Reviewerly/exHarmony huggingface dataset:
A critical overview of the (Im)practicability of solar radiation forecasting models

To size solar energy facilities, the availability of solar radiation data on a horizontal surface is inevitable. Based on the vast applications of energy from the sun in electricity generation, quality research has been dedicated to developing models capable of estimating solar radiation for various purposes. Although tremendous successes have been recorded while developing these models, some researchers have questioned the relevance of such models for practical applications. Aside from presenting an overview of existing solar radiation forecasting models, this study presents a perspective and critical discourse on the practicability or otherwise of solar irradiation prediction models available in the literature. The existing models are classified as satellite-driven models, regression models, statistical models, artificial intelligence-driven models, and hybrid solar irradiation forecast models. This study also presents research gaps that need to be examined in the future. Insights from the review reveal that although the generalization capabilities of solar radiation forecast models specifically for locations without measuring instruments have turned out to be an uphill task, some promising researches are being implemented and improved to bridge the gap of generalization of models. The subject discussed in this paper will be valuable to researchers, practitioners, funders, and policymakers interested in developing and utilizing these models to foster the growth of the solar energy industry.
Robust Autonomous Driving Control using Auto-Encoder and End-to-End Deep Learning under Rainy Conditions

A key element of autonomous vehicles is lane-keeping. To keep an automobile in its lane, numerous artificial intelligence technologies and vision systems are employed. Despite being widely used, current systems function effectively in favorable weather. Downstream computer vision models may perform worse due to rain and other weather-related obstacles. For this reason, rain, snow, and mist noise must be detected, recognized, and removed by vision systems to improve system performance. For these issues, the autoencoder neural network is particularly common. The main goal of using an autoencoder for image deraining is to maintain originality because autoencoders adhere to the backpropagation procedure instead of the traditional techniques, which results in the signal coming out being ultimately the altered version of the input signal. This study covers a method for reducing the amount of rain in photos utilizing image processing, deep learning methods with autoencoder implementation, and end-to-end learning to determine the best steering rotation setting for retaining a vehicle in its lane using a donkey car. After training, the end-to-end model may be able to directly guide the car using the information from the front view camera, unlike the conventional method which manually breaks down the autonomous driving issue into specific details. The convolutional neural network (CNN) model takes raw images as input and generates the proper steering angles. The AI Kit Donkey Car is used in this study to collect data, train models, and evaluate them. According to the experimental results, the combination of the deraining method and end-to-end deep learning produces good performance in rainy situations.
Traffic Lights Detection and Recognition Method using Deep Learning with Improved YOLOv5 for Autonomous Vehicle in ROS2

One of the most significant uses of autonomous cars in recent years is the detection of traffic light signals. Deep learning technology, which has a number of benefits including high detection accuracy and quick response to changes, is supporting the development of traffic light recognition under various environmental situations. In this paper, we use two methods to improve the traffic light detection and recognition method. First, we speed up training time by using the K-means clustering algorithm to compress image data. Second, a real time traffic light signal (red, yellow, green) identity based on the You Only Look Once (Yolov5) model is introduced. We utilised a variety of datasets including a freely available Roboflow dataset, a set of data obtained from Gazebo simulator, and a traffic light of CanTho city dataset to train and evaluate the proposed system. Furthermore, our algorithm was validated on a vehicle model in a simulated environment Gazebo of Robot Operating System 2 (ROS2).
Performance assessment of a bifacial PV system using a new energy estimation model

This study presents a comprehensive performance assessment of a bifacial photovoltaic (PV) system and real-time validation of a new energy estimation model. The system consists of 18 bifacial PV modules installed at a test site with a total capacity of 6.8 kWp. The system’s performance was evaluated based on its energy yield over one year. The energy estimation model was validated in real-time using data collected from the bifacial PV system. The results show that the bifacial PV system achieved an annual energy yield of 1569 kWh/kWp. The energy estimation model was accurate and reliable, with correlation coefficients of 1.04 and 1.40 for the front and rear sides, respectively. This study provides valuable insights into the performance of bifacial PV systems and the development of precise energy estimation models, which can help design and optimize PV systems in different environmental conditions.
Verifiable fuzzy keyword search supporting sensitive information hiding for data sharing in cloud-assisted e-healthcare systems

Nowadays, cloud-assisted e-healthcare systems are playing a more and more important role in intelligent medical services. By uploading Electronic Medical Records (EMRs) to the cloud, users can facilitate data sharing and decrease local data management overhead. Before EMRs are outsourced to the cloud, the sensitive information in them is usually encrypted for protecting the privacy. Though the techniques of traditional searchable encryption could achieve the retrieval of ciphertext, it results in EMRs unable to be shared with external researchers. Moreover, the existing searchable encryption schemes for cloud-assisted e-healthcare systems rarely consider fault-tolerant search and result verification. To address these issues, this paper proposes a verifiable fuzzy keyword search scheme supporting sensitive information hiding for data sharing in cloud-assisted e-healthcare systems. We encrypt sensitive information and share the rest information in EMRs among all users. We improve the approaches of keyword transformation to support the type of keyword in EMRs and realize fuzzy keyword search. For purpose of accomplishing the public verification, we adopt the BLS signature algorithm to generate two kinds of verification tags. We also generate the auxiliary match information to realize condition search. To enhance search efficiency, we build a secure index based on the balanced binary tree. In addition, the proposed scheme can also achieve dynamic update in real-time. We analyze the security and implement a series of experiments for evaluating the effectiveness of this scheme. The experimental results illustrate our scheme could reach high accuracy.
Multi-scale feature retention and aggregation for colorectal cancer diagnosis using gastrointestinal images

Colonoscopy is considered the gold standard for colorectal cancer diagnosis and prognosis. However, existing methods are less accurate and prone to overlooking lesions during gastrointestinal endoscopic examinations. Computer-assisted diagnosis combined with robot-assisted minimally invasive surgery (RMIS) can significantly help medical practitioners detect and treat lesions. Therefore, two novel architectures are developed for polyp and surgical instrument segmentation to aid colorectal cancer diagnosis, assessment, and treatment. Colorectal cancer segmentation network (CCS-Net) is the base network used in this study. It uses the maximum convolutional layers near the input image for effective feature extraction from low-level information. In addition, CCS-Net uses an efficient feature upsampling unit to efficiently increase the input spatial features' map size. Hence, CCS-Net is capable of providing a fair performance with satisfactory computational efficiency The multi-scale feature retention and aggregation network (MFRA-Net) is the final network in this study. MFRA-Net is developed to improve the segmentation accuracy of the CCS-Net further as it uses multi-scale feature retention to retain low-level spatial features and transfers them to deep stages of the network. MFRA-Net also combines multi-scale high-strided low-level information with high-level information to boost network segmentation performance. Finally, all the transferred multi-scale features from the early stages of the network are aggregated with high-level features in the deep levels of the network. This multi-scale feature retention and aggregation mechanism enables the network to maintain a better segmentation performance compared with other methods even with challenging blur, specular reflection, low contrast, and high variation cases. We evaluated both architectures on four challenging datasets: Kvasir-SEG, CVC-ClinicDB, Kvasir-Instrument, and the UW-Sinus-Surgery-Live dataset. The proposed method achieves dice similarity coefficients of 95.98%, 94.19%, 92.81%, and 88.57% for the CVC-ClinicDB, Kvasir-SEG, Kvasir-Instrument, and UW-Sinus-Surgery-Live datasets. The proposed method achieves superior segmentation performance compared with state-of-the-art methods and requires only 4.9 million trainable parameters for complete training. Therefore, the proposed networks can effectively assist health professionals in surgical procedures and colorectal cancer diagnosis through surgical instruments and polyp segmentation, respectively.
A Largely Unsupervised Domain-Independent Qualitative Data Extraction Approach for Empirical Agent-Based Model Development

Agent-based model (ABM) development needs information on system components and interactions. Qualitative narratives contain contextually rich system information beneficial for ABM conceptualization. Traditional qualitative data extraction is manual, complex, and time- and resource-consuming. Moreover, manual data extraction is often biased and may produce questionable and unreliable models. A possible alternative is to employ automated approaches borrowed from Artificial Intelligence. This study presents a largely unsupervised qualitative data extraction framework for ABM development. Using semantic and syntactic Natural Language Processing tools, our methodology extracts information on system agents, their attributes, and actions and interactions. In addition to expediting information extraction for ABM, the largely unsupervised approach also minimizes biases arising from modelers’ preconceptions about target systems. We also introduce automatic and manual noise-reduction stages to make the framework usable on large semi-structured datasets. We demonstrate the approach by developing a conceptual ABM of household food security in rural Mali. The data for the model contain a large set of semi-structured qualitative field interviews. The data extraction is swift, predominantly automatic, and devoid of human manipulation. We contextualize the model manually using the extracted information. We also put the conceptual model to stakeholder evaluation for added credibility and validity.
A novel hybrid PSO- and GS-based hyperparameter optimization algorithm for support vector regression

Hyperparameter optimization is vital in improving the prediction accuracy of support vector regression (SVR), as in all machine learning algorithms. This study introduces a new hybrid optimization algorithm, namely PSOGS, which consolidates two strong and widely used algorithms, particle swarm optimization (PSO) and grid search (GS). This hybrid algorithm was experimented on five benchmark datasets. The speed and the prediction accuracy of PSOGS-optimized SVR models (PSOGS-SVR) were compared to those of its constituent algorithms (PSO and GS) and another hybrid optimization algorithm (PSOGSA) that combines PSO and gravitational search algorithm (GSA). The prediction accuracies were evaluated and compared in terms of root mean square error and mean absolute percentage error. For the sake of reliability, the results of the experiments were obtained by performing 10-fold cross-validation on 30 runs. The results showed that PSOGS-SVR yields prediction accuracy comparable to GS-SVR, performs much faster than GS-SVR, and provides better results with less execution time than PSO-SVR. Besides, PSOGS-SVR presents more effective results than PSOGSA-SVR in terms of both prediction accuracy and execution time. As a result, this study proved that PSOGS is a fast, stable, efficient, and reliable algorithm for optimizing hyperparameters of SVR.
A dual mobile eye tracking study on natural eye contact during live interactions

Human eyes convey a wealth of social information, with mutual looks representing one of the hallmark gaze communication behaviors. However, it remains relatively unknown if such reciprocal communication requires eye-to-eye contact or if general face-to-face looking is sufficient. To address this question, while recording looking behavior in live interacting dyads using dual mobile eye trackers, we analyzed how often participants engaged in mutual looks as a function of looking towards the top (i.e., the Eye region) and bottom half of the face (i.e., the Mouth region). We further examined how these different types of mutual looks during an interaction connected with later gaze-following behavior elicited in an individual experimental task. The results indicated that dyads engaged in mutual looks in various looking combinations (Eye-to-eye, Eye-to-mouth, and Mouth-to-Mouth) but proportionately spent little time in direct eye-to-eye gaze contact. However, the time spent in eye-to-eye contact significantly predicted the magnitude of later gaze following response elicited by the partner's gaze direction. Thus, humans engage in looking patterns toward different face parts during interactions, with direct eye-to-eye looks occurring relatively infrequently; however, social messages relayed during eye-to-eye contact appear to carry key information that propagates to affect subsequent individual social behavior.
Exploring Consumer Sentiment on Central Bank Digital Currencies: A Twitter Analysis from 2021 to 2023

Abstract Between the worldwide digital currencies one can also pinpoint those of central banks being a part of the move towards a cashless society. Several worldwide central banks are already planning to issue them, while others are conducting studies on them. Literature of the topic is heavily increasing, including understanding central bank digital currencies (CBDCs), their risks, and central banks’ future position towards them. This paper analyzes Twitter data tagged with the “cbdc” hashtag and posted between January 2021 and January 2023, with the aim of highlighting the change regarding citizens’ perceptions towards central banks’ digital currencies. The authors extracted 124,946 positive, negative and neutral tweets from Twitter which they further analyzed by using a Python script, in the end highlighting different views on the potential benefits and drawbacks of CBDCs. The results show a growing debate and discussion around the use of CBDCs, with citizens expressing concerns about their potential consequences on civil liberties and financial control, while others highlight the benefits of CBDCs such as financial inclusion and tackling money laundering and terrorism. The paper enriches literature related to the study of consumer sentiment towards digital currencies, highlighting the significance of social media platforms for sharing opinions on emerging financial technologies. Central banks can use social media tools to shift citizens’ sentiments and perspectives, including on topics such as CBDCs, by publishing explainers, replying to comments on relevant topics, and increasing posts’ numbers, as they analyze whether and how CBDCs will be implemented. Research on consumer sentiment on this topic is useful as it can help central banks in adapting their strategies accordingly so that they can better achieve their objectives.
Read It, Don't Watch It: Captioning Bug Recordings Automatically

Screen recordings of mobile applications are easy to capture and include a wealth of information, making them a popular mechanism for users to inform developers of the problems encountered in the bug reports. However, watching the bug recordings and efficiently understanding the semantics of user actions can be time-consuming and tedious for developers. Inspired by the conception of the video subtitle in movie industry, we present a lightweight approach CAPdroid to caption bug recordings automatically. CAPdroid is a purely image-based and non-intrusive approach by using image processing and convolutional deep learning models to segment bug recordings, infer user action attributes, and generate subtitle descriptions. The automated experiments demonstrate the good performance of CAPdroid in inferring user actions from the recordings, and a user study confirms the usefulness of our generated step descriptions in assisting developers with bug replay.
Commit Message Matters: Investigating Impact and Evolution of Commit Message Quality

Commit messages play an important role in communication among developers. To measure the quality of commit messages, researchers have defined what semantically constitutes a Good commit message: it should have both the summary of the code change (What) and the motivation/reason behind it (Why). The presence of the issue report/pull request links referenced in a commit message has been treated as a way of providing Why information. In this study, we found several quality issues that could hamper the links' ability to provide Why information. Based on this observation, we developed a machine learning classifier for automatically identifying whether a commit message has What and Why information by considering both the commit messages and the link contents. This classifier outperforms state-of-the-art machine learning classifiers by 12 percentage points improvement in the F1 score. With the improved classifier, we conducted a mixed method empirical analysis and found that: (1) Commit message quality has an impact on software defect proneness, and (2) the overall quality of the commit messages decreases over time, while developers believe they are writing better commit messages. All the research artifacts (i.e., tools, scripts, and data) of this study are available on the accompanying website [2].
MTTM: Metamorphic Testing for Textual Content Moderation Software

The exponential growth of social media platforms such as Twitter and Facebook has revolutionized textual communication and textual content publication in human society. However, they have been increasingly exploited to propagate toxic content, such as hate speech, malicious advertisement, and pornography, which can lead to highly negative impacts (e.g., harmful effects on teen mental health). Researchers and practitioners have been enthusiastically developing and extensively deploying textual content moderation software to address this problem. However, we find that malicious users can evade moderation by changing only a few words in the toxic content. Moreover, modern content moderation software's performance against malicious inputs remains underexplored. To this end, we propose MTTM, a Metamorphic Testing framework for Textual content Moderation software. Specifically, we conduct a pilot study on 2, 000 text messages collected from real users and summarize eleven metamorphic relations across three perturbation levels: character, word, and sentence. MTTM employs these metamorphic relations on toxic textual contents to generate test cases, which are still toxic yet likely to evade moderation. In our evaluation, we employ MTTM to test three commercial textual content moderation software and two state-of-the-art moderation algorithms against three kinds of toxic content. The results show that MTTM achieves up to 83.9%, 51%, and 82.5% error finding rates (EFR) when testing commercial moderation software provided by Google, Baidu, and Huawei, respectively, and it obtains up to 91.2% EFR when testing the state-of-the-art algorithms from the academy. In addition, we leverage the test cases generated by MTTM to retrain the model we explored, which largely improves model robustness 0% ~ 5.9% EFR) while maintaining the accuracy on the original test set. A demo can be found in this link <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
AChecker: Statically Detecting Smart Contract Access Control Vulnerabilities

As most smart contracts have a financial nature and handle valuable assets, smart contract developers use access control to protect assets managed by smart contracts from being misused by malicious or unauthorized people. Unfortunately, programming languages used for writing smart contracts, such as Solidity, were not designed with a permission-based security model in mind. Therefore, smart contract developers implement access control checks based on their judgment and in an adhoc manner, which results in several vulnerabilities in smart contracts, called access control vulnerabilities. Further, the in-consistency in implementing access control makes it difficult to reason about whether a contract meets access control needs and is free of access control vulnerabilities. In this work, we propose AChecker - an approach for detecting access control vulnerabilities. Unlike prior work, AChecker does not rely on pre-defined patterns or contract transactions history. Instead, it infers access control implemented in smart contracts via static data-flow analysis. Moreover, the approach performs further symbolic-based analysis to distinguish cases when unauthorized people can obtain control of the contract as intended functionality. We evaluated AChecker on three public datasets of real-world smart contracts, including one which consists of contracts with assigned access control CVEs, and compared its effectiveness with eight analysis tools. The evaluation results showed that AChecker outperforms these tools in terms of both precision and recall. In addition, AChecker flagged vulnerabilities in 21 frequently-used contracts on Ethereum blockchain with 90% precision.