Reviewerly/exHarmony huggingface dataset:
Engineering archive management model based on big data analysis and deep learning model

Abstract In the background of the era of big data, the information management system of engineering archives has become more comprehensive and perfect because of the application of information technology. The application of deep learning model makes the management of engineering archives more systematic, scientific and standardized, which greatly improves the quality and efficiency of engineering archives management. The progress of society and the development of the times have put forward higher requirements for digital storage technology. This paper combines the characteristics of the new technological era, analyses the characteristics of traditional information management in the context of data processing, artificial intelligence, deep learning and other data, proposes a method for developing and managing web archives based on Bootstrapping technology, introduces an information meta-evaluation mechanism to improve the quality of mining, and uses a long and short-term memory model to extract multi-type fine-grained archival information elements in the corpus. Finally, the Alex network was established to manage the archives in a categorised manner. The experimental results show that the query results of the proposed method for the target archives are 100% accurate, and the query time for individual archives is basically within 5s, which has good management effect.
Local Bit-Plane Domain 3D Oriented Arbitrary and Circular Shaped Scanning Patterns for Bio-Medical Image Retrieval

A new feature descriptor called local bit-plane domain 3D oriented arbitrary and circular shaped scanning pattern (LB-3D-OACSP) is proposed for biomedical image retrieval in this study. Unlike the circular, zigzag and other scanning structures, the LB-3D-OACSP descriptor calculates the association between reference-pixel and its surrounding pixels in bit-plane domain in a 3D plane using multi-directional 3D arbitrary and 3D circular shaped scanning patterns. In contrast to other scanning structures, the multi-directional 3-D arbitrary shaped patterns provide more continual angular dissimilarity among the sampling positions with the aim to capture more frequent changes in the local textures. The total of sixteen number of discriminative 3D arbitrary and 3D circular shaped patterns oriented in various directions are applied on a 3D plane constructed using respective bit-planes of three multi-scale images which ensures the maximum extraction of inter-scale geometrical information across the scales which very effectively captures not only the uniform but non-uniform textures too. The multi-scale images are generated by processing the input image with Gaussian filter banks generating three multi-scale images. The LB-3D-OACSP descriptor is able to capture most of the very fine to coarse image textures through encoding of bit-planes. The performance of LB-3D-OACSP is tested on three popular biomedical image databases both in terms of % average retrieval precision (ARP) and % average retrieval recall (ARR). The experiments demonstrate an encouraging enhancement in terms of %ARP and %ARR as compared to many existing state of the art descriptors.
Refining Large Integrated Identity Graphs Using the Unique Name Assumption

The Unique Name Assumption (UNA) supposes that two terms with distinct identifiers from the same knowledge base do not refer to the same real-world entity. The UNA can be used to detect errors in large integrated knowledge bases. For example, some identity link can be erroneous if they are in a path that connects two entities (that refer to different real-world objects) defined in the same knowledge base. For large knowledge bases, however, the UNA does not always hold due to redundant IRIs that capture various encodings, languages, namespaces, versions, letter cases, etc. The UNA can still be useful for identifying erroneous links provided good adaption to the exceptions. For this, we propose a concrete definition of the UNA with tolerance towards multiple exceptions, namely the internal UNA (iUNA). To compare the iUNA and other variants of the UNA, we propose a generic algorithm that can be used for refinement. The algorithm employs an SMT (Satisfiability Modulo Theory) solver and takes advantage of the latter’s ability to efficiently reason over equality. For evaluation, we identify erroneous links in an identity graph of half a billion triples extracted from the LOD Cloud, and compare our approach against community detection methods (Louvain and Leiden) as well as other identity refinement approaches.
THE IMPORTANCE OF EMPLOYEE ENGAGEMENTIN EFFECTIVE MANAGEMENT: AN EMPIRICAL STUDY

The major objective of this present study is to let us understand, what is meant by employee engagement and why it is, crucial towards effective management.During the present research work, various factors have been discussed with respect to its effect on employee retention and their performance.It additionally helps in retaining a better level of dedication.Employee engagement can also be referred, as the level of worker's commitment and their participation towards their organisation as well as values.The organisational success totally depends on employee's productivity which can only be accelerated through, employee engagement practices.It was also found that employees are being praised to keep themselves most influential towards the organisations common goal.Employee engagement is rapidly, gaining popularity in different workplaces all around the world in many ways.This paper also aims to highlight, that employee engagement is a burning topic and also support towards maintaining a higher level of commitment.Through, such practices over the past few decades', employee engagement in an organisation have been trying to improve work culture by building better work as well as customer relationships.The present study has found the satisfactory level and a positive attitude among employees and they are actively engaged towards company.
Reading Instruction And Interventions In Multigrade Schools In Quezon

This study aimed to undermine the realities of reading instruction in three multigrade schools in Quezon province during the COVID-19 pandemic. They analyzed data obtained from Phil-IRI in the past three years and focused on how teachers implemented reading instruction in multigrade settings. The researchers investigated the interventions they employed. Results revealed that parental involvement played a critical role in reading instruction. Teachers espoused varied reading strategies and focused on material selection, integration, and contextualization. Formative strategies, home visitations, and interventions that involved parents were implemented. Keywords: multigrade, reading, reading instruction, strategies, reading skills, last mile school
BOOST HARDSKILL WITH CTF COMPETITIONS

The paper considers the principles underlying the CTF competitions, describes the main types of tasks. The authors reviewed the skills and knowledge that students who take part in the CTF receive and improve. It is shown that independent passage of CTF sites and information security competitions gives students not only an understanding of the current pressing problems of information security, but also gives them the opportunity to self-determination in the field of information security and information protection.
RSA Cryptosystem Speed Security Enhancement (Hybrid and Parallel Domain Approach)

Encryption involves every aspect of working with and learning about codes.Over the last 40 years, it has grown in prominence to become a prominent scholarly discipline.Because most interactions now take place online, people require secure means of transmitting sensitive information.Several modern cryptosystems rely on public keys as a crucial component of their architecture.The major purpose of this research is to improve the speed and security of the RSA algorithm.By employing Linear Congruential Generator (LCG) random standards for randomly generating a list of large primes; and by employing other selected algorithms, such as the Chinese Remainder Theorem (CRT) in decryption, exponent selection conditions, the Fast Exponentiation Algorithm for encryption, and finally, a comparison of the enhanced RSA versus the normal RSA algorithm that shows an improvement will be provided.
Crowdsourcing Segmentation of Histopathological Images Using Annotations Provided by Medical Students

Segmentation of histopathological images is an essential task for cancer diagnosis and prognosis in computational pathology. Unfortunately, Machine Learning (ML) techniques need large labeled datasets to train accurate segmentation algorithms that generalize well. A possible solution to alleviate this burden is crowdsourcing, which distributes the effort of labeling among a group of (non-expert) individuals. The bias and noise from less experienced annotators may hamper the performance of machine learning techniques. So far, crowdsourcing approaches in ML leveraging these noisy labels achieve promising results in classification. However, crowdsourcing segmentation is still a challenge in histopathological images. This paper presents a novel crowdsourcing approach to the segmentation of Triple Negative Breast Cancer images. Our method is based on the UNet architecture incorporating a pre-trained ResNet-34 as a backbone. The noisy behavior of the annotators is modeled with a coupled network. Our methodology is validated on a real-world dataset annotated by medical students, where five classes are distinguished. The results show that our method with crowd labels achieves a high level of accuracy in segmentation (DICE: 0.7578), outperforming the well-known STAPLE (DICE: 0.7039) and close to the segmentation model using expert labels (DICE: 0.7723). In conclusion, the initial results of our work suggest that crowdsourcing is a feasible approach to segmentation in histopathological images https://github.com/wizmik12/CRowd_Seg .
Survey on Sentiment Analysis Hate Speech Detection on Twitter using NLP

Abstract: Hate speech detection has substantially increased interest among researchers in the domain of natural language processing (NLP) and text mining. The number of studies on this topic has been growing dramatically. Thus, the purpose of this analysis is to develop a resource that consists of an outline of the approaches, methods, and techniques employed to address the issue of Twitter hate speech. This study can beused to aid researchers in the development of a more effective model for future studies. This review focused on studies published over the past eight years, i.e., from 2015 to 2022. This systematic search was carriedout in December 2020 and updated in July 2022. Ninety-one articles published within the mentioned period met the set criteria and were selected for this review. From the evaluation of these works, it is clear that a perfect solution has yet to be found. To conclude, this paper focused on presenting an in-depth understanding of current perspectives and highlighted research opportunities to boost the quality of hate speech detection systems. In turn, this helps social networking services that seek to detect hate messages generated by users before they are posted, thus reducing the risk of targeted harassment.
An Enhanced Security in Medical Image Encryption Based on Multi-level Chaotic DNA Diffusion

A novel medical image encryption technique has been proposed based on the features of DNA encodingdecoding in combination with Logistic map approach. The approach is proven for encryption of highly sensitive medical images with 100 percent integrity or negligible data loss. Testing is done on both high and low-resolution images. Proposed encryption technique consists of two levels of diffusion using the actual structure of the DNA. In the first level of diffusion process, we have used DNA encoding and decoding operations to generate DNA sequence of each pixel. The originality of the work is to use a long DNA structure stored in a text file stored on both sender and receiver’s end to improve the performance of the proposed method. In this initial level of diffusion, DNA sequences are generated for each pixe-land in each of the DNA sequence. Index values are obtained by employing a search operation on the DNA structure. This index values are further modified and ready to be used for next diffusion process. In the second level diffusion, a highly chaotic logistic map is iterated to generate sequences and is employed to extract the chaotic values to form the cipher images. The correlation coefficient analysis, Histogram analysis, Entropy analysis, NPCR, and UACI exhibit significant results. Therefore; the proposed technique can play an important role in the security of low-resolution medical images as well as other visible highly sensitive images.
Development of an Exe-Learning-based Mathematics E-module on Sequences and Series

This study aims to produce Exe-Learning-based mathematics e-modules on sequences and series material for grade XI Vocational High School (SMK) that are valid, effective, and practical. This research is development research (RD) with the ADDIE development model. The research subjects were math teachers and class XI students at SMK Putra Jaya Centre. The research instruments were expert assessment sheets, posttest questions, and teacher and student response assessment sheets. Based on the results, the e-module is categorized as very valid with an average score for content eligibility was 3.41, presentation feasibility was 3.60 and language was 3.50. Furthermore, the assessment of media experts obtained an average score on the aspect of graphic feasibility of 3.61 and media feasibility of 3.50. Testing the effectiveness of the e-module based on the results of the students' posttest obtained classical completeness results of 91% in the very effective category, while the teacher and student response questionnaire for the practicality of the e-module was 90% and 88% in the practical category. Thus, the Exe-Learning-based mathematics e-module on sequences and series material for grade XI Vocational High School students that has been developed is declared valid, effective, and practical.
Efficient Multi-Goal Reinforcement Learning via Value Consistency Prioritization

Goal-conditioned reinforcement learning (RL) with sparse rewards remains a challenging problem in deep RL. Hindsight Experience Replay (HER) has been demonstrated to be an effective solution, where HER replaces desired goals in failed experiences with practically achieved states. Existing approaches mainly focus on either exploration or exploitation to improve the performance of HER. From a joint perspective, exploiting specific past experiences can also implicitly drive exploration. Therefore, we concentrate on prioritizing both original and relabeled samples for efficient goal-conditioned RL. To achieve this, we propose a novel value consistency prioritization (VCP) method, where the priority of samples is determined by the consistency of ensemble Q-values. This distinguishes the VCP method with most existing prioritization approaches which prioritizes samples based on the uncertainty of ensemble Q-values. Through extensive experiments, we demonstrate that VCP achieves significantly higher sample efficiency than existing algorithms on a range of challenging goal-conditioned manipulation tasks. We also visualize how VCP prioritizes good experiences to enhance policy learning.
An Explainable AI Approach for Breast Cancer Metastasis Prediction Based on Clinicopathological Data

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> Breast Cancer is the most prevalent cancer and the first cause of cancer deaths among women worldwide. In 90% of the cases, mortality is related to distant metastasis. Computer-aided prognosis systems using machine learning models have been widely used to predict breast cancer metastasis. Despite that, these systems still face several challenges. First, the models are generally biased toward the majority class due to datasets unbalance. Second, their increased complexity is associated with decreased interpretability which causes clinicians to distrust their prognosis. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> To tackle these issues, we have proposed an explainable approach for predicting breast cancer metastasis using clinicopathological data. Our approach is based on cost-sensitive CatBoost classifier and utilises LIME explainer to provide patient-level explanations. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> We used a public dataset of 716 breast cancer patients to assess our approach. The results demonstrate the superiority of cost-sensitive CatBoost in precision (76.5%), recall (79.5%), and f1-score (77%) over classical and boosting models. The LIME explainer was used to quantify the impact of patient and treatment characteristics on breast cancer metastasis, revealing that they have different impacts ranging from high impact like the non-use of adjuvant chemotherapy, and moderate impact including carcinoma with medullary features histological type, to low impact like oral contraception use. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/IkramMaouche/CS-CatBoost</uri> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> Our approach serves as a first step toward introducing more efficient and explainable computer-aided prognosis systems for breast cancer metastasis prediction. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Significance:</i> This approach could help clinicians understand the factors behind metastasis and assist them in proposing more patient-specific therapeutic decisions.
Perbandingan algoritma kriptografi simon dan vigenere dalam mengamankan citra digital

Digital image files or images are sometimes a valuable asset. Digital images that are private and confidential are very vulnerable to interception by other parties, especially if the image is distributed via the internet. To increase the security of digital images so that their confidentiality can be maintained, a special technique is needed to protect digital image messages, namely with cryptographic techniques. This study aims to determine the performance of the SIMON algorithm for digital image security. SIMON algorithm performance results are compared with the vigenere cipher algorithm in terms of time and image file size produced. In this study used base64 encode for the encryption process and base64 decode for the decryption process. The performance of the SIMON algorithm in securing digital images results in an average encryption time of 969 ms and an average decryption time of 1537 ms. The SIMON algorithm requires a longer time for the encryption and decryption process when compared to the Vigenere algorithm. The cipher image encrypted by the SIMON algorithm has a size larger than the original file by 36%. However, when compared to the cipher image encrypted by the Vigenere algorithm, there is no significant difference. The UACI value obtained from the SIMON algorithm cipher image obtained an average yield of 18.94%. Based on the theory of differential analysis, it can be said that this value is still vulnerable to differential attack. This is based on the UACI value which has not met the minimum threshold value of 33%.
Remuneration: Performance-Based Payment System in Higher Education Institutions

This study aims to solve payroll problems at Universitas Binamika Tangerang (UBT) by identifying factors that are considered to play a role in the preparation and determining the stages of the payroll system on the remuneration basis. Data collection and processing were carried out by means of focused group discussions using fishbone diagrams to obtain data related to the old payroll system and the new payroll system with the remuneration model. Discussions involving fifteen personnel as informants, documentation studies and observations were also carried out to complete data acquisition. The results of the study identified eight elements that are key to the successful preparation and implementation of a remuneration payroll system, namely type of employment, staffing level, payroll information system, layer determination, payroll element, performance indicator, system models, and implementation stage.
A Study on the Evaluation Model of In-depth Learning for Oral English Learning in Online Education

The trend of globalization in the world is becoming increasingly frequent, and people from different regions are communicating more closely.Therefore, the demand for a second language is constantly expanding, accelerating the development of the field of English oral evaluation and also accelerating the development of online education.The study proposes a text priori based oral evaluation model, which is based on the Transformer model and uses target phonemes as input to the Decoder.The model successfully predicts the relationship between actual pronunciation and error labels.At the same time, a self-supervised oral evaluation model with accent is constructed, which simulates the training process of misreading data by calculating semantic distance.The experimental results show that when the training set ratio reaches its maximum in the Speed Ocean dataset and the L2 Arctic dataset, the F1 values of the proposed method are 0.612 and 0.596, respectively; the length of the target phoneme has a smaller impact on this model compared to other models.Experiments have shown that the proposed deep learning method can alleviate deployment difficulties, directly optimize the effectiveness of oral evaluation, provide more accurate feedback, and also provide users with a better learning experience.This has practical significance for the development of the field of oral evaluation.
Query Context Expansion for Open-Domain Question Answering

Humans are accustomed to autonomously associating prior knowledge with the text in a query when answering questions. However, for machines lacking cognition and common sense, a query is merely a combination of some words. Although we can enrich the semantic information of the given query through language representation or query expansion (QE) , the information contained in the query is still insufficient. In this paper, we propose an effective passage retrieval method named query context expansion-based retrieval (QCER) for open-domain question answering (OpenQA) . QCER associates a query with domain information by adding contextual association information based on the pseudo-relevance feedback (PRF) . QCER uses a dense reader to select top-n expansion terms for QE. We implement QCER by appending reader predictions, theoretically present in candidate passages, as contextual information to the initial query to form the new query. QCER with sparse representations (BM25) can improve retrieval efficiency and accelerate query convergence so that the reader can find the desired answer using fewer relevant passages, e.g., 10 passages, as soon as possible. Moreover, QCER can be easily combined with dense passage retrieval (DPR) to achieve even better performance, as sparse and dense representations are often complementary. Remarkably, we demonstrate that QCER achieves state-of-the-art performance in three tasks, passage retrieval, passage reading, and passage reranking, on the Natural Questions (NQ) and TriviaQA (Trivia) datasets under an extractive QA setup.