Reviewerly/exHarmony huggingface dataset:
Evaluasi Bimbingan Bahasa Inggris Menggunakan Model Kirkpatrick

In this day and age, English is also known as a much needed international language. People are increasingly required to be able to speak English both passively and actively. English language tutoring is one of the programs implemented at SMAN 13 Bone, which is evaluated using the Kirkpatrick model. The purpose of this study was to assess how the reaction, learning, application, and impact of English tutoring for students at SMAN 13 Bone. This study uses a qualitative approach with an exploratory case study method. This research was conducted at SMAN 13 Bone in the academic year 2022/2023. Data collection techniques used are interviews and observation. The results of the study showed that the implementation of English language guidance went well. This can be seen from the 4 stages, including: (1) Reactions caused by positive responses given by students regarding English language guidance, because the learning and the material are interesting. (2) Learn here about vocabulary, correct pronunciation, spelling, and even learning materials in class. (3) Its application involves the frequent application of English in daily life. (4) The impact of the English language guidance is an increase in students' understanding and report cards.
PERAN LITERASI DIGITAL DALAM MENINGKATKAN PEMAHAMAN SISWA KELAS XII (STUDI KASUS DI SMA NEGERI 1 TEGALOMBO)

This study aims to reveal: (1) the form of digital literacy used in SMA Negeri 1 Tegalombo (2) The causes of the implementation of digital literacy in SMA Negeri 1 Tegalombo (3) The role of digital literacy in improving the understanding of class XII students of SMA Negeri 1 Tegalombo. The research approach used in this field research is a qualitative approach, with the type of case study research. The data collection in this research was conducted by using interview, observation and documentation techniques. In order to perform data analysis, the technique used is the data analysis technique of the Cresswell model. To check the validity of the data so as to obtain valid and reliable data, the researchers used technical triangulation and data source triangulation. The results of the research show that at SMA Negeri 1 Tegalombo there are several forms of digital literacy, but the one that has the most existence and is often used is one of the aids in the form of an Independent Learning Pavilion (ABM) called kipin school. The cause of the implementation of digital literacy in the form of kipin school is due to the limitations of printed references in schools and also obstacles to accessing wifi due to frequent power outages. Kipin school has a big role for class XII high school students because there are many cross-level references and covers books with various scientific fields. So that it can support the process of students understanding the learning material.
Multi-Versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis on Arabic Corpus

Sentiment analysis (SA) of the Arabic language becomes important despite scarce annotated corpora and confined sources. Arabic affect Analysis has become an active research zone nowadays. But still, the Arabic language lags behind adequate language sources for enabling the SA tasks. Thus, Arabic still faces challenges in natural language processing (NLP) tasks because of its structure complexities, history, and distinct cultures. It has gained lesser effort than the other languages. This paper developed a Multi-versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis (MVODRL-AA) on Arabic Corpus. The presented MVODRL-AA model majorly concentrates on identifying and classifying effects or emotions that occurred in the Arabic corpus. Firstly, the MVODRL-AA model follows data pre-processing and word embedding. Next, an n-gram model is utilized to generate word embeddings. A deep Q-learning network (DQLN) model is then exploited to identify and classify the effect on the Arabic corpus. At last, the MVO algorithm is used as a hyperparameter tuning approach to adjust the hyperparameters related to the DQLN model, showing the novelty of the work. A series of simulations were carried out to exhibit the promising performance of the MVODRL-AA model. The simulation outcomes illustrate the betterment of the MVODRL-AA method over the other approaches with an accuracy of 99.27%.
Performance Improvement through Novel Adaptive Node and Container Aware Scheduler with Resource Availability Control in Hadoop YARN

The default scheduler of Apache Hadoop demonstrates operational inefficiencies when connecting external sources and processing transformation jobs. This paper has proposed a novel scheduler for enhancement of the performance of the Hadoop Yet Another Resource Negotiator (YARN) scheduler, called the Adaptive Node and Container Aware Scheduler (ANACRAC), that aligns cluster resources to the demands of the applications in the real world. The approach performs to leverage the user-provided configurations as a unique design to apportion nodes, or containers within the nodes, to application thresholds. Additionally, it provides the flexibility to the applications for selecting and choosing which node’s resources they want to manage and adds limits to prevent threshold breaches by adding additional jobs as needed. Node or container awareness can be utilized individually or in combination to increase efficiency. On top of this, the resource availability within the node and containers can also be investigated. This paper also focuses on the elasticity of the containers and self-adaptiveness depending on the job type. The results proved that 15%–20% performance improvement was achieved compared with the node and container awareness feature of the ANACRAC. It has been validated that this ANACRAC scheduler demonstrates a 70%–90% performance improvement compared with the default Fair scheduler. Experimental results also demonstrated the success of the enhancement and a performance improvement in the range of 60% to 200% when applications were connected with external interfaces and high workloads.
GEOLOGICAL POSITION AND GOLD-BISMUTH MINERALIZATION OF THE NAMOVSKOYE DEPOSIT (SOUTHERN SIKHOTE-ALIN)

Based on the results of a comprehensive geological and mineralogical-geochemical study of the ores of the Namovskoye deposit, new data have been obtained that reflect the specifics of mineralization. The ores of the deposit were formed in close connection with the manifestation of Early Cretaceous monzonitoid magmatism against the background of active left-hand movements along the Central Sikhote-Alin fault. U-Pb dating of the ore-bearing dike yielded an age of 103 Ma. The ores of the deposit, in addition to native gold, contain high concentrations of Ag, Bi, and Cu. A variety of bismuth minerals were found in the ores: sulfide (bismuthinite), telluride (hedleyite), sulfotellurides (tetradymite, joseite-А and joseite-В), Ag sulfobismuthite (matildite), Pb-Bi sulfosalts (aschamalmite, cannizzarite, cosalite, lillianite, nuffieldite, galenobismuthite), an intermetallic compound of gold (maldonite), and native bismuth. Silver minerals were also found: chloride (cerargyrite), sulfide (acanthite), and telluride (hessite). The typomorphic features of ore minerals and the geological structure of the Namovskoye deposit assign it to the gold deposits formed in a transform continental margin setting. A mantle source of ore mineralization is suggested.
An Authenticity Verification Method for Metallic Colored Artifacts

We propose a method that could be able to verify metallic-colored artifacts' authenticity. This method consists of two techniques. One is for forming a coating film and it uses flaky metal powder and functional material (it transmits visible light while absorbing near-infrared light). Another one is for photo shooting of color and near-infrared light images for authenticity verification by using image matching. Two types of images are shot by low-magnification microscopes. Authors calculated each type of image's similarity by using a Phase-Only Correlation method and found that the similarity of color images was lower than near-infrared light images. The degree of the artifacts' authenticity could be verified multi-modally by using two FAR/FRR curves depicted from two types of images.
Hybrid Optimization Method Using Simulated-Annealing-Based Ising Machine and Quantum Annealer

Ising machines have the potential to realize fast and highly accurate solvers for combinatorial optimization problems.They are classified based on their internal algorithms.Examples include simulated-annealing-based Ising machines (non-quantum-type Ising machines) and quantum-annealing-based Ising machines (quantum annealers).Herein we propose a hybrid optimization method, which utilizes the advantages of both types.In this hybrid optimization method, the preprocessing step is performed by solving the non-quantum-annealing Ising machine multiple times.Then sub-Ising models with a reduced size by spin fixing are solved using a quantum annealer.The performance of the hybrid optimization method is evaluated via simulations using Simulated Annealing (SA) as a non-quantum-type Ising machine and D-Wave Advantage as a quantum annealer.Additionally, we investigate the parameter dependence of the proposed hybrid optimization method.The hybrid optimization method outperforms the preprocessing SA and the quantum annealing machine alone in fully connected random Ising models.
Group action and its application on classification of low order groups

The classification of finite groups is an important topic in mathematics throughout history of mathematics. The topic of this paper is to use group action as a tool, to classify some special finite groups and some low order groups. First this paper introduces some concepts of group action. Then this paper states and proves some important theorems related to group action. For example, the Sylows theorem, which is very important in this paper. Research has found that, groups of specific order, such as groups whose order are 2p,p^2, pq(p, q are distinct prime numbers), p3(p is prime) can be classified using group action and the technique of semi-direct product, and groups whose order are no more than 15 are classified which can be seen as the special situations of the above ones. But in general, to make classification of a larger range of finite groups, more tools should be introduced.
Teknik Mind Mapping untuk Meningkatkan Keterampilan Menulis Teks Deskriptif pada Peserta Didik Kelas VII di SMP Negeri 1 Glenmore

The mind mapping technique is an innovative learning approach that can creatively develop ideas. This research aims to determine the use of mind mapping to improve the descriptive text writing skills of 7th-grade students at SMP Negeri 1 Glenmore. The method used in this research is Classroom Action Research (CAR) or action research involving 2 cycles, with each cycle consisting of 2 meetings. From the research results, the researcher found that the mind mapping technique can enhance the descriptive text writing skills of the students. The average scores of the students before and after being taught using the mind mapping technique show that before the intervention (58.81), in the first cycle (67.40), and in the second cycle (71.14). It can be concluded that there is an improvement in the students' descriptive text writing skills. In conclusion, English learning with the application of the mind mapping technique to enhance descriptive text writing skills has a significant impact on the learning outcomes of 7th-grade students at SMP Negeri 1 Glenmore.
Convergence Rate of Gradient-Concordant Methods for Smooth Unconstrained Optimization

The article discusses the class of gradient-concordant numerical methods for smooth unconstrained minimization where the descent direction is restricted to a subset of the descent cone. This class covers a wide range of well-known optimization methods, such as gradient descent, conjugate gradient method, and Newton’s method. While previous research has demonstrated the linear convergence rate of gradient-concordant methods for strongly convex functions, many practical functions do not meet this criterion. Our research explores the convergence of gradient-concordant methods for a broader class of functions. We prove that the Polyak-Łojasiewicz condition is sufficient for the linear convergence of the gradient-concordant method. Additionally, we show sublinear convergence for convex functions that are not necessarily strongly convex.
Interpretable Geometry Problem Solving Using Improved RetinaNet and Graph Convolutional Network

This paper proposes an interpretable geometry solution based on the formal language set of text and diagram. Geometry problems are solved using machines; however, machines encounter challenges in natural language processing and computer vision. Significant progress has improved existing methods in the extraction of geometric formal languages. However, the neglect of the graph structure information in the formal language and the lack of further refinement of the extracted language set can lead to poor theorem prediction and poor accuracy in problem solving. In this paper, a formal language graph is constructed using the extracted formal language set and applied to theorem prediction using a graph convolutional network. To better extract the relationship set of diagram elements, an improved diagram parser is proposed. The test results indicate that the improved method has good results when solving interpretable geometry problems.
Effective Model Compression via Stage-wise Pruning

Automated machine learning (AutoML) pruning methods aim at searching for a pruning strategy automatically to reduce the computational complexity of deep convolutional neural networks (deep CNNs). However, some previous work found that the results of many Auto-ML pruning methods cannot even surpass the results of the uniformly pruning method. In this paper, the ineffectiveness of Auto-ML pruning, which is caused by unfull and unfair training of the supernet, is shown. A deep supernet suffers from unfull training because it contains too many candidates. To overcome the unfull training, a stage-wise pruning (SWP) method is proposed, which splits a deep supernet into several stage-wise supernets to reduce the candidate number and utilize inplace distillation to supervise the stage training. Besides, a wide supernet is hit by unfair training since the sampling probability of each channel is unequal. Therefore, the fullnet and the tinynet are sampled in each training iteration to ensure that each channel can be overtrained. Remarkably, the proxy performance of the subnets trained with SWP is closer to the actual performance than that of most of the previous AutoML pruning work. Furthermore, experiments show that SWP achieves the state-of-the-art in both CIFAR-10 and ImageNet under the mobile setting.
The AI Gold Rush

The recent frenzy of commercial interest in large language models could be compared to a gold rush. We consider the metaphor and where it illuminates the mechanisms shaping business decisions. If the rush indicates the presence of a large long-term opportunity, then expect the supply chain of equipment and software to develop to support it.
Lightweight multi-target detection algorithm for unmanned aerial vehicle aerial imagery

Compared with the image captured in the natural scene, the image obtained by unmanned aerial vehicle (UAV) aerial photography has a more complex background and many dense small targets, which puts forward higher requirements for the detection accuracy of the target detection algorithm. However, because the UAV is a kind of small mobile device, how to ensure its real-time detection effect has been a problem. Aiming at these problems, the lightweight YOLOv7 algorithm, namely LRT-YOLOv7, is designed. First, the enhance feature fusion module and the transformer efficient layer aggregation networks module are proposed to improve the performance of feature extraction and fusion to enhance the efficiency of small target detection. Second, aiming at the problems of small target size and complex background in the UAV images, the detection head structure is redesigned in the YOLOv7-tiny algorithm to enhance the multi-scale feature fusion ability of the algorithm and thereby improve the algorithm’s detection accuracy for small targets. Finally, ablation, comparison, and visualization validation experiments were conducted using precision, recall, mean average precision, and frames per second (FPS) as evaluation indicators. The results show that the detection speed of the LRT-YOLOv7 algorithm on the self-made traffic target dataset is 133.8 FPS, and the precision indicator is 84.58%. Therefore, the LRT-YOLOv7 algorithm has high accuracy and real-time performance in traffic target detection tasks for UAV aerial imagery.
Scalable and Precise Refinement Types for Imperative Languages

In formal verification, there is a dichotomy between tools which are scalable and easy to use but imprecise, like most pluggable type systems, and tools which are expressive and precise but badly scalable and difficult to use, like deductive verification. Our previous research to create a formal link between refinement types and deductive verification allows programmers to use a scalable type system for most of the program, while automatically generating specifications for a deductive verifier for the difficult-to-prove parts. However, this is currently limited to immutable objects. In this work, we thus present an approach which combines uniqueness and refinement type systems in an imperative language. Unlike existing similar approaches, which limit refinements such that they cannot contain any reference-typed program variables, our approach allows more general refinements, because even if a type constraint is not provable in the type system itself, it can be translated and proven by a deductive verifier.
Usability Enhancements to a Prototype Clinical Decision Support System for Combat Medics

A Clinical Decision Support System that provides just-in-time medical guidance at the point of injury is being developed. To develop a user interface, a user-centered design approach was taken.To evaluate the system, personas of the users were created, a comparative analysis of the system against the Tactical Combat Casualty Care Card and Battlefield Assisted Trauma Distributed Observation Kit was completed, and user testing was performed.Many design recommendations were gathered from the user-centered design approach including replacing buttons with a homunculus, replacing prompts with a tree and node system, and allowing more user freedom in working with the system.Through multiple different evaluations, design recommendations for a clinical decision support system were implemented in an iterative process. More iterations and more formalized user testing are planned to maximize the usability of the system.
Human Activity Recognition by Edge Computing Based Convolutional Neural Network

Human activity recognition has become a hotly debated research topic due to its numerous important and futuristic applications, such as automated surveillance, automated vehicles, language interpretation, and human-computer interfaces. (HCI). Extensive and in-depth research has been conducted, and good progress is been achieved in this area of research. The proposed system is designed by taking health monitoring as an application and implemented on a Raspberry Pi board, which can be used for monitoring and surveillance purposes. The activities considered in our proposed work are standing, jogging, climbing upstairs, and climbing downstairs. The surveillance camera will watch people’s movements to see if they are performing the assigned job. We propose a CNN-based technique to recognize different actions. An alert will sound and the system will report that the person’s designated task has been disrupted if the camera determines that they are engaged in another activity.
A Constraint Optimization path planning for Dynamic Environment

Optimal path planning is a major challenge for the operation of autonomous unmanned ground vehicles. In dynamic mobile path planning, the goal is to find a collision-free path for a robot from an initial position to a goal position in an environment with obstacles. However, in a dynamic environment, the challenge is bigger than static. In this paper, a modified ABC and an adaptive constraint PSO algorithm are proposed to deal with the dynamic environment; the algorithms were chosen to have a good balance between exploration and exploitation, with a penalty function used to have a collision-free path.
Planar weibull quantum circuit genetic algorithm with strong search ability and its implementation

Abstract Genetic Algorithm (GA) is a widely used search method, and Quantum Genetic Algorithm (QGA) has been pointed out for several years. While common QGA only refers to the concept of quantum mechanics, and still uses mathematical representations of quantum states to simulate quantum entanglement and superposition, it can’t be realized on a quantum computer. This paper presents an implementable quantum circuit for a genetic algorithm, named as Planar-Weibull Quantum Circuit Genetic Algorithm (PW-QCGA), employing the Weibull function and excitation function through planar matrix coding. It is not only a self-adaptive algorithm that adjusts parameters dynamically, but it can also be executed on quantum computers by constructing parameterized quantum circuits. Experiments on three typical applications show that PW-QCGA not only has obvious advantages on convergence accuracy, search ability, convergence speed, and robustness, but also has higher fidelity on a quantum computer. We hope this work can bring inspiration to other quantum intelligent algorithms.
Virtual and Augmented Reality in Library Collections

An overview of virtual and augmented reality (VR/AR) technologies and their potential applications in library collections. It covers the unique challenges in cataloging and preserving VR/AR content including the lack of accepted standards and workflows. Best practices for instruction and outreach are discussed along with ethical concerns and emerging issues. This entry serves as a comprehensive introduction for librarians and professionals interested in integrating VR /AR into their collections.
Security analysis of the Classic McEliece, HQC and BIKE schemes in low memory

With the advancement of NIST PQC standardization, three of the four candidates in Round 4 are code-based schemes, namely Classic McEliece, HQC and BIKE. Currently, one of the most important tasks is to further analyze their security levels for the suggested parameter sets. At PKC 2022, Esser and Bellini restated the major information set decoding (ISD) algorithms by using nearest neighbor technique and then applied these ISD algorithms to estimate the bit security of Classic McEliece, HQC and BIKE under the suggested parameter sets. However, all major ISD algorithms consume a large amount of memory, which in turn affects their time complexities. In this paper, we theoretically reestimate the security levels of the parameter sets suggested by these three schemes in low memory by applying K-list sum algorithms to ISD framework. Compared with Esser–Bellini’s algorithms, our results achieve the best gains for Classic McEliece, HQC, and BIKE, with reductions in security levels of 11.09, 12.64, and 12.19 bits, respectively.