Reviewerly/exHarmony huggingface dataset:
A systematic review and analysis of deep learning-based underwater object detection

Underwater object detection is one of the most challenging research topics in computer vision technology. The complex underwater environment makes underwater images suffer from high noise, low visibility, blurred edges, low contrast and color deviation, which brings significant challenges to underwater object detection tasks. In underwater object detection tasks, traditional object detection methods often perform poorly in terms of accuracy and generalization capabilities. Underwater object detection requires accurate, stable, generalizable, real-time and lightweight detection models, for which many researchers have proposed various underwater object detection techniques based on deep learning. Although many outstanding results have been achieved on underwater object detection over the years, the research status of underwater object detection techniques are still lack of unified induction, and some existing problems need to be further probed from the latest perspective. In addition, previous reviews lack analysis on the relationship between underwater image enhancement and object detection. Therefore, this paper provides a comprehensive review of the current research challenges, future development trends, and potential applications of underwater object detection techniques. More importantly, this paper has explored the internal relationship between underwater image enhancement and object detection, and analyzed the possible implementation manners of underwater image enhancement in the object detection task in order to further enhance its benefits. The experiments show the performances of current underwater image enhancement and state-of-the-art object detection algorithms, point out their limitations, and indicate that there is not a strict positive correlation between underwater image enhancement and the accuracy improvement of object detection. The domain shift caused by underwater image enhancement cannot be ignored. This paper can be regarded as a guide for future works on underwater object detection.
Computer-aided breast cancer detection and classification in mammography: A comprehensive review

Cancer is the second cause of mortality worldwide and it has been identified as a perilous disease. Breast cancer accounts for ∼20% of all new cancer cases worldwide, making it a major cause of morbidity and mortality. Mammography is an effective screening tool for the early detection and management of breast cancer. However, the identification and interpretation of breast lesions is challenging even for expert radiologists. For that reason, several Computer-Aided Diagnosis (CAD) systems are being developed to assist radiologists to accurately detect and/or classify breast cancer. This review examines the recent literature on the automatic detection and/or classification of breast cancer in mammograms, using both conventional feature-based machine learning and deep learning algorithms. The review begins with a comparison of algorithms developed specifically for the detection and/or classification of two types of breast abnormalities, micro-calcifications and masses, followed by the use of sequential mammograms for improving the performance of the algorithms. The available Food and Drug Administration (FDA) approved CAD systems related to triage and diagnosis of breast cancer in mammograms are subsequently presented. Finally, a description of the open access mammography datasets is provided and the potential opportunities for future work in this field are highlighted. The comprehensive review provided here can serve both as a thorough introduction to the field but also provide indicative directions to guide future applications.
Dual-Domain Image Encryption in Unsecure Medium—A Secure Communication Perspective

With the growing demand for digitalization, multimedia data transmission through wireless networks has become more prominent. These multimedia data include text, images, audio, and video. Therefore, a secure method is needed to modify them so that such images, even if intercepted, will not be interpreted accurately. Such encryption is proposed with a two-layer image encryption scheme involving bit-level encryption in the time-frequency domain. The top layer consists of a bit of plane slicing the image, and each plane is then scrambled using a chaotic map and encrypted with a key generated from the same chaotic map. Next, image segmentation, followed by a Lifting Wavelet Transform, is used to scramble and encrypt each segment’s low-frequency components. Then, a chaotic hybrid map is used to scramble and encrypt the final layer. Multiple analyses were performed on the algorithm, and this proposed work achieved a maximum entropy of 7.99 and near zero correlation, evidencing the resistance towards statistical attacks. Further, the keyspace of the cryptosystem is greater than 2128, which can effectively resist a brute force attack. In addition, this algorithm requires only 2.1743 s to perform the encryption of a 256 × 256 sized 8-bit image on a host system with a Windows 10 operating system of 64-bit Intel(R) Core(TM) i5-7200U CPU at 2.5 GHz with 8 GB RAM.
A reinforcement learning-based metaheuristic algorithm for solving global optimization problems

The purpose of this study is to utilize reinforcement learning in order to improve the performance of the Sand Cat Swarm Optimization algorithm (SCSO). In this paper, we propose a novel algorithm for the solution of global optimization problems that is called RLSCSO. In this method, metaheuristic algorithm is combined with reinforcement learning techniques to form a hybrid metaheuristic algorithm. This study aims to provide search agents with the opportunity to perform efficient exploration of the search space in order to find a global optimal solution by using efficient exploration and exploitation to find optimal solutions within a given search space. A comprehensive evaluation of the RLSCSO has been conducted on 20 benchmark functions and 100-digit challenge basic test functions. Additionally, the proposed algorithm is applied to the problem of localizing mobile sensor nodes, which is NP-hard (nondeterministic polynomial time). Several extensive analyses have been conducted in order to determine the effectiveness and efficiency of the proposed algorithm in solving global optimization problems. In terms of cost values, the RLSCSO algorithm provides the optimal solution, along with tradeoffs between exploration and exploitation.
Perceived satisfaction of university students with the use of chatbots as a tool for self-regulated learning

Chatbots are a promising resource for giving students feedback and helping them deploy metacognitive strategies in their learning processes. In this study we worked with a sample of 57 university students, 42 undergraduate and 15 Master's degree students in Health Sciences. A mixed research methodology was applied. The quantitative study analysed the influence of the variables educational level (undergraduate vs. master's degree) and level of prior knowledge on the frequency of chatbot use (low vs. average), learning outcomes, and satisfaction with the chatbot's usefulness. In addition, we examined whether the frequency of chatbot use depended on students' metacognitive strategies. The qualitative study analysed the students' suggestions for improvement to the chatbot and the type of questions it used. The results indicated that the level of degree being studied influenced the frequency of chatbot use and learning outcomes, with Master's students exhibiting higher levels of both, but levels of prior knowledge only influenced learning outcomes. Significant differences were also found in students' perceived satisfaction with the use of the chatbot, with Master's students scoring higher, but not with respect to the level of prior knowledge. No conclusive results were found regarding frequency of chatbot use and the levels of students' metacognitive strategies. Further studies are needed to guide this research based on the students' suggestions for improvement.
MFO-SFR: An Enhanced Moth-Flame Optimization Algorithm Using an Effective Stagnation Finding and Replacing Strategy

Moth-flame optimization (MFO) is a prominent problem solver with a simple structure that is widely used to solve different optimization problems. However, MFO and its variants inherently suffer from poor population diversity, leading to premature convergence to local optima and losses in the quality of its solutions. To overcome these limitations, an enhanced moth-flame optimization algorithm named MFO-SFR was developed to solve global optimization problems. The MFO-SFR algorithm introduces an effective stagnation finding and replacing (SFR) strategy to effectively maintain population diversity throughout the optimization process. The SFR strategy can find stagnant solutions using a distance-based technique and replaces them with a selected solution from the archive constructed from the previous solutions. The effectiveness of the proposed MFO-SFR algorithm was extensively assessed in 30 and 50 dimensions using the CEC 2018 benchmark functions, which simulated unimodal, multimodal, hybrid, and composition problems. Then, the obtained results were compared with two sets of competitors. In the first comparative set, the MFO algorithm and its well-known variants, specifically LMFO, WCMFO, CMFO, ODSFMFO, SMFO, and WMFO, were considered. Five state-of-the-art metaheuristic algorithms, including PSO, KH, GWO, CSA, and HOA, were considered in the second comparative set. The results were then statistically analyzed through the Friedman test. Ultimately, the capacity of the proposed algorithm to solve mechanical engineering problems was evaluated with two problems from the latest CEC 2020 test-suite. The experimental results and statistical analysis confirmed that the proposed MFO-SFR algorithm was superior to the MFO variants and state-of-the-art metaheuristic algorithms for solving complex global optimization problems, with 91.38% effectiveness.
Sentiment Analysis and Topic Modeling Regarding Online Classes on the Reddit Platform: Educators versus Learners

The world is witnessing an unpredictable COVID-19 pandemic that has impacted all levels of online education, shaping future trends. However, this shift was so sudden and drastic that unrevealed puzzles exist regarding the public’s authentic opinion towards online classes, even though three years have passed. Many experts and policymakers have conducted qualitative and quantitative research to explore effective pedagogies, the satisfaction of different stakeholders, and factors influential on learners’ performance. However, scant studies have examined personal opinions and concerns toward online classes hidden behind people’s anonymous posts on social media. This research investigates the sentiments, concerns, and their variance with time regarding online classes by learners and educators on Reddit, which is a dominant social network among them. Data were collected via the official API from identified relevant subreddits and keyword search results across Reddit. Sentiment analysis was applied to reveal their emotions and their changes. Topic modeling was conducted to discover the concerns hidden in the posts. The results revealed the concerns about online classes, such as severe cheating behaviors, and showed doubts about previous strategies to solve disadvantages in online classes. In addition, the results verified the habitual difference and motivations of social media usage between educators and learners.
A Survey on Blockchain-Based Trust Management for Internet of Things

Internet of Things (IoT) aims to create a vast network with billions of things that can seamlessly create and exchange data, establishing intelligent interactions between people and objects around them. It is characterized with openness, heterogeneity, and dynamicity, which inevitably introduce severe security, privacy, and trust issues that hinder the widespread application of IoT. Trust management (TM) holds great promise in identifying malicious nodes, maintaining trust relationships, and enhancing system security. Traditional TM systems (TMSs) can be classified into centralized, semi-centralized, and distributed ones, all three of which suffer from critical challenges and thus are not sufficient for facilitating IoT development. Blockchain, as a disruptive technology, can help addressing the challenges of TM in IoT, thanks to its advanced features, such as decentralization, consistency, and tamper-proofing. As a result, blockchain-based TM (BC-TM) has been extensively studied in recent years to achieve decentralized TM in IoT. However, it still lacks a comprehensive survey on the current state of the arts. To fill this gap, in this article, we conduct a serious survey on BC-TM in IoT. We first propose a set of evaluation criteria that should be met by a TMS in IoT. Then, we propose a taxonomy of TMSs and continue with a thorough review on BC-TM in IoT by employing the proposed criteria. In the end, based on the review, a series of open issues are identified, and future research directions are suggested.
Internet of Medical Things Privacy and Security: Challenges, Solutions, and Future Trends from a New Perspective

The Internet of Medical Things (IoMT), an application of the Internet of Things (IoT) in the medical domain, allows data to be transmitted across communication networks. In particular, IoMT can help improve the quality of life of citizens and older people by monitoring and managing the body’s vital signs, including blood pressure, temperature, heart rate, and others. Since IoMT has become the main platform for information exchange and making high-level decisions, it is necessary to guarantee its reliability and security. The growth of IoMT in recent decades has attracted the interest of many experts. This study provides an in-depth analysis of IoT and IoMT by focusing on security concerns from different points of view, making this comprehensive survey unique compared to other existing studies. A total of 187 articles from 2010 to 2022 are collected and categorized according to the type of applications, year of publications, variety of applications, and other novel perspectives. We compare the current studies based on the above criteria and provide a comprehensive analysis to pave the way for researchers working in this area. In addition, we highlight the trends and future work. We have found that blockchain, as a key technology, has solved many problems of security, authentication, and maintenance of IoT systems due to the decentralized nature of the blockchain. In the current study, this technology is examined from the application fields’ points of view, especially in the health sector, due to its additional importance compared to other fields.
Adaptive Feature Projection With Distribution Alignment for Deep Incomplete Multi-View Clustering

Incomplete multi-view clustering (IMVC) analysis, where some views of multi-view data usually have missing data, has attracted increasing attention. However, existing IMVC methods still have two issues: 1) they pay much attention to imputing or recovering the missing data, without considering the fact that the imputed values might be inaccurate due to the unknown label information, 2) the common features of multiple views are always learned from the complete data, while ignoring the feature distribution discrepancy between the complete and incomplete data. To address these issues, we propose an imputation-free deep IMVC method and consider distribution alignment in feature learning. Concretely, the proposed method learns the features for each view by autoencoders and utilizes an adaptive feature projection to avoid the imputation for missing data. All available data are projected into a common feature space, where the common cluster information is explored by maximizing mutual information and the distribution alignment is achieved by minimizing mean discrepancy. Additionally, we design a new mean discrepancy loss for incomplete multi-view learning and make it applicable in mini-batch optimization. Extensive experiments demonstrate that our method achieves the comparable or superior performance compared with state-of-the-art methods.
TFSFB: Two-stage feature selection via fusing fuzzy multi-neighborhood rough set with binary whale optimization for imbalanced data

Obtaining informative features is crucial in imbalanced classification. However, existing neighborhood rough set-based feature selection approaches easily overlook the diversity and complexity of data distributions, and it is difficult to obtain this global optimal feature subset from imbalanced and high-dimensional datasets. To tackle these limitations, we construct a new two-stage feature subset selection scheme by fusing the fuzzy multi-neighborhood rough set (FMRS) with the binary whale optimization algorithm (BWOA) for imbalanced data. First, to evaluate those distributions of different features, this standard deviation coefficient is introduced to construct a fuzzy multi-neighborhood radius set. Then, the fuzzy multi-neighborhood granule and fuzzy membership degree are presented to establish the novel FMRS, and the feature significance measure in the view of algebra is developed to balance the approximate properties and influences of different features in the negative and positive classes. Second, fuzzy multi-neighborhood conditional entropy is defined to maximize information quantity of class- imbalanced data from the information view, and then by fusing the two evaluation perspectives above, this mixed metric is provided to fully assess this uncertainty of class-imbalanced datasets. These internal and external significant metrics are designed to obtain this preselected candidate set of features based on the filter FMRS model at this first stage. Third, a control factor can be developed to dominate the whale position update, and a novel fitness function will be constructed when fusing the dependency degree and entropy measure with the reduction ratio to evaluate this optimal subset of features. Adopting population partitioning and local interference schemes can prevent the BWOA from becoming trapped within a local optimum. To reduce this search space of evolution, the dynamic bitmask is used to improve the BWOA, and then an optimal subset of features is acquired through continuous iterations of this wrapper BWOA at this second stage. Finally, a new two-stage algorithm for feature subset selection by fusing FMRS and BWOA is provided to process class-imbalanced data, where this particle swarm optimization algorithm confirms those optimized parameters. Experiments on 31 datasets show that our algorithm is efficient and can achieve excellent classification efficiency for binary and multiclass imbalanced data.
Machine learning and deep learning for sentiment analysis across languages: A survey

The inception and rapid growth of the Web, social media, and other online forums have resulted in the continuous and rapid generation of opinionated textual data. Several real-world applications have been focusing on determining the sentiments expressed in these data. Owing to the multilinguistic nature of the generated data, there exists an increasing need to perform sentiment analysis on data in diverse languages. This study presents an overview of the methods used to perform sentiment analysis across languages. We primarily focus on multilingual and cross-lingual approaches. This survey covers the early approaches and current advancements that employ machine learning and deep learning models. We categorize these methods and techniques and provide new research directions. Our findings reveal that deep learning techniques have been widely used in both approaches and yield the best results. Additionally, the scarcity of multilingual annotated datasets limits the progress of multilingual and cross-lingual sentiment analyses, and therefore increases the complexity in comparing these techniques and determining the ones with the best performance.
Machine learning and analytical model hybridization to assess the impact of climate change on solar PV energy production

The United Nations' (UN) Sustainable Development Goals (SDGs) agenda has triggered numerous countries to harness solar energy from solar photovoltaic (PV) modules to increase the share of renewable energy in the global energy mix. However, geographical and climatic factors have a significant impact on the electrical performance of solar PV modules. In addition, since solar PV energy production models are the only physics-based approach to transferring ground-measured PV energy production to other locations, the authors developed 294 physical models from six different PV power technologies and validated them for the model's adaptability. To facilitate the possible determination of PV electric energy generation in the unique geographical and climatic environment of the experiment site, these models were built using machine learning, Gumbel's probabilistic approach (GPM), and hybridization of the two. The major challenges in this study are in developing the hybridized machine learning with the Gumbel probabilistic functional model, which resides in the mathematical transformation process, which required a great deal of repeated mathematical science knowledge to arrive at the final transformed and efficient model for predicting the potential of solar PV output. With a thorough coefficient of determination (R2) of 0.9998% and a root mean square error (RMSE) of 0.0063 kwh, the hybrid model with only the measurable solar radiation parameter is the closest to the measured PV energy production of all technologies. The best hybridized model was used to explore the potential impacts of climate change on the different solar PV technologies. This was achieved by using energy parameters from the Australian Community Climate and Global System Simulation (ACCESS-CM2) in Phase 6. On an annual basis, the effects of climate change on various PV technologies have had a small adverse impact (less than 1%) on these renewable energy technologies. It was also found that, compared to other technologies, CIGS thin film technology produced the least negative effects on climate change, with 10.94%–36.75% in the best-case, 35.71%–36.36% in the moderate-case, and 33.33–40.00% in the worst-case scenario for shared socioeconomic pathways (SSP126, SSP245, and SSP585) emissions. This suggests the intrinsic properties of Copper Indium Gallium Selenide (CIGS) thin film modules are more effective at withstanding high temperatures as they contribute 60.00–89.66% of their intrinsic module properties to PV energy production compared to other technologies. However, taking into account the time, resource availability, cost-effectiveness, commercialization, and consumption of various PV technologies studied in this era of global sustainability, poly-crystalline (p-Si) technology is highly recommended for harvesting solar PV energy products in Alice Springs, Australia.