Reviewerly/exHarmony huggingface dataset:
Comparison of K-Nearest Neighbor, Naive Bayes Classifier, Decision Tree, and Logistic Regression in Classification of Non-Performing Financing

The Non-Performing Financing (NPF) indicator of one of the Islamic Banks in Indonesia in the 1st to 3rd quarter of 2021 in a row of 9.69%; 9.97%; 9.46%. The NPF movement tends to improve slightly from time to time but still exceeds the maximum limit stipulated in Bank Indonesia’s Regulation Number 23/2/PBI/2021, which is no more than 5%. This shows that the Islamic bank has a financing performance that can be said to be less good. Preventive steps that can be taken to reduce the NPF ratio in order to improve the health of the bank is to classify prospective financing customers. This research was conducted using the K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree, and Logistics Regression classification methods to predict potential financing customers. The dataset is divided into 80% training and 20% testing. It was found that the best classification result was the Naive Bayes Classifier in the proportion of distribution of 80% training data and 20% testing data with an accuracy value of 84.69%, sensitivity of 58.25%, and specificity of 90.16%.
On the logic of theory change iteration of KM-update

We present a model for iterating the Katsuno and Mendelzon Update (KM-Update) process, which is inspired by models developed for iterated belief revision in the AGM framework. Our model adapts the postulates of belief revision proposed by Darwiche and Pearl, including the independence postulate. We characterize two classes of iterated update operators based on natural and lexicographic revision. The semantics of our models are characterized using the framework of possible worlds.
Severe oligodontia: Towards fully planned pre-prosthetic surgery

We study the Spohn conditional independence (CI) variety CX of an n-player game X for undirected graphical models on n binary random variables consisting of one edge. For a generic game, we show that CX is a smooth irreducible complete intersection curve (Nash conditional independence curve) in the Segre variety (P1)n−2×P3 and we give an explicit formula for its degree and genus. We prove two universality theorems for CX: The product of any affine real algebraic variety with the real line or any affine real algebraic variety in Rm defined by at most m−1 polynomials is isomorphic to an affine open subset of CX for some game X.
The Effect Internet to Learning English for Young Learner

English is an international language used to communicate with people all over the world, so learning English is very important. English also helps learners provide themselves withknowledge and progress. In this article, we'll explore how learning English over the Internet affects young learners. The Internet is currently the most popular source of information to get the information you need. This can affect the progress of English-speaking children. The researcher's goal in preparing this article is to find the positive and negative effects of learning English for young learners via the Internet. The method used in this study is a case study by interviewing a subject analysis of 25 mothers with children attending Islamic Schools TK Khalifah, TK DWI ASIH 2, and TK CINTA BANGSA. In the interview, mothers are asked about the positive and negative effects of learning English for their children over the internet. As a result of the analysis,learning English to children via the Internet generally has positive and negative effects. This hasthe positive effect that children are more likely to follow what they hear and see on the Internet. As children pronounce English, they can develop creativity and children can develop an interestin learning English. In other words, the negative effects of learning English over the Internet develop semi-linguisticism in which children cannot master the language to the fullest, impair their psychological development, and children are not fluent in Indonesian. Children tend to like gadgets. As a result, researchers can find the negative and positive effects of learning English for young learners via the Internet.
Parallel Colorful <i>h</i> -Star Core Maintenance in Dynamic Graphs

The higher-order structure cohesive subgraph mining is an important operator in many graph analysis tasks. Recently, the colorful h -star core model has been proposed as an effective alternative to h -clique based cohesive subgraph models, in consideration of both efficiency and utilities in many practical applications. The existing peeling algorithms for colorful h -star core decomposition are to iteratively delete a node with the minimum colorful h -star degree. Hence, these methods are inherently sequential and suffer from two limitations: low parallelism and inefficiency for dynamic graphs. To enable high-performance colorful h -star core decomposition in large-scale graphs, we propose highly parallelizable local algorithms based on a novel concept of colorful h -star n -order H-index and conduct thorough analyses for its properties. Moreover, three optimizations have been developed to further improve the convergence performance. Based on our local algorithm and its optimized variants, we can efficiently maintain colorful h -star cores in dynamic graphs. Furthermore, we design lower and upper bounds for core numbers to facilitate identifying unaffected nodes in presence of graph updates. Extensive experiments conducted on 14 large real-world datasets with billions of edges demonstrate that our proposed algorithms achieve a 10 times faster convergence speed and a three orders of magnitude speedup when handling graph changes.
On-chain off-chain blockchain model for IoT using IPFS

Blockchain has become the most revolutionary and trending technology since 2008 with the development of Bitcoin. Besides bitcoin, blockchain can be applied in different sectors, such as finance, healthcare, IoT, security, etc. Blockchain and IoT are two new emerging technologies. IoT has become an integral part of human digital life with the exponential growth of digitally connected devices to the internet. With the development of connected devices, data communication has also increased. Till now, IoT data management has been based on a centralized server. Blockchain technology can protect IoT devices against malicious attacks and single-point failure. This study analyzes the integration of blockchain technology with IoT and also discusses the advantages, challenges in integration, and associated cyber security threats. Also, it provides an architecture for blockchain-enabled secure IoT devices with the Interplanetary File System (IPFS).
Who’s Reading Who? Exploring the State of Third-Party Tracking Technology in Open Access Journal Content

The shift in scholarly communication from print to electronic format—and from ownership to access-based delivery models—has changed the dynamics of control related to library collections (Breeding 2019; Singley 2020). External providers now deliver most scholarly content to users, reducing library control to primarily print collections. User data gained through tracking technology, which often collects information without a user’s knowledge or agency, is a product of this shift that has been identified by previous studies (Hinchliffe, Zimmerman, and Altman 2018; Hanson 2019). This tracking data, aggregated by third parties who use it to form user profiles that can then be commodified, “challenges libraries’ historical assumptions about privacy and anonymity” (Hanson 2019, under “Aggregated Identities”).
Random Oracle Combiners: Breaking the Concatenation Barrier for Collision-Resistance

Suppose we have two hash functions $$h_1$$ and $$h_2$$ , but we trust the security of only one of them. To mitigate this worry, we wish to build a hash combiner $$C^{h_1, h_2}$$ which is secure so long as one of the underlying hash functions is. This question has been well-studied in the regime of collision resistance. In this case, concatenating the two hash function outputs clearly works. Unfortunately, a long series of works (Boneh and Boyen, CRYPTO'06; Pietrzak, Eurocrypt'07; Pietrzak, CRYPTO'08) showed no (noticeably) shorter combiner for collision resistance is possible. In this work, we revisit this pessimistic state of affairs, motivated by the observation that collision-resistance is insufficient for many interesting applications of cryptographic hash functions anyway. We argue the right formulation of the "hash combiner" is to build what we call random oracle (RO) combiners, utilizing stronger assumptions for stronger constructions. Indeed, we circumvent the previous lower bounds for collision resistance by constructing a simple length-preserving RO combiner $$\widetilde{C}_{\mathcal {Z}_1,\mathcal {Z}_2}^{h_1,h_2}(M) = h_1(M, \mathcal {Z}_1) \oplus h_2(M, \mathcal {Z}_2),$$ where $$\mathcal {Z}_1, \mathcal {Z}_2$$ are random salts of appropriate length. We show that this extra randomness is necessary for RO combiners, and indeed our construction is somewhat tight with this lower bound. On the negative side, we show that one cannot generically apply the composition theorem to further replace "monolithic" hash functions $$h_1$$ and $$h_2$$ by some simpler indifferentiable construction (such as the Merkle-Damgård transformation) from smaller components, such as fixed-length compression functions. Finally, despite this issue, we directly prove collision resistance of the Merkle-Damgård variant of our combiner, where $$h_1$$ and $$h_2$$ are replaced by iterative Merkle-Damgård hashes applied to a fixed-length compression function. Thus, we can still subvert the concatenation barrier for collision-resistance combiners while utilizing practically small fixed-length components underneath.
Coefficient Grouping for Complex Affine Layers

Designing symmetric-key primitives for applications in Fully Homomorphic Encryption (FHE) has become important to address the issue of the ciphertext expansion. In such a context, cryptographic primitives with a low-AND-depth decryption circuit are desired. Consequently, quadratic nonlinear functions are commonly used in these primitives, including the well-known $$\chi $$ function over $$\mathbb {F}_2^n$$ and the power map over a large finite field $$\mathbb {F}_{p^n}$$ . In this work, we study the growth of the algebraic degree for an SPN cipher over $$\mathbb {F}_{2^n}^{m}$$ , whose S-box is defined as the combination of a power map $$x\mapsto x^{2^d+1}$$ and an $$\mathbb {F}_2$$ -linearized affine polynomial $$x\mapsto c_0+\sum _{i=1}^{w}c_ix^{2^{h_i}}$$ where $$c_1,\ldots ,c_w\ne 0$$ . Specifically, motivated by the fact that the original coefficient grouping technique published at EUROCRYPT 2023 becomes less efficient for $$w>1$$ , we develop a variant technique that can efficiently work for arbitrary w. With this new technique to study the upper bound of the algebraic degree, we answer the following questions from a theoretic perspective: Based on this, we show (i) how to efficiently find $$(h_1,\ldots ,h_w)$$ to achieve the exponential growth of the algebraic degree and (ii) how to efficiently compute the upper bound of the algebraic degree for arbitrary $$(h_1,\ldots ,h_w)$$ . Therefore, we expect that these results can further advance the understanding of the design and analysis of such primitives.
Explainable Multi-type Item Recommendation System Based on Knowledge Graph

Knowledge graphs and recommender systems have significant potential for improving recommendation accuracy and interpretability. However, most existing methods focus on single-type item recommendations and offer limited explainability of their recommendations. In this paper, we propose a novel framework knowledge graph transformer network (KGTN) for explainable multiple types of item recommendation, which aims to recommend items with different formats and categories to users simultaneously. Unlike previous methods that rely on predefined meta-paths, KGTN mines hidden path relationships in a collaborative knowledge graph. The KGTN integrates a transformer-based model with a meta-path-based graph convolutional network to effectively learn user-item representations and capture the complex relationships among users, items, and their corresponding attributes. Finally, we use the critical path in the learned useful meta-path graph as an explanation. Experimental results on two real-world datasets demonstrate KGTN’s superiority over state-of-the-art methods in terms of recommendation performance and explainability. Furthermore, KGTN is shown to be effective at handling data sparsity and cold-start problems.
WA-ResUNet: A Focused Tail Class MRI Medical Image Segmentation Algorithm

Medical image segmentation can effectively identify lesions in medicine, but some small and rare lesions cannot be well identified. Existing studies do not take into account the uncertainty of the occurrence of diseased tissue, and the problem of long-tailed distribution of medical data. Meanwhile, the grayscale image obtained from Magnetic Resonance Imaging (MRI) detection has problems, such as the features being difficult to extract and invalid features being difficult to distinguish. In order to solve these problems, we propose a new weighted attention ResUNet (WA-ResUNet) and a class weight formula based on the number of images contained in the class, which improves the performance of the model in the low-frequency class and the overall effect of the model by improving the degree of attention paid to the valid features and invalid ones and rebalancing the learning efficiency among the classes. We evaluated our method on an uterine MRI dataset and compared it with the ResUNet. WA-ResUNet increased Intersection over Union (IoU) in the low-frequency class (Nabothian cysts) by 21.87%, and the overall mIoU increased by more than 6.5%.
Personalized Teaching Questioning Strategies Study Based on Learners’ Cognitive Structure Diagnosis

Personalized education has been a widely shared goal pursued by Chinese and foreign educators. As the primary method of teacher–student interaction, the importance of personalized questioning is self-evident. Due to a lack of technical support, teachers rely on their teaching experience to ask questions without considering the learning situation of learners. This results in teaching questioning being unable to support learners’ learning. These questions are relatively shallow and cannot promote the construction and transfer of learners’ knowledge. Cognitive diagnostic technology could diagnose learners’ cognitive states and provide services for personalized teaching. Therefore, a personalized teaching questioning strategy based on learners’ cognitive structure diagnosis was proposed in this study. Firstly, we diagnosed learners’ cognitive structure through usability, distinguishability, and stability. Secondly, we discussed the types of questions that teachers should raise when facing learners in different situations. We also discussed the application of personalized teaching questioning strategies. The experiment took place at M Primary School in Ningxia, China, with the participation of one teacher and ninety-seven fourth-grade students. Seven lessons were observed and videotaped across a range of topics. The study revealed that personalized teaching questioning strategies could improve learners’ academic performance and subject literacy. They can also increase the number of teacher questioning, change the depth of teacher questioning content, and expand the scope of questioning subjects.
EMG Based MIDI Controller

MIDI is a standard that has been used for musical performance since the early eighties and has been gaining increasing popularity ever since for its ease of use and versatility. The objective for this work is to implement a MIDI controller that takes input from the user’s hand/arm motion, and as such, send MIDI data to a MIDI interface. Hand movement was captured using three EMG sensors (Olimex Shield-EKG-EMG) along the forearm region to capture muscle activity. These will be connected to an Arduino UNO board and send sensor data into a Raspberry PI 4B board for data analysis. Then, in order to compare and determine which one is best to detect four different hand gestures, said activity will be analyzed using three different machine learning algorithms, namely Random Forest (RF), Support Vector Machines (SVM) and a three hidden layer neural network (NN). After classification, depending on the gesture, the controller converts these movements into their correspondent MIDI note for musical performance. Finally, performance will be tested using virtual instruments on a digital audio work station (DAW), namely Ableton Live 9.
ML-Aided SOP Compensation to Increase Key Exchange Rate in QKD Systems

Secure communications have become a requirement for virtually all kind of applications.Currently, two distant parties can generate shared random secret keys by using public key cryptography.However, quantum computing represents one of the greatest threats for the finite complexity of the mathematics behind public key cryptography.In contrast, Quantum Key Distribution (QKD) relies on properties of quantum mechanics, which enables eavesdropping detection and guarantees the security of the key.Among QKD systems, polarization encoded QKD has been successfully tested in laboratory experiments and recently demonstrated in closed environments.In this paper, we propose a Machine Learning (ML) -based polarization tracking and compensation that is able to keep shared secret key exchange to high rates even under large fiber stressing events.Exhaustive results using both synthetic and experimental data show remarkable performance, which can simplify the design of both quantum transmitter and receiver, as well as enable the use of aerial optical cables, thus reducing total QKD system cost.
Enhancing Locally Adaptive Smoothing of Graph Neural Networks Via Laplacian Node Disagreement

Graph neural networks (GNNs) are designed to perform inference on data described by graph-structured node features and topology information. From the perspective of graph signal denoising, the typical message passing schemes of GNNs act as a globally uniform smoothing that minimizes disagreements between embeddings of connected nodes. However, the level of smoothing over different regions of the graph should be different, especially for those inter-class regions. This deviation limits the expressiveness of GNNs, and then renders them fragile to over-smoothing, long-range dependencies, and non-homophily settings. In this paper, we find that the node disagreements of initial graph features can present more trustworthy constraints on node embeddings, thereby enhancing the locally adaptive smoothing of GNNs. To spread the inherent disagreements of nodes, we propose the Laplacian node disagreement to jointly measure the initial features and output embeddings. With such a measurement, we then present a new graph signal denoising objective deriving a more effective message passing scheme and further incorporate it into the GNN architecture, named Laplacian node disagreement-based GNN (LND-GNN). Learning from its output node representations, we integrate an auxiliary disagreement constraint into the overall classification loss. Experiments demonstrate the expressive ability of LND-GNN in the downstream semi-supervised node classification task.
USER EXPERIENCE ANSWER SYSTEM AUTOMATICALLY WITH USER CENTERED DESIGN AND USER EXPERIENCE QUESTIONNAIRE-SHORT

The interface of the automatic answer assessment system is plagued by several issues, including an unfamiliar layout, unresponsive design, inconsistency in elements, and a lack of clarity in presenting information. These problems significantly hinder the overall user experience. As a result, this study aimed to address these challenges by designing a user-centered experience for the automatic answer assessment system, using a high-fidelity prototype tailored to meet user needs. The user-centered design method involved four crucial stages: Specify The Context Of Use, Specify Requirements, Create Designs, and Evaluate Designs. Through rigorous usability testing with teachers, the design achieved an impressive effectiveness rating of 90%, firmly establishing it as a "very effective" solution. Additionally, it demonstrated high efficiency with a value of 0.01307 goals/sec, and teachers expressed positive feedback, confirming the satisfaction and usability of the new interface. Similarly, students' usability testing yielded noteworthy results, with a 90% effectiveness rating, also classified as "very effective." The interface showcased a high level of efficiency, with a value of 0.0849 goals/sec. While the satisfaction value fell below the PSSUQ norm, students still found the interface to be user-friendly and satisfactory. Furthermore, the user experience testing, utilizing the UEQ-S, provided valuable insights. For teachers, the pragmatic aspect scored 1.85, the hedonic aspect scored 2.33, and the overall aspect received a commendable score of 2.09, all of which fell within the excellent category on benchmarks. Similarly, students' ratings were highly positive, with scores of 2.14 for both pragmatic and hedonic aspects, and an overall score of 2.14, signifying an excellent user experience.The retrospective think-aloud validation test reaffirmed the positive response from prospective users. Overall, this research, employing a user-centered design approach, successfully delivered a highly satisfactory and effective user experience for both teachers and students using the automatic answer assessment system.
Knowledge Sharing and Students’ Development: R- Based Bibliometric Study

With a focus on bibliometric analysis, this study intends to analyze the patterns in the literature regarding knowledge sharing and student development. To find studies that were specifically focused on the topic under research, the authors searched Scopus using the Boolean operators AND, OR and NOT, together with the terms "knowledge sharing" and "student development," as well as their synonyms. In general, the study did not take the publication date into account. There were 1,154 documents in total in the final data set. The data were analyzed using Biblioshiny and VOSviewer software based on R. The use of bibliometric analysis to examine the connection between knowledge sharing and student development revealed several themes, influential authors, highly cited papers, prominent organizations and powerful nations. In addition, the study showed how the relationship between knowledge sharing and student development has changed over time and how it may interact with student performance to give educational institutions a long-term competitive advantage. To the author's knowledge, this study is the first to conduct a bibliometric analysis on knowledge sharing and student development. This study can be a starting point for scholars interested in understanding how knowledge sharing can relate to student development.