Reviewerly/exHarmony huggingface dataset:
Pengelompokan Pelanggan Toko Kerajinan Menggunakan K-Means dengan Model RFM dan LRFM

Customer groups within a company or shop are important to identify for determine the right sales strategy in a competitive market. This research was conducted at one of the online handicraft shops in Bali which was affected by the intense competition in the market. The handicraft industry in Bali is considered to provide a large contribution to the community's economy. One solution that can be done is to identify the customer groups in the store using the clustering technique. This research aims to get the best customer data cluster and model. The stages in this research start from preprocessing data on historical data and customer orders to generate data model Recency, Frequency, Monetary (RFM) and Length, Recency, Frequency, Monetary (LRFM), then the clustering process with K-Means and evaluation of cluster quality with Silhouette Coefficient (SC). Results Based on the research, the RFM model becomes a data model that produces the best clustering results with an SC value of 0.545 when k = 2 which is included in the medium structure. LRFM model only produces the largest SC value when k = 3 with a value of 0.415. The SC value calculated from each data model tends to increase when the percentage of data is added. Cluster 1 has 817 members with the last transaction taking a long time, but has a below average ordering and monetary frequency. Cluster 2 has 158 members, making the last transaction at the most recent time, and has an average order and monetary frequency.&#x0D; &#x0D; Keywords: Clustering, Customer, Craft, K-Means, RFM, LRFM, Silhouette Coefficient
Upaya Meningkatkan Keterampilan Membaca melalui Media Kartu Huruf Siswa Kelas I SD Negeri 30 Cakranegara

This study aims: 1) To find out the use of letter card media in learning Indonesian can improve the reading skills of grade I students at SD Negeri 30 Cakranegara; 2) To find out the improvement of speaking skills in learning Indonesian through the media of letter cards for first grade students of SD Negeri 30 Cakranegara. This research was designed using Classroom Action Research. The subjects in this study were the first grade students of SD Negeri 30 Cakranegara, totaling 29 people, consisting of 17 female students and 15 male students. While the research procedure goes through the stages of planning, implementing actions, observing, and reflecting. The results showed that the application or use of letter card media in Indonesian language learning can improve student learning outcomes, when teaching and learning activities take place, namely from the number of students, namely 29 people, whose scores are equal to or more than the KKM totaling 27 people, and if the percentage is 93, 10%. This shows that there is an increase from the initial conditions and the first cycle, namely from the number of students who completed as many as 19 people 65.56% then to 24 people 82.76% and increased again to 27 people 93.10%. Based on the results of this study, it can be concluded that learning Indonesian by applying or using letter card media can improve the reading skills of first grade students at SD Negeri 30 Cakranegara.
Virtual Shooting Action Simulation System Based on Intelligent VR Technology

Basketball is a competitive sport that scores points by putting the ball into the basket. To win the game, you must hit as many baskets as possible within the effective time. Therefore, shooting training is the most important training subject in daily training, and it is also the training program with the strongest skills and the longest practice accumulation process. To train shooting skills, you can not only practice on the real basketball court, but also practice in the virtual scene. The virtual shooting action simulation system designed in this paper applies computer, intelligent VR technology, Vega virtual simulation technology, database and other technologies to system training. Moreover, the system can restore the real basketball court, allowing users to use the system to play basketball.
The Interplay Between Missing Data and Out-of-Order Measurements using Data Fusion in Wireless Sensor Networks

Multi-data transmission is the most important processing of target detection with a reduction in delay in the transmission of the data. This may occur in certain technological circumstances, and it happens significantly often in wireless sensor networks—processing such data to keep track of and make predictions about targets of interest might result in errors due to the inherent nature of the data. The Kalman filter and other algorithms with equivalent functionality are most useful for their principal application, estimating the states of dynamic systems. This difficulty of modeling and filtering such delayed states and missing data is dealt with synergistically throughout this proposed work. This is done to ensure that the best possible results are obtained. Filtering methods similar to the optimal Kalman filter are most utilized in fusing measurement data at different levels. This relatively creative technique includes filtering delayed states while also using observations that have been randomly excluded, then putting those screened delayed states and words to use in a process that involves fusing data. One of these applications is the fusion of images. To successful the task of performance evaluation for the integrated plan, the use of numerical simulations is essential. The state delay, as well as the data that is absent at random, are both included in four distinct alternative algorithms. These algorithms are then investigated, and the results are given in this paper. Referring to the gain fusion, the H-infinity a posteriori filter, the H-infinity risk sensitive filter, and the H-infinity risk sensitive filter. To accommodate a scenario that involves MATLAB and the integration of sensor data, global filtering approaches are being updated and evaluated with the use of numerical simulations that are being carried out. In addition, we provide a nonlinear observer based on the gain of the continuous time data fusion filter. Using the Lyapunov energy function, we can conclude on asymptotic convergence in the system. These observers are presented after the previous step. Therefore, the filtering algorithms and the observers described in the current proposed work make a definite step towards improvement for controlling state delays and randomly missing data synergistically for wireless sensor networks.
Incremental Update of Locally Optimal Classification Rules

Incremental learning is a traditional topic that has particularly gained importance in the wake of big data and stream mining. Discrete symbolic representations do not easily allow for gradual refinements of the learned concept. While the problem is less severe for incremental induction of decision trees, it is much harder for incremental rule learning in that there are hardly any incremental rule learning algorithms which are really successful. In this paper, we introduce iLord algorithm, an adaptation of a recently proposed rule learning algorithm Lord, which aims at finding the best rule for each individual example, to an incremental learning setting. After being initialized with a first batch of training examples, iLord relies on efficient data structures to summarize the information contained in the training examples, which can be quickly updated and allows to retrieve the best rule for each incoming example. The behavior of iLord is evaluated with different parameterizations, and compared to other best-known incremental symbolic learning algorithms such as HoeffdingTree and VFDR.
An Efficient Scheme for Facial Expression Recognition using Random Forest and CNN

As humans, we use our facial expressions to communicate our thoughts and feelings without saying a word. The recognition of these facial expressions can provide a better idea of people’s thoughts or opinions. At present, the high performance of deep learning procedures has influenced the high efficiency of image recognition systems. This paper proposes a Facial Expression Recognition System (FER) using Convolutional Neural Network (CNN). We have made use of features such as Gabor wave coefficients, Histogram of Oriented Gradient (HOG), Speed-Up Robust Features (SURF), and Local Binary Patterns (LBP). We have used the Random Forest algorithm for the selection of the best features from the extracted feature set, accordingly, the learning procedure can be faster. The widely used CK+ database can be used for the appraisal of our proposed system. We have also compared our proposed method against similar FER systems and the consequences are satisfactory.
Research on Music Education Method Based on Voice Recognition Algorithm

Today’s society pays more and more attention to the quality education and all-round development of talents. Music education, as an important discipline for the all-round development of talents, has also been paid more and more attention. In music teaching, teachers should deeply explore the new curriculum standards, combine them with students’ interests, carry out music innovation education and practice, and constantly improve students’ various innovation abilities, so as to make music teaching more valuable and meaningful. This paper studies and discusses the methods of music education based on voice recognition algorithm.
Navigation, SLAM, and Goals

In this chapter, our intrepid rover will explore the Egyptian catacombs. Any autonomous rover needs to know where it is in its environment; i.e., its current position. That way, the rover can travel from one location to the next without user input. To do this, the rover must construct an internal map of its environment as it travels. This chapter introduces the simultaneous localization and mapping (SLAM) algorithm. SLAM generates maps from navigation data, such as the odometry data from the rover’s wheel encoders and the laser range data from the LiDAR sensor.
Light-weight Security Protocol in IoT with Less Computational Cost

Firewalls are the emerging technology to secure the internal network resources from outsider attacks. On the other hand, Authentication, integrity, and confidentiality are the main security challenges for firewalls. To achieve these security challenges, recently, two multicast signcryption schemes have been contributed to the literature. These two Signcryption suffer from two main flaws: computational cost and our head communication. Keeping in view these two flaws, we designed a new multi-receiver signcryption scheme that is lightweight. A lightweight and well-secured protocol is presented for smart-IOT-based homes that are proven to be secured against impersonation, replay, and exposed session key attacks. The Proposed technique is experimented with using the AVISPA tool to ensure that the various attacks do not crack it. The efficiency and security of the proposed scheme are based on a hyperelliptic curve cryptosystem. The hyperelliptic curve cryptography is the subtype of an elliptic curve cryptosystem, and the key size is low from the elliptic curve. Our scheme provides all the security requirements provided by the existing multi-receiver signcryption schemes with low computational and communication costs.
Basics of Quantum Information Processing

In this chapter, we present a collection of basic ideas in quantum information processing. First idea to present is the quantum gate operations, where a set of single-qubit gates and at least one two-qubit gate allows for the preparation of an arbitrary quantum state. We put particular emphasis on the derivation of quantum gates from the physical Hamiltonians. The second idea is quantum measurement and quantum state/process tomography. Then we scratch the surface of the quantum error correction next. The chapter ends with the introduction of celebrated DiVincenzo criteria, which nicely characterize the sound quantum systems in quantum computation and quantum networking.
Isolate-Set-Based In-Memory Parallel Subgraph Matching Framework

Subgraph matching is a classical graph task, which finds all subgraphs that are isomorphic to the query graph in a labeled data graph. According to the theoretical basis, there are three types of subgraph matching methods: exploration-based methods, state-space representation methods, and multi-way join methods. Each of these methods has its advantages, but their common feature is a large amount of computation. In order to improve the computation efficiency, some representative subgraph matching methods have their own parallel optimization methods, but these parallel optimization methods are not universal. To overcome the drawbacks and improve the efficiency of parallel computing, we propose an isolate-set-based parallel subgraph matching framework. The parallel framework can flexibly adapt different subgraph matching methods without changing the sequential methods themselves. We partition the search tree into several decoupled subtrees and prune the subtrees to reduce computation overheads. And then, parallel subgraph matching is implemented on the decoupled pruned subtrees. To demonstrate the proposed method’s effectiveness empirically, we parallelize several representative subgraph matching methods in our framework and compare their performance with the sequential methods. The results show that our parallel framework is adapted to representative sequential methods, outperforms the sequential methods by a large margin, and achieves a speedup of 10x-50x on a 64-core x86 machine.
Microsoft Teams Phone (Voice) Configuration and Management

Teams brings together calling, meeting, chat collaboration, and apps, thereby helping users easily stay connected right in their workflow. Microsoft Teams provides calls (1:1 and group) and multiparty meetings with optimal audio/video quality. Apart from VoIP communication, Teams provides external calling (PSTN calling) through calling plans, Operator Connects, and Direct Routing. Teams (Microsoft 365) Phone System is Microsoft's technology for enabling call control and PBX capabilities in the Microsoft 365 cloud.
Wavefunction and Notations in Quantum Mechanics

In this chapter, we review the properties of wavefunction and different representations. The introductory quantum mechanics courses usually start out with the wave-mechanics based on wavefunction representation, while it is often more convenient to use the matrix representation in the context of quantum information sciences. However, there are always some exceptions, and we often switch back and forth between two representations; thus, it is important for readers to be familiar with both representations.
A New Dynamic Routing Network for Monocular Depth Estimation

Depth estimation is an essential task for understanding the geometry of 3D scenes. Compared with multi-view-based methods, monocular depth estimation is more challenging for the requirement of integrating not only global information but also local cues from various parts. Recently, numerous Convolutional Neural Networks (CNNs) based approaches relying on pre-defined static architectures have been reported. Although many of them have achieved remarkable improvements in accuracy, there remains problem with the generality of the static model when it comes to diverse scenes. In this paper, a novel deep network with dynamic routing architecture is proposed with a dynamically adjustable structure with respect to different inputs, therefore leading to not only improvements on both accuracy and generality but also decrease of the model parameters. Extensive experiments of monocular depth estimation have been conducted on two well-known challenging datasets (i.e., NYUD-v2 and Make3D) as well as the mixed dataset, and have verified the effectiveness of the proposed dynamic framework.
Spam Detection with Integrated Review Text and Reviewer Behavior

The user-centered nature of the Web 2.0 era has prompted some merchants to use spam reviews to manipulate ratings and attract users. Since the performance of traditional methods for identifying spam reviews needs to be improved, this paper reviews and summarizes the existing methods for identifying spam reviews, and proposes a spam review identification model based on the review text and reviewer behaviors. The model extracts feature from the user’s review text and behavior separately, generates multiple decision trees using the random forest algorithm, integrates features for classification, and finally realizes the identification of spam reviews. The experimental results show that although the text of the spam reviews gradually becomes more and more like real ones, the spammers usually have obvious behavioral patterns. Compared with the identification models using only textual features or behavior characteristics, this model has significantly improved accuracy. Meanwhile, this model does not require large training sets and has strong generalization ability.
Circuit level implementation of the Reduced Quantum Genetic Algorithm using Qiskit

Genetic Algorithm (GA) are common probabilistic optimization methods inspired by the process of natural selection. Quantum computers promise substantial speedups over conventional machines, and libraries allow the emulation of circuits on a quantum computer in different highly configurable noise models and even run on quantum computers. Therefore, we need to analyze this class of heuristic methods in the quantum context. We propose a circuit level implementation of the Reduced Quantum Genetic Algorithm (RQGA) using Python and Qiskit. Our main goal is to analyze the circuit complexity from the perspectives of the number of qubits required and the number of quantum gates used. To achieve our goal, we instantiate the framework for solving the knapsack problem, examine the results in a simulated environment, and analyze the circuit's complexity.
PENGEMBANGAN STUDENT’S WORKSHEET BERBASIS CTL MENGGUNAKAN SAKEM

This research is a developmental research that aimed to produce CTL-based Students' Worksheets using SAKEM (Waste Packaging) in Class X High School Descriptive Text material. The instruments in this study were material and design / media assessment sheets, teacher assessment questionnaires and participant response questionnaires. The study was conducted at SMAN 11 Tebo. Product validation is carried out by material experts, design / media experts. Product testing is done by means of individual trials and small group trials. The results of the study indicated that Students' Worksheets are feasible for use by teachers and students. This can be proven by the validity and practicality of Students' Worksheets. The Students' validity Worksheets could be seen from the results of the validation of material and design / media experts with the category "very good". And also the practicality of English teachers assessment and the students response which a very positive category.
EfficientRec: An Unlimited User Scale Recommendation System Based on Clustering and User’s Interaction Embedding Profile

Recommendation systems are highly interested in technology companies nowadays. The businesses are constantly growing users and products, causing the number of users and items to continuously increase over time, to very large numbers. Traditional recommendation algorithms with complexity dependent on the number of users and items make them difficult to adapt to the industrial environment. In this paper, we introduce a new method applying graph neural networks with a contrastive learning framework in extracting user preferences. We incorporate a soft clustering architecture that significantly reduces the computational cost of the inference process. Experiments show that the model is able to learn user preferences with low computational cost in both training and prediction phases. At the same time, the model gives a very good accuracy. We call this architecture EfficientRec with the implication of model compactness and the ability to scale to unlimited users and products.
MACFE: A Meta-learning and Causality Based Feature Engineering Framework

Feature engineering has become one of the most important steps to improving model prediction performance, and producing quality datasets. However, this process requires non-trivial domain knowledge which involves a time-consuming task. Thereby, automating such processes has become an active area of research and interest in industrial applications. In this paper, a novel method, called Meta-learning and Causality Based Feature Engineering (MACFE), is proposed; our method is based on the use of meta-learning, feature distribution encoding, and causality feature selection. In MACFE, meta-learning is used to find the best transformations, then the search is accelerated by pre-selecting “original” features given their causal relevance. Experimental evaluations on popular classification datasets show that MACFE can improve the prediction performance across eight classifiers, outperforms the current state-of-the-art methods on average by at least 6.54%, and obtains an improvement of 2.71% over the best previous works.
The Development of Chinese Law Under the International Debate on Adequate Intellectual Property Protection

China’s integration into the international regime of copyright law correlates with its measures to improve copyright law step by step. Chinese copyright law has undergone its third comprehensive amendment, which includes the definition of work, types of works, the content of the copyright, related rights, copyright limitation and exception rules, and more effective implementation measures are also involved. One highlight is in the suggested reforms to limitation rules, which reflecting the intention of the Chinese legislature to introduce more flexibility into the copyright law in view of quickly developing information technology, which is deserving of praise. The concrete technologies and processes involved in legislation must, however, be improved.
Breast cancer malignancy classification based on breast histopathology images using convolutional neural network

Breast cancer is one of the main causes of women’s death in Indonesia. The prediction of the breast by medical personnel to classified the type of the breast histopathology image (BreakHis) with high accuracy in a short time is needed. This study aims to determine BreakHis’ malignancy classification, including in the benign or malignant class using the CNN (Convolutional Neural Network) algorithm and determine the optimization’s results of the accuracy benign class and malignant class using architectures of MobileNetV2 and ResNet50V2. In this study, 7891 BreakHis datasets are used with 40×, 100×, 200×, and 400× factors from the Kaggle website. The whole image is resized to 224×224 pixels and used Jupiter with the Python programming language to perform this study. The results showed the highest accuracy in the ResNet50V2 model with accuracy values of 100% for training data, 95.8% for testing, and 97% for validation.