Reviewerly/exHarmony huggingface dataset:
Sustainable Development of Higher Education in Russia in Conditions of Territorial Inequality

The research is devoted to the current problems of developing the territorial structure of the higher school of the Russian. We also paid attention to its historical retrospection, considering that the territorial organization of higher education is an essential factor in the spatial organization of society in post￾industrial development. It is necessary to consider the regional inequalities that have developed in current Russia. We suggested that the territorial differentiation in higher education is exacerbated by regional economic disparities. There were also historical prerequisites for the established territorial differentiation of higher education, laid down almost 300 years ago. Given the current situation and its comparison with the economic situation at the regional level, it was right to suggest that such a system could not be sustainable. For Russia, with its high spatial natural and socio-economic differentiation, the policy of supporting a small number of universities located in capital cities is wrong.
Regional Information Management of Higher Education Based on Network Security and Grey Relational Analysis

The optimal allocation of higher education resources is related to the growth of the regional economy and the development of innovation ability. Therefore, it is essential to deal with the relationship between quality, scale, efficiency, and fairness in the process of regional educational resource allocation. This study uses a method that combines empirical and normative analysis to classify and integrate regional educational data using the grey relational method and to improve the status quo of regional educational resource distribution. The infrastructure and resource-sharing mode of a regional university information resource grid database is designed in combination with the network security method. Various performance indicators are used to evaluate the quality of resource sharing. The assessment of resources sharing using performance indicators can enhance the initiative and enthusiasm of resource sharing among colleges and universities in the region. The feasibility of the scheme is verified through the educational information integration test, which can effectively promote the regional development of higher education and improve the quality of educational services.
Quantum Information Transformation from One Particle onto another Particle by Using Hybrid and Hyper Entangled States

In this paper, we propose two protocols for the transfer of quantum information carried by one particle onto another particle by using the path-spin hybrid and hyper entangled states as the quantum resources. It has been shown that the double slit arrangement with a spin flipper at one of its openings can be used for the generation of path-spin hybrid and hyper entangled states. By using such path-spin entangled states as the quantum resource, the quantum information encoded in one particle is transferred to another particle via spin and path measurements.
A Novel Classification Method Based on a Two-Phase Technique for Learning Imbalanced Text Data

The problem of imbalanced data has a heavy impact on the performance of learning models. In the case of an imbalanced text dataset, minority class data are often classified to the majority class, resulting in a loss of minority information and low accuracy. Thus, it is a serious challenge to determine how to tackle the high imbalance ratio distribution of datasets. Here, we propose a novel classification method for learning tasks with imbalanced test data. It aims to construct a method for data preprocessing that researchers can apply to their learning tasks with imbalanced text data and save the efforts to search for more dedicated learning tools. In our proposed method, there are two core stages. In stage one, balanced datasets are generated using an asymmetric cost-sensitive support vector machine; in stage two, the balanced dataset is classified using the symmetric cost-sensitive support vector machine. In addition, the learning parameters in both stages are adjusted with a genetic algorithm to create an optimal model. A Yelp review dataset was used to validate the effectiveness of the proposed method. The experimental results showed that the proposed method led to a better performance subject to the targeted dataset, with at least 75% accuracy, and revealed that this new method significantly improved the learning approach.
Implementing Joyful Learning Strategy Using Wordwall in Order to Improve Reading Comprehension Skills

Wordwall become one of ICT development which ease students to develop reading skills. This game applies new words and unfamiliar words to the students. The design of this application is simplicity to use by the teachers and the students. There are several characteristic of the application, namely: cumulative collections of words, it can be designed for specific instructional purposes, interactive, printable and providing scaffolds conversation in order to create students’ critical thinking. On the other hand, this application requires more time to made by the teachers even it has charge. Furthermore the students will feel enjoy using this application during online learning.
Efektivitas Penggunaan Busy Book Terhadap Kecerdasan Logika Matematika Anak Di Taman Kanak-Kanak

This study aims to find out how effective the use of busy book on the logical intelligence of early childhood mathematics. This study linked 28 children as the object of research, including 14 children from the experimental class and 14 children from the control class. This type of research is quantitative with a quasi-experimental design, namely pre-test post-test control group. The data collection technique in this study used an assessment instrument by giving a score based on the achievement of child development in accordance with the indicators on the research instrument. Before carrying out the research, the instrument validity test was conducted, namely, joint testing with experts in the field of early childhood mathematics and testing of research instruments in order to obtain a valid and reliable instrument. The research data analysis technique uses the normality test to determine whether the research data is normally distributed and the homogeneity test to test whether the data is homogeneous. The next step is to measure whether there is a significant effect of using busy books on children's mathematical logical intelligence, then a hypothesis test and effect size test are carried out which obtain significant results of 0.003 which means significant or influential value, and the d value of effect size test is 1.23 which means it has an effect strong category
Center detection algorithm for printed circuit board circular marks based on image space and parameter space

A highly efficient circle positioning algorithm, called the two-step optimization Hough transform (TSHT), based on multi-resolution segmentation is proposed to solve the problems of the offset Hough transform, namely, its large memory overhead, long time consumption, and low recognition accuracy. First, using the image feature of the printed circuit board (PCB) circular identifier, the target circle is obtained using adaptive image preprocessing, and then, images of an acceptable quality are separated by shape quality inspection to improve their robustness. Second, using effective interval sampling strategies and gradually controlling the accumulative interval of parameters, the TSHT algorithm reduces the memory overhead and quickly locates the center at the pixel level. Finally, the center at the sub-pixel level is found by the least-squares method for circle fitting. The experiments prove that TSHT, as a result of its high robustness, strong anti-noise capability, fast recognition speed, and accuracy, can be successfully applied to a vision positioning system of a solder paste printing machine.
What Should the Future of Learning Look Like? Looking Back, Looking Forward

This paper explores a possible and desirable future of technology-enhanced teaching and learning in higher education. It takes a normative lens that defines what ‘ought to be,’ based on considerations grounded in the philosophy of education. In other words, its aim is more prescriptive than predictive. It will suggest we embrace technology only to the extent that it brings us closer to realizing the pedagogical ideals of educability, personalization, and active, experiential learning. This paper examines how these principles prove helpful in prioritizing the technologies worthy of being adopted and how technology can contribute in a meaningful way on all three fronts. In addition to the principles of pedagogical innovation, practical considerations for realizing the future state will be identified. In this context, it is argued that the envisioned future of technology-enhanced teaching and learning in higher education can come to fruition only when education becomes collaborative and course creation builds incrementally on previous educational iterations, made possible through institutional support and collaborative design.
Hands-on Educational Labs for Cyber Defense Competition Training

Cyber Defense Competitions provide students with challenging, hands-on, fun, and close to real world opportunities to learn, practice, and perform tasks that they will be expected to complete as cybersecurity professionals. The current availability of training resources focused on Cyber Defense Competitions is limited. We introduce CYOTEE: CYbersecurity Oriented Training Environment and Exercises. CYOTEE provides a set of nine fully modifiable and freely available hands-on laboratory activities intended to help students gain skills needed to be successful at Cyber Defense Competitions. This article provides details for two of those hands-on labs: (1) Linux Hardening and (2) Windows Active Directory Hardening. CYOTEE lab descriptions and setup scripts may be found on the GitHub repository (https://github.com/CenterForSecureAndDependableSystems/CYOTEE).
Prediction of Behavior and Activity of Patients through Wearable Devices Based on Deep Learning

With the aging problem becoming more and more serious, muscle pain has become a common symptom. In order to help patients with rehabilitation training, it is necessary to monitor their activities in time. We propose a real-time monitoring method based on wearable devices. This method uses a wireless body area network for health care. Specifically, in the first step, we developed a wearable device based on ZigBee with low cost and low weight. Secondly, if only classifying the action at the current time, it will not meet the requirements of real-time monitoring. So, we design an end-to-end neural network model called ATCRNN to infer the actions to be made by users at the next time according to the data of the past few times. This model uses CNN and RNN to extract the spatial and temporal features of data and captures the context characteristics through self-attention. Finally, four volunteers wore equipment to participate in the experiment. The activity categories in the experiment are walking, sitting down, running, and climbing stairs. The accuracy of behavior inference reached 97% with ATCRNN. The compared results demonstrate that the performance of the proposed approach is superior to the result of other deep learning networks.
Switchable selective interactions in a Dicke model with a driven biased term

In this work, we propose a method to investigate controllable qubit-resonator interactions in a Dicke model with driven biased term. The nonlinearity of the spectrum, which can be induced by qubit-resonator interactions, plays an important role in such controllable interactions. To gain insight into the mechanism of the nonlinearity, we perform a unitary transformation of the Hamiltonian. The results show that the nonlinearity of the transformed Hamiltonian depends on the qubit-resonator coupling strength. The general forms of the effective Hamiltonians are discussed in detail based on the frequency modulation approach. The dynamical evolution can be switched on and off by adjusting the modulation parameters. By utilizing such controllable interactions, we discuss the creation of Dicke states and the arbitrary superposition of Dicke states. We also consider the nonlinearity of the energy level for the limit of large qubit numbers. In the thermodynamics limit, the Kerr type nonlinearity is induced from "magnon"-resonator coupling, and the selective preparation of "magnon" Fock states can be studied under a "magnon" scenario.
The Factors that Influence Knowledge Sharing in Educational Institutions

The objective of the study is to identify the elements which have an impact on sharing of knowledge at Cihan University in Erbil. Although previous studies have identified a range of elements affecting knowledge sharing, further research is needed to understand the factors driving knowledge sharing, particularly among higher educational institutions in Iraq. Since there are few studies about the sharing of knowledge among Iraqi higher education institutions' staff and there is no current model that has all of the elements required to examine the sharing of knowledge, the researchers investigated the effects of organizational, individual, and technological variables on academics' knowledge sharing behavior. Cihan University staff in Iraq's Kurdistan Region completed a total of 78 validated questionnaires. The data were evaluated with the use of a structural equation model (PLS-SEM). According to the findings of the study, organizational and technological factors are important indicators of knowledge sharing in educational institutions.
Lampung Script Recognition Using Convolutional Neural Network

The Lampung script is often used in writing words in Lampung language. The Lampung language itself is used by native Lampung people and people who learn Lampung language. The Lampung script is difficult to learn because there are many combinations of parent characters and subletters. CNN is a method in the field of object recognition that has a specific layer, namely a convolution layer and a pooling layer that allows the feature learning process well. Handwriting recognition as in character recognition in MNIST, CNN produces better performance compared to other methods. From the advantages of CNN, the CNN method with DenseNet architecture was chosen as the best architecture to recognize each Lampung script. In this study, there are 2 main processes, namely preprocessing, and recognition. This study succeeded in applying the CNN method which can recognize Lampung script. The dataset is divided into 4 groups of characters that have different sounds. First, the parent character data get 98% accuracy. Second, the parent letter data with the above letters get 98% accuracy. Third, the parent character data with the sub-letters on the side get 98% accuracy. Fourth, the parent letter data with the lower letters get 97% accuracy.
Detecting and Mitigating Bias in Data Using Machine Learning with Pre-Training Metrics

In this paper, the proposed algorithm to detect the bias from the datasets and to mitigate the bias in the datasets was observed. The consequences of this work shows that not only bias in a model can be decreased without forfeiting model performance rate, but improving the performance. Class imbalance, KL divergence, sample disparity and Kolmogorov-Smirnov (KS) are the pre-training metrics used in the work. Each metric is given weightage and the features are detected based on the maximum weightage. The model is trained to learn the unbiased data and shows the significant improvement in the performance of the system. ROC curve, False Positive Rate and False Negative Rate is used for bias trade-off. The comparison between FPR and FNR before mitigating bias and after mitigating bias is performed and its results are significantly improved.
An Evaluation of Power Side-Channel Resistance for RNS Secure Logic

In this paper, residue number system (RNS) based logic is proposed as a protection against power side-channel attacks. Every input to RNS logic is encrypted as a share of the original input in the residue domain through modulus values. Most existing countermeasures enhance side-channel privacy by making the power trace statistically indistinguishable. The proposed RNS logic provides cryptographic privacy that also offers side-channel resistance. It also offers side-channel privacy by mapping different input bit values into similar bit encodings for the shares. This property is also captured as a symmetry measure in the paper. This side-channel resistance of the RNS secure logic is evaluated analytically and empirically. An analytical metric is developed to capture the conditional probability of the input bit state given the residue state visible to the adversary, but derived from hidden cryptographic secrets. The transition probability, normalized variance, and Kullback–Leibler (KL) divergence serve as side-channel metrics. The results show that our RNS secure logic provides better resistance against high-order side-channel attacks both in terms of power distribution uniformity and success rates of machine learning (ML)-based power side-channel attacks. We performed SPICE simulations on Montgomery modular multiplication and Arithmetic-style modular multiplication using the FreePDK 45 nm Technology library. The simulation results show that the side-channel security metrics using KL divergence are 0.0204 for Montgomery and 0.0020 for the Arithmetic-style implementation. This means that Arithmetic-style implementation has better side-channel resistance than the Montgomery implementation. In addition, we evaluated the security of the AES encryption with RNS secure logic on a Spartan-6 FPGA Board. Experimental results show that the protected AES circuit offers 79% higher resistance compared to the unprotected AES circuit.
Union-Retire for Connected Components Analysis on FPGA

The Union-Retire CCA (UR-CCA) algorithm started a new paradigm for connected components analysis. Instead of using directed tree structures, UR-CCA focuses on connectivity. This algorithmic change leads to a reduction in required memory, with no end-of-row processing overhead. In this paper we describe a hardware architecture based on UR-CCA and its realisation on an FPGA. The memory bandwidth and pipelining challenges of hardware UR-CCA are analysed and resolved. It is shown that up to 36% of memory resources can be saved using the proposed architecture. This translates directly to a smaller device for an FPGA implementation.
Developing of Image Detection System with JPG Format using Error Level Analysis Technique

Digital image manipulation is increasingly difficult to detect.Software for image processing is easily available, making it easier for someone to manipulate a photo.Image manipulation techniques produce fake photos that are difficult to detect.Crime cases that use photo manipulation are increasingly rampant in fake news (hoax).Detection of manipulated images in crime cases using digital forensics.The object of this research is the attack on the jpg image.The data collection method used in this research is the observation technique and literatul study (library).The method used in this research is Error Level Analysis (Ela).ELA is a forensic method for identifying parts of an image with different levels of compression.This research is concerned with detecting errors that occur due to a decrease in image quality.If a JPEG image is attached to another JPEG image, then saved as a JPEG, there will be a difference in the level of compression that occurs.
Detecting Hate Speech In Twitter Using Long Short-Term Memory and Naïve Bayes Method

The information technologi’s development has been very sophisticated and easy, so that it becomes a lifestyle for people throughout the world without exception Indonesia which also affected by the development of this technology. One of the benefits of information technology is the emergence various kinds of social networking sites or social media such as Facebook, Twitter and Instagram. Technological developments isn’t only have a positive impact, but also have a negative impact the crime of insult or hate speech. This study is aims to classify Indonesian hate speech sentences based on hate speech and neutral sentiments using the Long Short-Term Memory (LSTM) method. Research data is obtained from Indonesian-language tweets. In testing process, the LSTM method will be compared with the Naïve Bayes method
Implementation of K-means algorithm in data analysis

Some large companies have difficulty in providing products even though the products are still available in the warehouse. Based on these problems, a solution is needed in managing cosmetic products and can find the right strategy so that it can increase business in the field of sales and improve sales services by using algorithms in data mining that can overcome these problems, such as clustering techniques that use the K-means clustering algorithm as a way to measure proximity data between cosmetic products based on transactions that have occurred. The specialty of the analysis of the management of cosmetic products in this study is that it produces data on products that are not sold enough so that it can provide prevention so that the accumulation of these products does not occur. The use of K-means clustering also makes it easier to collect cosmetic sales transaction data, can solve problems in classifying cosmetic product sales transaction data and find out which products should be in cosmetics stock so as to increase sales profits
Implementation of Two-Way Voice and Text Communication System Using Fiber Optic Media

In this technology age, people's need for communication systems is growing. In various places and conditions, communication must be maintained to meet various needs. One of the solutions required in these circumstances is an optical fiber communication system. Fiber optic cable is a light-based data transmission technique. The advantages of optical transmission include the fact that the light spectrum does not spread like radio frequencies, making it impossible to intercept, the fact that it propagates at a very high speed, allowing it to go very large distances, and the ease with which it may be installed. To tackle this problem, we proposed to innovate by realizing and developing a two-way voice and text communication system using fiber optic cable media. The voice and text communication system created and deployed in this study is based on an Android application. This communication system does not require a server and internet network. Only Android smartphones, access points, fiber media converters, and fiber optic cables are required. The greatest throughput value measured by the system is 2708 bytes/second, while the lowest is 1020 bytes/second. The longest delay is 2.314 seconds, while the shortest is 1.99 seconds. In terms of packet loss, the value is 0%.