Reviewerly/exHarmony huggingface dataset:
Enabling Transformers to Understand Low-Level Programs

Unlike prior approaches to machine learning, Transformer models can first be trained on a large corpus of unlabeled data with a generic objective and then on a smaller task-specific dataset. This versatility has led to both larger models and datasets. Consequently, Transformers have led to breakthroughs in the field of natural language processing. Generic program optimization presently operates on low-level programs such as LLVM. Unlike the high-level languages (e.g. C, Python, Java), which have seen initial success in machine-learning analyses, lower-level languages tend to be more verbose and repetitive to precisely specify program behavior, provide more details about microarchitecture, and derive properties necessary for optimization, all of which makes it difficult for machine learning. In this work, we apply transfer learning to low-level (LLVM) programs and study how low-level programs can be made more amenable to Transformer models through various techniques, including preprocessing, infix/prefix operators, and information deduplication. We evaluate the effectiveness of these techniques through a series of ablation studies on the task of translating C to both unoptimized (-O0) and optimized (-01) LLVM IR. On the AnghaBench dataset, our model achieves a 49.57% verbatim match and BLEU score of 87.68 against Clang -O0 and 38.73% verbatim match and BLEU score of 77.03 against Clang -O1.
Implement Android Application for Determination and Monitoring Blood Chemistry

Examination of blood chemistry, especially blood glucose, uric acid and cholesterol in the body is a test carried out in routine health checks or in the process of diagnosing a disease. The research aims to produce an android-based application that can determine and monitor the results of blood chemistry examinations that are user-friendly for the community. The research method is literature study, design flowchart, design user interface (UI), implement application, and testing application. The first stage is a literature study to collect data and information about blood chemistry followed by design application regarding the flowchart design and interface of the application. The next stage was implement application regarding the application implementation process based on the design application using android studio and testing application using black box testing, white box testing and user acceptance testing (UAT). Overall the application has been successfully designed and built according to the research method. The results of testing application show that the application runs well and there are no errors. This indicates that the application has been successful and can be used by the public as a supporting application in routine blood chemistry examinations.
Time-based Performance Improvement for Early Detection of Conflict Potentials at the Central Java Regional Police Department

Early detection of conflict potentials around the community is vital for the Central Java Regional Police Department, especially in the Analyst section of the Directorate of Security Intelligence. Performance in carrying out early detection will affect the peace and security of the community. The performance of potential conflict detection activities can be improved using an integrated early detection information system by shortening the time after observation, report preparation, information processing, and analysis. Developed using Unified Process as a software life cycle, the obtained result shows the time-based performance variables of the officers are significantly improved, including observation time, report production, data finding, and document formatting.
Measuring Maintainability of Web Applications Using an Extensible MVC Architecture

The recurrent usage of web-based applications has an important part in our daily life. MVC (Model View Controller) architecture is used as an alternative architectural style to encode the user interface. It divides the parts of a user interface into three components with clear roles. This makes applications easy to test and evolve. The maintainability of web applications plays a crucial role in satisfying end-users. In this paper, we try to enhance the maintainability of web applications using a new MVC architecture. Our architecture is an extension of this architectural style. To improve maintainability, several metrics have been used such as complexity and coupling. This paper presents a methodical refinement and mapping of the maintainability to a set of metrics for good design. Our research is evaluated through an empirical study that shows the difference in maintainability between a web application that practices the traditional MVC architecture and the same one that uses our new architecture.
Improved Dark Channel Prior for Image Defogging

An image defogging algorithm with improved transmittance is proposed for the problem of residual fog in the defogged image obtained by the dark channel a prior algorithm. In this paper, the atmospheric light value in the scene is estimated using the quadrature method, and the minimum map in the dark channel algorithm is further optimized using the super pixel segmentation algorithm with median filtering and combined at the pixel level. The obtained transmittance map is then filtered with a guide to eliminate texture effects. Finally, the defogged image is transferred to the HSI (Hue-Saturation-Intensity) color space for image enhancement. The results of the experiment showed that compared with the classical dark channel algorithm, the algorithm in this paper has obvious defogging effect, complete image information retention, and lower algorithm complexity.
Improved Dark Channel Prior Dehazing Algorithm Combined with Sky Recognition

A dark channel a prior defogging improvement algorithm combined with sky recognition is proposed to address the problems of failure of dark channel a prior for sky regions, artifacts and detail loss. Firstly, a quadtree search algorithm is used to find the atmospheric light values; then, an adaptive weighted guide filter is used to improve the transmittance map to enhance the edge details; the sky region is segmented by setting the brightness threshold and sky features, and the lower limit of transmittance is corrected according to the results of sky region identification; finally, the solution of the recovered clear image is carried out by substituting the atmospheric scattering model. The experiments show that the improved algorithm can improve the problems of image detail loss, halo, appearing artifacts, etc. compared with other defogging algorithms, and effectively solve the phenomenon of failure to sky regions. Compared with the classical dark channel a prior algorithm, the algorithm in this paper improves 37.02% in PSNR and 47.56% in SSIM.
An Improved Method for Brightness Preserving Dynamic Histogram Equalization

Histogram equalization is an effective image enhancement method for improving image contrast, and several equalization methods have been developed. Brightness-preserving dynamic histogram equalization (BPDHE) is a sub-histogram-based equalization method, and there is scope for optimising the performance of BPDHE in the segmentation process of some image histograms and in maintaining image structure and information entropy. Therefore, this paper presents a method to improve BPDHE. First, the image is subjected to bilinear interpolation. Then, the probability density function of the grey level of the image is divided into two parts according to its mean value. Finally, different methods are used to find local maximums for each of these two parts. In this paper, Absolute Mean Brightness Error (AMBE), Structure Similarity Index Measure (SSIM), Information Entropy (Entropy) and Peak Signal to Noise Ratio (PSNR) are used to compare with the different methods. The results show that the method proposed in this paper effectively enhances the contrast of the image while preserving the image brightness. In the average of evaluation metrics of the sample images, SSIM, Entropy and PSNR were 0.219, 2.0811 and 6.6201 higher than those of BPDHE, respectively.
An Image Denoising Algorithm Based on Improved Wavelet Threshold Function

In the field of image denoising research, the technique of wavelet threshold denoising has been widely used. Aiming at the shortcomings of traditional hard threshold and soft threshold denoising, an improved threshold function is proposed for image denoising in this paper. Two tuning parameters are added to this threshold function to improve the flexibility of the function. In the evaluation of denoising performance, this paper uses the peak signal to noise ratio (PSNR) and mean square error (MSE) as evaluation indicators. Experimental results on Boats images show that algorithm proposed in this paper improves the PSNR by 0.1 dB and 0.12 dB and reduces the MSE by 2.35% and 2.81%, respectively, compared with the algorithms in reference [6] and reference [7]. The experimental results on other images also show that the algorithm proposed in this paper also has some improvement in evaluation indexes compared with several comparative algorithms.
RFID-based Sensing and Reconstruction of Human Movements for Safety Monitoring of Workers

Wireless sensing and reconstruction of human movements is a crucial task also for monitoring of workers’ safety. For such a purpose, in this work, a UHF RFID tag integrated with an Inertial Measurement Unit (IMU) sensor has been designed so to exploit the low-power-consumption of RFID technology, and then realized and tested. It leverages on a specific RFID chip, the Impinj Monza X, provided with a wired memory access in addition to the classical wireless one. The working range is guaranteed by boosting the chip sensitivity through an on-board battery, also used to supply an ultra-low power microcontroller and the IMU. To test the device in a realistic environment, such as occupational setting, some prototypes have been simultaneously applied on harms and torso of a subject and the transmitted data used as input of a virtual human-body biomechanical model. The capability of the system of correctly reconstruct the human body movements validates the proposed approach.
Learning to Compute with Sound in Nonlinear Disordered Cavities

Machine learning (ML) is now widely used for a variety of important tasks. To improve ML performance, several computational architectures have been investigated whose physical implementations promise compactness, physical robustness, and low energy cost. Here, we experimentally demonstrate an approach that uses a nonlinear disordered cavity as a complex reverberant acoustic environment for the physical realization and enhancement of the computational power of a type of ML known as reservoir computing (RC). We realize the required fading memory of RC systems by leveraging the internal memory of sound-propagation and the intrinsically large feedback of disordered structures to increase the expressive power and reduce the total size of proposed RC. In addition, the use of reconfigurable nonlinear electroacoustic resonators on the cavity's top side allows us to enhance the reservoir size and, as a result, the performance of the acoustical reservoir computer. We show the efficiency of our approach on a challenging benchmark problem: forecasting chaotic time series.
Deepfake Literacy of the Journalists and Initiatives of Media Organization in Bangladesh

Deepfake has become a recent concern in the field of journalism.Though earlier deepfake was mainly used in porn industry nowadays the unscrupulous people used it to provide false and fabricated information.Artificial Intelligence Algorithm is being used to make deepfake video.Journalism is becoming challenging in Bangladesh.Government has made several laws and some sections of those laws are playing a vigorous role against free fair journalism.The aim of this study is to find out the deepfake knowledge of the journalists in Bangladesh.The study interviews 100 journalists of 50 media outlets including television, print and online media.Among the journalists 80 per cent did not know about deepfake and those who heard about it earlier among them only 9 percent got knowledge from their respective organization.A good number of journalists did not get proper training on deepfake.This study concluded by proposing few recommendations to teach the journalists about deepfake.
Face Recognition for Examinee Verification

In the modern world, facial recognition is playing a vital role in the field of biometric technologies. The reason being simple, it’s a very efficient and developed method compared to the other methods. Its being so precise, errorless and effective gives it an edge over other technologies. There are lot of fields where this fast growing technology is yet to show its effectiveness, one of which is examinations, the identification of the students during examinations. Different kinds of biometric technologies are used in the examination sector in order to identify the students appearing for the exams. Biometric technologies use physical features to identify the person appearing in the exam but many of these traditional methods create room for errors and cheating which can be improved by execution of facial technology.In this research, the approach of Eigenface and fisherface has been used. These techniques are recent and have apparently promising performances, and are representing new trends in this field. Based on previous research that has been done by other researchers about the Eigen face and Fisher face algorithms, where facial image recognition uses Eigenface with different conditions of differentiation from expression, with success rates up to 75%. Ada-boost face recognition, Eigen face PCA and MySQL produce 80% of various different conditions. Face recognition with 93% success with Fisher face by 73 face trials of different expressions and different positions. From previous research, the author wanted to know which algorithm has speed in terms of time, distance and accuracy of photosensitivity in the facial recognition process. By tests of the two algorithms, it produces success percentages and accuracy charts. The Fisherface algorithm is faster than the Eigenface algorithm has an accuracy of 96% and the Fisherface algorithm has an accuracy of 97%.
Information Security Issues With E-Governance

The rise of e-government has been quite possibly the most striking advancements of the web. As the Internet upheld advanced networks develop, and accepting that they do to be sure develop to join people around the nation (and globally), they present the public governments with various difficulties and openings. In an e-government project, a generous measure of documentation is done like upkeep of land records, police records, etc. Every office is basic so that solitary approved individuals get into the organization and access the data. A comprehension of the data security innovation and the requirement for its usage is key for more secure, made sure about and smooth working of e-administration undertaking.
Analysis of Hybrid Securing Digital Payment System through Risk Perception

This study article discusses a novel method of electronic payment wherein a retailer is made incapable of obtaining a customer's payment details which therefore accomplishes a secure transaction. Customers' payment information, often a credit or debit card detail, faces a number of security issues when transmitted to a retailer via the Internet. Credit card data theft, credit card fraud, and data manipulation are all potential threats. A company has the option of either using or storing client information. If a retailer does not take enough precautions to protect its server or system against data thieves, spammers, spyware, malware, and hackers, then their customers' information might be stolen and exploited. The main parts included in this research work are transaction through token issuance and Pedersen commitment approach to provide better efficacy of successfully securing digital payment system. The usual data security requirements are followed by payment gateways, and the most secure techniques and technologies are used when communicating with banks and credit card firms.
The Effects of Changes in Daily Life and Activity Participation of Youth on Their Career Readiness since COVID-19

The purpose of this study is to investigate the influence of COVID-19-induced changes in daily life and activity participation for youth on their career readiness. To this end, a survey was conducted with 1,056 youths living in Busan, and the results were obtained using the hierarchical regression analysis method. For the analysis, changes in daily life and activity participation were used as independent variables. Changes in daily life consisted of changes in stress and life satisfaction, and youth’s participation in activities was comprised of participation and satisfaction. Accordingly, the relative influence of changes in their stress, life satisfaction, activity participation, and activity satisfaction since COVID-19 on their career readiness was investigated. As a result, it was found that youth activity satisfaction, activity participation, and changes in life satisfaction and stress had a positive effect on their career readiness. In particular, activity satisfaction showed the greatest influence. Career readiness can provide youth with powerful support to actively respond to various changes they are facing in the rapidly changing society since COVID-19 and to achieve their individual career goals. This study confirmed that career readiness is reinforced by youth activity participation satisfaction.
The Relationship between the college student" Learning Presence, School Happiness and Classroom Engagement in dance sports class with mixed course in the COVID-19 Pandemic

The purpose of this study is to explore the relationship between College Students" learning presence, classroom engagement and school happiness of college students who participating in sports dance class during COVID-19. The sampling method used the convenience sampling method to collect the college students in B wide area city in the first semester of 2021 and participating in dance sports as the object, Conducted two weeks of network and offline through Google questionnaire and paper questionnaire from June 7. A total of 171 questionnaires were collected. Among them, in addition to 14 parts of repeated answers and dishonest answers, it is finally analyzed through 157 parts of the questionnaire. SPSS 26.0 program was used for data analysis. The validity and reliability of frequency analysis, exploratory factor analysis, Cronbach"s αcoefficient calculation and survey tools were verified by correlation analysis, and simple regression analysis and multiple regression analysis were carried out for hypothesis test. The results of these procedures are as follows: first, classroom engagement has a normal impact on school happiness. Second, in the sense of learning presence, only cognitive presence and social presence have a normal impact on classroom engagement, while emotional presence has no significant impact on classroom engagement. Third, in the sense of learning presence, only cognitive presence and emotional presence have a normal impact on classroom engagement, while social presence has no significant impact on classroom engagement. Fourth, in the relationship between learning presence and school happiness, classroom Engagement can not play a media role. In the relationship between learning presence and school happiness, classroom engagement plays a media role. In the relationship between learning presence and school happiness, social engagement plays a media role. Fifth, in the relationship between learning presence and school happiness, classroom engagement plays a part of the media role.
Similarity Analysis for Medical Images Using Color and Texture Histogramss.

Medical databases usually contain a significant volume of images, therefore search engines based on low-level features frequently used to retrieve similar images are necessary for a fast operation. Color, texture, and shape are the most common features used to characterize an image, however extracting the proper features for image retrievals in a similar manner with the human cognition remains a constant challenge. These algorithms work by sorting the images based on a similarity index that defines how different two or more images are, and histograms are one of the most employed methods for image comparison. In this paper, we have extended the concept of image database to the set of frames acquired following wireless capsule endoscopy (from a unique patient). Then, we have used color and texture histograms to identify very similar images (considered duplicates) and removed one of them for each pair of two successive frames. The volume reduction represented an average of 20% from the initial data set, only by removing frames with very similar informational content.
Optimization of continuous variables quantum key distribution using discrete modulation

Continuous Variables Quantum Key Distribution (CV-QKD) tackles the problem of the generation and distribution of cryptographic keys without assuming any computational limitations while employing standard telecom equipment. Gaussian Modulation (GM) theoretically maximizes the information a CV-QKD system is capable of transmitting while exhibiting a higher resistance to excess channel noise. However, GM-CV-QKD protocols put an extreme burden on the transmitter’s random number source and tend to be more susceptible to imperfect state preparation. Due to these difficulties, most experimental implementations of CV-QKD have used Discrete Modulation (DM). The closer the DM constellation approaches a GM one, the closer the theoretical performance of the associated system will be to the optimum value. To achieve this, high-cardinality constellations, coupled with probabilistic shaping, can be explored. However, choosing a too complex constellation will cause the modulation stage imperfections to again become apparent. Thus, the choice of the constellation format is not direct and is of high importance. In this work we present a methodology to determine the optimum constellation for a given DM-CV-QKD system, taking into account the limitations of the modulation stage, choosing from a variety of M-QAM and M-APSK constellations coupled with probabilistic shaping. Our obtained methodology will allow for the optimum modulation format for each specific system to be selected.
Vehicular cloud computing: introduction to cloud-based vehicles

In the past few years, cloud computing (CC) has taken the attention of the scientific community and researchers. CC has been further extended to fog and edge computing (in terms of better efficiency). It can be used in vehicles to store data at a remote server and provide efficient users/passengers. In autonomous cars, cloud-based vehicles or vehicles based on cloud computing can react to users&apos; responses smartly and quickly. Vehicle CC (a hybrid technology of cloud and cars) can help society/governments in traffic management and road safety by instantly using vehicular resources, such as computing, storage, and the Internet, for decision-making. Hence, these services can be provided as an automated and intelligent vehicle to reduce the crowd over the road network. Also, the security and privacy of passengers/users will be protected against any cyber-attacks like a bot, worm, trojan, ransomware, etc. Hence, this chapter briefly details vehicular CC and its scope (including various interesting remarks).