Reviewerly/exHarmony huggingface dataset:
Application of the Hierarchic Memetic Strategy HMS in Neuroevolution

Quite recently some noteworthy papers appeared showing classes of deep neural network (DNN) training tasks where rather simple one-population evolutionary algorithms (EA) found better solutions than gradient-based optimization methods. However, it is well known that simple single-population evolutionary algorithms generally suffer from the problem of getting stuck in local optima. A multi-population adaptive evolutionary strategy called Hierarchic Memetic Strategy (HMS) is designed especially to mitigate this problem. HMS was already shown to outperform single-population EAs in general multi-modal optimization and in inverse problem solving. In this paper we describe an application of HMS to the DNN training tasks where the above-mentioned single-population EA won over gradient methods. Obtained results show that HMS finds better solutions than the EA when using the same time resources, therefore proving the advantage of HMS over not only the EA but in consequence also over gradient methods.
Super-Smart Healthcare System in Society 5.0

Society 5.0 is a concept of future super-smart, sustainable society that can empower economic development of its citizens while ensuring social upliftment and equality through modern technology-driven solutions. An integral part of such a human-centered society is super-smart healthcare system. The previous Information Society (Society 4.0) opened up the gates to information accessible via the Internet, which was then used for analysis by human beings. However, the Society 5.0 is more advanced that will automate the data management and analysis to derive value and solve economic as well as social problems. In this chapter, we introduce super-smart, sustainable healthcare system in Society 5.0 whose vision will be equitable, optimized, and reliable healthcare service delivery to all the people of the society. The underlying objectives of such a system will be presented. The prototypical design of super-smart healthcare system will be described to explain structure (components) as well as the functioning of proposed system built over modern technologies like Artificial Intelligence (AI), Big Data, Cloud Computing, Internet of Things (IoT). Our prototype will explain the roles and responsibilities of different stakeholders in the system. This chapter will discuss core technologies and their utility in development of such system. The different methods for healthcare data management and analysis will be described. Lastly, the ethical concerns to be taken care of regarding patient safety, data security and well-being of people will be discussed.
On Agent-Specific Fuzzy Entropy Functions

The concern of this article is to introduce versatile fuzzy and probabilistic-fuzzy entropy functions that could quantify the overall ambiguity of a system as perceived by the agent. The central principle behind the proposed functions is: besides the underlying ambiguity, an agent’s inherent outlook toward ambiguity casts a great influence on the perceived entropy. The proposed functions also consider the tendency to exxaggerate/downplay the ambiguity. More specifically, the proposed entropy functions take as argument the perceived ambiguity value, rather than the actual membership grade. Besides, the relationship between the perceived membership grade and ambiguity is closely modeled to compute the overall entropy. The variants of the proposed functions for the probabilistic-fuzzy domain are also presented. A real case study is included to demonstrate the significance of the work.
A High-Efficiency Blockchain Sharded Storage Expansion Model

Blockchain is a new decentralized storage system that combines cryptography, consensus algorithms, distributed storage and other technologies. The full node redundant storage of data on the blockchain provides extremely high security, but while the redundant storage of the blockchain improves the security of the system, it also puts a lot of storage pressure on the nodes. The storage scalability problem of the blockchain has become a shortcoming restricting the development of blockchain technology. Sharded storage is a solution to the scalability of blockchain storage. While the existing sharded storage solution relieves the storage pressure of nodes, it also increases the computing and communication consumption of nodes, and reduces the efficient of blockchain systems. This article proposes a highly efficient fragmented storage model, which classifies blocks according to the frequency and size of the blocks being accessed, and uses different storage schemes for blocks of different classifications, which reduces the storage pressure of the node while controlling the computing and communication consumption of the node.
Neural Network Machine Translation Model Based on Deep Learning Technology

Since the emergence of neural network machine translation (MT), it has attracted the attention of translators and related researchers. However, since neural network machine translation (NNMT) only uses neural networks to convert between natural languages, it also has shortcomings while simplifying the translation process. At the same time, in recent years, researchers have discovered that deep learning (DL) has solved the problems of semantic understanding errors and poor translation quality in statistical MT. Therefore, MT based on DL has become a research hotspot. Therefore, in order to obtain more excellent translation performance, this article combines DL technology and conducts research on NNMT models. This article adopts experimental analysis method and data analysis method. It is intended to construct a NNMT model through experimental research and DL technology, so as to obtain more excellent translation performance. According to the experimental research results, the BLEU value of the model analyzed a set of sample information reached 54.8, showing its excellent translation level, and has strong ability and acuity in recognizing sentence information.
Effect of speckle size on surface crack detection via digital image correlation

Digital image correlation (DIC) is an experimental stress analysis technique used in nondestructive tests. The accuracy of DIC in crack detection depends on various factors such as the sizes of speckles and pixels. In the current study, a speckle pattern based on the spreading of nanoparticles with small speckles is compared with a conventional sprayed pattern to understand whether crack detection via DIC is improved by reducing the sizes of speckles and pixels. Owing to the small size of nanoparticles, an optical microscope is used for magnification. The spreading method for crack detection is first investigated experimentally. Results show that cracks can be detected easily when a 250 nm opening appears in the crack edges. Subsequently, the spreading method is compared with the conventional DIC, in which the spraying method is used for patterning, in terms of crack detection. Results show that by reducing the speckle size and closely analyzing the speckle pattern, the DIC technique is considerably better than the conventional technique in detecting small cracks. Moreover, the conventional method is more suitable for detecting large cracks.
An Empirical Study of Secure and Complex Variants of RSA Scheme

In today’s cyberRaza Imam Faisal Anwer space, where large amounts of data are being exchanged and stored on remote storages, Cryptography plays a major role. Public key cryptography such as RSA is one its effective type, which uses two keys, one for encryption and one for decryption. Concerning the recent advancements in the domain of cryptography, many cryptographers have proposed various extended and enhanced form of RSA algorithms in order to improve the reliability and efficiency of the information security world. In this paper, we studied several extended forms of RSA algorithm. We implemented several and compare them in terms of its efficiency in terms of key generation, encryption and decryption time. Finally, we suggested a multipoint parallel RSA scheme to improve the overall algorithm execution speed compared to standard RSA. This method will also prove to be computationally less costly and more secure as compared to standard RSA.
Collaborative Virtual Environment for Distant and Blended Learning in the Higher Education Setting: A Systematic Review

Collaborative virtual environment (CVE) is a set of technologies that enable collaborative activities in virtual scenarios while users can participate in such activities remotely via various interfaces. The unique features of CVE make it a promising tool for distant and blended learning, especially when considering the impact of COVID-19 pandemic on conventional face-to-face learning and the much-improved accessibility of the relevant technologies nowadays. This paper reports a systematic review on this emerging area and aims to answer four research questions related to the subject areas being covered, instructional and pedagogical methods, interface technology and virtual world design, and learning outcomes and effectiveness. After applying the selection criteria, a total number of 11 studies, which were published between 1 January 2017 and 31 December 2021, were included in our analysis and discussion. Our findings support the use of CVE in the higher education setting and suggest further investigating the underlying mechanism of learning in CVE, exploring the potential and unique benefits of CVE in practicing distant and blended learning, studying the roles of teachers in CVEs, and evaluating the learning effectiveness on larger samples and through longitudinal studies.
The Dominican Republic and the Digital Competencies of Future Teachers in the Digital Age

Higher education institutions deal with many defiances as the world evolves constantly. Since the 1990s, many countries have progressively experienced several changes as they arrived at the digital age. In addition, the pace at which technology is evolving is ever increasing. Thus, future teachers in the Dominican Republic (DR) as digital natives would benefit from using technology during education. Consequently, this systematic review utilized four databases to describe the concept of digital competence in higher education and intended to identify how future teachers on the DR can use ICT. Also, to determine their digital literacy and the components frequently used to evaluate DR's future professor's competencies in these aspects. The review indicated a project from the DR government that seeks to improve education and knowledge by integrating digital technologies in schools. Teachers attribute a sufficient or suitable level of training in ICT, particularly in the technical handling of technological tools and Internet access. The extent of training decreases as the ICT content is more complex. Also, most teachers lack a research profile, basically because teachers lack a Ph.D., among other factors. It is interesting that although for decades the informality and lack of concrete plans to have teachers digitally competent to teach the future professors in the DR. Since 2015, that reality began to change with Regulation 09-2015. Then the Agenda 2030, in 2021, is perfectly complemented to establish the competencies required for future teachers in the DR.
Learner Modeling in Conversation-Based Assessment

Conversation-based assessments (CBAs) make use of dialogue systems to create situations that can be used to gather evidence of the decisions learners make on performance-based assessments such as computer‐based scenarios, simulations, or other forms of technology rich tasks. CBAs are adaptive in nature providing subsequent questions, feedback and eliciting additional information from learners about the given topic. However, the adaptivity can always be improved with additional evidence of the learner's knowledge, skills, and other attributes (KSAs) which can be encompassed in a learner model. Learner models based on CBA's data can then be used to support and improve adaptive sequencing of questions and conversations as well as adaptive feedback. This information can also aid in selecting appropriate conversations and additional tasks. These improvements may result in more engaging and relevant conversations and tasks. In this paper, we discuss some of the challenges and opportunities for learner modeling in dialogue systems.
Sensitivity-Aware Spatial Quality Adaptation for Live Video Analytics

To address the conflict between the limited network bandwidth and high DNN inference accuracy, live video analytics desires a bandwidth-efficient streaming approach. To this end, more and more works study spatially variable quality streaming where high quality is only used for important regions. The key challenges are to accurately identify the important regions and select the right qualities for them to maximize accuracy. Existing approaches use either cheap analytics models or low-quality videos to locate important regions, and employ heuristic rules to make quality decisions, which struggle to address the above challenges. Our key insight is that the region’s accuracy “sensitivity” obtained by running the expensive DNN model on the high-quality video provides a reliable indication of the region’s importance and allows to allocate the available bandwidth optimally over regions by explicitly maximizing the frame accuracy. This work presents a sensitivity-aware algorithm Orchestra, which incorporates sensitivity into the design of spatial quality adaptation, including video zoning and quality selection. The design of Orchestra entails three main contributions: a feasible way of sensitivity estimation, sensitivity-aware zoning, and deduction-based accuracy estimation. Extensive experiments over realistic videos and network traces show that Orchestra improves accuracy by upto 14.1% with comparable bandwidth usage or reduces bandwidth usage by upto 44.2% while maintaining higher accuracy compared to baselines.
Anthropographics in COVID-19 simulations

Data visualization researchers and designers have explored a range of approaches to ensure that non-expert audiences understand and derive value from their work. Using anthropomorphized data graphics—or anthropographics—is one strategy that can help create a connection between data and audiences. Anthropographics have been defined as “visualizations that represent data about people in a way that is intended to promote prosocial feelings (e.g. compassion or empathy) or prosocial behavior (e.g. donating or helping).” However, during the SARS-CoV-2 pandemic, anthropographics were used in data visualizations that had an expanded range of rhetorical goals beyond promoting prosocial feelings and behavior—for instance, informing people about the pandemic, persuading them to adopt certain behaviors, or memorializing those killed by the virus. In particular, anthropographics were used in visualized simulations to model possible futures for audiences, showing the spread and impact of the virus in various scenarios. These simulations used anthropomorphizing strategies in text as well as in graphics, along with interactive options that enabled audiences to explore personal connections with the data. As demonstrated through a close reading of several of these COVID-19 simulations, anthropographics can be viewed holistically as a design strategy that incorporates text and interactivity as well as graphical marks in representing data. Findings from this analysis suggest several additions to the design space for anthropographics.
Stochastic Modeling of a Time of Flight Sensor to Be Applied in a Mobile Robotics Application

In this paper it is presented the stochastic modeling of a time of flight sensor, to be applied in a mobile robotics application. The sensor was configured to provide data at a frequency 30 Hz, obtaining a tradeoff between reactiveness and accuracy. The sensor data was acquired using a microcontroller development board, being the sensor moved with a manipulator, in order to assure repeatability and accuracy in the data acquisition process. The sensor was modeled having in mind the targets color, ranging from black to white for the working range, its variance, standard deviation, offset, means and errors measures were estimated.
Utilizing Skipped Frames in Action Repeats for Improving Sample Efficiency in Reinforcement Learning

Action repeat has become the de-facto mechanism in deep reinforcement learning (RL) for stabilizing training and enhancing exploration. Here, the action is taken at the action-decision point and is executed repeatedly for a designated number of times until the next decision point. Although showing several advantages, in this mechanism, the intermediate states which stem from repeated actions are discarded in training agents, causing sample inefficiency. To utilize the discarded states as training data is nontrivial as the action, which causes the transition between these states, is unavailable. This paper proposes to infer the action at the intermediate states via an inverse dynamic model. The proposed method is simple and easily incorporated into the existing off-policy RL algorithms - integrating the proposed method with SAC shows consistent improvement across various tasks.
A Magnetic Field Imaging System Based on TMR Sensors for Banknote Recognition

The high sensitivity and MEMS-compatible process of tunneling magnetoresistance (TMR) sensors provide great opportunities to develop high-resolution magnetic field detection and imaging systems. In this work, we designed a magnetic field imaging (MFI) system based on TMR sensors to detect the uniaxial magnetic field components of banknotes including US dollars (USD) and Chinese Yuan (CNY), which clearly reveals the distribution of magnetic anti-counterfeiting markers inside the banknotes. Measurement results of the USD show that the magnetic ink signal is around μT level, and the person portraits on banknotes can be revealed by the sensor signals. The testing results of the CNY banknotes indicate magnetic color-shifting ink images, as well as a clear magnetic anti-counterfeiting thread. Different from the traditional optical detection, magnetic detection provides unique signals and identification methods. With a satisfactory recognition rate, the proposed MFI system based on TMR sensors demonstrates its promising potential forgery detection and denomination identification of banknotes.
Software-Defined Power Supply to Geo-Distributed Edge DCs

With ever-increasing demands for computation, communication, and storage, the worldwide data centers (DCs) are scaling out and scaling up rapidly. Such a continuous expansion of DCs incurs enormous energy consumption and has drawn much attention in recent years. Since 2010, the energy consumption of global DCs grew at a compound annual growth rate (CAGR) of 4.4%; up to 2018, the energy consumption of DCs around the world reached 205 terawatt-hours, accounting for about 1% of global electricity consumption [91]. Among all the energy consumption, a considerable proportion comes from "brown energy" (i.e., the energy produced by fossil fuel), bringing enormous carbon footprints and significant environmental concerns.
An Optimized Semi-blind Watermarking Technique for Digital Images using Bat Algorithm

In this paper, Bat Algorithm (BA) is used to optimize the proposed semi-blind watermarking scheme of digital images to attain better imperceptibility and robustness. The insertion of the watermark is performed by normalized block processing (NBP) and hybrid transforms such as IWT, DCT and SVD. The embedding and extraction processes have been performed with the parameters obtained from the bat algorithm. The experimental results prove that this scheme exhibits very good perceptual quality for the watermarked image and better robustness for the watermark. The proposed approach is also superior to the other existing methods in terms of PSNR.
A Quantitative Approach to the Stylistic Assessment of the Middle Chinese Texts

The stylistic assessment is an important research domain as a language is intrinsically a complex of different stylistic varieties. Aiming at a quantitative assessment of the colloquialization level of the Middle Chinese texts, the spoken and written words in the literature with a focus on some core words are examined. In our survey, each axis is composed of both written and spoken words corresponding to the same sense. Then a quantitative approach to stylistic assessment is proposed. Statistical survey on some typical texts of Chinese shows that there are complicated mixtures, both inside a certain text on different axes, and inside a certain category, either native texts or Chinese translated Buddhist scriptures, across different texts, which gives a glimpse of the dynamic language evolution in the Middle Ages.
Anomalous Trajectory Detection for Automated Traffic Video Surveillance

Vehicle trajectories extracted from traffic video sequences can be helpful for many purposes. In particular, the analysis of detected anomalous trajectories may enhance drivers’ safety. This work proposes a methodology to detect anomalous vehicle trajectories by using a vehicle detection, a vehicle tracking and a processing of the tracking information steps. Once trajectories are detected, their velocity vectors are estimated and an anomaly value is computed for each trajectory by comparing its vector with those from its nearest neighbours. The management of these anomaly values allows considering which trajectories are suitable to be potentially anomalous considered. Real and synthetic videos have been included in the experiments to perform the goodness of the proposal.
Enhanced Service Point Approach for Microservices Based Applications Using Machine Learning Techniques

The migration of service oriented architecture (SOA) based applications to microservices architecture is a current research trend in the domain of software engineering. Estimating the effort required for migration is a challenging task as the traditional methods are not suitable for this new architectural style of microservices. Service Points (SP) is one new approach proposed by us for estimating the effort required for the migration of SOA based applications to microservices architecture. However, the use of machine learning techniques gives promising benefits in software effort estimation. To improve the accuracy of the service points approach, multiple regression analysis with the Leave-N-Out policy is applied. The standard service points approach, service points approach with Karner's ratings and proposed machine learning based approach are considered for comparison with actual efforts of the chosen dataset of applications. The accuracy of the models is evaluated using different measures such as MRE, RMSE, MAE, etc. It is clear that the effort estimation using regression analysis gives higher accuracy. Using machine learning techniques improves the accuracy of the effort estimation and helps software architects in better planning and execution of the migration process.