Reviewerly/exHarmony huggingface dataset:
Efficient System for Criminal Face Detection Technique on Innovative Facial Features To Improve Accuracy Using LBPH In Comparison With CNN

Aim:The objective of this study is to use face recognition technology to identify the suspects through facial biometrics which helps in identification of criminals faster based on innovative facial features.Innovative Local Binary Pattern Histograms algorithm is used for the face recognition method.Materials and Methods: Face detection for identifying the criminals is performed using Innovative Local Binary Pattern Histograms (N=10) was iterated 10 times for efficient and accurate analysis based on labeled data with G power in 80% and threshold 0.05%, CI 95% mean and standard deviation.The split size of training and testing of 70% and 30% respectively.Results: It is found that the accuracy of LBPH is 90.80% which is higher than the CNN model 90.30% and attained the significance value of p = 0.0303 (p<0.05).Conclusion: For the face identification purpose Local Binary Pattern Histograms algorithm is preferred than Convolutional Neural Network algorithm.
Evolutionary approach for neural network based agents applied on time series data in the Cloud

One of the alternatives and most popular heuristics for global optimisation and for training artificial neural networks is the application of evolutionary searching method. This paper investigates the internal details of the evolutionary searching mechanism from theoretical aspects and also from experimental parallel execution aspects utilising commercial (AWS) and community (ELKH) cloud resources. The paper introduces and evaluates a procedure where the resources assigned for the execution of calculations are scalable horizontally in order to increase significantly the speed of processing.
Optimizing scheduling in cloud using a meta-heuristic approach

Cloud computing aims to optimal use of its resources by aggregating them to increase throughput and solve difficult computational problems in the most efficient way possible. Task scheduling problem is incompliant with exact solutions in cloud due to its NP-hard nature. To address this, various meta-heuristic strategies have been developed. A task scheduler should locate the optimal resources for the user’s job while taking into account specific cloud task parameter constraints. Here, a hybrid task scheduling strategy is described that incorporates deep learning and nature-inspired meta-heuristic optimization to maximize cloud throughput while minimizing completion time in an IaaS cloud. The scheduler succeeds towards cloudlet allocation resulted to shorter makespan and higher system throughput. The novel scheduling technique was evaluated against certain algorithms using the CloudSim software. When compared to existing algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), the experimental findings show that the suggested approach outperforms them.
RESEARCH OF THE STATE OF DIGITAL COMPETENCE OF TEACHERS OF HIGHER EDUCATION INSTITUTIONS IN THE CONDITIONS OF POST-COVID-19 EDUCATIONAL SPACE

The article examines the state of digital competence of teachers of higher education institutions in the conditions of post-COVID-19 educational space in the period from the beginning 2019-2020 academic year till the end of 2020-2021 academic year. The concepts of «educational space», «competence», «digital competence» are clarified and the concept of «post-COVID-19 educational space» is revealed. The structure of digital competence, conditions of its formation, tools and equipment for its increase are determined. A comparative analysis of the results of a survey of teachers of educational institutions proved to increase the level of their digital competence in the specified period in the post-COVID-19 educational space, which allowed the educational process to take place in full under quarantine.
Study on path planning of high-speed aircraft based on scene-ring matching in land-sea interface area

The path planning of high-speed aircraft at land-sea interface area determines the accuracy of the whole path planning. In this paper, based on the establishment of scene-ring, the scene matching method of SIFT-GPU algorithm is used to judge whether there is any deviation in the position and heading of high-speed aircraft at key waypoints, so as to adjust the cruising state of high-speed aircraft in time. It has certain practical value and reference significance for the study of path planning of high-speed aircraft.
Better safe than sorry: Smart Sheet Based Attendance System

It is very important for the effective functioning of an organization to maintain its attendance record properly, and because of this very reason we have created Smart Sheet Attendance System which provide us finest of details about attendance record of university students.
Otsuka Ochiai Energy Efficient and Probabilistic Extreme Load Balancing for WSN Assisted IoT

In the current era, both Internet of things (IoT) and Wireless Sensor Networks (WSNs) are said to be the paramount operational coercion for technology advancements. However, when load conditions dynamically fluctuate, an imbalance load between sensors. In this paper, we propose an energy-efficient data aggregation and load balancing for reliable WSN assisted IoT communication called, Otsuka Ochiai Energy-efficient and Probabilistic Extreme Load Balancing (OOE-PELB). Otsuka-Ochiai Energy-efficient Data Aggregation is applied for performing optimal clustering and selecting the energy-efficient cluster head to reduce data aggregation time. Next, Probabilistic Extreme Learning Machine Load Balancing model is applied for precise and reliable WSN assisted IoT communications. The performance of the proposed method is evaluated by extensive simulations. The simulation results reveal that it outperforms the existing state-of-the-art methods in terms of energy efficiency, data aggregation time and network lifetime.
A Dual-channel Text Classification Model based on an Interactive Attention Mechanism

Aiming at the problem that convolutional neural network(CNN) focuses on local features and lacks the ability of text context feature extraction, In this paper, we propose a dual-channel text classification model based on Interactive Attention Mechanism(IAM). The model uses skip-gram to embed words into dense low latitude vectors and obtains the text embedding matrix, which is input into the Gate Convolution Neural Network(GCNN) and Multi-Head Attention(MHA) at the same time, and then after Pointwise Convolution(PC), the features obtained from the feature extraction layer in the two channels are calculated by an IAM, and finally, the features are fused. Compared with CNN, LSTM, and other improved models, the classification effect of this hybrid model is improved.
Penerapan Clustering K-Means untuk Pengelompokan Tingkat Kepuasan Pengguna Lulusan Perguruan Tinggi

One way to evaluate the quality of graduates is to provide questionnaires to graduate users, namely agencies / companies in the world of work in order to assess the quality of graduates of each university. Questionnaires for graduates are generally carried out by filling out the questionnaire form physically and then returning to the college. The K-Means method is one of several non-hierarchical clustering methods. Data clustering techniques are easy, simple and fast. Many approaches to creating clusters or groups, such as creating rules that dictate membership in the same group/group based on the level of similarity between the members of the group. Other approaches such as creating a set of functions to measure multiple criteria from grouping as a function of some parameters of clustering/grouping. From the results and discussions, K-Means clustering succeeded in grouping graduate user satisfaction data into three clusters where the results shown by manual calculations and applications showed the same results where clusterS C1 as many as 48 alternatives, C2 as many as 1 alternative, and C3 as many as 2 alternatives. In the sense that the application that is built successfully implements K-Means clustering is evidenced by the comparison of applications with weka tools has similar percentage results. In terms of the percentage of graduate users or alumni from STMIK PPKIA Tarakanita Rahmawati 94.12% Very satisfied, 1.96% Satisfied and 3.92% Quite Satisfied.
Efficient ECDSA-Based Adaptor Signature for Batched Atomic Swaps

Adaptor signature is a novel cryptographic primitive which ties together the signature and the leakage of a secret value. It has become an important tool for solving the scalability and interoperability problems in the blockchain. Aumayr et al. (Asiacrypt 2021) recently provide the formalization of the adaptor signature and present a provably secure ECDSA-based adaptor signature, which requires zero-knowledge proof in the pre-signing phase to ensure the signer works correctly. However, the number of zero-knowledge proofs is linear with the number of participants. In this paper, we propose efficient ECDSA-based adaptor signature schemes and give security proofs based on ECDSA. In our schemes, the zero-knowledge proofs in the pre-signing phase can be generated in a batch and offline. Meanwhile, the online pre-signing algorithm is similar to the ECDSA signing algorithm and can enjoy the same efficiency as ECDSA. In particular, considering specific verification scenarios, such as (batched) atomic swaps, our schemes can reduce the number of zero-knowledge proofs in the pre-signing phase to one, independent of the number of participants. Last, we conduct an experimental evaluation, demonstrating that the performance of our ECDSA-based adaptor signature reduces online pre-signing time by about 60% compared with the state-of-the-art ECDSA-based adaptor signature.
Organizers

List of organizers of the Musical Metacreation workshop held at the Ninth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment.
An Improved Algorithm for Label Propagation Based on Rough Core

Complex networks are a hot topic in social science research today.Analyzing the structure of the relationships embedded in them through community discovery techniques is an important tool for the study of complex networks.To address the randomness problem of label propagation in label propagation algorithms, this paper proposes an improved algorithm for label propagation based on rough kernels.The algorithm first performs the extraction of rough kernels and assigns unique labels to each rough kernel to achieve fast label preprocessing; then intervenes in the label propagation process through the ranking of node influence to reduce its randomness and make the division results more stable; finally, the community division is performed by the distribution of network labels.Experiments are conducted in real networks, and the results show that the combined performance of the method in this paper is better than the traditional labeling algorithm in terms of modularity and NMI metrics, and the proposed improved algorithm leads to improved community division results.
Adaptive Neural Networks for Image-Based Visual Servoing with Uncertain Parameters

Visual servoing of robot manipulators can be viewed as an optimization problem and recurrent neural network is widely accepted as a powerful tool for solving optimization problems. Inspired by this, an adaptive neural network method is proposed for image-based visual servoing (IBVS) with uncertain parameters. It is the first work focused on IBVS simultaneously considering the uncertain camera configuration parameters and uncertain kinematics of robot manipulators in the framework of recurrent neural networks. Theoretical analysis including convergence and stability of the proposed method is presented. In order to verify the effectiveness of the theoretical results and the portability of the proposed method, simulations are conducted on different robot manipulators for different tracking tasks with excellent performance. In addition, comparisons with control schemes employing traditional gradient neural network (GNN), Kalman filter (KF) and model-based recurrent neural network highlight the great advantages of the proposed control system.
Understanding convolved Gaussian process priors for multivariate non linear regression

A Gaussian process as prior in regression analysis was developing last decade. The performance of the prediction has shown promising. Recently the data have become large and dependent on each other. It also has nonlinear characteristics. This paper will discuss the extension of the Gaussian process using convolution for multivariate nonlinear regression. The simulation shows that the performance of prediction is good compared with the existed approach.
Connectivity and Management of Technological Supplies in Educational Institutions during COVID 19

A documentary review was carried out on the production and publication of research papers related to the study of the variable Connectivity and Management of Technological Supplies in Educational Institutions during Covid-19 in Latin America.The purpose of the bibliometric analysis proposed in this document is to know the main characteristics of the volume of publications registered in Scopus database during the period between 2020 and the first semester of 2021 in Latin American countries, achieving the identification of 32 publications in total.The information provided by this platform was organized by means of graphs and figures, categorizing the information by Year of Publication, Country of Origin, Area of Knowledge and Type of Publication.Once these characteristics were described, the position of different authors regarding the proposed topic was referenced by means of a qualitative analysis.Among the main findings of this research, it is found that Mexico, with 10 publications, is the Latin American country with the highest production.The area of knowledge that made the greatest contribution to the construction of bibliographic material related to the study of Connectivity and Supply Management was Social Sciences with 19 published documents, and the type of publication that was most used during the period mentioned above was the journal article, which represents 66% of the total scientific production.
A Survey and Research on the Satisfaction of Part-Time Chinese as a Foreign Language Teachers on X Platform

Under the objective influence of digital development and the epidemic , the online teaching mode of Chinese as a foreign language has shown a vigorous development trend .Teachers have an indispensable position in teaching , and their satisfaction is an important factor affecting the quality of teaching and the development of the industry .This paper adopts the methods of questionnaire survey and semi-structured interviews , from the perspective of platform policy and teachers, through the investigation of X online teaching platform for Chinese as a foreign language, from the aspects of X platform policy, teachers ' daily work, teachers' salary, vacation, work pressure, etc. Started to conduct research, and made a summary in the field of Chinese as a foreign language education.On this basis, suggestions for the development of online platforms for teaching Chinese as a foreign language are put forward.
Some Reflections on Educational Reform

This article, under the guidance of the scientific methodology, discusses and illustrates the relationships of educational reforms and their historical experiences and realities, and dialectically analyzes the relational problems with regard to borrowing and localization, knowledge learning and capacity building, stress and relaxation, teachers and students, and teaching and learning emerged in these educational reforms.
PEMEROLEHAN BAHASA INDONESIA ANAK USIA 5—6 TAHUN STUDI KASUS MUHAMMAD RAGIL SATRIA PUTRA AGUNG DALAM KAJIAN PSIKOLINGUISTIK

AbstractThis study aims to describe the acquisition of Indonesian children aged 5-6 years who are examined on the types of Indonesian sentences and patterns of Indonesian sentence structure obtained by Muhammad Ragil Satria Putra Agung. The method used is descriptive method. The form of qualitative research. The data source is Muhammad Ragil Satria Putra Agung and the research data are all the utterances produced by Muhammad Ragil Satria Putra Agung in the form of sentences according to the selected problem formulation. The data collection technique used is the conversational engagement technique, recording technique and note-taking technique on the data card. The technique of testing the validity of the data is the persistence of observations and the adequacy of references. The results showed that: declarative sentences (news) as many as 272 sentences, imperative sentences (commands) as many as 32 sentences, interrogative sentences (questions) as many as 71 sentences. Based on the data classification, the patterns of Indonesian sentence structure obtained by Muhammad Ragil Satria Putra Agung include: (SP) 246 patterns, (SPO) 8 patterns, (SP-Pel) 138 patterns, (SP-Ket) 29 patterns, (SPO- Note) 3 patterns, (SPO-Mop) 5 patterns, and (SP-Pel-Ket) 14 patterns. From the results of data analysis, the most dominant sentences obtained are declarative sentences, which are 272 sentences, while the sentence structure pattern that is most widely used is the Subject-Predicate (SP) pattern, which is 246 patterns. Keywords: Indonesian language acquisition, children aged 5-6 years, psycholinguistics.
A Search Method Combining Average Distance of Particle Swarm and Crowding Distance of Particles

It is popular to constantly improve existing optimization algorithms by embedding intelligent operators. In this paper, with particle swarm optimization algorithm (PSO) as the framework, the average distance of particle swarm is combined with the crowding distance of particles to provide conveniences for selecting the global optimal particle and deleting redundant particles in Pareto optimal set (PS). What's more, exponential weight and adaptive mutation are used to control the trade-off between global and local exploration. The proposed method has been evaluated by inverted generation distance (IGD) and compared with several typical multi-objective optimization algorithms. Experimental results show that the proposed method can improve the ability of searching for unknown solutions and also obtain Pareto front (PF) with good distribution on most of standard functions.
IMPLEMENTASI PROGRAM KEGIATAN ORGANISASI KEMAHASISWAAN DALAM MENUMBUHKAN PERILAKU KEPEMIMPINAN MAHASISWA DI IAIN MADURA

Student organizations are forms and means of channeling ideas, creations and works that can support students. The results of the study show that: First; Each ORMAWA organizes RAKER, which will be programmed in its leadership period from short to long term, then the results of the mutually agreed upon RAKER are submitted in RAKERGAB, SENAT students hold RAKERGAB which is attended by all ORMAWA. The work plan program in the program does not deviate from the Bylaws, then the agreed upon results are carried out according to RAKERGAB. Supporting factors include: the existence of good service from the leadership to students to carry out their activities programs in improving student leadership behavior, the leadership provides opportunities for students to create and creativity as always willing and always accompany and provide direction and motivation to students. The inhibiting factors include: Limited land and facilities that are still inadequate, lack of places to carry out limited activities, and limited provision of stationery.
Making Citizens' systems more Secure: Practical Encryption Bypassing and Countermeasures

Cryptography is used to protect the confidentiality, integrity, and authenticity of information by preventing unauthorized users from accessing or modifying them. Encryption techniques are used to protect personal or company data. This work demonstrates practical scenarios where, under certain conditions, encryption may be bypassed. Bypassing encryption, either by recovering the encryption key, a password used to generate the encryption key, or a plaintext copy of the encrypted data, allows for accessing data which appear to be inaccessible in the first place. There are six categories for bypassing encryption: find the key, guess the key, compel the key, exploit a flaw in the encryption scheme, access unencrypted message when the device is in use and locate an unencrypted copy of the message. In this study we utilize publicly available software to demonstrate real-world scenarios that fall into most of the aforementioned categories and show how, in those specific cases, encryption may be successfully bypassed. Moreover, we underline that bypassing encryption is possible only when certain conditions are met (e.g., software misconfiguration, physical access to the target device, etc.) and we highlight each one of them so as to effectively suggest countermeasures to the demonstrated techniques for encryption bypassing. The main aim of this paper is to highlight how encryption can be bypassed and thus make citizens set up their system in such a way that it would be more difficult to be hacked. This is especially important for citizens that may have limited knowledge/exposure to technology as they can be, for example. people from certain diversity groups such as elderly and/or people of very low income.
Research on the improvement path of primary school teachers' instructional design ability under deep learning theory

Instructional design is a scheme for teachers to spread knowledge to students, and deep learning is a means to ensure the quality of teaching.However, it is easy to cause problems of chaotic teaching target design, overall teaching content design, compact teaching process design, and preliminary teaching reflection design due to the particularity of pupils when teachers carry out teaching design.Hence, teachers should carry out teaching design from four aspects: straight-forward teaching objective design, diversified teaching content design, detailed teaching process design, and teaching reflection design suitably under the theory of deep learning.Students can control their learning process through purposive, autonomous learning guided by teachers.
Deep Belief Neural Network (DBNN)-Based Categorization of Uncertain Data Streams

In the data mining era, the research field is paying attention to data stream mining, which offers a substantial influence on a variety of applications such as networking, wireless communications, education, economics, weather prediction, financial sector, and so on. Moreover, processing of this uncertain data stream faces two major challenges, which are computational difficulty and long processing time of data. Thus, to overcome this, this work proposes a technique that employs a deep belief neural network to categorize uncertain data streams. Initially, this work utilized a hybrid method that combines ensemble, grid, and density-dependent clustering approaches to acquire the local optimum value in uncertain data streams. Furthermore, for classification, a deep belief neural network (DBNN) has been used. As a result of mining, target semantics or chunks will be obtained from the classified data. The suggested technique performs well, and its effectiveness has been assessed in terms of time and accuracy. Thus, the proposed method outperforms the existing techniques.