Reviewerly/exHarmony huggingface dataset:
Electromagnetic Modulation Signal Classification Using Dual-Modal Feature Fusion CNN

AMC (automatic modulation classification) plays a vital role in spectrum monitoring and electromagnetic abnormal signal detection. Up to now, few studies have focused on the complementarity between features of different modalities and the importance of the feature fusion mechanism in the AMC method. This paper proposes a dual-modal feature fusion convolutional neural network (DMFF-CNN) for AMC to use the complementarity between different modal features fully. DMFF-CNN uses the gram angular field (GAF) image coding and intelligence quotient (IQ) data combined with CNN. Firstly, the original signal is converted into images by GAF, and the GAF images are used as the input of ResNet50. Secondly, it is converted into IQ data and as the complex value network (CV-CNN) input to extract features. Furthermore, a dual-modal feature fusion mechanism (DMFF) is proposed to fuse the dual-modal features extracted by GAF-ResNet50 and CV-CNN. The fusion feature is used as the input of DMFF-CNN for model training to achieve AMC of multi-type signals. In the evaluation stage, the advantages of the DMFF mechanism proposed in this paper and the accuracy improvement compared with other feature fusion algorithms are discussed. The experiment shows that our method performs better than others, including some state-of-the-art methods, and has superior robustness at a low signal-to-noise ratio (SNR), and the average classification accuracy of the dataset signals reaches 92.1%. The DMFF-CNN proposed in this paper provides a new path for the AMC field.
Turkish abstractive text summarization using pretrained sequence-to-sequence models

Abstract The tremendous amount of increase in the number of documents available on the Web has turned finding the relevant piece of information into a challenging, tedious, and time-consuming activity. Accordingly, automatic text summarization has become an important field of study by gaining significant attention from the researchers. Lately, with the advances in deep learning, neural abstractive text summarization with sequence-to-sequence (Seq2Seq) models has gained popularity. There have been many improvements in these models such as the use of pretrained language models (e.g., GPT, BERT, and XLM) and pretrained Seq2Seq models (e.g., BART and T5). These improvements have addressed certain shortcomings in neural summarization and have improved upon challenges such as saliency, fluency, and semantics which enable generating higher quality summaries. Unfortunately, these research attempts were mostly limited to the English language. Monolingual BERT models and multilingual pretrained Seq2Seq models have been released recently providing the opportunity to utilize such state-of-the-art models in low-resource languages such as Turkish. In this study, we make use of pretrained Seq2Seq models and obtain state-of-the-art results on the two large-scale Turkish datasets, TR-News and MLSum, for the text summarization task. Then, we utilize the title information in the datasets and establish hard baselines for the title generation task on both datasets. We show that the input to the models has a substantial amount of importance for the success of such tasks. Additionally, we provide extensive analysis of the models including cross-dataset evaluations, various text generation options, and the effect of preprocessing in ROUGE evaluations for Turkish. It is shown that the monolingual BERT models outperform the multilingual BERT models on all tasks across all the datasets. Lastly, qualitative evaluations of the generated summaries and titles of the models are provided.
review of SQLI detection strategies using machine learning

Various platforms and web apps to deliver material via the Internet are becoming more widespread. Web-based technologies accept and store sensitive information from users. Because of their Internet connectivity, these systems and the databases they link to are vulnerable to various information security vulnerabilities. The most dangerous threats are denial of service (DoS) and SQL injection assaults. SQL Injection attacks are at the top of the list for web-based systems. In this type of attack, the perpetrator will take sensitive and classified information that might hurt a firm or enterprise. Depending on the conditions, the corporation may incur financial losses, have private information disclosed, and have its stock market value drop. This work uses machine learning-based classifiers such as MLP, Support Vector Machine, Logistic Regression, Naive Bayes, and Decision Tree to identify and detect SQL Injection attacks. For the SQLI dataset learning strategies, we examined all five algorithms using the Confusion Matrix, F1 Score, and Log Loss. We discuss the benefits and drawbacks of the proposed AI-based SQLI techniques. Finally, we talk about making SQLI reach its full potential through more research in the coming years.
Examining and Mitigating the Impact of Crossbar Non-idealities for Accurate Implementation of Sparse Deep Neural Networks

Recently several structured pruning techniques have been introduced for energy-efficient implementation of Deep Neural Networks (DNNs) with lesser number of crossbars. Although, these techniques have claimed to preserve the accuracy of the sparse DNNs on crossbars, none have studied the impact of the inexorable crossbar non-idealities on the actual performance of the pruned networks. To this end, we perform a comprehensive study to show how highly sparse DNNs, that result in significant crossbar-compression-rate, can lead to severe accuracy losses compared to unpruned DNNs mapped onto non-ideal crossbars. We perform experiments with multiple structured-pruning approaches (such as, C/F pruning, XCS and XRS) on VGG11 and VGG16 DNNs with benchmark datasets (CIFAR10 and CIFAR100). We propose two mitigation approaches - Crossbar-column rearrangement and Weight-Constrained-Training (WCT) - that can be integrated with the crossbar-mapping of the sparse DNNs to minimize accuracy losses incurred by the pruned models. These help in mitigating non-idealities by increasing the proportion of low conductance synapses on crossbars, thereby improving their computational accuracies.
Gamification as a strategy in collaborative learning against virtual education in times of pandemic

This research focuses on the use of gamification in collaborative learning, which means, implementing game-based activities in the virtual education of students from Educaci&#x00F3;n General B&#x00E1;sica. Gamification is a strategy that seeks to increase the motivation and creativity of students in academic environments, to obtain better results in their learning. Therefore, the objective of this research was to develop author resources based on gamification tools in the collaborative learning of students at Educaci&#x00F3;n General B&#x00E1;sica. Therefore, the type of research is experimental - analytical, since it allows to analyze the experiment applied in the study population through a pretest and posttest, the ADDIE model was used for the development of resources, which consists of creating experiences in the educational process to enhance skills in students. To determine the reliability of the instrument, Cronbach&#x2019;s alpha statistic was used with a value of 0.842. Furthermore, the tools used were Powtoon and Kahoot which are important resources for students to improve their knowledge in an innovative and creative way. The results of this research were optimal, the students give a positive response for the development of gamification tools within the classroom since those help them to learn an interactive and dynamic way.
Indian female Twitter influencers’ perceptions of trolls

Abstract Social media interactions invariably include trolling, where disruptive or annoying comments are made online, and gender trolling is one aspect of trolling. Gender trolling in India has turned ugly, with mischievous and offensive tweets transforming into threats. However, far too little research has examined the perceptions of such trolling among women. What do Indian women on Twitter feel about trolling? Are they encountering prejudice on the platform as well? How do they cope with vitriolic trolls and online threats? This study employs a feminist narrative approach to evaluate perceptions of gender trolling among women on Twitter in India. The study aims to understand the impact of online trolling among Indian women Twitter influencers. Insights into their online trolling experiences from twenty-five selected women social media influencers on Twitter were gathered through detailed interviews. This study unravelled some critical observations and responses towards gender trolling among women Twitter influencers in India. The research also explored if the regularization of misogynistic laced gender trolling within the Indian online space alters the way women social media influencers respond to trolling.
AEON: a method for automatic evaluation of NLP test cases

Due to the labor-intensive nature of manual test oracle construction, various automated testing techniques have been proposed to enhance the reliability of Natural Language Processing (NLP) software. In theory, these techniques mutate an existing test case (e.g., a sentence with its label) and assume the generated one preserves an equivalent or similar semantic meaning and thus, the same label. However, in practice, many of the generated test cases fail to preserve similar semantic meaning and are unnatural (e.g., grammar errors), which leads to a high false alarm rate and unnatural test cases. Our evaluation study finds that 44% of the test cases generated by the state-of-the-art (SOTA) approaches are false alarms. These test cases require extensive manual checking effort, and instead of improving NLP software, they can even degrade NLP software when utilized in model training. To address this problem, we propose AEON for Automatic Evaluation Of NLP test cases. For each generated test case, it outputs scores based on semantic similarity and language naturalness. We employ AEON to evaluate test cases generated by four popular testing techniques on five datasets across three typical NLP tasks. The results show that AEON aligns the best with human judgment. In particular, AEON achieves the best average precision in detecting semantic inconsistent test cases, outperforming the best baseline metric by 10%. In addition, AEON also has the highest average precision of finding unnatural test cases, surpassing the baselines by more than 15%. Moreover, model training with test cases prioritized by AEON leads to models that are more accurate and robust, demonstrating AEON’s potential in improving NLP software.
An Exploratory Study on the Possibility of Metaverse-based Korean Language Subject Design

The purpose of this study is to explore the possibility of developing a metaverse-based Korean language subject reflecting the changing teaching/learning environment of university and the diversified characteristics and needs of foreign learners. Even though the utilization of the so-called metaverse in the field of Korean language education is still in its early stages, target language learning based on concrete and direct experience is very effective, just as emphasized in ‘The Cone of Experience’, Thus, if students can learn the Korean language through the metaverse based on such programs as Virtual Worlds and Mirror Worlds, it would help them increase their actual communication skills to a great extent. In order to design a metaverse-based Korean language subject, this study first examined the technical types of the metaverse, and then analyzed the functions of Gather.town and ifland, both of which are currently being developed. As a result, this study revealed the possibility of conducting a Korean language subject through the metaverse platform. After arguing for the necessity of establishing a ‘contextual scenario’ based on communication tasks, this study also presented a four-step instructional model according to each scenario. In the future, there should be follow-up research that proves the effectiveness of this instructional model by applying it to the actual education field.
Exploring the grammar of othering and antagonism as enacted in terrorist discourse: verbal aggression in service of radicalisation

Abstract The social, discursive practice of othering in violent extremist discourse serves to present outgroups as distant yet real threats to the ideological and physical territories of an ingroup which a terrorist claims to represent. However, the role of grammatical choices (namely, non/transactive construction, voice, and mood) in enacting the othering act within the context of radicalisation to terrorism remains to be empirically verified. This paper explores the patterning and pragmatic functions—namely in framing situations, coercing into violence, and legitimising hostile actions against Others—of the syntactic structure of the othering utterances. The othering utterances, as realized in a set of eight public statements produced by former al-Qaeda leader, Osama bin Laden, were sorted manually and analysed qualitatively to help understand and showcase how grammar was strategically leveraged in the process of radicalisation. Results show that the act of othering in the dataset operates within the victimization and injustice frameworks to morally sanction antagonism and aggression via: (i) overt othering, where transactive construction, only declarative mood and active voice are used, and (ii) covert othering, in which nontransactive construction, any mood type, and passive voice are utilized. Overt othering foregrounds, through assertions and statements of presumed facts, the negative agentive role of Others and the diagnostic framing of the causal relationships between Others and negative experiences. Covert othering backgrounds this agentive role to place prominence on immoral actions and to serve in the motivational function of framing. The grammatical patterns provide evidence of the strategic character of OBL’s verbal aggression and how different mood types tend to construct the directive, illocutionary point of the utterances and to enact prognostic framing. The analytical strategy aids in threat assessment and preventing radicalisation by sensitizing assessors to, first, the kind of semiotic clues to engagement in the social and discursive process of radicalisation where utterances count as calls for action and activators of a reality of deontology, and, second, to the social functioning of terrorist texts in: (i) promoting putative readers’ awareness of particular outgroups, and (ii) ideological positioning and encouraging and legitimating violence that is liberty, loyalty and care metavalues-based.
Blind image quality assessment based on progressive multi-task learning

Due to the lack of adequate training data and sufficient mining of prior knowledge related to perceived quality, most existing image quality assessment (IQA) methods show limited generalization performance. In this paper, we study the prior knowledge from the factors affecting perceived quality, and introduce a novel progressive multi-task learning based blind IQA method. Inspired by the definition of IQA: human comprehensive perception for degradation of image content, we firstly deconstruct IQA into three elements, i.e., image content, pattern of degradation, and intensity of degradation. Based on these elements, we design the corresponding auxiliary tasks for instructing the network to learn IQA. By statistical analysis on a great deal of data, we find that there is progressive relevance among the four tasks. Furthermore, we mathematically derive that introducing the progressive relevance into a multi-task learning network can effectively constrain the hypothesis space of the main task. Under the guidance of the derivation, we propose an end-to-end IQA framework based on progressive multi-task learning. Experimental results demonstrate the excellent generalization capability of the proposed method, which achieves state-of-the-art performance against these existing IQA methods.
Modeling of Online Learning Strategies Based on Fuzzy Expert Systems and Self-Directed Learning Readiness: The Effect on Learning Outcomes

This study proposes an online learning model based on fuzzy expert systems to recommend learning contents that are most appropriate for facilitating more efficient learning. The process used two computer applications which are Matlab for fuzzy analysis and the learning management system Moodle platform for online learning environment. Besides, considering the learner themselves by their self-directed learning readiness as the moderating variable in this study. To examine the learning strategies, we used aptitude treatment interaction for the learning model. Statistical analysis is used to examine the effect of this strategy and the students’ self-directed learning readiness on the learning outcomes. We found that students who are taught by online learning strategies based on fuzzy expert systems have better learning outcomes than students who are given traditional online learning strategies. Students who have high self-directed learning readiness have higher learning outcomes than students who have low self-directed learning readiness in both online learning strategies. Students who have low self-directed learning readiness are more appropriate to learn by the online learning strategies based on fuzzy expert systems to improve their learning outcomes
ST-TLF: Cross-version defect prediction framework based transfer learning

Cross-version defect prediction (CVDP) is a practical scenario in which defect prediction models are derived from defect data of historical versions to predict potential defects in the current version. Prior research employed defect data of the latest historical version as the training set using the empirical recommended method, ignoring the concept drift between versions, which undermines the accuracy of CVDP. We customized a Selected Training set and Transfer Learning Framework (ST-TLF) with two objectives: a) to obtain the best training set for the version at hand, proposing an approach to select the training set from the historical data; b) to eliminate the concept drift, designing a transfer strategy for CVDP. To evaluate the performance of ST-TLF, we investigated three research problems, covering the generalization of ST-TLF for multiple classifiers, the accuracy of our training set matching methods, and the performance of ST-TLF in CVDP compared against state-of-the-art approaches. The results reflect that (a) the eight classifiers we examined are all boosted under our ST-TLF, where SVM improves 49.74% considering MCC, as is similar to others; (b) when performing the best training set matching, the accuracy of the method proposed by us is 82.4%, while the experience recommended method is only 41.2%; (c) comparing the 12 control methods, our ST-TLF (with BayesNet), against the best contrast method P15-NB, improves the average MCC by 18.84%. Our framework ST-TLF with various classifiers can work well in CVDP. The training set selection method we proposed can effectively match the best training set for the current version, breaking through the limitation of relying on experience recommendation, which has been ignored in other studies. Also, ST-TLF can efficiently elevate the CVDP performance compared with random forest and 12 control methods.
A New Cyber-Alliance of Artificial Intelligence, Internet of Things, Blockchain, and Edge Computing

The world is witnessing an accelerated large-scale technology adoption with transformative effects on day-to-day operations, with many industries now depending on machine-controlled processes. These technologies include blockchain, artificial intelligence (AI), the Internet of Things (IoT), and edge computing (EC). Although a lot of resources have been used to secure these technologies, persistent cyber threats result in major drawbacks, and applications must be designed with utmost precaution considering threats against known and unknown vulnerabilities. However, the vulnerabilities do not apply equally across all four technologies, and research shows that weaknesses in one platform can potentially be overcome by converging two or more. While blockchain offers enhanced distributed security, AI offers efficiency on blockchain ledgers, IoT provides much-needed data input to AI, and EC provides all platforms with reduced latency. Traditionally, these have been implemented in silos or perhaps in combinations of two. This article explores the possibility of addressing cybersecurity concerns through a complete alliance of the four technologies. We examine the correlation and expediency of integrating blockchain, AI, IoT, and EC, and how the consolidation could provide new levels of cyber safety.
Unsupervised Underwater Image Clearness via Transformer

Underwater images are degraded by the adverse effects of light absorption and scattering in underwater transmission, which is embodied in the quality degradation problems such as color deviation and blurring. The degraded underwater image will hinder the development of various ocean-related researches, especially for the subsequent typical tasks of underwater image classification, detection and recognition. Therefore, how to obtain a clear underwater image is an important problem to be solved urgently in the field of scientific research and engineering. Especially, complicated and changeable underwater environment greatly limits underwater image clearness performance of existing methods. To solve the problem of underwater image degradation, we try to leveraging Transformer’s powerful ability of long-range context to capture the global information of underwater image. Moreover, the acquisition of large paired underwater dataset are expensive and logistically almost impossible to obtain, and then to improve the generalization ability of the method on the real-world data, we try to use air image to guide underwater image clearness. Extensive experiments demonstrate the effectiveness and superiority of our underwater image clearness method.
Underwater image enhancement based on color correction and complementary dual image multi-scale fusion

Underwater images often suffer from color cast, poor contrast, and detail loss owing to the scattering and absorption of light in water. To solve these problems, we propose what we believe to be a novel underwater image enhancement method based on color correction and dual image multi-scale fusion. We first use the color correction method to solve the problem of color cast, and we compensate the other two-color channels with the highest mean value color channel; further, all the color channels are dynamically stretched. Next, a complementary dual image multi-scale fusion method is used to improve the contrast, pairs of complementary adaptive gamma correction with weighted distribution enhanced images are used as the two inputs of multi-scale fusion, and appropriate weight maps are selected. Then, a multi-scale detail-sharpening method is used to enhance the image details. Qualitative and quantitative evaluations prove that the proposed method can produce high-quality underwater images. Moreover, the proposed method has relatively high evaluator values compared to the state-of-the-art methods.