Reviewerly/exHarmony huggingface dataset:
Computer Security and Privacy: Principles and Practice

Computer security refers to the protection of computer systems and networks from unauthorized access, theft, damage, or any other malicious activity. It involves various measures such as access controls, firewalls, antivirus software, encryption, and so on. The primary goal of computer security is to ensure the confidentiality, integrity, and availability of the data stored and processed by the computer systems. On the other hand, computer privacy refers to the protection of personal information that individuals share online or store on their computers. It involves the safeguarding of sensitive data such as credit card information, social security numbers, health records, and other personal information. Computer privacy measures may include data encryption, secure communication protocols, and access controls. In today’s world, where we rely heavily on technology and the internet for our personal and professional lives, computer security and privacy have become more critical than ever. With the rise of cyber-attacks and data breaches, individuals and organizations face serious risks of data theft, financial loss, and reputational damage. Therefore, it is essential to have effective computer security and privacy measures in place to protect our information and systems from cyber threats.
Sensitive information leakage analysis of database code by abstract interpretation

An information system stores outside data in the backend database to process them efficiently and protects sensitive data from illegitimate flow or unauthorised users. However, most information systems are made in such a way that the sensitive information stored in a database may be leaked explicitly or implicitly during data processing along with the control structure of the program to the output channels. Therefore, sensitive data leakage is one of the crucial security threat. In this paper, the main objective is to detect the illegitimate flow of confidential information in an information system. We propose a framework to detect sensitive information leakage through the data-flow paths of an information system. In particular, to compute the precise set of data-flow paths, we use the non-relational abstract property of the interval domain and the relational abstract property of the polyhedra domain that enables the framework to produce efficient security analysis results.
Field Application of Portable XRF with Glass Bead Certified Reference Material(CRM) in Nickel Laterite Deposit

Following QA/QC procedures, portable XRF (p-XRF) field tests were performed on borehole samples from nickel laterite deposits and the results were compared with lab-XRF results.To verify the reliability and accuracy of the results obtained by p-XRF in the field, certified reference materials were prepared as glass beads and analyzed together with samples.The results showed that the overall contents of each element were generally lower in wet samples than in pulp samples, and the trend in the element content by depth was related to the degree of weathering of the laterite.For the element of interest, Ni, correction was performed using Ni-Fe correlation and the calculated values were found to be similar to the lab-XRF results.The derived correction coefficient was applied to another borehole sample to confirm its applicability to other boreholes.The corrected values of the wet samples showed a decrease in the deviations from lab-XRF data, confirming the validity of the correction method.
Solving Dialogue Grounding Embodied Task in a Simulated Environment using Further Masked Language Modeling

Enhancing AI systems with efficient communication skills that align with human understanding is crucial for their effective assistance to human users. Proactive initiatives from the system side are needed to discern specific circumstances and interact aptly with users to solve these scenarios. In this research, we opt for a collective building assignment taken from the Minecraft dataset. Our proposed method employs language modeling to enhance task understanding through state-of-the-art (SOTA) methods using language models. These models focus on grounding multi-modal understandinging and task-oriented dialogue comprehension tasks. This focus aids in gaining insights into how well these models interpret and respond to a variety of inputs and tasks. Our experimental results provide compelling evidence of the superiority of our proposed method. This showcases a substantial improvement and points towards a promising direction for future research in this domain.
A Query Engine for Self-controlled Case Series, with an application to COVID-19 EHR data.

Self-controlled case series (SCCS) is a statistical method in epidemiological study design that uses individuals as their own controls, with comparisons made within the same individuals at different time points of observation. SCCS has been applied in settings where it is difficult to identify comparison or control groups. To provide computational support for SCCS, we introduce a query engine called Self-Controlled Case Query (SCCQ) and use it to extract cohorts of self-controlled case series from a large-scale COVID-19 Electronic Health Records (EHR) dataset. Visual summary of the queried population through the R-Shiny visualization framework offers SCCQ's query result dashboard to the researcher. SCCQ allows the export of query-generated raw data files with a portable format that researchers can extend to create more intricate and robust visualization capabilities without needing a high-level of technical or statistical background. Our validation and evaluation experiments uncovered COVID-19 outcomes to be consistent with existing research findings. With SCCQ, cohort exploration, data extraction, and information visualization can be provided for structured EHR data to lower the barrier for clinical and epidemiological research.
12 Preservice English language teachers and informal digital learning of English (IDLE) in Kazakhstan

Given that existing digital affordances offer contemporary language learners ample opportunities to be exposed to authentic language and culture learning experiences in online environments, this chapter adopts a critical approach for examining how undergraduate preservice English teachers negotiate and balance their language learner and language teacher identities. To do so, we adopt a qualitative research design informed by 32 semi-structured interviews with prospective English language teachers from Kazakhstani universities. Research findings allow us to address beliefs about the concept of informal digital learning of English (IDLE) and to provide possible clarifications for the results obtained. In addition, we discuss plausible explanations for the contradictions expressed by interviewees and propose further research pathways.
Anomaly Detection of Rolling Element Bearings Based on Contrastive Representation

Anomaly detection of rolling element bearing aims at detecting bearing defects based on its monitoring data of normal condition, to prevent unexpected shut-downs of rotating machines. Commonly adopted methods are based on hand-crafted features or data reconstruction, which are indirect to the anomaly detection task. In this work, a bearing anomaly detection method is proposed based on contrastive learning. To extract essential representations of normal condition, the time-domain vibration signal and its frequency spectra are compared and forced to provide common information. Hence the contrastively learned essential representation does not change regarding data transformation and is more faithful to the normal condition. And a straightforward distance-based detector is enough for the anomaly detection task. The proposed method is applied to benchmark bearing experimental data. Results show that the proposed method outperforms other commonly used anomaly detection methods.
An Overview of Blockchain Scalability for Storage

Blockchain mandates that every node store the whole chain’s history in order to address trust issues in the network. And the storage requirement becomes extremely high, severely affecting the chain’s scalability. To solve such a problem, many optimizations of storage have been proposed. In this paper, existing ways of blockchain storage scalability are described in two categories: off-chain and on-chain. The off-chain way is combined with various distributed and nondistributed storage systems. And on-chain is optimized by changing its block structure, storage rules, or technology. Blockchain technology with scalable storage has been applied in the medical industry. We assess and contrast the methods’ latency, security, and cost. And we point out the problems and challenges of the existing approaches and give an outlook on the future.
Ensuring security and data integrity in Multi Micro-Agent System Middleware with Blockchain Technology

Multi-agent systems offer the advantage of performing tasks in a distributed and decentralized manner, thereby increasing efficiency and effectiveness. However, building these systems also presents challenges in terms of communication, security, and data integrity. Blockchain technology has the potential to address these challenges and to revolutionize the way that data is stored and shared, by providing a tamper-evident log of events in event-driven distributed multi-agent systems. In this paper, we propose a blockchain-based approach for event-sourcing in such systems, which allows for the reliable and transparent recording of events and state changes. Our approach leverages the decentralized nature of blockchains to provide a tamper-resistant event log, enabling agents to verify the integrity of the data they rely on.
Bi-population Cooperative Moth-flame Optimization Algorithm for the Networking Mode Optimization

The moth-flame optimization (MFO) algorithm is extensively employed to attain the global optimization of the problem. The disadvantages of the original MFO algorithm include poor population variety, a sluggish rate of convergence, and an easy propensity to be drawn in by local optimum. This paper presents a bi-population cooperative moth-flame optimization algorithm (BCMFO) to address the issues. Utilizing the low discrepancy sequence (LDS), a random population with a uniform distribution is produced in the search space. Two subpopulations with similar sizes are updated using Gauss mutation and opposition learning. To improve the algorithm's ability to search globally, the elite method is used to eliminate the subpar solutions from the population. BCMFO is used to optimize the networking mode and is confirmed using the benchmark test suite in CEC 2017. Experimental results show that BCMFO outperforms state-of-the-art algorithms.
Approaching Cross-Disease Features for Improved Classification of Thyroid and Breast Cancer in Ultrasound Images

Thyroid and breast cancers are seen astwo diseases that mostly affect women worldwide. Ultrasonography is known to bean effective modality in interpreting these diseases. It is a safe, accessible,cost-effective, and non-invasive but highly operator-dependent technique.Ultrasound (US) images suffer from poor image quality. Thus, the need for anexperienced radiologist for an effective assessment of these images. Computer-Aided-Diagnosis (CAD) systems based on artificial intelligence can reliablyease the interpretation of US images and provide assistance to radiologists. Inthis article, we propose a previously validated Convolutional Neural Network(CNN) architecture for breast ultrasound to train and classify thyroid noduleswhile assessing the effectiveness of this approach by leveraging the shared USfeatures between the two diseases. We also evaluated five state-of-the-artpre-trained models on both breast and thyroid nodules using the sameclassifier. Our customized CNN achieved 94.84% and 85.35% accuracy whilemaintaining a low positive rate of 5.5% and 13.0% on both open-access datasetsfor breast and thyroid, respectively. Regarding Transfer Learning, InceptionV3and VGG16 are seen to outperform other models.
Impact of Network Resource Management On Quality of Industrial Processes

Mean Opinion Score (MOS) and Estimated Mean Opinion Score (EMOS) are originally defined for the evaluation of voice and video codecs. In this paper, we introduce the concept of the application of EMOS on industrial processes to support smart network management. We make measurements in the function of network performance on a cyber-physical system performing a sanding use case. A demonstration video is available at [1].
Variational Bayes Estimation of Off-grid Migrating Targets with a Delta Method

This paper describes a variational Bayes sparse recovery to estimate off-grid range-migrating targets unambiguously. Off-grid parameters depend nonlinearly on the observation and are thus challenging to estimate. Though, if not estimated, targets may be erroneously recovered in the velocity-ambiguity sidelobes of the true ones. A so-called Delta method is used and results in a fast and efficient algorithm able to leverage velocity ambiguity on synthetic and experimental data. The proposed approach is less complex than state-of-the-art algorithms, a desirable property for real-time applications.
An Effective and Efficient Self-Attention Based Model for Next POI Recommendation

Next point-of-interest (POI) recommender sys-tems that recommend the user's next POI according to past check-ins history are widely deployed on mobile devices. Recently, there has been remarkable progress on sequential models which improve recommendation performance by learning in-sequence check-in interactions, yet such models are memory and computationally costly. To fill the gap, we aim to develop an effective and efficient next POI recommen-dation algorithm on sequential models. For effectiveness, the spatial and temporal check-ins information are fed into the sequential model through our proposed self-attention structure, which fully utilizes the relative distance information to calculate the check-ins relationship and compensates by the check-in timings. For efficiency, we select representative elements in the self-attention structure to roughly represent the whole self-attention score, thereby reducing the memory and computational time costs. Our extensive experiments on real-world datasets show that our proposed method has state-of-the-art performance among all baselines, and the memory and time complexities of the self-attention component re-d uced from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(L^{2})$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(L\ln L)$</tex> .
ALGORITHM OF DEVELOPMENT OF LEADERSHIP QUALITIES IN FUTURE TECHNICAL SPECIALISTS AT THE NATIONAL TECHNICAL UNIVERSITY “KHARKIV POLYTECHNIC INSTITUTE”

Abstract: The results of research on the development of leadership qualities in the process of professional training of students of a technical institution of higher education are highlighted in the publication. Special attention is paid to the personality development of the future leader. The results of a pedagogical experiment based on the following conducted methods are highlighted in the article: the method of sociometric measurements, Kettel's multifactorial questionnaire, and the “Personal Differential” test. Based on the results of the analysis of respondents' answers, the main leadership qualities of students were established. The importance of mandatory study of leadership disciplines in a technical institution of higher education has been proven. In the article, a program for the development of leadership qualities of students of a technical orientation was developed and tested in the process of a formative experiment. The results of the control experiment proved the effectiveness of the application of the leadership potential development program in the educational process of professional training of future technical specialists.
Analysis of DSTU 8961:2019 in random oracle model

The paper provides a proof in the IND-CCA2 random oracle model of the security of the asymmetric encryption scheme described in the DSTU 8961:2019 standard, and the IND-CCA2 security of the corresponding key encapsulation mechanism. Since the standard contains only a technical description of transformations, a formalized mathematical model was introduced in Chapter 4 without unnecessary technical details that do not affect safety assessments. Since the system-wide parameters in the standard were chosen in such a way that the scheme did not contain decryption errors, it was possible to simplify significantly the proof. Section 5 provides a schematic overview of possible attack vectors on the DSTU 8961:2019, but a detailed analysis is the subject of further research. In addition to safety, the analysis also showed that the DSTU 8961:2019 has a certain disadvantage in terms of safety. The design can be significantly simplified and accelerated without loss of safety. Security, on the contrary, can be significantly increased.
Textual Classification on Multiple Domains by Weighted Fusion of Multiple Models

Text classification is an important task in the natural language processing while the increased number of samples from multiple domains may reduce the accuracy of the classification. In this paper, we proposed a novel method that can classify samples from multiple domains with the weighted fusion of the multiple models. For each sample, our method firstly predicts which domain it belongs to. Then, we apply a weighted fusion to the corresponding models that are trained on this domain to predict the label of this sample. The experiments on multiple domains proved that our method achieved the best performance compared with the other methods.
Analisis Sentimen Layanan Shopeefood Pada Twitter Dengan Metode K-Nearest Neighbor, Support Vector Machine, dan Decision Tree

Transportation is an infrastructure that connects one point to another. In an era like this, people's needs are getting wider, not just about delivering people, but more than that, for example delivering goods, groceries, or food and so on. Transportation that is supported by smartphones and the result of a combination of transportation services with communication technology is called online transportation. One of the online transportation that is on the rise is ShopeeFood. ShopeeFood is an online transportation that focuses on services that deliver food to its customers. The purpose of this study was to obtain classification results according to the level of accuracy of the Twitter community's perception of shopeeFood services. This study uses three methods, namely KNN, SVM and Decision Tree. The data used is twitter data obtained by crawling using RapidMiner software. Based on the test results, it was found that the KNN method has a higher value than the other methods with an accuracy of 71.49%, precision of 72.86% and recall of 76.94%.
Rethinking the Backward Propagation for Adversarial Transferability

Transfer-based attacks generate adversarial examples on the surrogate model, which can mislead other black-box models without access, making it promising to attack real-world applications. Recently, several works have been proposed to boost adversarial transferability, in which the surrogate model is usually overlooked. In this work, we identify that non-linear layers (e.g., ReLU, max-pooling, etc.) truncate the gradient during backward propagation, making the gradient w.r.t. input image imprecise to the loss function. We hypothesize and empirically validate that such truncation undermines the transferability of adversarial examples. Based on these findings, we propose a novel method called Backward Propagation Attack (BPA) to increase the relevance between the gradient w.r.t. input image and loss function so as to generate adversarial examples with higher transferability. Specifically, BPA adopts a non-monotonic function as the derivative of ReLU and incorporates softmax with temperature to smooth the derivative of max-pooling, thereby mitigating the information loss during the backward propagation of gradients. Empirical results on the ImageNet dataset demonstrate that not only does our method substantially boost the adversarial transferability, but it is also general to existing transfer-based attacks. Code is available at https://github.com/Trustworthy-AI-Group/RPA.
Impact of Adaptive Mean Filter as the Preprocessing Stage of Histopathological Image Classification of Breast Tumor Using Transfer Learning VGG16 for Various Magnifications

This study assesses the impact of using an Adaptive Mean Filter (AMF) as a preprocessing stage for classification of breast tumor histopathological images at various magnifications. The histopathological image was converted from red-green-blue (RGB) into grayscale before AMF is applied. In this study, AMF was performed with kernel sizes of 3 × 3 and 5 × 5 pixels. The datasets were extracted using transfer learning VGG16 before being classified using Bagging classifier. To obtain unbiased performance of the model, stratified K fold cross-validation with K = 10 was used. The dataset was divided into K-equal-sized folds. For each fold, the model was trained on the remaining K-1 folds then evaluated on the held-out fold. This process was repeated K times, with each fold used once as the validation set. The accuracy of the model was then averaged over the K folds to estimate its generalization performance. The AMF with a kernel size of 3 × 3 pixels improves the multi-class classification accuracy for magnifications of 40× and 200×, resulting in accuracy increases of 0.20% and 0.89%, respectively. However, at a magnification of 100×, the model's performance decreases. While the use of AMF with a kernel size of 3 × 3 pixels did not raise the accuracy at magnification 400×, it resulted in a lower standard deviation by 0.24%. In binary-class classification, the use of the AMF with a kernel size of 3 × 3 pixels improves accuracy by 1.10% for magnification 40× and by 0.85% for magnification 200×. However, when implemented at magnifications of 100× and 400×, the AMF filter results in decreased performance. In conclusion, the use of the AMF with a kernel size of 3 × 3 pixels as a preprocessing stage for the histopathological image classification of breast tumor has shown to have a positive impact on the accuracy of multi-class and binary-class classifications for magnifications of 40× and 200×, but not for magnifications of 100× and 400×. The results also indicate that the use of AMF filter can reduce the standard deviation compared to without AMF for some magnifications. However, caution should be considered when applying the AMF filter, as it can decrease the model performance in some cases.