Reviewerly/exHarmony huggingface dataset:
PID Tuning Using Differential Evolution With Success-Based Particle Adaptations

Proportional-Integral-Derivative (PID) is a simple and intuitive feedback-based control mechanism being useful to track set points and to reject disturbances. A key question in gradient-free optimization is to ascertain whether the class of optimization algorithms based on the difference of vectors generalize reasonably well to tackle a large class of PID control problems. For generalization and practical purposes, it would be desirable to render algorithms being able to tune PID controllers over a diverse and large set of control problems/tasks with minimal human intervention (self-adaptation features), and under tight computational budgets. In this paper, aiming to fill the above-mentioned gap, we propose and investigate the effectiveness of a new class of algorithm based on the difference of vectors and self-adaptation mechanisms for PID tuning. As such, we introduce a new class of Differential Evolution with success-based Particle Adaptations (DEPA), which unifies the principles of difference of vectors, particle schemes and trial/parameter adaptation through archive (memory) mechanisms. Our computational simulations using a large/relevant set of 25 control problem instances (tracking of linear, nonlinear, continuous, and discontinuous trajectories in motor position control, motor velocity control, magnetic levitation, inverted pendulum, crane stabilization), and the comparisons with a large set of closely related optimization algorithms, and their extended adaptive variants (23 optimization algorithms in total) has shown the outperforming benefits of the proposed approach in convergence performance under tight function evaluation budgets (1000 function evaluations). Also, the experiments on a real-world inverted pendulum device show the potential for transferability of the learned gains to unseen situations during training. Furthermore, we evaluated the algorithmic extension and the generalization towards diverse fitness landscapes in the CEC 2017 benchmark suite, showing the attractive/outperforming performance overall problem instances. In particular, the proposed framework performs better in 358 control instances compared to other algorithms for PID control tasks, and the algorithmic extension for general optimization landscapes performs better than the related algorithms in 182, 215 and 235 problem instances of CEC 2017 benchmark suite for 10, 30 and 50 dimensions, respectively. Our obtained results have the potential to further advance towards developing efficient and self-adaptive optimization algorithms based on the difference of vectors, which may find use in a wider set of optimization and control problems.
Simulated Annealing

Simulated annealing (SA) is a well-known local search metaheuristic for finding high-quality solutions to both discrete and continuous optimization problems. The algorithm was first proposed and used in statistical mechanics by Metropolis et al. (J Chem Phys 21:1087-1092, 1953). Yet, not until Kirkpatrick et al. (Science 220:671-680, 1983) and Cerny (J Optim Theory Appl 45:41–52, 1985) was SA implemented as a heuristic for a notoriously hard combinatorial optimization problem – the traveling salesman problem. Since then, SA has been successfully applied across a broad range of application areas such as finance, machine learning, operations research, etc., where the associated optimization problems can be computationally intractable for large problem instances. In these situations, SA is a serious contender as a convenient, yet effective, optimization tool as it requires no knowledge of the problem structure. Indeed, the key advantage of SA is in its simplicity, which facilitates quick implementation for solving many real-life applications.
How blockchain technology supports the business processes of clinical trials: a systematic review

Purpose The authors propose a blockchain platform for managing clinical trial data to enhance data validity, integrity, trust and transparency in the pharmaceutical research process. The authors also provide an extensive review of how blockchain technology supports the business processes of clinical trials. Design/methodology/approach A systematic literature review was conducted to identify the existing applications of blockchain in pharmaceutical process management. A conceptual design for a blockchain infrastructure to address clinical trial challenges is developed by outlining the entire clinical trial value chain and identifying the coordination and communication among its stakeholders. A stakeholder analysis is conducted to ensure that the clinical trial processes satisfy the requirements and preferences of each stakeholder. Findings The proposed blockchain platform offers a promising solution for enhancing integrity, trust and transparency in the clinical trial process. Additionally, blockchain can help streamline communication and collaboration between stakeholders by enabling multiple parties to access and share data in real time, lowering the possibility of delays or errors in data analysis and reporting. Practical implications The proposed blockchain platform can benefit patients by empowering them to have better-controlled access to their data and by allowing researchers to maintain adherence to reporting requirements. Additionally, the platform can benefit granting agencies, researchers and decision-makers by ensuring the integrity of clinical trial data and streamlining communication and collaboration between stakeholders. Originality/value This study builds on existing blockchain applications in pharmaceutical process management by developing a blockchain framework that can address clinical trial concerns from an integrated perspective.
Rethinking accountability in developing countries: an institutional pillars perspective

Purpose This paper aims to fill gap in the literature and explore policy options for resolving the problems of accountability by framing three research questions. The research questions are (i) whether certain elements of Scott’s (2014) institutional pillars attenuate (accentuate) corporate and public accountability; (ii) whether the presence of ruling party-affiliated enterprises (RPAEs) create an increase (decrease) in the degree of corporate (public) accountability; and (iii) whether there is a particular form of ownership change that transforms RPAEs into public investment companies. Design/methodology/approach Using a qualitative research methodology that involves term frequency and thematic analysis of publicly available textual information, the paper examines Mechkova et al. ’s (2019 forms of government accountability. The paper analyzes the gaps between the de jure and de facto accountability using the institutional pillars framework. Findings The findings of the paper are three. First, there are gaps between de jure and de facto in all three (vertical, horizontal and diagonal) forms of government (public) accountability. Second, the study finds that more than three fourth of the parties that contested the June 2021 election did have regional focus. They did not advocate for accountability. Third, Ethiopia’s RPAEs are unique. They have regional focus and are characterized by severe forms of agency and information asymmetry problems. Research limitations/implications The main limitation of the paper is its exploratory nature. Extending this research by using cross-country data could provide a more complete picture of the link between corporate (public) accountability and a country’s institutional pillars. Practical implications Academic research documents that instilling modern corporate (public) governance standards in the Sub Sahara Africa (SSA) region has shown mixed results. The analysis made in this paper is likely to inform researchers and policymakers about the type of change that leads to better corporate (and public) accountability outcomes. Social implications The institutional change proposed in the paper is likely to advance the public interest by mitigating agency and information asymmetry problems and enhancing government accountability. The changes make the enterprises investable, save scarce jobs, enhance diversity and put the assets in RPAEs to better use. Originality/value To the best of the authors’ knowledge, this is the first paper that uses the institutional pillars analytical framework to examine an SSA country's corporate (public) accountability problem. It demonstrates that accountability is a domestic and a (novel) traveling theory. The paper identifies the complexity of resolving the interlock between political institutions and business enterprises. It theorizes that it is impossible to instill modern corporate (public) accountability standards without changing regulatory, normative and cultural cognitive pillars of institutions. The paper contributes to the change management and public interest literature.
Fenomeni di elaborazione sintattica in Italiano Antico: Il caso della subordinazione a verbo non finito

This paper aims at providing a discussion on ’Ausbau‘ phenomena and on the influence of Latin syntactic models in the development of ancient Italian in the 14th century. This subject is addressed through the analysis of the characteristics of non-finite subordinate clauses in five ancient Italian texts: three translations from Latin classical texts and two ’original‘ chronicles. The comparison between these two types of texts will highlight the interactions between translations and ’original‘ texts and the relevance of the notion of discourse tradition in the analysis of ’Ausbau‘ phenomena in Medieval Italian texts. Finally, a general typology of the processes of syntactic elaboration and of the relationships between Medieval Italian syntactic structures and Latin models is also provided.
Practical Storage-Compute Elasticity for Stream Data Processing

Stream processing pipelines need to handle workload fluctuations (e.g., daily patterns, popularity spikes) by scaling up/down the resources contributed to running jobs. While there have been efforts proposing auto-scaling mechanisms for stream processing engines, prior work has overlooked the role of the storage system in ingesting and serving stream data. The absence of effective scaling for data streams is problematic given that the number of parallel partitions of a data stream limits both streaming data ingestion throughput and read parallelism for downstream streaming jobs. In this paper, we propose to augment the auto-scaling notion of stream processing engines with information about the source data stream. The key novelty of our approach lies in exploiting elastic data streams to ingest data, which is a unique feature of Pravega: a storage system for data streams part of the Dell's Streaming Data Platform. Pravega streams can dynamically change their parallelism based on the ingestion workload, and such information can in turn be exploited for auto-scaling the streaming job downstream. To this end, we have developed an Apache Flink connector for Pravega, as well as an auto-scaling orchestrator that feeds on data stream metrics. Our experiments show how a stream processing pipeline auto-scales by coordinating data stream and processing parallelism under workload fluctuations, with low operations cost.
Assessment of the large-scale extraction of photovoltaic (PV) panels with a workflow based on artificial neural networks and algorithmic postprocessing of vectorization results

Having a complete and high-quality geospatial catalogue of existing large-scale photovoltaic (PV) panels is very important nowadays, due to the rapid increase in the use of this type of installations. This catalogue could be used to estimate, with a higher level of granularity, the energy that can be produced from solar radiation forecasts, to operate the electricity system efficiently by adjusting supply to demand, as well as for fiscal reasons. However, most existing works in the specialized literature focus on the extraction of PV panels in reduced, favourable scenes. In this study, a processing strategy to obtain PV panel arrays geometries from aerial orthoimages at a very large scale, is proposed. The processing strategy includes operations for PV panel array classification and semantic extraction, and algorithmic improvement and simplification of the vectorization results. The processing workflow was trained and tested on two datasets containing aerial images of 256 × 256 pixels with the studied polygonal feature at two different spatial resolutions (with more than 185,000 and 690,000 images for spatial resolutions of 0.5 m and 0.25 m, respectively). The image tiles were labelled at pixel level with PV panel information for both spatial resolutions and cover representative, extended urban and rural regions of Spain. The accuracies achieved in the PV panel arrays classification are superior to 0.999, while the semantic segmentation performance is superior to 0.90 in the Intersection over Union score. The assessment of the proposed procedure of fifteen new, unseen areas distributed over the Spanish territory (covering approximately 8125 km2) demonstrated its suitability (in the analysis, percentage of incompletely detected installations of only 11 % were achieved) and indicated that future works focused on the improvement of the initial predictions are should be carried out.
Federated Computing in Electric Vehicles to Predict Coolant Temperature

Reducing greenhouse gas emissions in mobility is paramount to achieving a carbon-neutral society. However, battery-electrical vehicles (BEV) introduce unique engineering challenges to protect expensive electrical components from overheating. A centralized architecture for model-driven predictions of coolant temperatures poses privacy and legal issues. Additionally, the applications in a vehicle compete for the available resources and must use them as sparingly as possible. Therefore, we introduce a new federated computing (FC) use case to help transform the mobility sector. We evaluate the performance of two FC approaches (linear regression and machine learning) on hardware and privacy metrics by leveraging a real-world dataset from BEVs. Our findings show trade-offs between hardware utilization and model accuracy. The linear regression model yields the best performance and prediction metrics. FC with ML shows up to 761 % variances when comparing vehicle-specific models with models trained with the entire fleet and clustering the data into velocity profiles partly improves prediction performance.
Online evidence application platform

Due to the fact that one of the main activities carried out during the teaching of a course is the evaluation of evidence generated by students, safeguarding those evidence in an adequate way so that it can be graded later, becomes an important practice to carry out. This article addresses the development of a tool that allows the management of the evidence generated in a programming course of the Ingeniería en Informática in the Universidad Politécnica del Estado de Morelos (UPEMOR). This tool was developed under the software development model called improved cascade, using technologies that allow operation through the Internet, which would allow faster access to students' evidence, as well as keeping them better organized and accessible. In addition, this tool would allow defining the items that will be evaluated in each evidence, as well as being able to carry out the evaluation of them from the same place.
A Fuzzy Set-Based Model for Educational Serious Games with 360-Degree Videos

Currently, the use of 360° videos combined with virtual reality (VR) techniques in the educational context has been considered a promising field of research. However, there is still a shortage not only of educational serious games (ESG) that implement such resources but also of models that can enhance the player’s experience and evaluate their performance. This work presents a decision model for SGE with 360° videos. The model was developed from artificial intelligence (AI) techniques with the aim of enhancing the player’s learning using immersion mechanisms and pedagogical reinforcement strategies based on the player’s experience and performance evaluation. The model was integrated into the SG The Mystery of Pandora to evaluate its efficiency. The game was evaluated with a sample of 52 participants aged between 13 and 63 ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>M</mi> <mo>=</mo> <mn>33.55</mn> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mtext>SD</mtext> <mo>=</mo> <mn>12.14</mn> </math> ). The results showed adjustments made by the model in the final performance of the players based on their exploration in the 360° videos. Such adjustments allowed identifying players with learning difficulties and recommending pedagogical reinforcement to enhance learning. It was also possible to verify that players would win the game without the minimum knowledge expected about the subject if the decision model was not used.
Thai Conversational Chatbot Classification Using BiLSTM and Data Augmentation

Chatbot platforms, e.g., Facebook and Line, have revolutionized human interaction in the digital age. In order to develop an automatic chatbot classification, there are several challenges especially for Thai chat messages. Conversational messages are usually short and ambiguous. Therefore, it is difficult to find a dataset for constructing an effective classification model. To address the limited size of the dataset, data augmentation techniques can be possibly applied. Data augmentation involves generating synthetic messages by applying various transformations to existing data samples while preserving their original meaning. In this study, the size and diversity of the dataset is increased by two methods, i.e., text augmentation using word2vec from Thai2Fit and English-Thai machine translation models proposed by VISTEC. Based on the augmented messages, a Deep Learning technique, BiLSTM, is used to construct a chatbot classification model. The experimental obtained results demonstrate that data augmentation can help to increase the classification performance.
Web Usage Mining for Determining a Website’s Usage Pattern: A Case Study of Government Website

Web usage mining is a crucial research area that aims to uncover user behavior patterns from web log data because web usage mining can be used to analyze a website's usage. This study examined web usage mining to discover online users' usage patterns and used the results to redesign and improve the government website. This study aims to help online customers obtain a better experience. A dataset was collected from the Metropolitan Electricity Authority (MEA) website. Various algorithms, including association rule mining (Apriori and Frequent Pattern-Growth (FP-Growth)) and sequential pattern mining (Generalized Sequential Pattern (GSP) and Prefix-Span), were used to mine the user usage data. The first 30 frequently visited patterns of web usage mining results were selected for analysis and to develop a prototype. It revealed that most pages were accessed directly. Moreover, most users were interested in alternative energy information, the Fuel Adjustment Charge (at the given time) or the FT rate, the power outage announcement page, and reducing electric expenses information. Furthermore, the analysis indicated that customers were interested in making online processes, such as using the contact-us feature, downloading forms, and calculating their electric expenses. A survey was conducted with 30 participants and the results were compared with the usage data from the weblog. Welch's t-test was utilized to evaluate the prototype. The findings indicated that the newly implemented website, based on the user usage patterns, was more effective and reduced the user's usage time.
Improving Low Light Object Detection Using Image Enhancement Models

This paper explores the particular issue of object detection under low-light situations, in which, due to low exposure, object detectors may perform poorly. We present a comparative study of various state-of-the-art image enhancement models for the purpose of facilitating object detection under low light. We also propose a new method for robust low-light object detection that shows substantial improvements over state-of-the-art baselines. The proposed approach increases detection robustness to different lighting conditions and establishes a state-of-the-art mAP $$_{50}$$ of 79.5% on the ExDark dataset.
DeepFake Detection Using Deep Learning

A deepfake is a computer-generated video or image where one person's face is replaced with another face of a person which uses a generative adversarial network (GAN) to create and alter images that are practically impossible for humans to distinguish from authentic ones. The development of GAN technology has led to significant improvements in image generation. This progress has made it difficult for humans to differentiate between generated images and raised concerns about their misuse and also creating security threats for the society. Deepfake image detection is an essential challenge because the current methods often face inaccuracy and time-consuming issues. The research work employed GAN discriminators to address these issues. The experiment used the CelebA dataset, which contained diverse celebrity images. Moreover, the proposed model consists of components i.e., generator and discriminator. The generator creates fake data from the training images, while the discriminator distinguishes real from the fake images. The result shows that the proposed approach is out-performing with an accuracy of 95.8% in identifying deep fake images, even in the presence of various manipulations. The proposed model is helpful for enhancing trust and security in the society.
Maintenance Algorithms for 0-Complete Trees with Improved Space Utilization

A compact 0-complete tree is an indexing mechanism that can be effectively used for large databases with long and variable-size keys. Compared to B-trees, compact 0-complete trees eliminate search values from secondary indices altogether. They are replaced with small surrogates whose relatively short representation is adequate for most practical key lengths. Hence the secondary indices are simply hierarchical collections of (surrogate, pointer)-pairs. However, the overall storage performance of a compact 0-complete tree structure suffers from its dependency on the distribution of keys. We complete our previous study of structural modification of 0-complete trees, which improves their space utilization by reducing page-underfilling, with the design and implementation of coupled retrieval and maintenance algorithms. This is achieved by introducing the notion of a sequence set that is analogous to that of B $$^{+}$$ -trees.
Implementasi Aplikasi Mobile Augmented Reality Untuk Pengenalan Sejarah Ikon Kota Di Indonesia

This research is the result of observations made by researchers about the lack of interesting and educational learning media to introduce the history of city icons in Indonesia. Along with the times, there are many learning methods that utilize technological advances in their application. One technology that can be applied is Augmented Reality technology. Augmented Reality is a technology that unites two-dimensional or three-dimensional virtual objects into a real environment and then projects them in Real Time. This research uses a data collection process by conducting a literature study, namely collecting relevant information from thesis sources, scientific journals, and the internet. The results of this study are expected to add insight into the history of city icons in Indonesia and can add to the user experience about Augmented Reality technology.