Reviewerly/exHarmony huggingface dataset:
Multiple Differential Distinguisher of SIMECK32/64 Based on Deep Learning

Currently, deep learning has provided an important means to solve problems in various fields. Intelligent computing will bring a new solution to the security analysis of lightweight block cipher as its analysis becomes more and more intelligent and automatic. In this study, the novel multiple differential distinguishers of round-reduced SIMECK32/64 based on deep learning are proposed. Two kinds of SIMECK32/64’s 6–11 rounds deep learning distinguishers are designed by using the neural network to simulate the case of the multiple input differences and multiple output differences in multiple differential cryptanalysis. The general models of the two distinguishers and the neural network structures are presented. The random multiple ciphertext pairs and the associated multiple ciphertext pairs are exploited as the input of the model. The generation method of the data set is given. The performance of the two proposed distinguishers is compared. The experimental results confirm that the proposed distinguishers have higher accuracy and rounds than the distinguisher with a single difference. The relationship between the quantity of multiple differences and the performance of the distinguishers is also verified. The differential distinguisher based on deep learning needs less time complexity and data complexity than the traditional distinguisher. The accuracy of filtering error ciphertext of our 8-round neural distinguisher is up to 96.10%.
Human-centric Linguistic Summarization based on Analysis of Correlation between Linguistic Terms

Easy access to large amounts of data triggers an interest in various data analysis techniques. On multiple occasions, an insightful and meaningful ‘view’ of a phenomenon represented by the data is not easy to spot in the obtained results. The users would like to learn more about essential aspects in the context of their own perception of considered concepts and gain a more human-like and linguistic-based interpretation of the results. Yet, existing techniques cannot work with imprecise linguistic terms and do not provide users with results expressed in a human-friendly format.This paper proposes a simple data analysis technique based on calculating correlation. The method investigates the relationships between linguistic terms defined – individually by the users – on data features. It leads to personalized summarization of data that includes the essential relations. In addition, we use the proposed method on the temporal facet/angle of data allowing us to provide the users with the inside regarding the temporal volatility of relations.
Causal discovery for fuzzy rule learning

In this paper, we focus on allying fuzzy logic, which is a suitable model for human-like information, and causality, which is a key concept for humans to generate knowledge from observations and to build explanations. If a fuzzy premise causes a fuzzy consequence, then acting on the fuzzy premise will have an impact on the fuzzy consequence. This is not necessarily the case for common fuzzy rules whose induction is based on correlation. Indeed, correlations may be due to some latent common cause of fuzzy premise and consequence. In this case, a change in the value of the fuzzy premise may not affect the fuzzy consequence as it should. We propose an approach to construct a set of causality-based fuzzy rules from crisp observational data. The idea is to identify causal relationships on the set of fuzzified inputs and outputs by well-known constraints-based causal discovery algorithms such as Peter-Clark and Fast Causal Inference. The causal discovery algorithms are combined with entropy-based conditional independent testing that avoids making hypotheses on the data distribution. Experiments are conducted to evaluate our approach in terms of ability to recover causal relationships between fuzzy sets in the presence of a latent common cause. The results illustrate the interest of our approach compared to a correlation-based approach and state-of-the-art approaches.
On the Understanding of Anomalies in the Oculography Data and Their Classification with an Application of Fuzzy Aggregators

Modern medicine has been increasingly using information technologies and computer systems to improve decision-making processes. Important examples of such activities are the collection, processing, and analysis of oculographic data. The correct interpretation of the measurement results plays a very important role here. Medical examinations based on eye-tracking allow for early detection of many diseases or disfunctions of human brain. Moreover, it can be seen that implementation of such methods improves human-computer interfaces. In this study, we propose an innovative solution based on ten approaches of anomaly detection with the use of fuzzy aggregation of their results. Also, our experiment is supported by the analysis of obtained anomaly scores by the specialists in the field of medicine. The results of the automatic and expert evaluation show high potential of our method. There are also some differences in the perception of the anomaly by machine learning techniques and experts’ judgement. Hence, in this paper we try to understand and explain them.
A Service Model to Support Digital Transformation of SMEs

Various cloud applications have been developed to support digital transformation of LEs (Large Enterprises) in the world but there are not so many cloud applications to support digital transformation of SMEs (Small and Medium Enterprises) in Japan. One big reason is that it is not easy to develop low-cost cloud applications for which SMEs are capable of paying service fees. Neo Career which is one of the fastest growing cloud application vendors in Japan could develop low-cost cloud applications for SMEs and released a product by the name of “jinjer” in January 2016. jinjer has been rapidly deployed and about 11,000 SMEs are using jinjer as of October 2019. The survey was conducted to study an ideal service model to support digital transformation of SMEs. From the results of the survey for 1,000 jinjer users, it was found that the following 2 factors were most effective to accelerate digital transformation of SMEs. The first factor was that various workflows were automated and real-time and the second factor was that jinjer could accommodate various functions on a single platform. Also, an ideal service model for the future cloud application was proposed through analysis of the survey.
Multicriteria Optimization Techniques in SVM Method for the Classification Problem

The paper considers the procedures for solving the multiclass classification problem using a series of support vector machine optimization problems for the binary classification problem in the multicriteria formulation. In the traditional formulation, the objective function takes into account the width of the separating the classes hyperplane and the function of the penalty for classification errors. Multicriteria modifications of this problem allow us to study the influence of each criterion separately on the classification error. For the problem of multiclass classification, the use of the optimization problem of binary classification and its modifications is carried out within the strategies of the “one against many” or “elimination tournament”. The research of various procedures for solving a multiclass problem is carried out using the example of the intrusion detection problem.
Optimization Simulation of Balanced Distribution of Multimedia Network Modular Teaching Resources

The balanced distribution of multimedia wireless network modular teaching resources is improved by optimizing and adjusting the balanced distribution of multimedia wireless network modular teaching resources. Based on block grid balanced scheduling and resource spectrum fusion, a balanced distribution method of multimedia wireless network modular teaching resources is proposed. A blockchain Internet of Things accesses control network architecture system for the balanced allocation of multimedia wireless network modular teaching resources established using the priority business scheduling method. Modular resource allocation of multimedia wireless networks and adaptive forwarding are adjusted to control the data packets. The bandwidth guarantee mechanism is used for the equitable allocation of multimedia wireless network modular teaching resources information flow. Multi-service hierarchical management of multimedia wireless network modular teaching resources spectrum is controlled by block grid balanced scheduling and resource spectrum fusion. The high-energy physical computing system structure is used to allocate the resource spectrum balance of the modular teaching resources of the multimedia wireless networks, ensuring the bandwidth requirements of various services. The classified management and balanced allocation of the multimedia wireless networks’ teaching storage, system, and teaching spectrum resources are made possible. The results of the tests show that this method effectively reduces the delay in the allocation of modular multimedia wireless network teaching resources, improves the utilization rate of modular multimedia wireless network teaching resources, and significantly improves their reading and writing performance.
MDSNet: self-supervised monocular depth estimation for video sequences using self-attention and threshold mask

Solving the problem that features prediction of self-supervised monocular depth estimation is still ambiguous in low-texture regions and boundaries. We proposed an innovative self-supervised monocular depth estimation method, monocular depth self-supervised network, which integrates three effective strategies to construct an innovative self-supervised monocular depth estimation framework: (1) the attention mechanism and feature fusion module are adopted to enhance the semantic and spatial information of feature images, (2) the threshold segmentation mask is utilized to solve object motion and low-texture regions to increase image details, and (3) the residual pose module and deep reconstruction loss are used to enhance the feature extraction capability of the model to improve the accuracy of depth and pose estimation. Comprehensive experiments and visual analysis results demonstrate the effectiveness of each component in isolation. Compared to existing self-supervised methods, our model not only achieves outstanding results on KITTI and NYU Depth V2 datasets but also can be suitable to different environments.
Virtual terrors

Abstract A long‐standing aim of cinema – in particular of ‘extreme’, ‘unwatchable’ or ‘feel‐ bad’ cinema – has been to acquaint viewers with extreme suffering. In this article I first offer an explication of that aim in terms of recent work in philosophy of mind, then exploit the resulting framework to examine claims to the effect that a new technological development, Virtual Reality, provides cinema's best shot at achieving that aim.
Research synthesis of cybercrime laws and COVID-19 in Indonesia: lessons for developed and developing countries

The COVID-19 pandemic poses an unprecedented global challenge to the whole society. Previously, most of the physical activities were not conducted online and were not exposed to cybercrime. Thus, it necessitates paying attention to this emerging phenomenon and devising strategies for a safe online environment for all stakeholders. The current study presents a historical overview of cybercrime and the cybersecurity system and highlights cybercrimes’ legal importance globally and cybercrime regulations during the Covid-19 times, specifically in the Indonesian context. Using a normative juridical approach, this study collected secondary data from various books, journals, regulatory guidelines, institutional reports, research, and the Internet. Additionally, a systematic literature search methodology was used to collect relevant secondary data from the most popular database, i.e., Scopus. Results revealed that although many nations are actively working to control cybercrimes, the issue persists, and many new cyber threats, i.e., scams and phishing, emerged during the Covid-19 pandemic. In such situations, along with the existence of a promising cybersecurity system, cybercitizens should always consider hygienic cyber tips to avoid being victims of cyber coronavirus crimes. Finally, after presenting an overview of the current state of cybercrime legislation on COVID-19, key policy recommendations for developed and developing countries are given, along with future research directions.
PREDICTION MODEL FOR IN-HOSPITAL MORTALITY OF PATIENTS WITH HEART FAILURE BASED ON OPTUNA AND LIGHT GRADIENT BOOSTING MACHINE

Background and objective: Heart failure (HF) is a lethal public health problem in the field of cardiovascular diseases with high incidence, rehospitalization, and mortality rates. Therefore, the prediction of in-hospital mortality of patients with HF is of paramount significance in providing clinical information to doctors. To improve the accuracy of prediction, this study constructed a prediction model for in-hospital mortality of patients with HF based on machine learning algorithms. Methods: We obtained the medical data of 1901 patients with HF from a public database and performed preprocessing to extract 19 variables as inputs of the prediction model. A prediction model was constructed based on the light gradient boosting machine (LightGBM) and its performance was improved using the Optuna framework to optimize the LightGBM hyperparameters. To evaluate the proposed algorithm, five machine learning algorithms widely used in the field of biomedicine were selected for comparison: support vector machine, classification and regression tree, random forests, gradient boosting decision tree, and LightGBM. Further, we explained the proposed model based on Deep SHapley Additive exPlanations. We also quantified the importance of each variable and analyzed its correlation with the results. Results: The accuracy rate of Optuna–LightGBM was [Formula: see text], the precision rate was [Formula: see text], the recall rate was [Formula: see text], the [Formula: see text]-measure was [Formula: see text], and the area under the receiver operating characteristic curve was [Formula: see text]. The results show that this model outperformed other models on all evaluation indicators. Conclusions: The proposed method can be used to construct a prediction model for in-hospital mortality of patients with HF. Optuna–LightGBM can assist clinicians to quickly classify the high-risk patients with HF so that the clinicians can provide timely care and optimize hospital resources.
TOWARDS EYE-TRACKING-BASED TECHNOLOGY ON SIGHT INTERPRETATION PERFORMANCE IMPROVEMENT

This study used the recording of eye-tracking-based target domain (TD) fixation as the primary approach to explore the correlation between occupying fixation and sight interpretation (SI) performance. This paper records the gaze plot and gaze duration during the sight interpretation and analyzes the correlation between them and the interpretation performance. First, we designed the nine-point track calibration for a noninvasive study. Second, we carried out pre-experiments to find out the best experimental conditions. Finally, after eye rest, we performed the formal test. Extensive experiments were implemented to verify the factors that affect the SI performance, including the number of TD occupying fixations, the time-cost of TD occupying fixation, and the concentration of TD occupying fixation. Statistical analysis of experiments concluded that the psychological dictionary, Long-term Memory (LTM) information, and bilingual conversion skills are the main factors affecting the number and time of eye-tracking TD occupying fixation spots.
PREDIKSI KELULUSAN MAHASISWA TEKNIK INFORMATIKA UNIVERSITAS BANTEN JAYA MENGGUNAKAN ALGORITMA NEURAL NETWORK

The university strives to provide relevant knowledge. One way the government can use it is to measure the quality of the institution by the number of graduates. The higher the pass rate, the higher the quality of training, which can have a positive impact on the certifications awarded by BAN-PT. This allows researchers to see how research is being conducted at the University of Banten Jaya. To predict graduation rates, students can use a type of artificial neural network algorithm commonly known as neural networks. Artificial neural networks are machine learning techniques developed from Multilayer Perceptron (MLP) and designed to process two-dimensional data. Neural network algorithms belong to the type of deep neural network imaging used. There are several types of neural network techniques. That is, the steps of forward and reverse propagation training. Neural networks are similar to MLPs, but in neural networks each neuron is represented in two dimensions, as opposed to MLP, where each neuron has only one dimension. The results of student graduation in a timely manner and is expected to provide information and can provide input to universities in formulating policies for future improvements.
Medical Social Security System Considering Multisensor Data Fusion and Online Analysis

At present, multi-sensor data fusion technology has been applied in many fields, and we have to understand the algorithm principle of multisensor data fusion technology, master the basic theory of multisensor data fusion technology, and analyze the application field of multisensor data fusion technology. The technical challenges of its application area should be understood. The discussion on its development direction has promoted the wide application of multisensor data fusion technology. By improving the query planning, query interpretation, and cache query optimization mechanism of different data organization models, a scalable and efficient distributed hybrid online analytical processing system is designed and implemented. With the opening of the medical system reform, the construction of the medical security system will be continuously improved. The in-depth reform of the medical security system will affect the medical treatment of more than one billion people, involving thousands of households, and it is a great cause for the welfare of the people. In this paper, exploration and research are carried out, combined with the basic algorithm and typical application of data fusion, and the construction of urban residents’ medical insurance is studied. The experimental results show that the Cavani index of medical expenses generally shows a downward trend in urban and rural areas, and the urban area has decreased from −0.1916 in 2012 to −0.2995 in 2020. This paper focuses on the in-depth study of the development process, actual situation, and existing problems of the social medical insurance system for urban residents, compares the development models of urban medical insurance in other places, and tries to put forward valuable and constructive countermeasures for the medical insurance system for urban residents.
Two-qubit Entangling Gate for Superconducting Quantum Computers

The existence of entangled states is essential to completely harness the power of quantum computation and quantum information processing. At the absence of such states, quantum computers have no more power over its classical counterparts. Following the experimental setup carried out in [Nature Physics 6, 409 âˆ’ 413(2010)]. We consider the system combining two capacitively coupled Josephson superconducting phase qubits, in order to implement a natural two-qubit entangling gate - known recently as Dagwood Bumstead gate. The gate is of special significance. It belongs to the canonical family, and it could prove useful for the efficient execution of various quantum algorithms on a solid quantum processor.
Combining Machine Learning Models with Gsadf Test for Bitcoin Market Crash Prediction

Bitcoin market crashes bring huge economic loss and weaken the global financial system. Thus, predicting Bitcoin market crashes is very important for investors. However, due to the high volatility of Bitcoin prices, accurate prediction of crash events is difficult. In this study, we propose three fusing approaches to Bitcoin market crash prediction for better investment decision-making. First, we offer a new hybrid prediction approach that combines various machine learning models with the Generalized Supremum Augmented Dickey-Fuller (GSADF) test. Analysis using machine learning models trained on information from four long-term bubble cycles from January 2016 to June 2021 captured via the GSADF test revealed significantly improved predictive performance. Second, we employed the Synthetic Minority Oversampling Technique (SMOTE) to address the loss of predictive power due to the severe imbalance between crash and no-crash periods in real-world data. Each machine learning model was trained using a different oversampling strategy to determine the best predictive performance. Finally, SHapley Additive exPlanations (SHAP) interpretation of outcomes from the predictive model provide practical information for the establishment of efficient investment strategies. In our model, factors such as increased interest rates and the continuation of the bubble greatly increased the probability of future crashes in the Bitcoin market.
An efficient and secure data sharing scheme for cloud data using hash based quadraplet wavelet permuted cryptography approach

Summary Ensuring the reliable and secure data transmission in cloud systems is very essential and demanding task in the recent decades. For that purpose, various security frameworks have been deployed in the existing works, which are mainly focusing the improving the privacy and confidentiality of cloud data against the unauthenticated users. Yet, it facing the major problems of inefficient accessing, increased time consumption, computational complexity, storage complexity, and memory utilization rate. Hence, this research work intends to develop an advanced and efficient cryptography model, named as, hash based quadraplet wavelet permuted cryptography (HQWPC) for the secured cloud data storage and retrieval operations. Here, the bilinear mapping and group hash function generation processes are performed to generate the keys used for cryptographic operations. Also, the zig‐zag scanning, wavelet coefficients extraction, and permutation processes are accomplished for data encryption. Consequently, the inverse of these operations is performed while decrypting the data. In addition to that, an integrated signature control policy authentication mechanism is employed for validating the authenticity of the cloud data users. This kind of signature verification process could efficiently increase the security level of cloud data. For validating the performance of the proposed security framework, various evaluation metrics have been utilized during analysis. Then, the obtained results are compared with the recent state‐of‐the‐art models for proving the efficiency of the proposed technique over the other techniques.