Reviewerly/exHarmony huggingface dataset:
FPGA implementations of Fruit-v2, Fruit-80, and Grain-128AEAD stream ciphers

The use of devices with limited resources, such as wireless sensor network (WSN), radio-frequency identification (RFID), industrial Internet of Things (IIoT), has increased. Xilinx field-programmable gate array (FPGA) solutions provide real-time processing and flexibility. For all these, the main challenging problem is confidentially with limited resources. The eSTREAM portfolio was started to discover efficient lightweight stream ciphers. Later on, various stream ciphers have been proposed depending upon the requirement of applications. The minimum area, higher frequency, and higher throughput-area ratio are the main requirements. This can be achieved by implementing ciphers in three ways: the basic version, the serial version, and the parallel version. Using the basic version, one can achieve a higher operating frequency. The lesser area can be accomplished in the serial version. In the parallel version, the maximum throughput-area ratio is obtained. This paper discusses the design and implementation of the basic version, serial version, and parallel version of Fruit-v2, Fruit-80, and Grain-128AEAD. The main objective is to achieve the requirement based on the application.
Bearing Fault Diagnosis based on Fixed Threshold Wavelet Transform and ELM

In order to improve the efficiency and accuracy of bearing fault diagnosis, fixed threshold wavelet transform and extreme learning machine (ELM) are used to diagnose the fault data set. Firstly, the original signal underwent wavelet noise reduction by fixed threshold and heuristic threshold method, comparing SNR and mean square error, the processed signal was extracted, select cliff, margin factor, waveform factor, pulse factor, variance, mean, maximum and minimum 8 features, and the values were input into ELM for training and testing, and adjust the number of ELM neurons to check the fault identification accuracy, and compared with other diagnostic methods. The simulation results show that the proposed method provides a new idea for solving the bearing fault diagnosis problems.
Mechanical Engineering Students at Dalian Jiaotong University Should Keep Learning Hand-drawing Courses

With the development of electronic information, digital drawing like Solidworks has become an increasingly large part of the mechanical field.Also, followed by the development of technology, mechanical engineering students need to engage in more courses.This has led to a discussion about the need for mechanical engineering students to continue learn hand-drawing courses.This paper takes mechanical engineering students at Dalian Jiaotong University as the subject of the study.This paper argues that mechanical engineering students at Dalian Jiaotong University should continue to take handdrawing courses by introducing that digital drafting is just a tool, and using this tool requires spatial imagination, which can be developed by hand-drawing courses.This paper also verify the conclusion by experimental data, and show that there will be serious consequences if mechanical engineering students do not take hand-drawing courses.To sum up, mechanical engineering students at
A Survey on Various Representation Learning of Hypergraph for Unsupervised Feature Selection

Since multimedia technology is increasingly being developed, vast quantities of unlabeled data must be processed with a high dimension. Unsupervised feature selection considered to minimize the dimensionality of many machine learning and data mining activities is generally accepted as an important and challenging preliminary step. Feature selection is a big problem in machine learning and in recent years has been very important. Representation learning has become a field in itself in the community of machine learning, and both academia and industry are followed and feeding a substantial string of empirical successes by an exponential growth in research activity in the field of representation learning. Latent representation learning may help with multiple tasks in machine learning and data mining and also recently has become ever more drawn to network data particularly. A vertex set and hyperedge set are constructed of Hypergraph. In a hypergraph, each hyperedge can connect several vertices and easily extend the hyperedge, which contributes to a flexible hypergraph edge degree. In hypergraph learning, hypergraph construction is the first step for data modeling. In this survey paper, we have described various technologies like Data mining, Machine learning, and their types and also we deeply described feature selection methods and their types; classification methods explained the various learning methods such as hypergraph learning and Latent representation learning. Various optimization techniques are also discussed.
ANALISIS PENGEMBANGAN E-OFFICE DALAM PELAKSANAAN E-GOVERNMENT PADA DINAS KOMUNIKASI, STATISTIK DAN PERSANDIAN KOTA BEKASI

E-government is the use of information technology that can help, improve relations between the government and other stakeholders, both to the community, business circles and fellow governments. This study aims to analyze the development and implementation of e-office and what factors hinder the implementation of e-office in the Bekasi City Communication, Information, Statistics and Encryption Service. This study uses a qualitative research method with a case study design because the research focuses on the development of e-office in the implementation of e-government at the Office of Communication, Information, Statistics and Encryption Bekasi City. Data collection techniques through observation, interviews and documentation. Informants in this study included the head of the e-government development section, the head of the e-government governance section, and e-government staff. The data obtained from the results of the study were reviewed using data reduction techniques, data presentation, and drawing conclusions. The results of this study indicate that the development of e-office is running well. This can be identified through the elements of successful e-government development which include support, capacity, value. From the support element, Diskominfostandi supports the development of e-office with the instructions issued by the Mayor of Bekasi Number: 555.10/1132/Diskominfostandi.Egov regarding the Implementation of Electronic Service Manuscripts Through E-office Applications in Bekasi City. From the capacity element, the information technology infrastructure owned by Diskominfostandi is sufficient, where Diskominfostandi currently has its own server to facilitate e-office users in the Bekasi City SKPD. From the value element, it is quite easy for users to write correspondence and archive letters. The inhibiting factor for the implementation of e-office at the Bekasi City Communication, Information, Statistics and Encryption Service is that human resources are still limited because they are still looking for a replacement programmer and when the head of the service wants to do an online signature published by the Electronic Certification Center (BSrE) it is constrained by the process inadequate network and also because there are too many users.
The Misuse of Emojis in Social Media: The Case of Kurdish Facebook Users

Writing in online platforms does not have the animate qualities of face-to-face interactions.In a real context, people use body language, gestures, and facial expressions.These show different thoughts and emotions of the speaker to the listener.In the digital medium, lack of the above-mentioned qualities has resulted in creating an anonymous, vague and cold socialization.The invention of kaomoji, emoticon, and emoji was a unique step toward solving the issues in online writing.It was a step to make digital writing animate and alive.As there are different gestures, body language movements, and facial expressions, the same is true about having different emojis.There are thousands of emojis, and a lot of Kurdish Facebook users do not know where, when and how to use them.The misuse of emojis create misunderstandings on the level of language and socialization, between the addresser and the addressee.This study tried to understand the factors behind misusing emojis among Kurdish Facebook users.In this regard, there are a selected number of emojis used by Kurdish users to express their modes of feelings and thoughts in all circumstances.In most cases, the selected emoji does not fit the topic of the sender or vice versa.To tackle the current issue under consideration, ten visual documents are taken from the posts of Kurdish Facebook users in the year of 2021 in Erbil and Sulaimani, Iraq.These posts express different social situations.The researchers used Microsoft Excel to analyze the collected data statistically.To interpret the misuse of emojis, they used C. S. Pierce's theory of signs.The results of the current study demonstrate that most users fail to select a right emoji for the situation under consideration.Mostly, their use of emojis are not accurate and relevant.Of course, this misuse creates problems for both sides, the addresser and the addressee.The researchers recommend that Kurdish users should know the reasons behind their emoji selection, otherwise they should not use them.The same scenario is true for educational settings.Finally, the researchers present their recommendations for the educational institutions in order to avoid these issues of miscommunication.
Application and development of classifiers in breast cancer

Breast cancer is an aggressive form of cancer that kills many women. Although breast cancer has a high incidence, it death rate is not as high as incidence. This is thanks to the staging approach to breast cancer. Early detection of breast cancer can help doctors and patients better treat the disease. In the last century, the classification of breast cancer was left to the discretion of experienced pathologists. With the progress of technology, machine classification is replacing manual classification. The classifier will save society the time and cost of training pathologists. In the future, it may be possible for a highly accurate classifier algorithm to achieve even higher accuracy than manual classification. This paper summarizes the research direction and progress of breast cancer classifiers up to now and provides the latest research results for medical practitioners and researchers in the discussion section.
Combining Focus Cues and Depth Cues for Light Field Salient Object Detection

Light field cameras record the spatial information and angle information of a scene at the same time. The array of micro-lens added in front of the sensor makes it possible to capture sub-aperture images as well as refocus images. Thus, focus cues and depth cues are available simultaneously by only one shoot. However, previous deep learning based light field salient object detection (SOD) methods only extract features from one of them (focus cues or depth cues). In this paper, we propose a new deep convolutional network to realize SOD by using both focal stacks and depth maps. We construct the FSNet based on 3D octave convolution blocks to extract and retain the continuously changing focus cues in the focal stack, and the DepthNet to extract depth cues in the depth map. Then, the focal stack features and depth features are combined for the further SOD task. Experiments are conducted on three benchmark light field SOD datasets, and the results show that our method is effective.
Automatic Evaluation of French Research Projects in the Acquisition Process of Research Tax Credit (CIR)

In this work, we evaluated research projects of French companies using Natural Language Processing. To this end, we designed a system able to estimate the probability of obtaining a research tax credit (CIR) for a project based on its technical description. This system is designed around two modules whose outputs are concatenated and fed to a fully-connected neural network that predicts the probability of success for the project. The first module uses the FastText algorithm and a Convolutional Neural Network to extract a Text Embedding vector. The second module uses an unsupervised knowledge graph extraction method and a Graph Neural Network to extract a Graph Embedding vector. The texts used as data in this study describe the research projects of companies and are written in French. Due to their high confidentiality, no similar examples exist in the literature. This data is provided by a partner consulting firm whose work consists in helping companies raise funds for their research projects. Since the methods in the literature were not effective in extracting the knowledge graphs used in the second module for our data, we present a new Knowledge Graph extraction using an unsupervised Named Entities Recognition, NER, as our contribution.
Rail Track Monitoring System Using Quantum Key Distribution in IoT Scenario

Quantum key distribution (QKD) is a secure communication method implementing a cryptography protocol involving the theory of quantum mechanics. It allows two entities to generate a shared random secret key known only to them. This secret key can then be used to encrypt and decrypt messages on the respective ends of the communication. Many researches have been conducted on different aspects of the same and researchers came up with the celebrated BB84 protocol. The protocol presents a way of exchanging the secret keys using the polarizations of photons. This paper introduces an application of QKD for a railway track monitoring system backed up with IoT sensors. An approach toward simulation of quantum key distribution between an IoT device and a server to encrypt the data sent to the server is discussed in the paper. It also demonstrates the simplicity of this method and its efficiency in producing a QKD simulation. Also, it is demonstrated that for the IoT security, the quantum key length thus generated is used.
When the best reviews are not placed between extremes

Several research studies have demonstrated the strong influence that online reviews exert on consumers' purchasing decisions. Specifically, those with extreme opinions, both favorable and unfavorable, are often considered more useful. This paper is focused on enhancing the detection of extreme reviews through sentiment analysis. For this purpose, a real scenario is taken into account, using the examples of all classes and dealing with the imbalance between them, which is characteristic in online reviews. The main objective is to carry out the classification with a high certainty and incurring as few errors as possible in relation to the examples belonging to the rest of the classes. Therefore, the emphasis is on the quality of the predictions rather than on the quantity. Using XLNet, we show how the transfer of knowledge extracted from the source domain (i.e., the extreme reviews) improves their detection regarding the overall number of errors made in the target domain (i.e., multi-class classification).
MTKDSR: Multi-Teacher Knowledge Distillation for Super Resolution Image Reconstruction

In recent years, the performance of single image super-resolution (SISR) methods based on deep neural networks has significantly improved. However, large model sizes and high computational costs are common problems for most SR networks. Meanwhile, a trade-off exists between higher reconstruction fidelity and improved perceptual quality in solving the SISR problem. In this paper, we propose a multi-teacher knowledge distillation approach for SR (MTKDSR) tasks that can train a balanced, lightweight, and efficient student network using different types of teacher models that are proficient in terms of reconstruction fidelity or perceptual quality. In addition, to generate more realistic and learnable textures, we propose an edge-guided SR network, EdgeSRN, as a perceptual teacher used in the MTKDSR framework. In our experiments, EdgeSRN was superior to the models based on adversarial learning in terms of the ability of effective knowledge transfer. Extensive experiments show that the student trained by MTKDSR exhibit superior performance compared to those of state-of-the-art lightweight SR networks in terms of perceptual quality with a smaller model size and fewer computations. Our code is available at https://github.com/lizhangray/MTKDSR.
Requirements Elicitation Challenges and Strategies for GSD Based Projects

The geographically distributed software development has attained a significant attraction in software development industry since last few decades due to its wide acceptance across the globe. The main essence of geographically distributed software development is to expand company working by follow-the-sun strategy, get skilled labors at cheaper rates from low economy regions of world and pool-up company resources to raise their utilization graph. The requirements elicitation process is considered as one of the most challenging tasks during software development process in traditional as well as geographically distributed software development projects due to its social and collaborative nature. This article presents a comprehensive insight about major challenges faced by requirements elicitation teams engaged in geographically distributed software development projects. The article also elaborates the possible strategies, which can be adopted to minimize the negative impact of geological distribution of software development activities. The presented study will help the requirements elicitation teams to better plane their requirements elicitation sessions for geological distributed software development projects by encountering the expected hurdles.
Structural social capital articles in business administration literature on the Web of Science database: co-word analysis

This paper aims to investigate the focal and bridging themes of structural social capital articles in business administration literature on the Web of Science database from 2015 to April 1, 2020, through co-word analysis. Since structural social capital has received little attention to date, the term is also searched by its components in this study. The author did not find any articles focusing on this topic and beyond the scope of this study. The results generally state that social networks and innovation are the focal and bridging themes. This study shows the gaps by giving the readers signals for unfocused themes. It also shows that what one can see in the literature focusing on the articles' title, another method in the co-word analysis can be different from reality by focusing on author keywords and searching the literature on the components of structural social capital.
Enabling Negotiating Agents to Explore Very Large Outcome Spaces

This work presents BIDS (Bidding using Diversified Search), an algorithm that can be used by negotiating agents to search very large outcome spaces. BIDS provides a balance between being rapid, accurate, diverse, and scalable search, allowing agents to search spaces with as many as $$10^{250}$$ possible outcomes on very run-of-the-mill hardware. We show that our algorithm can be used to respond to the three most common search queries employed by 87% of all agents from the Automated Negotiating Agents Competition. Furthermore, we validate one of our techniques by integrating it into negotiation platform GeniusWeb, to enable existing state-of-the-art agents (and future agents) to scale their use to very large outcome spaces.
Principal Component Analysis-Improved Fuzzy Genetic Algorithm

Since traditional intelligent algorithms composed of air quality prediction are commonly used, but such algorithms still have shortcomings for the validity of data, especially the problem of time-series prediction data. In order to investigate the problem of traditional intelligent algorithms for effective time-series data, this paper proposes a principal component analysis -improved fuzzy genetic algorithm (PCA-IFGA), in order to get more effective data in predicting air quality. the PCA-IFGA algorithm just divide into two modules. The first is PCA (Principal Component Analysis, PCA) which solves the problem of"dimensionality reduction" of air quality data by analyzing the largest individual differences revealed by taking the principal components and discovering the characteristics that affect the data in air quality prediction. The IFGA (Improved Fuzzy Genetic Algorithm) improves the traditional FGA (Fuzzy Genetic Algorithm) by increasing the variation rate of the algorithm to enhance the population diversity and facilitate the algorithm to jump out of the local optimum, while preserving the superior population diversity, increasing the crossover rate and reducing the variation rate of the algorithm to accelerate the convergence of the algorithm, thus the convergence efficiency and correct rate of the algorithm are improved. The experimental results show that PCA-IFGA is significantly better than BP algorithm, LSTM and SVM algorithms in terms of stability and full correctness.
Online Learning System of Learning Mathematics at SMAN 1 Kampar Kiri Hilir: Learning Difficulties Analysis Study

This study aims to determine the factors behind the literacy difficulties of undergraduate mathematics in online literacy at SMAN 1 Kampar Kiri Hilir. The subjects in this study were class XI wisdom students at SMAN 1 Kampar Kiri Hilir for the academic year 2021/2022 and class XI wisdom students with a population of 68 ulama consisting of 15 male and 53 female scholars. This form of exploration is descriptive qualitative. The data collection method in this study was carried out using a check system, videlicet, by distributing online questionnaires through Google Forms which were transferred via WhatsApp Group. Based on the study results, it can be concluded that online literacy is carried out. Overall, there are 50 who find it difficult in the online tutoring and literacy process. 48.53 students find it difficult in the network aspect of sharing in the online tutoring and literacy process, 41.18 students have difficulty in the high order when sharing in the online learning process. In tutoring and online literacy, 45,59 students have difficulty understanding the material guided by school teachers, 92,65 students have difficulty installing communication either in the form of laptops or androids, 80,88 students have difficulty communicating with teachers
Similarity based text extractive summarization using relevance measure and semantic analysis

The search and extraction of knowledge is based on fairly basic principles of identifying individual words, analyzing their morphology, statistical patterns and the joint occurrence of linguistic units in a scientific text. One of the most significant and promising areas of semantic analysis is the use of abstracting technologies to automate the process of extracting scientific frames. The implementation of the ontology of frames with accompanying frames with additional information in the form of associative and cause-and-effect relationships is proposed. An algorithm for extractive summarization is developed based on the deterministic selection of relevant information from the segments of the object semantic network. The result of the algorithm is a frame extracted from scientific texts with relation to the reference multiset of keywords. A frame analysis is performed of how the system of scientific knowledge is organized in the reports of the scientific and technical program, in what systemic relations are its concepts.