Reviewerly/exHarmony huggingface dataset:
Development of scientific-based electronic modules using sigil application

This research aims to develop scientific-based e-modules using sigil applications. The research method used the research and development of model 4D with stages of define, design, develop, and disseminate. This scientific-based e-module is designed using sigil software that can convert word files into e-pub extension files that can be used online and offline. The resulting e-module can be an e-pub format that can be opened on laptops and computers using redium and reasily applications for android. This scientific-based e-module consists of a description of material with images, videos, audio and interactive exercises packed with scientific approaches so that students can be more active in the learning process both in the classroom, learning from home and self-study. The result e-module is validated by an expert validator based on aspects of content feasibility, suitability of e-module format with scientific approach, e-module characteristics using sigil application, language assessment, and physical form of e-module with a value of 88.1% with a very valid category. The results of the practicality e-module tested on a limited basis to high school students of class X SMAN Batusangkar were 89.5% with a very practical category. In conclusion, scientific-based e-modules designed using this sigil application can be used in learning.
A Multi-tasking and Multi-stage Chinese Minority Pre-trained Language Model

The existing multi-language generative model is considered as an important part of the multilingual field, which has received extensive attention in recent years. However, due to the scarcity of Chinese Minority corpus, developing a well-designed translation system is still a great challenge. To leverage the current corpus better, we design a pre-training method for the low resource domain, which can help the model better understand low resource text. The motivation is that the Chinese Minority languages have the characteristics of similarity and the adjacency of cultural transmission, and different multilingual translation pairs can provide the pre-trained model with sufficient semantic information. Therefore, we propose the Chinese Minority Pre-Trained (CMPT) language model with multi-tasking and multi-stage strategies to further leverage these low-resource corpora. Specifically, four pre-training tasks and two-stage strategies are adopted during pre-training for better results. Experiments show that our model outperforms the baseline method in Chinese Minority language translation. At the same time, we released the first generative pre-trained language model for the Chinese Minority to support the development of relevant research (All the experimental codes and the pre-trained language model are open-sourced on the website https://github.com/WENGSYX/CMPT ).
A Survey on the Usability and User Experience of the Open Community Web Portals

Web-based portals enable a new communication paradigm that could provide variety of benefits and support to both the customers and companies. Customers can have continuous access to the services, information, support, and payments on the portal with the possibility of personalisation. This paper presents a survey on the usability and user experience studies relevant to open community web portals and information sharing platforms. The objective of the work presented in this paper was to produce an overview of how literature reported on usability in relation to information sharing web portals. A systematic mapping method has been applied to identify and quantify primary studies focusing on the usability and user experience of the open community web portals.
Development of a hybrid mapping scheme using matrix mapping and unicode in elliptic curve cryptography

We are now living in a digital universe. The wide world of technology with high level advancements has all started to gravitate towards our working lives, personal lives, and finances. This pervasive phenomenon, sadly, leaves us at more risk than ever to spiteful attacks, privacy stalking, fraud, and other such unpleasant things. Hence cyber security is such a crucial part of a stable and secure world. Elliptic curve cryptography is mainly used for encryption and decryption as an asymmetric encryption system in which, if you have to encrypt a message, first mapping the message by using an efficient mapping algorithm, they try to plot the message to a different point on the elliptic curve. Even though the arithmetic behind Elliptic curve cryptography is highly complex in nature, it is difficult to break. In this paper, we focus on various message mapping schemes in the cryptosystem of elliptic curves, the drawbacks of each strategy, and how sensitive to cryptanalysis they are. We are proposing a new mapping method that is unaffected by frequency analysis and other types of data analysis of crypto attacks in the later part of this paper.
Image style transfer based on decoupled representation

The present methods of image style transfer lack the ability to express the image content, which leads to the poor quality and lack of diversity of stylized images. This paper proposes a style transfer method based on image decoupled representation. Generator network is composed of content encoder and style encoder for feature extraction. Decoder generates target image; The discriminator enhances the rationality of the target image. Define two mapping functions to map content features in domain-crossed shared content space to domain independent content spaces. Using Photo2Monet, Cat2Dog, and photographic 2portrait data sets, we found that the domain independent content spaces better represents the content, and style transfer effect is better. The average FID index value was 56.59. The average value of LPIPS index was 0.3749.
Complexity Weights Parameter Optimization of Use Case Points Estimation using Chaotic PSO

Use Case Point has been used in software effort estimation to calculate a project cost. One of the parameters used is the Use Case complexity weight which significantly affects estimation accuracy. Nevertheless, the current complexity weight levels result in unreliable measurement and abrupt classification caused by discontinuous weight levels. This paper proposes a chaotic Particle Swarm Optimization for optimizing the Use Case complexity weight parameters. The optimum complexity weight parameters that minimize the mean absolute error are selected using the particle swarm optimizer. The proposed algorithm is compared with standard PSO to evaluate their performance. The experimental procedure showed that Bernoulli chaotic map yielded the best mean solution of 1008.82 and was statistically significant with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> -values less than 0.05 (0.018). The experiment proved that the proposed algorithm is robust in finding the optimal Use Case complexity weight.
Ultra-short-term Interval Prediction Model for Photovoltaic Power Based on Bayesian Optimization

Photovoltaic power generation has great potential to replace the traditional coal-fired power generation which has high pollution and high energy consumption. However, photovoltaic power generation has great uncertainty, so how to accurately predict the photovoltaic power generation is an urgent issue to be solved. Currently, Artificial Intelligence provides a promising way to improve prediction accuracy and reliability. In addition, compared with deterministic prediction, probabilistic prediction can analyze the probability distribution of photovoltaic power generation at a certain time period to obtain more favorable results. In this paper, based on a deep learning algorithm, a probabilistic algorithm of time series prediction based on Bayesian optimization is proposed. The input data are optimized by the Bayesian networks, and deterministic results are predicted by using the LSTM network. A case study of prediction results shows that the probabilistic prediction interval of photovoltaic power under 15%-95% incredible conditions can be effectively obtained. The actual operation data of the photovoltaic power station are used to verify and the prediction results show that the precision of the optimized data is increased by about 20%, which shows good engineering application
Watch the Gap: Making code more intelligible to users without sacrificing decentralization?

The potential for blockchain technology to eliminate the middleman and replace the top-down hierarchical model of governance with a system of distributed cooperation has opened up many new opportunities, as well as dilemmas. Surpassing the level of acceptance by early tech adopters, the market of smart contracts is now moving towards wider acceptance from regular (non-tech) users. For this to happen however, smart contract development will have to overcome certain technical and legal obstacles to bring the code and the user closer. Guided by notions from contract law and consumer protection we highlight the ‘information gap’ that exists between users (including judges/legal bodies) and the source code. We present a spectrum of low-code to no-code initiatives that aim at bridging this gap, promising the potential of higher regulatory acceptance. Nevertheless, this highlights the ‘The Pitfall of the Trustless Dream’, because arguably solutions to the information gap tend to make the system more centralized. In this article, we aim to make a practical contribution of relevance to the wide-spread adoption of smart contracts and their legal acceptance by analyzing the evolving practices that bring the user and the code closer.
Towards Making Unikernels Rejuvenatable

Software rejuvenation is a simple but powerful method for improving the availability of computer systems. Software rejuvenation faces a challenge to apply itself to a new type of application, the Unikernel which is a library OS where OS functions are linked to the target applications like libraries. Since the unikernel layer is tightly coupled to applications, rebooting the unikernel layers involves the applications' reboots, eliminating and reconstructing memory contents unrelated to the unikernels. This paper presents VampOS that allows us to rejuve-nate the only unikernellayer. VampOS performs component-level rejuvenation of the unikernel by logging interactions between the components and replaying them to restarted components while simultaneously keeping the linked applications running. We implemented a prototype of VampOS, not well-optimized, on Unikraft 0.8.0 and the experimental results show that its runtime overhead is up to 13.6x and the VampOS-linked SQLite mitigates the effects of the intentionally injected memory leak bugs without any downtime. This paper also describes the next directions for efficient rejuvenation of the unikernel-linked applications.
Establishing an Information Model for the «Philologist Automated Workplace» Concept for Launching and Implementing Cross-Disciplinary Research Projects at the Interface of Socio-Humanitarian and Technical Science

A series of processes leading to dramatic changes in all aspects of our lives are ushering in the fourth industrial revolution, the foundation of which is the development of fast-growing digital technologies. Among these are big data, blockchain, artificial intelligence and neural networks, and the Internet of Things (IoT). In order to have an understanding of digital processes and their social effect, it is necessary to describe social systems and social sectors of the economy in new terms and through a new understanding of the impact of the technologies of the fourth industrial revolution. One element of such a description is the problem of developing and transforming an academic ecosystem to launch and implement interdisciplinary projects at the intersection of the social and technical sciences. That is why we have developed the concept of the "Philologist Automated Workplace" as an element of the academic ecosystem for launching and implementing interdisciplinary research projects at the intersection of social and technical sciences.
Performance Comparison of Cryptosystems in Context to Internet of Things

The future of the internet―Internet of Things (IoT), is where billions of intelligent physical devices communicate with each other without human invention, making a better world. Real-time applications like smart cities, smart healthcare, medical, military, connected cars, etc., include massive data processing and transmission. As the IoT paradigm capturing a market, security has become a significant concern in IoT deployment. Cryptographic algorithms are a classical approach to protecting the communication channel. However, the IoT's low-power, heterogeneous and resource-constrained nature makes incorporating traditional security features extremely challenging. In this work, we compare applications, encryption (decryption) speed, area, and power overhead for three different encryption methods: RSA (public-key cryptosystem), AES (symmetrical block cipher), and monoalphabetic substitution cipher in the context of IoT. Further, to improve the resilience of the popular and robust AES cipher against side-channel attacks, we implement the masked S-box and analyze the overhead in contrast to the unmasked implementation.
Cyberattacks: Modeling, Analysis, and Mitigation

Industrial cybersecurity has risen as an important topic of research nowadays. The heavy connectivity by the Internet of Things (IoT) and the growth of cyberattacks against industrial assets cause this risen and attract attention to the cybersecurity field. While fostering current software applications and use-cases, the ubiquitous access to the Internet has also exposed operational technologies to new and challenging security threats that need to be addressed. As the number of attacks increases, their visibility decreases. An attack can modify the Cyber-Physical Systems (CPSs) quality to avoid proper quality assessment. They can disrupt the system design process and adversely affect a product’s design purpose.This working progress paper presents our approach to modeling, analyzing, and mitigating cyberattacks in CPS. We model the normal behavior of the application as well as cyberattacks with the help of Microsoft Security Development Lifecycle (SDL) and threat modeling approach (STRIDE). Then verify the application and attacks model using a model checking tool and propose mitigation strategies to decrease the risk of vulnerabilities. The results can be used to improve the system design to overcome the vulnerabilities.
Machine Learning-Based Abnormal Event Detection and Classification

The utilization of digital networks has improved the ease of monitoring and control and significantly optimized the operations in industrial control systems (ICSs). With the rapid growth of artificial intelligence techniques and hardware in recent years, industries are moving toward nearly or fully autonomous operation, i.e., industry 4.0, where smart machines with improved communication, control, and monitoring capabilities are introduced into ICSs. Recently, nuclear industry, where safety is of utmost importance, is migrating toward digitalization as well.
Cognitive assemblages: The entangled nature of algorithmic content moderation

This article examines algorithmic content moderation, using the moderation of violent extremist content as a specific case. In recent years, algorithms have increasingly been mobilized to perform essential moderation functions for online social media platforms such as Facebook, YouTube, and Twitter, including limiting the proliferation of extremist speech. Drawing on Katherine Hayles’ concept of “cognitive assemblages” and the Critical Security Studies literature, we show how algorithmic regulation operates within larger assemblages of humans and non-humans to influence the surveillance and regulation of information flows. We argue that the dynamics of algorithmic regulation are more liquid, cobbled together and distributed than it appears. It is characterized by a set of shifting human and machine entities, which mix traditional surveillance methods with more sophisticated tools, and whose linkages and interactions are transient. The processes that enable the consolidation of knowledge about risky profiles and contents are, therefore, collective and distributed among humans and machines. This allows us to argue that the cognitive assemblages involved in content moderation become a cobbled space of preemptive calculation.
Anti-Forensic Deepfake Personas and How To Spot Them

Forensic systems have recently been studied to detect and prevent deepfakes abuses such as fake personas, frauds, misinformation, or harassment. At the same time, anti-forensic deepfakes are being investigated to understand the gaps in these detection systems and pave the way for improvement. In this paper, we investigate the threat of anti-forensic fake personas, where a fraudster creates a fake personal profile from multiple anti-forensic deepfake images portraying a single identity. To comprehensively study this threat model, we consider three approaches that an attacker may use to conduct such attacks, encompassing both white- and black-box scenarios. A range of defense strategies is then proposed with the aim to improve the robustness of current forensic systems against such threats. Experimental result shows that while the attacks can bypass current detection, our proposed defense approaches that consider the multi-image nature of a fake persona can effectively mitigate this threat by lowering the attack success rate.
On the Effectiveness of Synthetic Data Sets for Training Person Re-identification Models

Person re-identification is a prominent topic in computer vision due to its security-related applications, and to the fact that issues such as variations in illumination, background, pedestrian pose and clothing appearance make it a very challenging task in real-world scenarios. State-of-the-art supervised methods require a huge manual annotation effort for training data and exhibit limited generalisation capability to unknown target domains. Synthetic data sets have recently been proposed as one possible solution to mitigate these problems, aimed at improving generalisation capability by encompassing a larger amount of variations in the above mentioned visual factors, with no need for manual annotation. However, existing synthetic data sets differ in many aspects, including the number of images, identities and cameras, and in their degree of photorealism, and there is not yet a clear understanding of how all such factors affect person re-identification performance. This work makes a first step towards filling this gap through an indepth empirical investigation, where we use existing synthetic data sets for model training and real benchmark ones for performance evaluation. Our results provide interesting insights towards developing effective synthetic data sets for person re-identification.
Precise Recommendation Technology Based on Mobile User Profile Situation Information

The accurate recommendation technology of situation information based on mobile user portrait is a kind of inherent information such as identity, address and time provided by mobile users when logging in the system. Combined with the user's historical browsing records, dynamic real-time accurate portrait of each mobile user. Then, the situation information generated in real time on major network platforms is intelligently and accurately recommended to every mobile user.
YOLOX based Underwater Object Detection for Inshore Aquaculture

Currently, underwater works by divers are inefficient and dangerous, which encourages the development of underwater drones. Particularly, object detection methods are a key technique to assist underwater drones. In this paper, state-of-the-art real-time object detection algorithms are trained and inferred on underwater images of a hypothetical inshore aquaculture operation to investigate model selection and hyper-parameters for object detection in underwater images. The simulation results show that 54.2% mean average precision is achieved by YOLOX-m and 97.1 frames per second inference processing confirmed by YOLOX-tiny. In addition, saliency maps indicate almost no difference between missed detection object and the detected object. Therefore, the detection rate of objects in underwater images can be increased by the dataset augment and additional training.
Research on Hybrid Teaching Assessment Driven by Big Data

With the continuous development of the world epidemic, online and offline mixed teaching has become the main way of higher education teaching. Combining the assessment of students' performance in mixed teaching with big data will make the assessment and evaluation of students' performance more quantitative and scientific. Based on the mixed teaching mode of ̋MOOC + SPOC + flipped classroom + enterprise practice”, this paper puts forward the whole process quantitative evaluation path of student achievement under the empowerment of big data, which is ;front-line course data + flipped classroom data in class + enterprise practice data integration after class”.
Human activity recognition using smartphone sensors for basic life support

Human Activity Recognition expects to recognize the activities completed by an individual from a progression of perceptions on their actions and the natural conditions. This recognition is accomplished by using the data recovered from different sources, such as ecological, body-worn, and smartphone sensors. The six everyday activities which a human can Perth a tare to walk, to walk upset walking, walking upstairs, walking downstairs, sitting, standing, and lying else various movements can be recorded as the tri-axial accelerometer and gyroscopic data from the smartphone for measuring linear acceleration and angular velocity, respectively. These signals are preprocessed for noise reduction and to improve the quality of data. After preprocessing the data, the dataset is loaded, and the activities are classified using the Multilayer Perceptron algorithm. The time-series data that is obtained using the intelligent phone dataset is used to monitor daily life activities.
Formation of a digital footprint based on gamification in professional self-determination of preschool and school age children

The article presents the experience of personalized career guidance and identifying features in the indicator of involvement for each type of profession of preschool and school children based on the analysis of a digital footprint in a mobile application, which is the result of choosing professional preferences formed in the process of self-determination. A conscious choice of personal self-determination, based on life values and work motivation (WANT), as well as personal qualities, psychological characteristics and medical indications (CAN), allows the child and his parents to formulate accessible goals, build an original professional perspective. The diagnostic program proposed in the article will make it possible to form an idea of the predisposition and identify trends in the child's involvement in one of the types of professions: man-man, man-nature, man-technology, man-sign system, man-artistic image on the digital footprint left in the mobile application. A differentiated approach to the presentation of game content makes it possible to take into account the age characteristics of children and transform the components of their vigorous activity in proportion to individual achievements.