Reviewerly/exHarmony huggingface dataset:
Optimization-based pseudo random key generation for fast encryption scheme

Data encryption is being widely employed to secure data in order to provide confidentiality, authenticity and data privacy. In proposed method a novel method for (encryption, decryption) message through optimization using genetic algorithm (GA) is presented which aims to maintain the security of the message via increasing the complexity of the key used in the encryption. The proposed method is named stream cipher randomization using GA (SCRGA). It is characterized by its secrecy by hiding the statistical properties for the input message. The GA increases the key diffusion in order to eliminate the likelihood of using the statistical analysis and cryptanalysis techniques to obtain the original text. A key (k) will be supplied as input to pseudorandom bit generator and then it produces a random 8-bit output, The SCRGA evaluates the candidate keys and it would select the one that achieves the best value of the fitness function by maximization three criteria considered in matching the input message and the key. The test results were based on the comparison of the proposed method with two other state-of-the-art methods, that the SCRGA outperformed the other two methods in terms of the execution time (encryption, decryption) and encryption rounds key size.
A systematic review of non-functional requirements mapping into architectural styles

Fortunately, the software attracted enough businesses to the market, allowing them to earn money in less time with less work and more accurate results. Software development life cycle (SDLC) is used for software development as it is responsible for system functionality, efficiency, maintainability, and any other non-functional system requirements. Each stage of the SDLC process is critical. However, software requirements and software architecture are both fundamental activities that play a vital role in all other SDLC stages. Non-functional requirements are critical to the success of any software because they explain all system quality attributes such as complexity, reliability, security, and maintainability, among others. The architectural styles assist you in determining which architecture may be best for your project requirements. This paper discusses several of the most important architectural styles that are best suited for mapping desired non-functional requirements for software development, as well as their comparison based on various quality attributes (non-functional requirements).
Optimization and application of web crawler architecture

For monitoring of cutting-edge technologies by obtaining the massive data on the internet, the pyspider framework is used to regularly crawl the information of a large number of websites, and the crawled data is regularly imported into the business application system through python scripts. At the initial stage of the project, the distributed architecture is used as officially recommended by pyspider. Later, it is optimized as the clustered architecture in order to adapt to the actual network environment and the characteristics of crawler tasks. After testing, the work efficiency and stability of the improved architecture have been greatly improved and the expected results have been achieved.
THE EFFECT OF HEALTH PROMOTION USING EMO DEMO VIDEO ON THE SELECTION OF FOOD CONTAINS IRON FOR ANEMIA PREVENTION IN ADOLESCENTS: STUDY AT ISLAMIC SENIOR HIGH SCHOOL IN YOGYAKARTA

Background. Emotional Demonstration (Emo Demo) is a technique to educate the public through the latest approach from the theory of Behavior Center Design as a reference. This strategy can be an alternative to help young women in choosing food ingredients to prevent anemia. Given the adverse effects of anemia, it is necessary to overcome them. Objective. This study aims to assess the effect of health promotion through Emo Demo video media on selecting foods containing iron to prevent anemia in adolescent girls. Method. The research design was a pre-post test with a control group design and quasi-experimental design. The research design used in this research is "Control Group Design". In this study, there were two groups, namely the intervention group using the Emo Demo video method and the control group using e-leaflets. Results. Emo Demo video promotion has a positive effect on knowledge (p value 0.000), attitudes (p value 0.000), and behavior (p value 0.000) in adolescent girls to prevent anemia compared to e-leaflets. Conclusion. Emo Demo video as a learning media is more effective in increasing knowledge, attitudes, and behavior in choosing foods high in iron in adolescent girls than in the group using e-leaflets. It is important to provide continuous Emo Demo videos and do the monitoring of adolescent girls to prevent anemia.
ROLE OF KNOWLEDGE MANAGEMENT IN WORKER’S PRODUCTIVITY AND ORGANIZATIONAL PERFORMANCE: EMPIRICAL STUDY OF SOUTH PUNJAB, PAKISTAN

This study exploited effect of knowledge management in knowledge worker productivity and empirically measured via cross-sectional study and data was collected from banks in south-Punjab Pakistan. There is lack of literature that deliberate interplay of knowledge management-enablers, knowledge worker’s productivity, knowledge management process as well as the organizational performance. Thus, the findings as obtained through the statistical procedures provide significant information that helps in reaching the desired conclusion. The finding reveals positive significant effect of the knowledge management (KM) enablers on the knowledge management (KM) practices and process and subsequently, knowledge management practices and process has positive impact upon workers’ productivity and organizational performance. Likewise, the knowledge workers productivity also significantly enhancing the firms’ performance.
Utilization of deep learning architectures for MIMO detection

Applications of deep learning in communications systems are becoming popular today with their powerful solutions to complex problems. This study considers the utilization of deep learning detectors for small-scale multiple-input multiple-output systems. Deep neural network, long short-term memory, and one-dimenisonal convolutional neural network architectures are discussed and the bit error rate performances of these deep learning based detectors are compared with the optimal maximum likelihood and sub-optimal minimum mean square error detectors. Simulation results show that the deep neural network architecture has the best detection performance among the discussed deep learning detectors and may outperform the sub-optimal minimum mean square error detector. For small-scale multiple-input multiple-output systems, the performance of the deep learning based detector is close to that of the optimal detector.
Efficient Elliptic Curve Arithmetic for Lightweight Cryptographic Schemes for IoT Applications

The Internet of Things’ (IoT) market is expected to grow exponentially at the global level in the coming years, due to the proliferation of more reliable and faster networks resulting from the extensive rollout of 5 to 10 G mobile networks. By 2025, it is expected that worldwide projection of IoT connected devices will be pegged at 30.9 billion units. Despite the potential benefits of the new technology, security in IoT is a major threat. According to HP, 70% of IoT devices are vulnerable to sniffing attacks and reliable solution is yet to be found. The standard cryptographic algorithms such as RSA and AES provide good security but their utilization in IoT is questionably due to hardware and energy constraints for computationally expensive encryption schemes. However, elliptic curve- based cryptography, a recent paradigm in public key cryptography, achieves the same level of security with smaller key sizes. On the other hand, the total score of performance of an elliptic curve-based cryptosystem depends largely on the efficiency of the arithmetic operations performed in it. It is against this background that this paper proposes efficient elliptic curve arithmetic for implementing ECC based schemes suitable for IoT systems implementations. Elliptic curve point arithmetic implementations in projective coordinate systems over binary extension fields introduce higher efficiencies in software. In this regard, this paper has proposed an improved López-Dahab point arithmetic methods on non-supersingular elliptic curves over . The results show 69.20% improvement in Point Doubling, 44.68% in Point Addition and the scalar point multiplication execution time is decreased by 48.80%.
Pengembangan Media Pembelajaran Interaktif Berbasis Mind Mapping pada Era Pembelajaran Jarak Jauh

The limited use of learning media in the era of distance learning makes schools need to optimize each material with alternative developments to evaluate self-evaluation, formula calculation schemes, and curve evaluation forms. The emergence of the offline version of desktop economic mind mapping media development in the era of distance learning has the following objectives: 1) to prioritize mind mapping-based interactive learning media; 2) to analyze the structure of practicality; 3) to analyze how significant the changes in student learning outcomes were before and before being given the media that had been developed; 4) to analyze the analysis related to assessing the responses, reactions, and perceived impacts after the use of developer media. The testing phase is limited to 20 students in class X IPS 1 and X IPS 2 at SMA Muhammadiyah 3 Surabaya. Researchers in collecting data using documentation methods, interviews, review sheets and expert validation, and questionnaires. The adoption of 4D stages (define, design, develop, dessiminate) is the method used to develop the product in this study with an application output in .EXE format (file extension) that is suitable for use on a Windows computer system. The results of the study refer to the development of the media which is stated to be very feasible with the percentage of material testing 93%, media testing 87%, evaluation testing 84%. The media level is stated to be very practical with a percentage of 90%. The effectiveness of the media shows an increase in understanding that is significantly more dominant than the results of measurements with other media. 88% positive responses were given by students to this development media. So that mind mapping-based interactive learning media can support students' understanding and be applied in the era of distance learning.
Bellman's, Knut's, Lupanov's, Pippenger's problems and their variations as generalizations of the addition chain problem

We consider various generalizations of the effective exponentiation problem, also known as the addition chains problem in additive terminology. We attempt to evaluate and consistently compare the results and the methods that were used to obtain them. The considered generalizations are complexity problems (i.e. finding the minimum number of required operations) for the computation a monomial of many variables, a set of powers, elements of finite Abelian groups, elements of free semigroups, systems of monomials, systems of integer linear forms, systems of elements of free Abelian groups along with some other problems. For all these directions of research we overview both the classical results and the results of the author and his followers.
Presenting the pattern of personality components and psychological factors affecting teachers' teaching during the outbreak of Covid-19

Presenting the pattern of personality components and psychological factors affecting teachers' teaching during the outbreak of Covid-19
Remedies to the Irreversibility of Smart Contracts in Colombian Private Law

Abstract The application of blockchain technology to contractual activity has led to a decrease in transaction costs, but also an enormous potential cost of judicial solutions that order reversal of the effects of smart contracts. This research addresses solutions, both from the perspective of updating the unforeseen theory and from clauses that are introduced to the legal smart contracts to allow reducing the costs associated with judicial decisions that declare an anomaly in the contracts.
VALIDITAS DAN RELIABILITAS TERHADAP INSTRUMEN KEPUASAN KER

Research instruments with appropriate and consistent measuring instruments in research can be carried out with validity and reliability tests. Validity test can be done using Corrected Item to Total Correlation method and reliability test using Cronbach's Alpha method. The test uses a two-sided test with a significance level of 0.05 or 5%.Data retrieval using Google Forms with a trial of 50 respondents in the form of ordinal data with SPSS tools analyzed by variability test using Corrected Item to Total Correlation method and reliability test using Cronbach's Alpha method.As many as 4 instrument numbers out of 50 instrument numbers were declared invalid, namely numbers 7, 9, 12 and number 14, while the other instrument item numbers were declared valid. In the reliability test using Cronbach's Alpha method with Cronbach's Alpha value of 0.987 0.60 then all the instrument questions are declared reliable.
CLIP THINKING AS A TOOL OF SUBSTITUTED REALITY

The phenomenon of clip thinking in the youth environment is considered. Its main features are indicated. The influence of the existing education system on the formation of clip thinking is shown. The impact of social networks, messengers, audio and video hosting on the formation of consciousness is analyzed in detail. The relationship between clip thinking and the substitution of reality is shown, the negative aspects of such a relationship are explained
Protectability of IBMQ Qubits by Dynamical Decoupling Technique

We study the current effectiveness of the dynamical decoupling technique on a publicly accessible IBM quantum computer (IBMQ). This technique, also known as bang-bang decoupling or dynamical symmetrization, consists of applying sequences of pulses for protecting a qubit from decoherence by symmetrizing the qubit–environment interactions. Works in the field have studied sequences with different symmetries and carried out tests on IBMQ devices typically considering single-qubit states. We show that the simplest universal sequences can be interesting for preserving two-qubit states on the IBMQ device. For this, we considered a collection of single-qubit and two-qubit states. The results indicate that a simple dynamical decoupling approach using available IBMQ pulses is not enough for protecting a general single-qubit state without further care. Nevertheless, the technique is beneficial for the Bell states. This encouraged us to study logical qubit encodings such as |0⟩L≡|01⟩,|1⟩L≡|10⟩, where a quantum state has the form |ψab⟩=a|0⟩L+b|1⟩L. Thus, we explored the effectiveness of dynamical decoupling with a large set of two-qubit |ψab⟩ states, where a and b are real amplitudes. With this, we also determined that the |ψab⟩ states most benefiting from this dynamical decoupling approach and slowed down the decay of their survival probability.
A Nighttime and Daytime Single-Image Dehazing Method

In this study, the requirements for image dehazing methods have been put forward, such as a wider range of scenarios in which the methods can be used, faster processing speeds and higher image quality. Recent dehazing methods can only unilaterally process daytime or nighttime hazy images. However, we propose an effective single-image technique, dubbed MF Dehazer, in order to solve the problems associated with nighttime and daytime dehazing. This technique was developed following an in-depth analysis of the properties of nighttime hazy images. We also propose a mixed-filter method in order to estimate ambient illumination. It is possible to obtain the color and light direction when estimating ambient illumination. Usually, after dehazing, nighttime images will cause light source diffusion problems. Thus, we propose a method to compensate for the high-light area transmission in order to improve the transmission of the light source areas. Then, through regularization, the images obtain better contrast. The experimental results show that MF Dehazer outperforms the recent dehazing methods. Additionally, it can obtain images with higher contrast and clarity while retaining the original color of the image.
Knowledge management implications to the financial performance

Knowledge and effective knowledge management are critical sources of sustainable competitive advantage. Literature reveals that knowledge management positively impacts the overall company performance. For this reason, many companies strive to improve their knowledge management capabilities to enhance their organizational performance. Many studies have confirmed the direct positive influence that knowledge management resources and capabilities can have on corporate financial performance. This paper’s research focuses on evaluating the impact of knowledge management capability on the financial performance of a company sample from Bosnia and Herzegovina in sectors with high levels of knowledge. Research findings revealed a positive relationship between knowledge management capabilities and financial performance, specifically between knowledge creation and application and the financial performance of companies.
Multi-Scale Residual Aggregation Feature Pyramid Network for Object Detection

The effective use of multi-scale features remains an open problem for object detection tasks. Recently, proposed object detectors have usually used Feature Pyramid Networks (FPN) to fuse multi-scale features. Since Feature Pyramid Networks use a relatively simple feature map fusion approach, it can lead to the loss or misalignment of semantic information in the fusion process. Several works have demonstrated that using a bottom-up structure in a Feature Pyramid Network can shorten the information path between lower layers and the topmost feature, allowing an adequate exchange of semantic information from different layers. We further enhance the bottom-up path by proposing a multi-scale residual aggregation Feature Pyramid Network (MSRA-FPN), which uses a unidirectional cross-layer residual module to aggregate features from multiple layers bottom-up in a triangular structure to the topmost layer. In addition, we introduce a Residual Squeeze and Excitation Module to mitigate the aliasing effects that occur when features from different layers are aggregated. MSRA-FPN enhances the semantic information of the high-level feature maps, mitigates the information decay during feature fusion, and enhances the detection capability of the model for large objects. It is experimentally demonstrated that our proposed MSRA-FPN improves the performance of the three baseline models by 0.5–1.9% on the PASCAL VOC dataset and is also quite competitive with other state-of-the-art FPN methods. On the MS COCO dataset, our proposed method can also improve the performance of the baseline model by 0.8% and the baseline model’s performance for large object detection by 1.8%. To further validate the effectiveness of MSRA-FPN for large object detection, we constructed the Thangka Figure Dataset and conducted comparative experiments. It is experimentally demonstrated that our proposed method improves the performance of the baseline model by 2.9–4.7% on this dataset and can reach up to 71.2%.
SOCIAL MEDIA AND FILTER BUBBLES

The aim of this paper is to analyze the media bubbles and media footprint in social medias. The authors reflect the ways numerous online hubs are personalizing the Internet for their users, so people have media bubbles as an effect when the algorithms of social networks and search engines personally select what posts and links to show. The information bubble is related to the peculiarities of the cognitive activity. People choose information that supports their point of view and isolate what will confirm their opinion from the whole array of facts and information, a “Confirmation Bias”. Turning students’ attention to their own filter bubbles on social media, a space where they may have already developed an array of literacies, the authors warn the students that media bubbles can be dangerous. There are tasks to analyze the media fields of students and also there are ways to filter out information flows, preserve and develop critical thinking. The authors give examples of tasks for bachelor level students in the discipline “Info-Media Literacy and Critical Thinking”.
A word embedding trained on South African news data

This article presents results from a study that developed and tested a word embedding trained on a dataset of South African news articles. A word embedding is an algorithm-generated word representation that can be used to analyse the corpus of words that the embedding is trained on. The embedding on which this article is based was generated using the Word2Vec algorithm, which was trained on a dataset of 1.3 million African news articles published between January 2018 and March 2021, containing a vocabulary of approximately 124,000 unique words. The efficacy of this Word2Vec South African news embedding was then tested, and compared to the efficacy provided by the globally used GloVe algorithm. The testing of the local Word2Vec embedding showed that it performed well, with similar efficacy to that provided by GloVe. The South African news word embedding generated by this study is freely available for public use.
PREGNANT WOMEN EMPOWERMENT IN MONITORING FETAL WELLBEING THROUGH FETAL MOVEMENT COUNT MOBILE APPLICATION

Fetal movement is one of the main indicators of fetal well-being in the womb. For this reason, every pregnant woman should be able to calculate and interpret the results of fetal movement calculations every day, so that early detection of fetal pathology can be identified by the family. Most pregnant women are not used to it and have not received information about how to calculate fetal movement. Another obstacle in calculating fetal movements is the forgetting factor. For this reason, a tool is needed to familiarize mothers with counting fetal movements every day. Existing tools still have some drawbacks. This study aims to develop an android-based application called "Kick Count", as a tool for pregnant women to count fetal movements every day. The research method uses R&amp;D which consists of: exploring potential problems, gathering information, product design, design testing, design improvement, and product testing. The results of product trials by media experts, material experts, and groups of pregnant women state that this product is very suitable for use by pregnant women in the community. Things that still need to be perfected for the next stage are reducing memory and integrating installation instructions into the application.
Small-signal Modeling of InP HBT based on PSO-ELM Neural Network

Extreme Learning Machine (ELM) is a new learning algorithm for single-hidden layer feedforward neural network, which has been widely used in lots of fields. However, it still has the insufficiency of randomly determining the hidden layer threshold and output weight, which leads to ill-conditioned output. In order to avoid the risk of decreasing prediction accuracy caused by this possibility, the ELM is optimized using particle swarm algorithm. A Particle Swarm Optimization (PSO) enhanced ELM algorithm is proposed to accurately model the small-signal properties of InP Heterojunction Bipolar Transistors (HBTs). PSOELM algorithm solves the problem of unstable prediction data caused by random determination of input weights in ELM. Comparing the modeling effects of the PSO-ELM model and the ELM model under different bias conditions for a 1 μm×15 μm InP HBT, it is proved that the PSO-ELM algorithm has better consistency with the measured data.