audience or the users with very little or no data regarding any language. The project can aim to be helpful for each the traditional yet because the person with speaking or hearing incapacity. Although, the scope of the project shall be on the far side the compatibility, still it's herewith tried to usher in the module or a locality of the appliance for the speech impaired person. Future scope of the project aims to develop the appliance to assist person with all ability. Keywords: Hand Sign Recognition, American-Sign-Language, Yolo, Object Detection, Gesture to Speech, CNN, Machine Learning.
Multilingual Speech Recognition Methods using Deep Learning and Cosine Similarity

The paper includes research on discovering new methods for multilingual speech recognition and comparing the effectiveness of the existing solutions with the proposed novelty approaches. The audio and textual multilingual dataset contains multilingual sentences where each sentence contains words from two different languages - English and Kannada. Our proposed speech recognition process includes preprocessing and splitting each audio sentence based on words, which is then given as input to the DL translator (using MFCC features) along with next word predictions. The use of a Next Word Prediction model along with the DL translator to accurately identify the words and convert to text. Similarly the other approach proposed is the use of cosine similarity where the speech recognition is based on the similarity between word uttered and the generated training dataset. Our models were trained on an audio and textual dataset that were generated by the team members and the test accuracies were measured based on the same dataset. The accuracy of our speech recognition model, using the novelty method, is 71%. This is a considerably good result compared to the existing multilingual translation solutions. Communication gap has been a major issue for many natives and locals trying to learn or move ahead in this tech-savvy English-speaking world. To communicate effectively, it is not only essential to have a single language translator but also a tool that can help understand a mixture of different languages to bridge the gap of communication with the non-English speaking communities. Integrating a multilingual translator with the power of a smart phone voice assistant can help aid this process.
An Interpretable Prediction of FCM driven by Small Samples for Energy Analysis based on Air Quality Prediction

In order to achieve prevention and control of air pollution through energy consumption adjustment in advance, the paper proposes an Fuzzy Cognitive Map (FCM) of various energy resources affecting air quality, an incremental prediction algorithm of FCM and gradient descending method used to learn the FCM based on the small sample data on various energy consumptions and concentration of air pollutants. The FCM as an interpretable prediction method not only can predict future air quality more accurately, but also can analyze and interpret the affecting of various energy types on the future air quality. As the time delay of various energy consumptions affecting concentration of air pollutants, the quantitative time sequence influencing relationships (causality) in the FCM is mined directly from these data, and the air quality affected by various types of energy consumptions is predicted based on the FCM. Accordingly, the energy types affecting air pollution can be obtained for prior decision of energy consumption structure adjustment. The experimental results in Beijing-Tianjin-Hebei show that the FCM modeling is better than Support Vector Regression (SVR), Linear Regression (LR), Principal Component Analysis (PCA)-based forecasting, Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) methods in predicting air quality affected by energy resources, meanwhile according to the interpretable prediction results of the FCM, we obtain some interesting results and suggestions on energy consumption types in Beijing-Tianjin-Hebei regions in advance.Implications: At present, China's air pollution control has entered the deep-water area, and the biggest challenge is how to adjust the energy (consumption) structure. Therefore, this study completed the two important tasks: (1) driven by small sample data of energy consumptions, the paper provides an interpretable prediction model and method with better performance to achieve prevention and control of air pollution through energy consumption adjustment in advance; (2) according to the interpretable prediction results, the paper obtains some interesting results used to guide energy consumption adjustment in Beijing-Tianjin-Hebei regions. This study will provide beneficial suggestions and strategies for air pollution prevention and control in Beijing-Tianjin-Hebei, will help improve the air quality and energy consumption structure in Beijing-Tianjin-Hebei, and also can be extended to other regions.
Implementasi Algoritma C4.5 Prediksi Produksi Komoditas Tanaman Perkebunan Berdasarkan Luas Lahan

The Blue-blue Plant Protection Development Unit (UPPT) is an agency that plays a role in assisting the community in increasing the production of plantation crop commodities in their fostered areas, both in terms of quality and quantity. However, sometimes there are still problems experienced by farmers, namely the incompatibility of the production of plantation crop commodities with the area of ​​land they have, so that it can affect the level of income of the farmers themselves. One way to process this data is to apply data mining techniques, where data mining is a process of analyzing large data that is extracted into information to determine the pattern of analysis on plantation crop commodity production data based on land area. The C.45 algorithm will later explore existing data and will form a decision tree, where the decision tree will later assist the Blue-blue Plant Protection Development Unit (UPPT) in taking good actions in helping to increase the production of plantation crop commodities. and provide counseling to fostered farmers to anticipate losses to farmers
Agency in augmented reality: exploring the ethics of Facebook’s AI-powered predictive recommendation system

The development of predictive algorithms for personalized recommendations that prioritize ads, filter content, and tailor our decision-making processes will increasingly impact our society in the upcoming years. One example of what this future might hold was recently presented by Facebook Reality Labs (FRL) who work on augmented reality (AR) glasses powered by contextually aware AI that allows the user to "communicate, navigate, learn, share, and take action in the world" (Facebook Reality Labs 2021). A major feature of those glasses is "the intelligent click" that presents action prompts to the user based on their personal history and previous choices. The user can accept or decline those suggested action prompts depending on individual preferences. Facebook/Meta presents this technology as a gateway to "increased agency". However, Facebook's claim presumes a simplistic view of agency according to which our agentive capacities increase parallel to the ease in which our actions are carried out. Technologies that structure people's lives need to be based on a deeper understanding of agency that serves as the conceptual basis in which predictive algorithms are developed. With the goal of mapping this emerging terrain, the aim of this paper is to offer a thorough analysis of the agency-limiting risks and the agency-enhancing potentials of Facebook's "intelligent click" feature. Based on a concept of agency by Dignum (Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Springer International Publishing, Cham, 2019), the three agential dimensions of autonomy (acting independently), adaptability (reacting to changes in the environment), and interactivity (interacting with other agents) are analyzed towards our ability to make self-determining choices.
Analisa Kualitas Layanan Website Gofeeder Terhadap Kepuasan Mahasiswa STAH Dharma Nusantara Jakarta dengan Metode Webqual

Gofeeder website as a means of academic information used in STAH Dharma Nusantara Jakarta, currently the utilization of the website has not been maximally carried out by STAH DNJ students, but has begun to be effective within four years. Obstacles that are often experienced by students have difficulty in logging in to customize the website, payments that are still manual have not been integrated with the Virtual Account and not yet the maximum function of the website service. For this reason, researchers analyzed student satisfaction as users of Gofeeder website services on website quality by using the webqual 4.0 method which focuses on 3 dimensions of webqual 4.0, namely Usability, Information Quality and Interaction Service Quality, to measure user satisfaction. The study used a statement instrument based on webqual dimension 4.0, using questionnaires shared with 65 respondents taken based on simple random sampling from a population of 157 active college students. Data processing uses SPSS version 26. The results of the F test stated that the common influence of 3 dimensions of Usability, Information Quality, Interaction Service Quality on user satisfaction, with a P-Value or significance value of 0.000, meaning a probability value of &lt; 0.05. The result of the test t showed the variable Usability had no significant effect on user satisfaction, with a p-value of 0.405 (probability value &gt; 0.05), the variable that affected was the quality of information (value of information). p-value 0.002), and interaction service quality (p-value 0.000). The results of the determination coefiesien test showed the effect of 3 dimensions of webqual 4.0 on user satisfaction (user satisfaction) of 86.10%, and the remaining 13.90% was influenced by other factors not used in this study
New Bounds and a Generalization for Share Conversion for 3-Server PIR

Private Information Retrieval (PIR) protocols, which allow the client to obtain data from servers without revealing its request, have many applications such as anonymous communication, media streaming, blockchain security, advertisement, etc. Multi-server PIR protocols, where the database is replicated among the non-colluding servers, provide high efficiency in the information-theoretic setting. Beimel et al. in CCC 12’ (further referred to as BIKO) put forward a paradigm for constructing multi-server PIR, capturing several previous constructions for k≥3 servers, as well as improving the best-known share complexity for 3-server PIR. A key component there is a share conversion scheme from corresponding linear three-party secret sharing schemes with respect to a certain type of “modified universal” relation. In a useful particular instantiation of the paradigm, they used a share conversion from (2,3)-CNF over Zm to three-additive sharing over Zpβ for primes p1,p2,p where p1≠p2 and m=p1·p2, and the relation is modified universal relation CSm. They reduced the question of the existence of the share conversion for a triple (p1,p2,p) to the (in)solvability of a certain linear system over Zp, and provided an efficient (in m,logp) construction of such a sharing scheme. Unfortunately, the size of the system is Θ(m2) which entails the infeasibility of a direct solution for big m’s in practice. Paskin-Cherniavsky and Schmerler in 2019 proved the existence of the conversion for the case of odd p1, p2 when p=p1, obtaining in this way infinitely many parameters for which the conversion exists, but also for infinitely many of them it remained open. In this work, using some algebraic techniques from the work of Paskin-Cherniavsky and Schmerler, we prove the existence of the conversion for even m’s in case p=2 (we computed β in this case) and the absence of the conversion for even m’s in case p>2. This does not improve the concrete efficiency of 3-server PIR; however, our result is promising in a broader context of constructing PIR through composition techniques with k≥3 servers, using the relation CSm where m has more than two prime divisors. Another our suggestion about 3-server PIR is that it’s possible to achieve a shorter server’s response using the relation CSm′ for extended Sm′&sup;Sm. By computer search, in BIKO framework we found several such sets for small m’s which result in share conversion from (2,3)-CNF over Zm to 3-additive secret sharing over Zpβ′, where β′>0 is several times less than β, which implies several times shorter server’s response. We also suggest that such extended sets Sm′ can result in better PIR due to the potential existence of matching vector families with the higher Vapnik-Chervonenkis dimension.
COMPUTER-BASED ENGLISH COMPETENCY ASSESSMENT FOR SCHOLARSHIP SELECTION

The need for a safe and versatile testing system during COVID-19 pandemic for scholarship selection prompted the Financial Education and Training Agency (FETA), an echelon-1 unit under the Ministry of Finance, to design the Aplikasi Ujian Online computer-based test platform. This paper focused on exploring challenges in developing and administering English competency assessment through the computer-based platform and the strategies to overcome them. The research used the descriptive method with direct observation and questionnaire for data collection. Three receptive skills (listening, structure, and reading comprehension) were measured on 337 applicants of the Ministerial Scholarship offered by the Ministry of Finance in 2021. A post-test questionnaire was administered to investigate test takers' perceptions. The findings showed that loading time and login difficulty were the most common issues. However, preformulated risk-mitigation strategies were found to be effective in resolving the issues. Overall, the questionnaire result showed a significantly positive perception of the computer-based English competency test.
Addressing Motion Blurs in Brain MRI Scans Using Conditional Adversarial Networks and Simulated Curvilinear Motions

In-scanner head motion often leads to degradation in MRI scans and is a major source of error in diagnosing brain abnormalities. Researchers have explored various approaches, including blind and nonblind deconvolutions, to correct the motion artifacts in MRI scans. Inspired by the recent success of deep learning models in medical image analysis, we investigate the efficacy of employing generative adversarial networks (GANs) to address motion blurs in brain MRI scans. We cast the problem as a blind deconvolution task where a neural network is trained to guess a blurring kernel that produced the observed corruption. Specifically, our study explores a new approach under the sparse coding paradigm where every ground truth corrupting kernel is assumed to be a “combination” of a relatively small universe of “basis” kernels. This assumption is based on the intuition that, on small distance scales, patients’ moves follow simple curves and that complex motions can be obtained by combining a number of simple ones. We show that, with a suitably dense basis, a neural network can effectively guess the degrading kernel and reverse some of the damage in the motion-affected real-world scans. To this end, we generated 10,000 continuous and curvilinear kernels in random positions and directions that are likely to uniformly populate the space of corrupting kernels in real-world scans. We further generated a large dataset of 225,000 pairs of sharp and blurred MR images to facilitate training effective deep learning models. Our experimental results demonstrate the viability of the proposed approach evaluated using synthetic and real-world MRI scans. Our study further suggests there is merit in exploring separate models for the sagittal, axial, and coronal planes.
Об отборе кандидатов в аналитики и их подготовке

The authors try to find answers to the following questions: how to select candidates for analytics, so as not to waste time on the incompetent ones; what to prepare them for and what to teach them. It is supposed to study in dynamics the psychological characteristics of future analysts and analytical activity in general. The article suggests the professionally structured training program consisting of three stages: acquaintance with the theoretical foundations (theory of cognition, general theory of systems, management theory, synergetics, and game theory), study of the specifics of analytical activity and practical development of the necessary skills.
A REAL-TIME IMPROVED PEDESTRIAN DEAD RECKONING TRAJECTORY TRACKING ALGORITHM

Abstract. Accurate location of pedestrians plays a crucial role in emergency relief, traffic control, crowd behavior analysis and other aspects. Especially in the context of the COVID-19 pandemic in recent years, pedestrian location technology can help relevant departments to complete target screening more quickly.However, the Pedestrian Dead Reckoning algorithm can only calculate the target trajectory through the sensor return value, but can not carry out real-time trajectory correction and location.With the rapid development of deep learning, object detection and tracking technology based on computer vision has been applied to pedestrian location, but there are two challenges in the application process.Firstly, in the pedestrian gathering scene, the target number base is large, so the accuracy of the current detection algorithm needs to be improved and the model drift index of the tracking algorithm needs to be reduced. Secondly, there is a certain distortion between the real three-dimensional coordinate space of pedestrians and the two-dimensional image captured by the camera, and the transformation of the spatial coordinate of the target point is a technical difficulty.In this regard, first of all, to improve the accuracy of pedestrian target detection in crowded scenes, this paper adopts the method of improving the generalization of network to pedestrian target, and uses k-means algorithm to find the best prior frame of pedestrian, and sets the width to height ratio suitable for the target.Secondly, to solve the problem of model drift in the above tracking process, this paper proposes a binary classification model based on target appearance difference, which introduces target context information as a new target distinguishing feature when two or more targets are similar.Finally, in order to obtain more accurate coordinate position information, this paper combines the inverse perspective algorithm to calculate the target coordinates into the coordinates in the world coordinate system, and calculates the exact position of the target in the aerial view, as well as the distance between the targets or the current flow of people.In order to evaluate the effectiveness of the proposed algorithm, experiments on target detection, tracking and precise positioning were carried out in different intensity scenarios to verify the feasibility of the proposed method.
The Effects of Social Media Tools Towards Online Retail Businesses in the Consumer Electronics Industry

Businesses nowadays have been adapting to technological advancements since the 21st century to promote their products. The majo]rity of the global population has been exposed to the digital world, and retail businesses have engaged themselves in the online environment. Social media tools have become a significant factor in advertising products and have taken big steps to create innovations in their platforms. Using social media has given online businesses options on managing their performance towards their brand and how to interact with consumers. These digital enhancements toward flourishing online retail businesses have boosted the consumer electronics industry. The study's objective was to provide extensive knowledge of how the different social media platforms and tools significantly affected the performance of online retail businesses belonging to the consumer electronics industry. A quantitative research approach was utilized in this study. The researchers conducted online survey questionnaires and were distributed to 185 respondents. Lastly, the study aims to benefit retail businesses, consumers, and future researchers, who engage in online businesses and how they can stand out from prospective competitors.
FABRICATION OF PHOTOTHERAPY TOOLS WITH SENSORS TEMPERATURE AND TIMER BASED MICROCONTROLLER ARDUINO

Phototherapy is a technology used for therapy in infants who suffer from hyperbilirubinemia or jaundice, this disease is due to the accumulation of bilirubin in the blood which is marked by the skin or the whites of the eyes turning yellow, babies who suffer from this disease are usually called yellow babies or icterus. This Phototherapy tool works by giving a blue light to the baby's skin directly for a certain period of time (depending on the level of bilirubin in the baby). The baby will be exposed to blue light which has a wavelength in the range of 450-490 nm for a certain period of time until the level of bilirubin in the baby's body drops to a safe level. With the advancement of technology, we want to add a little innovation by applying the use of a microcontroller to the phototherapy tool, in this case we add the time and temperature sensor DS18B20 as an indicator that will be displayed on the LCD screen so that it can make it easier for the user to turn off the tool and automatically and can notify the temperature of the baby who is getting sick. is doing this blue light therapy. In this case the time can be set according to user needs. From this research, it was concluded that phototherapy tools can be used to treat bilirubin babies. Advantages of this tool that users can monitor the baby's temperature and turn off the device automatically because we added a temperature sensor and timer to the phototherapy tools.
Image Encryption Algorithm of Chaos System Adding Cosine Excitation Function

In order to increase the chaotic performance of the chaotic system, the chaotic system A-S proposed by Sprott is improved by adding a cosine excitation function to a controller. A series of new chaotic systems are obtained, and the chaotic performance of the improved system is verified. The image is encrypted by the chaotic sequence generated by the improved A chaotic system. In the scrambling part of the image encryption algorithm, the zigzag transformation is improved, and different directions are selected to start the traversal, so that the scrambling process is not easy to be restored. The diffusion part draws on the traditional IDEA algorithm to perform diffusion operations on the image. Finally, the encryption algorithm is analyzed and tested, and the results show that the algorithm has fast encryption and decryption speed, sufficient key space, and can resist statistical analysis attacks well. The algorithm can provide better guarantee for the security of images.