Reviewerly/exHarmony huggingface dataset:
COGNITIVE STRUCTURE ANALYSIS: ASSESSING ELEMENTARY SCHOOL STUDENTS IN MATH TO DETERMINE THE TYPES OF KNOWLEDGE THEY HAVE

Assessment has been a key part of education, playing the role of determining how much students have learned. Traditionally, assessments have focused on whether students give the correct answer to problems, implying that the number of correctly answered test items is a valid measure of how much students know. Unfortunately, the focus on correct answers has also resulted in neglecting the potential ability of assessments to provide diagnostic feedback to educators as to what concepts students have mastered and where the gaps in their knowledge are, thus potentially informing the day-to-day teaching process. The present paper describes an assessment technique called Cognitive Structure Analysis (CSA) that is derived from John Leddo’s integrated knowledge structure framework (Leddo et al., 1990) that combines several prominent knowledge representation frameworks in cognitive psychology. Previous studies with middle and high school students revealed strong correlations between students’ factual, procedural, strategic and rationale subject matter knowledge and their problem solving performance. Given that the Leddo et al. (1990) research showed that all four types of knowledge were present in highly experienced people but that novices tended to be characterized by factual and procedural knowledge, the question arises as to whether elementary school students would show significant correlations between only factual and procedural knowledge and problem solving performance on school subject matter due to their lack of academic experience. The present study investigated this question using fifth graders studying math. 20 fifth graders were taught beginning and advanced concepts in solving distance = rate * time problems. Afterwards, they were assessed on their concept knowledge using CSA and given practical problems to solve. Results showed significant correlations between factual and procedural knowledge as assessed by CSA and problem solving performance. Correlations between strategic and rationale knowledge were not statistically significant. These results suggest that elementary school students not only have less subject matter knowledge than their older counterparts, but that the type of knowledge they have is also qualitatively different. This finding may have implications for how to teach students in order to nurture advanced thinking skills.
Unlocking the CISSP Journey: An Introduction

Information security has become paramount in the digital age, where our lives are intricately intertwined with technology. As the world becomes progressively interconnected, the rate and sophistication of cyberattacks and data breaches are escalating at an alarming pace. This unstable environment has compelled organizations globally to prioritize and invest in a robust information security program. At the heart of this initiative lies the Certified Information Systems Security Professional (CISSP), a globally recognized cybersecurity certification governed by the International Information System Security Certification Consortium (ISC2).
Asset Security

Asset Security is a pivotal domain within the CISSP examination, emphasizing the processes, principles, and techniques required to protect an organization’s informational assets. As we delve into this chapter, we aim to equip you with a comprehensive understanding of the information security concepts and the best industry practices outlined in the CISSP CBK. But it’s crucial to remember that the CISSP exam requires a holistic understanding of these principles, not mere rote memorization.
Multiple channel adjustment based on composite backbone network for underwater image enhancement

In order to heighten enhancement effects for underwater images under different conditions, multiple channel adjustment based on composite backbone network (MC-CBNet) was proposed that skilfully combine the enhancement effect from RGB colour space as well as HSV and Lab. MC-CBNet consists of a preliminary enhance block, a multi-space adjust block and a confidence map block. Preliminary enhance block and multi-space adjust block adjust the images from RGB, HSV and Lab colour spaces respectively. The confidence map is obtained by the ultimate block to fuse the results of different channels. Besides, preliminary enhance block and confidence map block are formed from composite backbones. Experimental results on underwater image enhancement benchmark (UIEB) indicate that our method gets better grades than existing methods under both reference subset and challenging subset evaluation.
MEDIA CULTURE AND MODERN CHALLENGES IN THE PARADIGM OF NETWORK COMMUNICATION

At the turn of the XX-XI centuries, humanity received a unique opportunity for network communication. Having a powerful self-organizing potential, it offered unlimited horizons for the production and broadcasting of operational information, which became available to the maximum number of participants-communicants connected to the network. At the same time, inclusion in the information network in modern society is considered a sign of status, success, presentability. It is obvious that this potential is capable of not only informing, but also involving the audience in active activity in the shortest possible time. The claim for additional activities opens the account of socio-cultural challenges. Network communication becomes a kind of "algorithm factory" of not always prosocial action outside the network environment. Having absorbed all the advanced expressive means of TV and methods of their formation, network communication has acquired a huge resource for influencing the consciousness of the audience. Aesthetic challenges about due to the contradictory nature of media access in the conditions of network media communication. The aesthetic metamorphoses are also influenced by the systemic constant signs and socio-cultural models of network media communication behavior that we have discovered. In the light of their functionality, a large-scale influence on the chronotope of network media communication is noted. Which in turn leads to an aesthetic imbalance of its ontological essence.
Deep learning for software defect prediction

One of the most important jobs in the SDLC’s Testing Phase is software defect prediction. It determines which modules are prone to failure and require extensive testing. This allows for efficient use of testing resources while staying within the limits. Predicting software faults is a significant and effective method for increasing software quality and reliability. Modeling which components in a large software system will have the most difficulties in the next release helps project managers better manage projects, such as early detection of potential release delays and cost-effectively guiding remedial actions to improve software quality. Developing robust fault prediction models, on the other hand, is a difficult task, and numerous solutions have been proposed in the literature. In this survey, we look at convolutional neural networks, defect prediction via attention mechanism, and other deep learning techniques that can help detect software defects. Program defect prediction is used to aid developers in spotting probable flaws and prioritizing their testing efforts in order to increase software reliability.
On the Measurement attaining the Quantum Guesswork

The guesswork quantifies the minimum cost incurred in guessing the state of an ensemble, when only one state can be queried at a time. In the classical case, it is well known that the optimal strategy trivially consists of querying the states in their non-increasing order of posterior probability. In the quantum case, on the other hand, the most general strategy to obtain the optimal ordering in which to perform the queries consist of a quantum measurement. Here, we solve such an optimization problem by deriving the quantum measurement attaining the guesswork for a broad class of ensembles and cost functions.
A ConvLSTM-based Blind Receiver for Physical Layer Wireless Communication

Recently, machine learning based blind detection of modulation and coding scheme (MCS) has been proven highly effective in achieving dynamic wireless communications in accordance with channel quality, thereby ensuring reliable and stable communication. To reduce the complexity of receivers using separate MCS detection and demodulation, we propose a ConvLSTM-based blind receiver to realize a unified solution with both blind MCS detection and low complexity demodulation. In achieving concurrent MCS detection and demodulation as a task of multi-label classification in deep learning, the ConvLSTM-based receiver includes three parts: data preprocessing, feature extraction, and multi-label classification. The data preprocessing is to guarantee a uniform input for all considered MCSs. Then, the feature extraction extracts the spatiotemporal features of the signals. Finally, the multi-label classification transforms the extracted features into bit streams. Simulation results show that the proposed ConvLSTM-based receiver can improve the bit error rate (BER) performance with low storage and time complexities compared with the baselines.
F.I. Chaliapin and K.P. Pyatnitsky. The History of Their Friendship

While A.M. Gorky’s friendship with F.I. Chaliapin is quite well known, we seem to know next to nothing about Chaliapin’s friendship with one of the founders of the book-publishing partnership “Znanie” K.P. Pyatnitsky. The paper deals with the documents that could help to trace the story of their acquaintance and friendship, which are currently stored in the A.M. Gorky Archive and introduced into academic circulation for the first time. Pyatnitsky’s letters and his contemporaries’ recollections state that his first meeting with Chaliapin took place in Podolsk on November 8, 1901. Later on, Pyatnitsky’s correspondence with Gorky and his diaries allow us to trace some of his meetings with Chaliapin between late 1902 and 1904. Gradually, their friendship grew stronger and became more trusting. However, Pyatnitsky’s activity in the publishing house and Chaliapin’s concert performances did not allow them to see each other often. The history of these meetings can be traced only through some references in Pyatnitsky’s correspondence with Gorky and Е.P. Peshkova, as well as through Pyatnitsky’s diaries. In 1908 hard times hit Pyatnitsky, when his relationship with Gorky began to deteriorate due to their disputes about the work of the “Znanie” publishing house. An indication of this fact can be found in Pyatnitsky’s diary entries from 1911 to 1914 and in his letters to friends and acquaintances of those years. Chaliapin tried his best to reconcile his former friends and to support Pyatnitsky. This is evidenced by his letter to V.A. Maklakov of April 28, 1914. The last documentary evidence of Chaliapin’s friendship with Pyatnitsky is an official invitation from Chaliapin to visit the consecration of an infirmary he arranged for wounded soldiers on October 14, 1914.
AdaPyramid: Adaptive Pyramid for Accelerating High-resolution Object Detection on Edge Devices

Deep convolutional neural network (NN)-based object detectors are not appropriate for straightforward inference on high-resolution videos at edge devices, as maintaining high accuracy often brings about prohibitively long latency. Although existing solutions have attempted to reduce on-device inference latency by selecting a cheaper configuration (e.g., choosing a more lightweight NN or scaling a frame to a smaller size before inference) or eliminating a background containing no object, they often ignore various high-resolution features and fail to optimize for those videos. We thus present AdaPyramid, a framework to reduce as much on-device inference latency as possible, especially for high-resolution videos, while achieving the accuracy demand approximately. We observe that the cheapest configuration to achieve the accuracy demand varies significantly across both different frames and different regions in a frame. The underlying reason is that object features (e.g., the location, size and category of objects) are more uneven in high-resolution videos, both temporally and spatially. Moreover, we observe that the object size presents a prominent hierarchical distribution in high-resolution frames. AdaPyramid thus partitions each frame hierarchically just like a pyramid and chooses a content-aware configuration for each region, which is adapted online based on the feedback. We evaluate the performance of AdaPyramid on a public dataset and our collected real-world videos. The obtained results show that under comparable accuracy to the state-of-the-art solutions, AdaPyramid can decrease inference latency by 40% on average, with up to 2.5× speed-up.
Blockchain Enabled Secure Distributed Event Logging in the Industrial Internet of Things

Blockchain technology has found applications across diverse domains owing to its ability to establish trust in a decentralized manner. Nevertheless, the integration of blockchain into critical infrastructure domains encounters significant challenges posed by the computational demands and storage requirements associated with the proof-of-work puzzle during the mining process. This scenario becomes particularly complex in the context of applications within the Industrial Internet of Things (IIoT), where stringent timeliness constraints are inherent, notably in functions such as intrusion detection and control. This paper presents a novel solution that takes into account the time-sensitive nature of application constraints within the IIoT. Specifically, we focus on online functions involving intrusion detection and control. By doing so, we address the imperative need for timely and secure data delivery, crucial in maintaining the integrity of hard-to-tamper ledger blocks. These blocks encapsulate measurements that are seamlessly utilized by various system functions and components. The proposed approach optimizes the utilization of heterogeneous resources governing blockchain computations. This optimization ensures that the desired properties for logging within the blockchain are met, enabling the prompt delivery of measurements. The novel collaborative mining technique entails the sharing of nonce ranges among miners, which effectively reduces the overall mining time and enhances the efficiency of the process.
FLOW: A Robust Federated Learning Framework to Defend Against Model Poisoning Attacks in IoTs

Federated Learning (FL) is a promising distributed learning approach to enable intelligent Internet of Things (IoT) applications. However, FL is vulnerable to model poisoning attacks in which malicious clients abate the accuracy of the global model by committing crafted local model updates to the server. Existing defense methods either rely on a validation dataset or simply remove the detected malicious clients from the subsequent training process to handle attacks from a large number of malicious clients. Thus, the performance of existing methods deteriorates drastically in many scenarios where the data distributions of clients are unpredictable. To address these deficiencies, we propose a framework called FL overwatch (FLOW) to efficiently defend against model poisoning attacks taking advantages of the local model updates in current and historical training iterations. On one hand, FLOW detects malicious clients in each iteration by measuring the cosine distances between the local model updates of clients, such that malicious updates are eliminated from the current aggregation. On the other hand, FLOW gracefully punishes the previously identified malicious clients rather than removes them from the whole training process. As a result, FLOW can embrace a richer reliable set of local model updates than existing methods in aggregation. Extensive experiments on widely-used benchmark datasets show that FLOW can achieve higher success defending ratio and higher accuracy of global models over existing Byzantine-robust FL methods under typical untargeted attacks and targeted attacks. Furthermore, FLOW also shows significant effectiveness in defending against adaptive attacks tailored to FLOW.
Gradient-Based Spectral Embeddings of Random Dot Product Graphs

The Random Dot Product Graph (RDPG) is a generative model for relational data, where nodes are represented via latent vectors in low-dimensional Euclidean space. RDPGs crucially postulate that edge formation probabilities are given by the dot product of the corresponding latent positions. Accordingly, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">embedding</i> task of estimating these vectors from an observed graph is typically posed as a low-rank matrix factorization problem. The workhorse Adjacency Spectral Embedding (ASE) enjoys solid statistical properties, but it is formally solving a surrogate problem and can be computationally intensive. In this paper, we bring to bear recent advances in non-convex optimization and demonstrate their impact to RDPG inference. We advocate first-order gradient descent methods to better solve the embedding problem, and to organically accommodate broader network embedding applications of practical relevance. Notably, we argue that RDPG embeddings of directed graphs loose interpretability unless the factor matrices are constrained to have orthogonal columns. We thus develop a novel feasible optimization method in the resulting manifold. The effectiveness of the graph representation learning framework is demonstrated on reproducible experiments with both synthetic and real network data. Our open-source algorithm implementations are scalable, and unlike the ASE they are robust to missing edge data and can track slowly-varying latent positions from streaming graphs.
Serial verb reduplication in the Mabia languages of West Africa

This paper introduces and discusses the notion of Serial Verb Reduplication (SVR) in two Mabia languages of West Africa, Dagaare and Kusaal. The authors show that the phenomenon of SVRs, though under-represented in the literature, has a wide scope occurrence in natural language usage within serializing languages. Theoretically, two lexical semantic notions: semantics of verbs (verb meaning) and pluraction, are advanced to explain the intricacies of the syntax and semantics of SVRs. The paper identifies two groups of SVRs: canonical SVRs and pluractional SVRs and proposes that semantically bleached verbs can only be reduplicated in pluractional benefactive and causative SVRs in these languages.
Performance Indicators for Energi Management of Tourism in The Pangururan District Kabupaten Samosir

. The Lake Toba area is a national tourist area visited by various tourists both from within and outside the country, and is included in the 88 national tours which are the parent of tourism development in Indonesia. One of the infrastructures that must be considered is the availability of electrical energy sources in the tourist area of ​​Lake Toba. Pangururan District as one of the tourism and administrative areas in the Lake Toba area must have sufficient electrical energy needs and become sustainable tourism. The purpose of this study was to examine the Performance of Tourism Energy Management in Pangururan District, Samosir Regency with an approach to analyzing the level of energy use and conservation, energy management programs, and utilization of renewable energy sources. The research method used is direct field observation and literature study, both theory and supporting documents in the research, which is then analyzed by interpreting the data obtained based on the theory and applicable policies. The results of this study are to identify that the need for electrical energy in the tourist area in the Pangururan sub-district is quite evenly distributed and the need for electrical energy is also met, but in the tourist area in the Pangururan sub-district conservation activities and the use of sustainable energy sources carried out by the government and the tourism business unit are still very low. a little. In addition, this research is expected to be a source of knowledge about Energy Management in the Tourism Sector.
Marginalising dyslexic researchers is bad for science

Communication in the sciences is often based on text, which places researchers with dyslexia at a disadvantage. However, this means that science is missing out on the original insights and specific strengths in exploration that dyslexic researchers bring to their disciplines. Here we discuss how the scientific community can address the challenges that dyslexic researchers face, and how science stands to benefit as a result. We discuss this in the context of a new theoretical framework proposing the existence of complementary learning strategies that could play a key role in scientific progress, particularly with regard to accelerating innovation.
Comparison of the Accuracy of Drug User Classification Models Using Machine Learning Methods

Cases of drug abuse are on the rise, with many users entering the addiction phase, often resulting in overdose and death. Drugs are chemical compounds that are capable of affecting biological functions, can induce feelings of happiness and reduce pain. To address this growing problem, a proactive measure is needed. Therefore, this study aims to classify drug users and non-users, so that health workers and therapists can educate about the dangers of drugs to non-users and rehabilitate drug users. This study uses drug consumption data taken from the UCI Irvine Machine Learning Repository. The data consists of 1885 rows with 32 attributes and 2 classes, where there are 18 types of legal and illegal drugs. This research utilizes machine learning methods, specifically Artificial Neural Network (ANN), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), in addition to evaluation methods such as Confusion Matrix and Area Under Curve (AUC). The results showed that RF outperformed the other methods, with accuracy, precision, and recall of 93%, and an f1-score of 89%, while the AUC value was still suboptimal at 0.66. DT had the worst results, with 82% accuracy, 87% precision, 82% recall, 84% f1-score, and an AUC value of 0.56. With these results, this research can be continued into an application that can classify drug users and non-users.
Analysis of the Saintekmu Website Quality on User Satisfaction Using the Modified System Usability Scale and Webqual 4.0 Method

At present, websites are a major means of finding or providing information. The Saintekmu website was created to offer top-notch service to students. One way to ensure that the website's services are appropriate and that information technology is being used to its fullest potential is to evaluate the level of service provided and improve its quality. This study aims to compare the results of two methods - the System Usability Scale (SUS) and Webqual - used to determine the quality and expectations of website users. The study distributed questionnaires online using Google Forms and had a sample size of 20 students. The data collected were analyzed using the SPSS program. Results from the SUS method indicated that the website's acceptability range was in the Marginal category, with a score of 69.9 and a classification rating of OK. The Webqual method yielded an R Square of 0.948, indicating that the website's usability, quality, and interaction variables had a significant effect on user satisfaction. All WebQual 4.0 dimensions had a positive and significant effect on user satisfaction, both partially and simultaneously. This study provides Muhammadiyah Saintek University with reference material for evaluating their website in the future.