Reviewerly/exHarmony huggingface dataset:
PEMS: Custom Neural Machine Translation System - Making subtitling of Portuguese TV shows and movies on the African continent work

The boom in audio-visual content such as movies and TV shows has meant that to increase global accessibility, this content needs to be localised into the target audience&#x0027;s language. Neural Machine Translation (NMT) presents a solution to address this at scale. Despite current NMT models having success on general-purpose text, they fail to generalise to the language nuances of media subtitles, primarily because of training data bias. In this work, we propose PEMS - Portuguese Engine for Machine Subtitling, a custom NMT model for English (en) to Portuguese (pt-pt) translation of TV show and movie subtitles. We conducted a thorough evaluation based on automatic metrics and human evaluation. The PEMS model demonstrates superior performance based on automatic evaluation metrics compared to other state of the art (SOTA) models, both open-source and cloud-based. Human evaluation by two professional translators indicated significant performance gains of post-editing machine translation compared to translating from scratch. Overall the productivity increase for PEMS, compared to translation from scratch, was 40&#x0025; and 33&#x0025; for each of the two translators, respectively. In addition, PEMS had fewer edits and higher adequacy and fluency scores compared to other models. The ablation study highlights sources of performance that could be applied to improve NMT in general. Specifically, the results demonstrate the importance of adapting NMT systems to the domain of interest (i.e. media). Most importantly, this work highlights the performance benefits of using NMT models such as PEMS to improving subtitling of content and, ultimately, to increase the proliferation and accessibility of media content, both on the African content and for Portuguese speakers.
Ultrametrics and Outlier Identification

Outliers in clustered data are objects that do not fit well into existing clustering structures. Starting from properties of a special product of matrices with non-negative elements we present an algorithm that uses variations of silhouettes of objects to identify outliers relative to a given clustering.
Assessing EFL Teachers’ Self-Efficacy Regarding Technological Pedagogical Content Knowledge at Chinese Universities

The in-depth integration of information technology and education implies the fundamental pursuit of education informatization. To this end, assessing teachers’ self-efficacy regarding technological pedagogical content knowledge (TPACK) assists in unlocking the complexity and tacit nature of TPACK. Relevant studies are relatively rare in the English as a Foreign Language (EFL) field compared with studies in other areas. The assessment of 264 EFL teachers at Chinese universities and the exploration of their TPACK revealed significant differences in these teachers’ self-efficacy in terms of their teaching experience and educational background. Additionally, they did not rate highly in the specific constructs of TPACK. It is suggested that in-service EFL teachers broaden their horizons and perceive technological breakthroughs more scientifically and holistically. More opportunities need to be created to support EFL teachers’ application of technology to actualize the integration of information technology and teaching practices. EFL teachers’ demographic characteristics need to be considered to design appropriate teacher training programs. Peer support and teaching communities can also be enhanced to improve teachers’ self-efficacy concerning TPACK.
Indonesian Clickbait Detection Using Improved Backpropagation Neural Network

Clickbait has been considered a problem in the modern age of technology, especially in Indonesia. Various attempts have been made to research clickbait detection, however, researches which use Indonesian data are relatively scarce compared to other languages such as English because the availability of Indonesian datasets are lacking. Backpropagation Neural Network was used in clickbait detection on article's title and has achieved quite good accuracy result, however there is still a chance to improve the accuracy. This paper shows the results of using a modified backpropagation neural network algorithm to detect clickbait using article titles when compared to the standard algorithm. The research compares the results of standard stochastic gradient descent algorithm, mini-batch gradient descent algorithm, and a version of stochastic gradient descent with Adam optimizer and three hidden layers. The results show that using Adam optimizer and three hidden layers in stochastic gradient descent algorithm significantly improves the results compared to the standard architecture. The modified algorithm shows a precision score of 78% and a recall and F1 score of 76%, where the standard algorithm has a precision score of 67% and a recall and F1 score of 66%. The resulting algorithm is then implemented to a desktop application, which is considered easy to use.
Comparing recent Swarm Algorithms with Information Theoretic Filter criterion for Feature Selection

Feature selection is getting significant importance in the fields of machine learning specially for dealing with high dimensional datasets. Feature selection techniques such as swarm algorithms have proven to be very effective in extracting meaningful information from large amounts of high dimensional data, thus improving prediction performance and reducing computation time. In this research, we evaluate the effectiveness of introducing mRMR (minimum Redundancy-Maximum Rel-evance) as an information theoretic filtering criterion on top of swarm optimization algorithms to further improve on existing techniques. We also present a comparison of the performances of two recent swarm algorithms against the well-known Artificial Bee Colony algorithm after incorporating mRMR-based filtering to demonstrate the applicability of this technique.
From Digital Resource Construction to Public Cultural Services: The Innovation Approaches of Digital Culture Construction of Public Libraries

In recent years, public libraries have undertaken an important social responsibility in digital resource construction and public cultural services. Based on summarizing the development status of digital culture construction of public libraries, it was found that there were contradictory and inconsistency in the aspects of the relationship between supply and demand of resources construction, supply-demand relationship in resources construction, resources integration, service effect and efficiency, marketing promotion and social participation. Through learning from successful experiences and models of public cultural institutions at home and abroad, this paper has discussed several innovative approaches in digital cultural construction of public libraries. Firstly, based on the needs of readers, to promote the supply of digital resources. Secondly, to strengthen resource integration and to work on the perfection of the construction of ‘one-stop platform’. Thirdly, to establish a reader feedback mechanism and to customize personalized services. Fourthly, to carry out cross-border library cooperation of ‘Internet +’ to attract public participation. Finally, to build the library brand IP and to value much more the creativity of service marketing.
Quality Level of the Teaching-Learning Process Based on the Application of Technological Tools: Teaching Experience in Virtual Education against COVID-19

The purpose of this research was to demonstrate that the application of technological tools in the current context significantly influences the level of quality of the teaching-learning process in a Public University of the city of Ica-Peru. The research is of applied type, explanatory level and quasi-experimental design with a single group in the pre-test and post-test modality, where the influence of the independent variable on the dependent variable was established, the sample consisted of 52 university teachers. The results obtained showed that there is influence of the application of technological tools in the improvement of the quality level of the teaching-learning process since the Z test for the difference of population means showed a significant influence between the study variables with a level of significance of 5% (0.05) and a confidence level of 95% (0.95).
Group Signature Based Federated Learning Approach for Privacy Preservation

Federated Learning (FL) is a recently developed machine learning technique for updating the learning parameters in distributed devices. Traditionally in FL, the server receives local updates from the devices in the network and then aggregates them to form a new learning model. This new information is again shared with all the network devices. The actual information is not revealed during the repeated learning process since only the updates are sent from local devices. Hence, the privacy of information is better preserved in FL than the conventional centralized machine learning algorithms. However, the existing algorithms proposed for FL require all client devices to communicate with many other clients to share their secrets and preserve privacy. This increases the computation cost and communication overhead exponentially. Therefore, we propose a group signature-based federated learning process requiring members to sign more efficiently using their group&#x0027;s signatures instead of their signatures. We have shown that our algorithm is safe, and the computational and communication cost is significantly less than existing protocols.
Effectiveness of Explaining a Program to Others in Finding Its Bugs

Explaining a program to others helps get others to find bugs and for the explainer him/herself to find bugs. However, to the best of our knowledge, there is no quantitative evidence that explaining a program to others helps the explainer find bugs. This study aims to show quantitatively, using an experimental evaluation, that the explainer himself can find new bugs by explaining the program to others. In the experiment, subjects first review a program that contains many bugs and try to find as many bugs as possible. Next, they are required to explain the program aloud to others. We see if they notice any new bugs themselves during the explanation. As a result of the experiment, five out of the six subjects could find new bugs when explaining the program to others. According to the questionnaire to the subjects, the subjects who find many bugs feel that they can understand the program better by explaining it to others.
-1D Fabrication: Investigating Decoupling Dimensions of Fabrication Output and Fabrication Machine Size

Fabrication devices often appear as “large, clunky cubes”, and the more capable a machine is, the larger it usually is as well. Minus 1D Fabrication investigates decoupling the output dimensions of fabrication machines from the form factor of the machines themselves. We propose designs for pen-shaped laser cutters and book-sized 3D printers, including designs that can increase the build volume without increasing the size of the machine. The resulting form factors assist the integration of fabrication into daily life.
Black-Box Adversarial Attack by Evolution Strategy with Binary Search

Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples which will cause fateful consequences in real-world applications with security-sensitive purposes. In the black-box setting, when attacking the deployed machine learning service, attackers can only acquire the input-output correspondences of the target model. The major drawback of existing black-box attacks is the need for expensive model queries. To bridge this gap, we propose an Evolution Strategy with Binary Search for the black-box adversarial attack that can efficiently find adversarial examples with high qualifications in the query-limited setting. Our approach is universal and precise as it finds a probability density distribution, such that a sample drawn from this distribution is likely to be an adversarial example. According to the testing against DNNs, it outperforms state-of-the-art black-box methods for most test cases.
Security-Cost Efficiency of Competing Proof-of-Work Cryptocurrencies

Proof-of-Work cryptocurrencies consume vast energy to rule out potential attacks. Therefore, evaluating and improving the security-cost efficiency, the cost of attacking the system per unit of operating cost, is important. In this paper, we demonstrate that the stability of miners' supply of work over time is essential for the security-cost efficiency, and it is determined by the Difficulty Adjustment Algorithm (DAA) of the currency and the reward elasticity of the miner's supply of work. To this end, we develop a model of the multicurrency mining market and estimate the own- and cross-elasticity of the hash supply to the reward by exploiting the reward shock event, called halving. We use the estimated model to simulate the mining market and evaluate the security-cost efficiency. We find that Bitcoin is stable because of the inelastic miners, regardless of the DAA, whereas other smaller coins face highly elastic miners and can be stable only with efficient DAAs. Upgrading all relevant currencies' DAAs to the state-of-art one substantially improves the security-cost efficiency and saves the energy-consumption rate by 0.21 GW or 3.2% while maintaining the security level.
EmergencyBlock: A Blockchain-based Public Emergency Alert System

In the smart emergency detection systems, which automatically detects emergencies and disseminates proper alerts to the citizens and local authorities, accuracy and security is significant concerns. To overcome these concerns, we propose a blockchain-based open emergency alert system, namely EmergencyBlock. Our envisioned system has decentralized structure helps the system’s availability during the crisis situations that cause high load. Nodes in the system have a shared public ledger that holds emergency event data and no other data that may create privacy concerns. Because of the system’s public nature, the blockchain is used to prevent false alerts by protecting the integrity, and also asymmetric encryption is used to protect the confidentiality of the system.
Agile Mindset Adoption in Student Team Projects

Developing and maintaining complex software systems requires both technical expertise as well as soft skills such as teamwork and good communication abilities. These become even more important as the size and geographical distribution of teams grow. The adoption of Agile development methodologies further emphasizes interaction, collaboration and adaptability to change, all with the purpose of prioritizing working software. Organizations of higher education have acknowledged this by incorporating capstone or team projects as part of their curricula. Our exploratory study reports the experience of 47 teams of 3rd year Computer Science students, each tasked with developing a medium-sized software application as part of a Team Project course. We focus our work on the issues reported by student teams planning to take up Agile values and practices. We report the most important challenges faced during application development and present them from both the students’ and their mentors’ perspectives. We show that soft skills remain an important component of adopting an Agile mindset when teaching collaborative software engineering. We provide an open source replication package that allows repeating or extending our investigation.
Applications of quantum computing to optimization

The paper presents the basic aspects of the logic behind Quantum Computing and shows its fields of application and the state of the industry, focusing on the special case of the work at Universidad de Montevideo and the startup Quantum-South, born within it: optimization applications for air cargo transport and investment portfolio optimization applications. The paper content has been presented in a plenary session at IEEE CHILECON 2021. It has been intended to motivate professionals and students to work on Quantum Computing in the field of optimization.
Hacker and Non-Attributed State Actors

Chapter 3 sets the scene by exploring some challenges from both a technical and societal viewpoint and contrasts situations against an undertow of cyber-attacks. This chapter investigates various cases of how vulnerabilities originating from the software supply chain can have catastrophic outcomes when weaknesses slip through the net such as unpatched software or software misconfigurations during an organization's software maintenance regime. Examples are provided of high-profile hacks, security breaches, and cyber-attacks undertaken by hackers suspected of being affiliated to foreign states. These case studies provide various salient contexts as well as examples of threats, vulnerabilities, and their resultant impacts; ultimately, the consequence of flaws that create vulnerabilities occur through misconfigurations or from unpatched software weaknesses.
Verification method of SCD file in smart substation based on improved k-nearest neighbor algorithm

The status data of smart substation presents the trend of multi-dimensional redundancy, and the current methods are difficult to support the requirements of efficient SCD file verification. To solve this problem, this paper proposes an smart substation file verification method based on improved k-nearest neighbor (KNN) algorithm. Firstly, the method realizes the visual processing of the SCD file of the smart substation, and the distributed verification calculation of the SCD file of the substation is carried out based on the KNN algorithm optimized by K-means clustering. The verification analysis is carried out from the perspectives of interval structure, similar IED and dual configuration. The simulation results show that the verification accuracy of the proposed verification method for different scenarios can reach more than 98%, and the calculation processing time is less than 0.758s, which has good engineering applicability.
The Future of Virtual Reality in Education

For the last few years, even decades, we have been hearing about the exciting possibilities and, in some cases the actuality of Virtual Reality (VR). There are many examples in the entertainment industry but what about education? This paper provides a brief history of VR and considers some of the possible uses in some of the various fields of education, including uses in science and training.
Robust Color Image Watermarking Scheme with High Payload Capacity using FRT - SVD

When any color image transfers through an open-source communication channel, then the security of the color image is compromised against various manipulations. Therefore, the attacker has easily stolen any important color image when transferred from one place to another. For this problem, one of the solutions is digital watermarking, which provides security when data communicate through a channel. But most existing watermarking schemes can hide a small amount of owner identity into a color image to generate a secure watermarked color image. In this paper, a ridgelet transform and singular value decomposition-based watermarking scheme is proposed to tackle this problem, which provides higher payload capacity. Here, the color watermark logo is inserted into singular value of ridgelet coefficients of the color cover image to get the color watermarked image. After that, scrambling-based encryption is applied to it to get an encrypted color watermarked image that provides security before transmission. The experimental results of the proposed scheme show that this new scheme offers better payload capacity and performs better than existing color watermarking schemes.
Hierarchical Learning-based Particle Swarm Optimizer

Particle Swarm Optimization (PSO) is an optimization technique that has been applied to solve various optimization problems. Its traditional strategy adopts elitism, in which only the personal and global best positions are utilized as leaders to guide all particles&#x2019; update and discard other potentially excellent positions around which the global optimum may be found. In a human society, people fall into different classes. They tend to learn from better ones, not just from the best ones in the whole society. Inspired from the learning behavior in a human society, this work considers particles in a swarm as people belonging to different classes and proposes a hierarchical learning-based particle swarm optimizer (HLPSO). In it, particles hierarchically learn from the ones in either the same or upper level ones. The levels of particles are updated according to their fitness after each iteration. Since all particles determine respective leaders according to their own levels, the population hierarchically learns from a large number of potentially excellent positions, which greatly maintains the diversity of population and brings HLPSO a powerful exploration capability. The diversity analyses of HLPSO reveal that the hierarchical utilization of diversified leaders maintains population diversity. HLPSO and eight popular PSO contenders are tested on 28 CEC2013 benchmark functions. Experimental results indicate its high effectiveness and efficiency.
Blockchain Enabled IoMT and Security

The wide adoption of Internet of Medical Things (IoMT) devices can benefit the medical field and its patients greatly. Security and privacy concerns are barring IoMT technology and devices from reaching their full potential and distribution. Blockchain technology poses a solution to IoMT&#x0027;s security and privacy related issues while also allowing for traceability of pandemic origin. We will present in this article, blockchain-enabled IoMT technologies, their benefits, structure, and discuss future possibilities of a wider-connected world of IoMT connected devices.
Extension of Reverse Engineering Approach of SWS for the Resolution of Semantic Interoperability of EIS

Enterprise Information System (EIS) must cover the interoperability criteria inside its “application view” scope. Nevertheless, the urbanization approach, on which we rely to implement this EIS, has to deal with “horizontal fit” and “traversal fit” problems: lack of intra and inter-applicative communications problems. To overcome these deficiencies, we show in this paper our solutions to ensure semantic interoperability between the applications of different EIS, which are involved to exploit collaboratively business processes. For that, we rely on the Semantic Web Services (SWS) to allow enterprises that use multiple software systems for a business management project, to exchange easily business data between these services. In this context, we propose to reuse and extend the reverse engineering approach that serves to describe the SWS. Three phases permit to describe our contribution: extraction of relevant terms, analysis of the filtered terms and construction of Ontology Design Patterns (ODP). We demonstrate that our strategy enhance the semantic interoperability criteria between the enterprises.