Reviewerly/exHarmony huggingface dataset:
RETRACTED: Creative thinking as a driver for students’ transition to university 4.0 model

The dynamics of the modern world and the transformation of the labor market put forward new requirements for education, aimed today at the formation of the necessary skills and the development of the ability to innovate, which becomes the basis of a new paradigm of education, embedded in the University 4.0 model. The purpose of the article is to study the readiness of students for creative and innovative activities in the context of the transition to the University 4.0 model on the example of [BLINDED] University. A study of the intellectual prerequisites of readiness for innovative activities of 236 physics students was carried out before and after training. At the beginning of the experiment, psychological testing was carried out to determine the characteristics of thinking and the creative component of a personality using valid psychodiagnostic techniques. Students completed training sessions for 1 month; the training included solving logical and situational tasks, business games, and brainstorming. The training was aimed at developing cognitive shifting, concentration of attention (a high level of intellectual lability before training was observed in 24.6% of students, after training - in 40.7% of students), inductive, logical, practical thinking (a high level of these indicators before training was found in 23.3% of students, after training - in 36.9% of students), imagination (the number of students with a high level of image changed from 28.4% to 51.7%), creative, entrepreneurial, and managerial abilities (a high level of which after the training was shown by 11.0% of students, in comparison with 6.4% before the training). The results of the study showed a statistically significant improvement in all the parameters. The results obtained allow concluding that it is necessary to revise and improve curricula and programs to bring them in line with the new paradigm of education within the framework of the University 4.0 model with the obligatory addition of moral, ethical, and spiritual components. The novelty/value of this research lies in the study of the intellectual prerequisites of students for innovative activities and the proof of the possibilities of their development in the process of studying at a university.
Randomized Oblivious Transfer for Secure Multiparty Computation in the Quantum Setting

Secure computation is a powerful cryptographic tool that encompasses the evaluation of any multivariate function with arbitrary inputs from mutually distrusting parties. The oblivious transfer primitive serves is a basic building block for the general task of secure multi-party computation. Therefore, analyzing the security in the universal composability framework becomes mandatory when dealing with multi-party computation protocols composed of oblivious transfer subroutines. Furthermore, since the required number of oblivious transfer instances scales with the size of the circuits, oblivious transfer remains as a bottleneck for large-scale multi-party computation implementations. Techniques that allow one to extend a small number of oblivious transfers into a larger one in an efficient way make use of the oblivious transfer variant called randomized oblivious transfer. In this work, we present randomized versions of two known oblivious transfer protocols, one quantum and another post-quantum with ring learning with an error assumption. We then prove their security in the quantum universal composability framework, in a common reference string model.
Optimal Online Liveness Fault Detection for Multilayer Cloud Computing Systems

Efficient online liveness fault detection is crucial to cloud systems. Most current online liveness fault detection techniques, such as system layer heartbeating, use a single unreliable detector to detect cloud system liveness. A single unreliable detector requires a certain amount of time to detect faults to avoid misjudgment, regardless of the type of fault detected. However, many faults can be detected by other detectors more quickly. Therefore, this article proposes an efficient online liveness fault detection mechanism for cloud systems that integrates existing detectors to quickly detect faults. We compared the fault detection efficiency of the proposed mechanism with those of counterpart mechanisms. According to the results, relative to system layer heartbeating without any auxiliary detection mechanism, our proposed mechanism had a 70.3 percent shorter fault detection time.
ADAM-CS: Advanced Asynchronous Monotonic Counter Service

Trusted execution environments (TEEs) offer the technological breakthrough to allow several applications to be deployed and executed over untrusted public cloud environments. Although TEEs (e. g., Intel SGX, ARM TrustZone, AMD SEV) provide several mechanisms to ensure confidentiality and integrity of data and code, they do not offer freshness out of the box, a critical aspect yet often overlooked, for instance, to protect against rollback attacks. Monotonic counters are a popular way to detect rollbacks, as their counter values cannot be decremented. However, counter increments are slow (i.e., 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> of milliseconds), making their use impractical for distributed services and applications processing thousands of transactions simultaneously, for which an order of magnitude improvement is needed. ADAM-CS is an asynchronous monotonic counter service to protect such high-traffic applications against rollback attacks. Leveraging a set of distributed monotonic counters and specific algorithms, ADAM-CS minimizes the maximum vulnerability window (MVW), i.e., the amount of transactions an adversary could successfully rollback. Thanks to its asynchronous nature, ADAM-CS supports thousands of increments per second without introducing additional latency in the transactions performed by applications. Our measurements indicate that we can keep the MVW well below 10ms while supporting a throughput of more than 21K requests/s when using eight counters.
X-Secure T-Private Federated Submodel Learning

The problem of (information-theoretic) X-secure T-private federated submodel learning represents a setting where a large scale machine learning model is partitioned into K submodels and stored across N distributed servers according to an X-secure threshold secret sharing scheme. Various users wish to successively train (update) the submodel that is most relevant to their local data while keeping the identity of their relevant submodel private from any set of up to T colluding servers. Inspired by the idea of cross-subspace alignment (CSA) for X - secure T -private information retrieval, we propose a novel CSA-RW (read-write) scheme for efficiently (in communication cost) and privately reading from and writing to a distributed database. CSA-RW improves significantly upon available baselines from prior work and is shown to be asymptotically/approximately optimal in download/upload cost. It also answers an open question previously noted by Kairouz et al. by exploiting synergistic gains from the joint design of private read-write.
Toward NMR Quantum Reservoir Computing

Reservoir computing is a framework used to exploit natural nonlinear dynamics with many degrees of freedom, which is called a reservoir, for a machine learning task. Here we introduce the NMR implementation of quantum reservoir computing and quantum extreme learning machine using the nuclear quantum reservoir. The implementation utilizes globally controlled dynamics of nuclear spin qubits in solid state and it has been demonstrated.
A credibility-aware swarm-federated deep learning framework in internet of vehicles

Federated Deep Learning (FDL) is helping to realize distributed machine learning in the Internet of Vehicles (IoV). However, FDL's global model needs multiple clients to upload learning model parameters, thus still existing unavoidable communication overhead and data privacy risks. The recently proposed Swarm Learning (SL) provides a decentralized machine-learning approach uniting edge computing and blockchain-based coordination without the need for a central coordinator. This paper proposes a Swarm-Federated Deep Learning framework in the IoV system (IoV-SFDL) that integrates SL into the FDL framework. The IoV-SFDL organizes vehicles to generate local SL models with adjacent vehicles based on the blockchain empowered SL, then aggregates the global FDL model among different SL groups with a proposed credibility weights prediction algorithm. Extensive experimental results demonstrate that compared with the baseline frameworks, the proposed IoV-SFDL framework achieves a 16.72% reduction in edge-to-global communication overhead while improving about 5.02% in model performance with the same training iterations.
Particle Swarm Optimization for Feature Selection in Emotion Categorization

Emotion categorization plays an important role in understanding human emotions by artificial intelligence systems such as robots. It is a difficult task as humans express many features, which vary over time when showing an emotion. Thus, existing classification techniques are overwhelmed, and the creation of a subset of appropriate features is needed. Feature selection can be used to improve the performance of an emotion categorization task by selecting a subset of features. This removes irrelevant features. Particle swarm optimization (PSO) is a meta-heuristic algorithm which has demonstrated excellent performance in feature selection tasks. However, traditional PSO algorithms often get trapped in local optima as they use their personal best and global best to determine their search direction, which may lead to premature convergence. In this paper, we present a time-based PSO variant by introducing a time-constant into the velocity update function of the PSO algorithm to avoid premature convergence, particularly in an emotion video-frame dataset. The method has been incorporated into binary and continuous PSO, then compared with the two standard versions on an emotion video-frame (CK+) dataset, as well as on static emotional datasets (i.e. the JAFFE and NIMH-ChEFS) to ensure that bias has not been introduced into the algorithm. While the time-based PSO variant (both binary and the continuous PSO) have achieved non-significantly higher performance than the standard PSO algorithms on the JAFFE (77.15% vs 75.61%) and NIMH-ChEFS (71.57% vs 70.53%) dataset, the performance is significantly higher on the CK+ (96.19% vs 94.06%) dataset.
Automatic Tissue Attenuation-Based Contrast Enhancement of Low-Dynamic X-Ray Images

In medical imaging, X-ray images are an important part to diagnose the disease of the human body. The acquisition of X-ray images through X-rays, having a low-dynamic range and low contrast, such that the identification of bones and organs is more difficult. So that the contrast improvement is more needful for the X-ray images to identify the body organs. To improve the contrast of the low-dynamic X-ray images, a novel concept automatic tissue attenuation-based contrast enhancement (ATACE) method has been proposed. In this method, the X-ray image is decomposed into two components, i.e., detailed components and tissue components. The tissue component is modified using a removable factor and produced the removable component for enhancing the visible contrast of both dark and bright regions of the X-ray image. The automatic adjustment of the contrast parameter is calculated using local maximum and removable components to enhance the contrast of the X-ray image. The enhanced image is obtained using simple calculation with the help of removable components and contrast parameters. The proposed technique is applied to the X-ray dataset picked from the Japanese Society of Radiological Technology (JSRT). The parametric evaluation metrics of the proposed technique are achieved promising scores.
DMDIT: Diverse multi-domain image-to-image translation

Cross-domain image translation studies have shown brilliant progress in recent years, which intend to learn the mapping between two different domains. A good cross-domain image translation model should meet the following conditions: (1) do not rely on paired dataset, (2) can deal with multiple domains, (3) obtain diverse outputs with the same source image. Most state-of-art studies are devoted to addressing two of them i.e., either (1) and (2), or (1) and (3). In this paper, we construct a unified diverse multi-domain image to image translation framework (DMDIT) which can satisfy the above three requirements simultaneously. Different from traditional approaches, the proposed generator can achieve diverse and multi-label image-to-image translation while retaining the underlying features of the input image. The diverse outputs are obtained through a latent noise sampled from the normal distribution randomly. To further improve the multiplicity of the outputs, we propose a novel style regularization loss to restrain the latent noise. The mode collapse problem usually occurs due to the lack of constraints on the noise, so we embed a noise separation module in the discriminator to avoid this issue. In addition, we apply an attention mechanism to make the model attentively focus on the most attribute-relevant regions, helping to improve the quality of the generated images. Extensive qualitative and quantitative evaluations clearly demonstrate the effectiveness of our approach.
Identifying Illicit Drug Dealers on Instagram with Large-scale Multimodal Data Fusion

Illicit drug trafficking via social media sites such as Instagram have become a severe problem, thus drawing a great deal of attention from law enforcement and public health agencies. How to identify illicit drug dealers from social media data has remained a technical challenge for the following reasons. On the one hand, the available data are limited because of privacy concerns with crawling social media sites; on the other hand, the diversity of drug dealing patterns makes it difficult to reliably distinguish drug dealers from common drug users. Unlike existing methods that focus on posting-based detection, we propose to tackle the problem of illicit drug dealer identification by constructing a large-scale multimodal dataset named Identifying Drug Dealers on Instagram (IDDIG). Nearly 4,000 user accounts, of which more than 1,400 are drug dealers, have been collected from Instagram with multiple data sources including post comments, post images, homepage bio, and homepage images. We then design a quadruple-based multimodal fusion method to combine the multiple data sources associated with each user account for drug dealer identification. Experimental results on the constructed IDDIG dataset demonstrate the effectiveness of the proposed method in identifying drug dealers (almost 95% accuracy). Moreover, we have developed a hashtag-based community detection technique for discovering evolving patterns, especially those related to geography and drug types.
Enhancement of email spam detection using improved deep learning algorithms for cyber security

Email has sustained to be an essential part of our lives and as a means for better communication on the internet. The challenge pertains to the spam emails residing a large amount of space and bandwidth. The defect of state-of-the-art spam filtering methods like misclassification of genuine emails as spam (false positives) is the rising challenge to the internet world. Depending on the classification techniques, literature provides various algorithms for the classification of email spam. This paper tactics to develop a novel spam detection model for improved cybersecurity. The proposed model involves several phases like dataset acquisition, feature extraction, optimal feature selection, and detection. Initially, the benchmark dataset of email is collected that involves both text and image datasets. Next, the feature extraction is performed using two sets of features like text features and visual features. In the text features, Term Frequency-Inverse Document Frequency (TF-IDF) is extracted. For the visual features, color correlogram and Gray-Level Co-occurrence Matrix (GLCM) are determined. Since the length of the extracted feature vector seems to the long, the optimal feature selection process is done. The optimal feature selection is performed by a new meta-heuristic algorithm called Fitness Oriented Levy Improvement-based Dragonfly Algorithm (FLI-DA). Once the optimal features are selected, the detection is performed by the hybrid learning technique that is composed of two deep learning approaches named Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). For improving the performance of existing deep learning approaches, the number of hidden neurons of RNN and CNN is optimized by the same FLI-DA. Finally, the optimized hybrid learning technique having CNN and RNN classifies the data into spam and ham. The experimental outcomes show the ability of the proposed method to perform the spam email classification based on improved deep learning.
Baggage Tracking Using RFID and Blockchain Technology

The airlines lose millions of bags each year costing them hundreds of millions of dollars each year. Though the luggage isn't insured by the airlines they bear the expenses for damaged goods in the luggage. They are also liable for baggage that has been damaged, delayed or lost in transit. Passengers face a lot of problems when their baggage goes missing, as the procedure to claim reimbursement could get complicated also the claim should be filed within a certain time period which depends on the policies which are specific to each airline. The airlines must compensate the passengers for provable loss. The proposed Baggage Tracking System using RFID and Blockchain helps overcome the above problems faced by both entities. The RFID tags is used to sense the location of the luggage and track them at checkpoints and simultaneously update the blockchain. Blockchain Technology can be translated as a timestamped record and hence can be used to trace the location of the bag at a particular point in time after it is added to the blockchain. It will help us handle baggage in a more transparent and efficient way since the copy of the baggage details will be with every node in the system. Proposed system will benefit the airlines by reducing the compensation cost incurred on every botched luggage and it will also decrease trouble caused to the passengers as both the airlines and passengers would be able to keep a track of the luggage on the go.
Batik AR ver.1.0: Augmented Reality application as gamification of batik design using waterfall method

Abstract During the Covid-19 pandemic, it’s time to implement asynchronous learning. The problem is, elementary school students have difficulty learning cultural arts, especially batik design. This study aims to create an application base on augmented reality (AR) with batik design content that can be used to introduce batik design as cultural art to elementary school students. The method used in developing this application is a waterfall which consists of (1) feasibility study, (2) requirements, (3) System design, (4) Encoding, (5) Testing system, (6) Acceptance Test. The results of this study were the validators rated more than 85% of media validation consisting of the quality of visual and auditory perceptions, ease of interaction, ease of interaction, and ease of use, while user responses consisted of 80% efficiency, 85% usability, 78% cognitive absorption, and enjoyment. 87%. This implies that the AR application can provide benefits for students who learn cultural sessions, especially batik design.
Transfer learning for face recognition using fingerprint biometrics

Biometrics is a set of highly automated methods used for recognition purposes including analyzing physical traits of people and statistically measuring them. In forensic applications, fingerprint and face images are mostly used for recognition. There is a need during a criminal investigation to find out a face image of a person from its fingerprint. The proposed method able to identify the face of a person using an associated fingerprint. In this paper, Bregman divergence regularization is used to learn and optimize transferring subspace. This method first gleans knowledge from samples that are meant for training and transfer it to the testing samples. This regularization helps to minimize the probability distribution differences between two different domains. It helps to find a common subspace that boosts the performance of independent and identically distributed (i.i.d.) condition of samples. Practically the samples violate this i.i.d. condition. However, it will help to identify the correct suspect.
A Non-Intrusive Method for Smart Speaker Forensics

With the rapid development of the Internet of Things technology, smart speakers have become increasingly popular. However, smart speaker security is an ensuing threat. At present, smart speakers are activated by voice, and they monitor users’ voices 24 hours per day. Consequently, there may be problems with user privacy leakage. In this paper, we propose a non-intrusive digital forensic method for smart speakers. The main contribution of the paper is an effective method of combining network traffic analysis with the extraction of user intent and alarms about abnormal network traffic to support the investigation of security. We use Xiaomi smart speakers as an example in an experiment to verify our forensic method. The evaluation results show that our method works well for detecting security risks.
Evaluation of Web Application Vulnerability Scanner for Modern Web Application

Current needs and developments encourage the increasing use of digital-based applications. One of them is a web-based application that is easy to access and used by today’s society. Along with these developments, it is common for vulnerabilities to exist in web applications that the owners are unaware of. It creates the risk of data leakage or damage to the organization’s reputation as the application owner. In addition, the number of web applications owned by an organization or company leads to challenges in finding vulnerabilities in these applications. This happened due to time and resource constraints for conducting manual assessments. Therefore, there is necessary to use a web application vulnerability scanner, which performs vulnerability scanning automatically, to be able to help and streamline the search for vulnerabilities. There are many types of web application vulnerability scanners that can be used for free or commercially. This study evaluated the capabilities of WAVS (Web Application Vulnerability Scanners) tools such as OWASP ZAP, Wapiti, Arachni, and Burp Suite Professional with NodeJS-based benchmark targets, namely Damn Vulnerable NodeJS Application (DVNA) and NodeGoat. This study found that the four WAVS have an average f-measured value between 0.4-0.6. Burp Suite Professional had the best True Positive (TP) and Recall values, while Arachni for perfect Precision valued for both benchmark targets.