Reviewerly/exHarmony huggingface dataset:
Efficient Unpredictable Multi Authority Attribute based Encryption

Data access control is an effective way to ensure data security in the cloud. Due to data outsourcing and untrusted cloud servers, the data access control becomes a challenging issue in cloud storage systems. Attribute-based encryption (ABE) is usually adopted for cloud storage, both for its achievement of fine grained access control over data, and for its guarantee of data confidentiality. Single-authority Attribute-Based Encryption (SA-ABE) has its obvious drawback in that only one attribute authority can assign the users’ attributes, enabling the data to be shared only within the management domain of the attribute authority, while rendering multiple attribute authorities unable to share the data. On the other hand, multi-authority attribute-based encryption (MA-ABE) has its advantages over SA-ABE. It can not only satisfy the need for the fine-grained access control and confidentiality of data, but also make the data shared among different multiple attribute authorities. In this paper, on the basis of the cryptography, an efficient revocable multi-authority attribute-based encryption (RMA-ABE) scheme for cloud storage is proposed. Multi-authority attribute-based encryption was very suitable for data access control in a cloud storage environment. However, efficient user revocation in multi-authority attribute based encryption remains a challenging problem that prevents it from practical applications. A multi-authority attribute-based encryption scheme with efficient revocation was proposed with proved statically secure and revocable in the random oracle model.
A study on the effect of different machine learning algorithms on soccer footwork recognition under trajectory tracking theory

Abstract This paper aims to investigate the effectiveness and influencing factors of different machine learning algorithms on soccer footwork recognition. In this paper, we use inertial sensors to obtain the basic data of soccer players’ movements, then convert them into initial data of footwork using pose representation and pose-solving filtering. The value of K mainly influences the classification accuracy of KNN, and the highest accuracy of 67.23% is achieved when K is 5. The classification accuracy of SVM is related to the choice of the distance function. The accuracy of CNN is mainly affected by the size of the convolutional kernel and the convolutional step size, and the highest accuracy is 73.82%. The machine learning-based soccer step recognition can improve the recognition accuracy of traditional physical methods and provide scientific sports guidance for soccer players’ training.
English-Chinese translation strategies based on BERT and multi-similarity fusion of English and American literature

Abstract Under the background of big data development, more intelligent technologies make work and life convenient, and the traditional English-US literature translation methods and means are difficult to adapt to the fast-paced era of big data, so this paper conducts research and analysis on this problem. In terms of the English-Chinese translation strategy based on BERT and multi-similarity fusion of English and American literature, the article first introduces the concept and principle of BERT, and multi-similarity fusion algorithm then constructs the model of BERT and multi-similarity algorithm fusion, analyzes and discusses the English and American literature translation dataset respectively, and compares it with three different similarity algorithms in terms of three indicators. The data shows that BERT has the greatest effect on the accuracy rate, followed by BLEU value and Manhattan distance. Compared with the fusion of the three methods, the data analysis of a single method is better, and the three selected index values all remain above 90%, and this study is conducive to improving the accuracy and efficiency of English and American literature work, which is of great historical significance for international literary communication.
Deep Learning Based Sports Video Classification Research

Abstract In this paper, we study sports video classification technology based on a deep learning algorithm, using a convolutional neural network and deep learning gradient descent algorithm as the main research method to classify and regress the image features of sports video output. A sports video classification model is built to clarify the full research idea, and the results of extracted data are verified one by one with OTB target tracking dataset and specifically analyzed the accuracy and check-all rate of various types of videos, and then conclusions are drawn. The data show that the accuracy of the sports video classification test set with deep learning algorithm reaches 98.3%, and the detection rate of tennis, badminton, and table tennis all reach 92%. Soccer and basketball have a lower accuracy rate but can reach 83%. The accuracy rate of capturing detailed actions is 97.6%. The sports video classification based on deep learning accurately and effectively captures faces and actions accurately, which is important for viewers to find interesting sports video categories quickly.
Human-object Behavior Analysis Based on Interaction Feature Generation Algorithm

Aiming at the problem of insufficient utilization of interactive feature information between human and object, this paper proposes a two-stream human-object behavior analysis network based on interaction feature generation algorithm. The network extracts human-object’s feature information and interactive feature information respectively. When extracting human-object features information, considering that ResNeXt has powerful feature expression ability, the network is used to extract human-object features from images. When extracting interactive features information between human and object, an interaction feature generation algorithm is proposed, which uses the feature reasoning ability of graph convolutional neural networks. A graph model is constructed by taking human and objects as nodes and the interaction between them as edges. According to the interactive feature generation algorithm, the graph model is updated by traversing nodes, and new interactive features are generated during this process. Finally, the humans’ and objects’ features information and the human-object interaction feature information are fused and sent to the classification network for behavior recognition, so as to fully utilize the humans’ and objects’ feature information and the interaction feature information of human-objects. The human-object behavior analysis network is experimentally verified. The results show that the accuracy of the network has been significantly improved on HICO-DET and V-COCO datasets.
Integrating Transfer Learning and Deep Neural Networks for Accurate Medical Disease Diagnosis from Multi-Modal Data

Effective patient treatment and care depend heavily on accurate disease diagnosis. The availability of multi-modal medical data in recent years, such as genetic profiles, clinical reports, and imaging scans, has created new possibilities for increasing diagnostic precision. However, because of their inherent complexity and variability, analyzing and integrating these varied data types present significant challenges. In order to overcome the difficulties of precise medical disease diagnosis using multi-modal data, this research suggests a novel approach that combines Transfer Learning (TL) and Deep Neural Networks (DNN). An image dataset that included images from various stages of Alzheimer's disease (AD) was collected from kaggle repository. In order to improve the quality of the signals or images for further analysis, a Gaussian filter is applied during the preprocessing stage to smooth out and reduce noise in the input data. The features are then extracted using Gray-Level Co-occurrence Matrix (GLCM). TL makes it possible for the model to use the information gained from previously trained models in other domains, requiring less training time and data. The trained model used in this approach is AlexNet. The classification of the disease is done using DNN. This integrated approach improves diagnostic precision particularly in scenarios with limited data availability. The study assesses the effectiveness of the suggested method for diagnosing AD, focusing on evaluation metrics such as accuracy, precision, miss rate, recall, F1-score, and the Area under the Receiver Operating Characteristic Curve (AUC-ROC). The approach is a promising tool for medical professionals to make more accurate and timely diagnoses, which will ultimately improve patient outcomes and healthcare practices. The results show significant improvements in accuracy (99.32%).
BIG DATA CONCEPT ANALYSIS FOR AGRICULTURAL SUITABLE LAND GEOGRAPHIC INFORMATION SYSTEM APPROACH

Big data analysis for agriculture provides farmers with a comprehensive view of the concept of increasing agricultural productivity using the effectiveness of irrigation canals, predicting rainfall to determine outcrop patterns, and identifying the adequacy of agricultural land. It also allows farmers to optimize irrigation, increasing yields while reducing costs and environmental impact. It also will enable farmers to optimize irrigation; Rainfall predictions are used to determine cropping patterns and identify suitability for permits. It can also be used to deal with weather patterns and climate change, allowing farmers to adapt their practices to reduce the impact of climate change, ultimately protecting their crops and currency. This research aims to develop plant productivity through several stages of research and the use of methods. The methods used in this study are 1)Prediction of water discharge using the linear regression method; 2)Prediction of Rainfall for Planting Pattern Training using the SARIMA method, and 3)Suitability of Agricultural Land using the Cluster Area Analysis Approach. The results of this study are that in the Sleman region, the adequacy of water for agricultural areas is in the excellent category (fulfilled), cropping pattern spending is divided into 2, namely dry and wet months. In the wet months (high rainfall), rice is suitable for planting from January to May; for the dry months between June and October, tobacco, soybeans, corn, peanuts, green beans, cassava, and sweet potatoes. As for land suitability, it consisted of 46025.36 Ha (81%) suitable and 10811.48 Ha not suitable for use.
Pengaruh Buku Suplemen Digital Terintegrasi Mitigasi Bencana terhadap Keterampilan Kolaborasi Peserta Didik

Collaboration skills are a process carried out in groups to discuss differences through listening, giving suggestions, listening to discussions and respecting differences of opinion. Student collaboration skills can be dangerous in the learning process, one of which is by utilizing digital-based learning media. Digital supplement books are complementary books to printed books which contain reading text, images, video, audio and other information that can be read on smartphones, laptops and other portable devices. The purpose of this study was to determine the effect of disaster-integrated digital supplement books on students' training skills. This research was conducted using the Quasi Experiment research method (pseudo-experiment). The data collection technique for this research was an observation sheet filled in by two observers at each meeting and a questionnaire filled in individually by students. The average observation sheet for the collaboration skills of students in the experimental class was higher than that of the control class, namely 71 for the experimental class and 59 for the control class. At the level of student collaboration skills, the average value of the experimental class was 91% greater than that of the control class, which was 79%. From this study it can be interpreted that the integrated digital supplement book on the impact of disasters has an effect on the skills of the trainees.
A Lightweight ABE Security Protection Scheme in Cloud Environment Based on Attribute Weight

Attribute-based encryption (ABE) is a technique used to encrypt data, it has the flexibility of access control, high security, and resistance to collusion attacks, and especially it is used in cloud security protection. However, a large number of bilinear mappings are used in ABE, and the calculation of bilinear pairing is time-consuming. So there is the problem of low efficiency. On the other hand, the decryption key is not uniquely associated with personal identification information, if the decryption key is maliciously sold, ABE is unable to achieve accountability for the user. In practical applications, shared message requires hierarchical sharing in most cases, in this paper, we present a message security hierarchy ABE scheme for this scenario. Firstly, attributes were grouped and weighted according to the importance of attributes, and then an access structure based on a threshold tree was constructed according to attribute weight. This method saved the computing time for decryption while ensuring security and on-demand access to information for users. In addition, with the help of computing power in the cloud, two-step decryption was used to complete the access, which relieved the computing and storage burden on the client side. Finally, we simulated and tested the scheme based on CP-ABE, and selected different security levels to test its performance. The security proof and the experimental simulation result show that the proposed scheme has high efficiency and good performance, and the solution implements hierarchical access to the shared message.
A Novel Parallel Computing Confidentiality Scheme Based on Hindmarsh-Rose Model

Due to the inherent insecure nature of the Internet, it is crucial to ensure the secure transmission of image data over this network. Additionally, given the limitations of computers, it becomes even more important to employ efficient and fast image encryption techniques. While 1D chaotic maps offer a practical approach to real-time image encryption, their limited flexibility and increased vulnerability restrict their practical application. In this research, we have utilized a 3D Hindmarsh-Rose model to construct a secure cryptosystem. The randomness of the chaotic map is assessed through standard analysis. The proposed system enhances security by incorporating an increased number of system parameters and a wide range of chaotic parameters, as well as ensuring a uniform distribution of chaotic signals across the entire value space. Additionally, a fast image encryption technique utilizing the new chaotic system is proposed. The novelty of the approach is confirmed through time complexity analysis. To further strengthen the resistance against cryptanalysis attacks and differential attacks, the SHA-256 algorithm is employed for secure key generation. Experimental results through a number of parameters demonstrate the strong cryptographic performance of the proposed image encryption approach, highlighting its exceptional suitability for secure communication. Moreover, the security of the proposed scheme has been compared with state-of-the-art image encryption schemes, and all comparison metrics indicate the superior performance of the proposed scheme.
Cross-Domain Data Traceability Mechanism Based on Blockchain

With the application and development of blockchain technology, many problems faced by blockchain traceability are gradually exposed. Such as cross-chain information collaboration, data separation and storage, multi-system, multi-security domain collaboration, etc. To solve these problems, it is proposed to construct trust domains based on federated chains. The public chain is used as the authorization chain to build a cross-domain data traceability mechanism applicable to multi-domain collaboration. First, the architecture of the blockchain cross-domain model is designed. Combined with the data access strategy and the decision mechanism, the open and transparent judgment of cross-domain permission and cross-domain identity authentication is realized. And the public chain consensus node election mechanism is realized based on PageRank. Then, according to the characteristics of a nonsingle chain structure in the process of data flow, a data retrieval mechanism based on a Bloom filter is designed, and the cross-domain traceability algorithm is given. Finally, the safety and effectiveness of the traceability mechanism are verified by security evaluation and performance analysis.
DEVELOPMENT OF SCHEDULING SYSTEM WITH GENETIC ALGORITHM IN WEBSITE-BASED SMK NEGERI 1 SINE

Scheduling is an information that has limited conditions that must be met. Preparation of the schedule will take quite a long time if it is done using conventional media such as writing on paper or books. Scheduling optimization is needed to provide effectiveness and efficiency so that the implementation of learning activities can run more optimally. The genetic algorithm approach method is used to get the optimum schedule. This algorithm produces the best combination for subject pairs and teaching teachers as a whole by determining the initial population and initializing the chromosomes, determining the fitness value, then carrying out crossover selection, and carrying out mutations to produce the best fitness value which will be used to determine the final value of scheduling. The results of the entire algorithm process are consistent with the original prediction data, and the same teacher is not scheduled to teach more than once at the same time. The results of the subject scheduling process using the genetic algorithm obtain a fairly good optimization in subject scheduling.
FORENSIC RECOVERY ANALYSIS OF LOST RAID 0 CONFIGURATION ON NETWORK ATTACHED STORAGE AS EVIDENCE IN COURT

In the modern world of globalization, Digital Forensic science has emerged as a pivotal investigative tool for solving complex cases. This discipline proves particularly effective in deciphering digital evidence. In this study, we ventured to examine a Network Area Storage (NAS) device, specifically one containing three hard disks. Our objective was to validate that the Hash value generated before the duplication process matches identically with the Hash value post-imaging or duplication. Our findings demonstrate that through reconfiguration and recovery of RAID 0, previously deleted files can be restored using the methods outlined in our research. In essence, this study establishes that: (1) Reconstruction of RAID configurations is feasible, (2) Recovery of deleted files from a RAID 0 system is achievable, and (3) Such restored data can serve as admissible evidence in court.
PENGEMBANGAN VIDEO TUTORIAL PRODUKSI ANIMASI PEMBELAJARAN SEKOLAH DASAR MENGGUNAKAN MICROSOFT POWER POINT

This study attempts to: (1) develop media tutorials in making of primary school animated learning use application Microsoft power point, (2) know feasibility media tutorials in making of primary school animated learning use application Microsoft power point. This is a Research and Development Research (R D), which are generally trough four stages, namely: study introduction, design development, product development, and product evaluation. Data collection was in the form of sheets of material validation, media experts and users to know the feasibility of video tutorial media. The product of the development is in the form of video tutorials media that can be stored in compact disk (CD). The research results show as follows: (1) media tutorial video developed was for teaching the ability for the production of simple animation in primary school learning, (2) evaluation results of material experts obtains a judgment 3.7 (good category) by inference worth to be used, (3) evaluation of media experts obtains a judgment 4.1 (god category) by inference worth to be used, (4) evaluation results by users get average 4.3 (very good category) by inference worth to be used.
Internet of Things for 4.0 Industry Revolution

In this training, participants were given an understanding of the IoT (Internet of Things) for the Industrial Revolution 4.0 and were accompanied by instructors to get to know the technology. The approach method is based on research results that have been published in electrical engineering. We CO (Collaborated Overseas) with UTHM (Universiti Tun Hussein Onn Malaysia) in the CS (Community Service) programme, with the aim of providing guest lectures on the advancement of IoT technology and IR (Industry Revolution) 4.0. Thus, the contribution to this CS activity is to support the improvement of the quality of skilled labor from partner participants to be in line with industry needs. Therefore, the leadership of the institution approved and held the IoT for 4.0 IR training. Universitas Mercu Buana through the Institute for Research and Community Service participated in the field of ICT (Information and Communication Technology) with the long-term target of developing wireless sensors and actuators, and computing methods in embedded systems and the cloud. The conclusion of this CS activity is that participants were very enthusiastic about this activity and and gained the benefits of science, as well as an understanding of telecommunications.
TGMIL: A hybrid multi-instance learning model based on the Transformer and the Graph Attention Network for whole-slide images classification of renal cell carcinoma

The pathological diagnosis of renal cell carcinoma is crucial for treatment. Currently, the multi-instance learning method is commonly used for whole-slide image classification of renal cell carcinoma, which is mainly based on the assumption of independent identical distribution. But this is inconsistent with the need to consider the correlation between different instances in the diagnosis process. Furthermore, the problem of high resource consumption of pathology images is still urgent to be solved. Therefore, we propose a new multi-instance learning method to solve this problem. In this study, we proposed a hybrid multi-instance learning model based on the Transformer and the Graph Attention Network, called TGMIL, to achieve whole-slide image of renal cell carcinoma classification without pixel-level annotation or region of interest extraction. Our approach is divided into three steps. First, we designed a feature pyramid with the multiple low magnifications of whole-slide image named MMFP. It makes the model incorporates richer information, and reduces memory consumption as well as training time compared to the highest magnification. Second, TGMIL amalgamates the Transformer and the Graph Attention's capabilities, adeptly addressing the loss of instance contextual and spatial. Within the Graph Attention network stream, an easy and efficient approach employing max pooling and mean pooling yields the graph adjacency matrix, devoid of extra memory consumption. Finally, the outputs of two streams of TGMIL are aggregated to achieve the classification of renal cell carcinoma. On the TCGA-RCC validation set, a public dataset for renal cell carcinoma, the area under a receiver operating characteristic (ROC) curve (AUC) and accuracy of TGMIL were 0.98±0.0015,0.9191±0.0062, respectively. It showcased remarkable proficiency on the private validation set of renal cell carcinoma pathology images, attaining AUC of 0.9386±0.0162 and ACC of 0.9197±0.0124. Furthermore, on the public breast cancer whole-slide image test dataset, CAMELYON 16, our model showed good classification performance with an accuracy of 0.8792. TGMIL models the diagnostic process of pathologists and shows good classification performance on multiple datasets. Concurrently, the MMFP module efficiently diminishes resource requirements, offering a novel angle for exploring computational pathology images.