Reviewerly/exHarmony huggingface dataset:
Uncovering the Strength of Capsule Networks in Deepfake Detection

Information is everywhere, and sometimes we have no idea if what we read, watch or listen is accurate, real or authentic. This paper focuses on detecting deep learning generated videos, or deepfakes - a phenomenon which is more and more present in today's society. While there are some very good methods of detecting deepfakes, there are two key elements that should always be considered, i.e., no method is perfect and deepfake generation techniques continue to evolve, sometimes even faster than detection methods. In our proposed architectures, we focus on a family of deep learning methods that is new, has several advantages over traditional Convolutional Neural Networks and has been underutilized in the fight against fake information, namely the Capsule Networks. We show that: (i) state-of-the-art Capsule Network architectures can be improved in the context of deepfake detection, (ii) they can be used to obtain accurate results using a very small number of parameters, and (iii) Capsule Networks are a viable option over deep convolutional models. Experimental validation is carried out on two publicly available datasets, namely FaceForensics++ and CelebDF, showing very promising results.
ToxLex_bn: A curated dataset of bangla toxic language derived from Facebook comment

Toxic Language in social media is a newly emerging virtual disorder of human society. Detecting toxic language is an NLP task that requires a Dataset of utterances [1]. For the Bangla language, very few datasets have been developed on toxicity or similar concepts [2]. A dataset has been developed using user-generated content from Facebook and that will cover the demographic and thematic distribution of Bangla toxic language generated on the web. Therefore, 2207590 comments have been collected, annotated, and thus extract about 1959 unique bigrams as utterances, which were considered as base-entry of a toxic language dataset. The core derivatives of the dataset are bigram-based wordlists, which are annotated inductively and divided into 08 thematic classes that give some ideas on toxicity variations found in the Bengali community. These thematic classes cover political hate speech [3] and misogynist bullies dominantly. However, these thematic labels will serve as classifiers in the text classification process through machine learning. In addition to the thematic classification labels, this dataset includes some additional features such as imprecise meanings in English, IPA transliteration, real occurrences in the source pages, spelling standards, and degree of toxicity. As this is a dataset of utterance, it has de-identified and anonymous entries and no difficulties for public disclosure. Therefore, we consider this dataset as Toxic lexicon (Toxlex) as an exhaustive wordlist that is essentially a curated value-added and analyzed dataset which can be used as classifier material to detect toxicity in social media.
A Sojourn-Based Approach to Semi-Markov Reinforcement Learning

Abstract In this paper we introduce a new approach to discrete-time semi-Markov decision processes based on the sojourn time process. Different characterizations of discrete-time semi-Markov processes are exploited and decision processes are constructed by their means. With this new approach, the agent is allowed to consider different actions depending also on the sojourn time of the process in the current state. A numerical method based on Q -learning algorithms for finite horizon reinforcement learning and stochastic recursive relations is investigated. Finally, we consider two toy examples: one in which the reward depends on the sojourn-time, according to the gambler’s fallacy ; the other in which the environment is semi-Markov even if the reward function does not depend on the sojourn time. These are used to carry on some numerical evaluations on the previously presented Q -learning algorithm and on a different naive method based on deep reinforcement learning.
The Implementation of Cognitively Challenging Tasks: The Role of Science Teachers’ Professional Development and Teaching Experience

This study deals with the implementation of cognitively challenging tasks for students in science education following professional development interventions for teachers based on the theory of change. These tasks require non-routine behaviors and encourage students to work beyond instructions and accept a high level of responsibility. This study explores how the content of professional development, the duration of professional development, and the extent of the teaching experience of science teachers are associated with the implementation of cognitively challenging science tasks in science education. A secondary data analysis of Trends in International Mathematics and Science Study (TIMSS) 2019 data from three countries (Singapore, Lithuania, and South Africa) was conducted. Ordinal logistic regression (OLR) was used to predict an ordinal dependent variable (the implementation of cognitively challenging science tasks) according to the independent variables (the content of professional development, the duration of professional development, and the extent of the teaching experience of science teachers). The results of the OLR confirmed that the implementation of cognitively challenging tasks is associated with professional development content (improving students’ critical thinking or inquiry skills and science content) and the extent of the teaching experience of science teachers from the selected countries.
Lessons Learned and Future Considerations for Designing Remotely Facilitated Co-Design Studies with Children Focused on Socio-Emotional Experiences

The IDC community has a rich history of developing new methods for involving children in design research. However, few papers discuss developing new remotely facilitated co-design approaches. Fewer still focus on the challenges of eliciting discussion and generating design ideas around subjective experiences involving emotions, feelings, and thoughts. We argue these are of growing need in a post-Covid world. In this paper we contribute a methodological design rationale for a remotely facilitated co-design study aimed at addressing challenges related to ethically eliciting reflection on, promoting ideation around and capturing data of children and families about their personal Covid-19 experiences. To illustrate our methods, we provide exemplar cases of data collected from our participants to show the type of data that can be elicited using our methods. Lastly, we contribute considerations for future methods design based on a selection of our lessons learned.
Tennis Technology Recognition and Training Attitude Analysis Based on Artificial Intelligence Sensor

In order to solve the problem of traditional tennis serving technique teaching, subjective, and experience-based teaching methods, the author proposes a method to extract tennis training movements based on artificial intelligence sensor video analysis. This method requires the use of ordinary cameras and computer technology; video analysis technology is used to guide tennis teaching and training. The result obtained is as follows: in the experimental group and the control group, at the end of the teaching stage, the movement characteristics of each link of the body are quite different, which can reach 15%-20%; when the experimental group and the control group did the “scratching back” movement during the teaching period, at the end of teaching, the students in the experimental group were close to high-level tennis players in their movement skills, and the movement gap was only about 5%. It is proved that the method proposed by the author provides an objective and scientific basis for measuring the referee’s penalty level. In the grass-roots tennis teaching and training, it has important value and significance and has a great promotion prospect.
A Technical and Operational Perspective on Quality Analysis of Stitching Images with Multi-Row Panorama and Multimedia Sources for Visualizing the Tourism Site of Onshore Wind Farm

A virtual tour of the onshore wind farm near Gaomei Wetland, Taichung City, Taiwan, was produced by producing panoramic images of the site by stitching images captured with a full-frame digital single-lens reflex camera and a multi-row panorama instrument, which automatically and precisely divided each scene into several images. Subsequently, the image stitching quality was improved by calculating the root mean square error (RMSE) of tie point matching and adjusting the tie points. Errors due to eccentricity attributed to the camera’s relative position to the rotational axis of the multi-row panorama instrument were examined and solved; the effect of the overlap rate on image stitching quality was also investigated. According to the study results, the overlap rate between the original images was inversely proportional to the RMSE and directly proportional to the time required for photography and image processing. The stitching quality was improved by resolving eccentricity and by increasing the number of tie points. The RMSEs of the panoramas of all stations were all less than 5 pixels. Subsequently, multimedia materials providing information on wind turbine attributes were combined with the panorama platform to establish a virtual reality tour platform. The content of the platform could be accessed with a smartphone and viewed with a virtual reality device and could promote both tourist attractions and wind energy.
On the security of deep learning novelty detection

Deep learning is a type of machine learning that adapts a deep hierarchy of concepts. Deep learning classifiers link the most basic version of concepts at the input layer to the most abstract version of concepts at the output layer, also known as a class or label. However, once trained over a finite set of classes, some deep learning models do not have the power to say that a given input does not belong to any of the classes and simply cannot be linked. Correctly invalidating the prediction of unrelated classes is a challenging problem that has been tackled in many ways in the literature. Novelty detection gives deep learning the ability to output “do not know” for novel/unseen classes. Still, no attention has been given to the security aspects of novelty detection. In this paper, we consider the case study of abstraction-based novelty detection and show its weakness against adversarial samples. We show the feasibility of crafting adversarial samples that bypass the novelty detection monitoring and fool the deep learning classifier at the same time. In other words, novelty detection itself ends up as an attack surface. Moreover, we call for further research from a defender’s point of view. We investigate auto-encoders as a plausible defense mechanism and assess its performance.
Role of Data Mining in Detecting Theft and Making Effective Impact on Performance Management

This paper analyses the effectiveness of DM (Data Mining) classification techniques for company detection. Different approaches of data mining are accessible for the process of data mining. In many applications, data mining techniques are used to identify and prevent various kinds of theft. While data mining research and possible measures for the detection and identification of different forms of theft are already in progress, there is limited study that reconstructs many elements of theft that employ data mining methods. The use of data mining techniques to detect theft is a responsibility. We are also classifying theft into three groups with respect the use of data mining as an aid for the identification and prevention. This report examines the efficacy in determining false financial statements of policymakers, artificial neural networks, and Bayesian beliefs networks. Management theft, Theft against Customers, and computer-based theft are all three kinds of theft.
The Impact of Big Data Technology on Phrase and Syntactic Coherence in English Translation

In the era of big data, data are ubiquitous. The proliferation of data leads to a surge in the demand for communication, which in turn promotes a surge in the demand for language services. Big data technology is a comprehensive technology, and its important feature is a more active technological factor, where technological development and technological innovation dominate, which will have an incalculable impact on the development of the translation industry. In the language service industry, much information that used to be difficult to quantify will be transformed into data for storage and processing, and a large number of complex items to be translated will gradually surface. Therefore, it is the general trend of the translation industry to stimulate and utilize the unexplored values hidden inside the data and develop the blue ocean of the language service industry. Traditional translation researchers are confined to the study of language and text and are not fully aware of it. The huge role played by translation technology in today’s business environment, and the traditional translation theory can hardly describe and explain the new modern translation technology phenomenon and translation technology activities. Whether we are ready for it or not, the rapid development of translation technology in the era of big data will lead to significant changes in translation inquiry and translation teaching on a global scale. The integration and development of translation with cloud computing, big data, the Internet, and artificial intelligence are driving a series of innovations in the traditional translation model. The development of translation technologies and tools is breaking new ground, rapidly expanding into all aspects of the translation industry and triggering disruptive changes in the language service industry.
The Future Impact of Technological Developments on Digital Marketing Through Artificial Intelligence

Digital marketing includes a combination of marketing activity and implication of computer science to optimise the activity of advertisement and sales generation. In this study a comprehensive discussion is conducted to evaluate the aspect of technological advancement in Artificial intelligence and its implication in digital marketing activities. Different methods of digital marketing are discussed in this regard that facilitate modern companies to attain an effective customer engagement through direct interaction. SEO, PPC, Chatbots, SMM and other methods are found to be used extensively in recent times to optimise the marketing operation in the global market. Implication of AI is significant in this regard to enhance the aforementioned methods and improve the process of online marketing. AI facilitates the business entities in gathering customer’s data at the time of searching about products. Brain modelling, time series prediction, image recognition and other techniques are used by AI to offer more relevant data to the business entities regarding customer behaviour and purchase intention. Augmented reality is another important feature in this regard that facilitates customers to see and feel products offered by companies that contribute to increasing the amount of sales volume in the targeted market. Such kind of technological advancement contributes in evaluating customers’ perception about the product and services that helps the marketer to implement suitable strategies to meet customer demands. It can be done by optimising the quality of customer services and products by differentiating it from others available in the similar market. Hence, AI influences to enhance the technique of product promotion to improve the sales volume in target market by effective technical advancements.
Automated Machine Learning-Based Gestational Monitoring Framework in Wearable Internet of Things Environment

Recently, Internet of Things (IoT) and wearable technologies have become popular in diverse areas, and smart health is an important application area. Specifically, IoT and wearable technologies find useful in linking different medicinal gadgets, sensors, and physicians in offering better healthcare services in remote areas. It results in enhanced patient safety, minimum healthcare cost, improved healthcare service accessibility, and high operational efficiency in the healthcare sector. At the same time, high-quality care during pregnancy is essential to determine the probable difficulties earlier and guarantee the healthiness of mother as well as fetus. Several research works have been existed in the literature to monitor the maternal lifetime. But they are developed for a particular health issue and based on short-term data collection approaches. Since maternal monitoring necessitates long-term services, this paper designs an automated machine learning-based gestational monitoring framework in wearable IoT environment. The goal of this paper is to derive a new improved salp swarm optimization (ISSO) with extreme learning machine (ELM), called ISSO-ELM model for continual maternal health monitoring. The ISSO-ELM model encompasses different sub-processes namely data acquisition, pre-processing, classification, and parameter optimization. The presented model enables IoT devices and wearables to gather healthcare data about pregnant women. Then, the gathered data are pre-processed to remove the unwanted noise. Besides, the ELM model is employed as a classifier to determine the occurrence of difficulties. Moreover, ISSO algorithm is applied as a parameter tuning technique to optimally adjust the parameters that exist in the ELM model. The extensive experimental analysis highlighted the enhanced performance of the ISSO-ELM model over the recent state-of-art methods with the precision, recall, and accuracy of 94.00, 88.00, and 90.07%.
On the impact of prior distributions on efficiency of sparse Gaussian process regression

Gaussian process regression (GPR) is a kernel-based learning model, which unfortunately suffers from computational intractability for irregular domain and large datasets due to the full kernel matrix. In this paper, we propose a novel method to produce a sparse kernel matrix using the compact support radial kernels (CSRKs) to efficiently learn the GPR from large datasets. The CSRKs can effectively avoid the ill-conditioned and full kernel matrix during GPR training and prediction, consequently reducing computational costs and memory requirements. In practice, the interest in CSRKs waned slightly as it became evident that, there is a trade-off principle (conflict between accuracy and sparsity) for compactly supported kernels. Hence, when using kernels with compact support, during GPR training, the main focus will be on providing a high level of accuracy. In this case, the advantage of achieving a sparse covariance matrix for CSRKs will almost disappear, as we will see in the numerical results. This trade-off has led authors to search for an “optimal” value of the scale parameter. Accordingly, by selecting the suitable priors on the kernel hyperparameters, and simply estimating the hyperparameters using a modified version of the maximum likelihood estimation (MLE), the GPR model derived from the CSRKs yields maximal accuracy while still maintaining a sparse covariance matrix. In fact, in GPR training, modified version of the MLE will be proportional to the product of MLE and a given suitable prior distribution for the hyperparameters that provides an efficient method for learning. The misspecification of prior distributions and their impact on the predictability of the sparse GPR models are also comprehensively investigated using several empirical studies. The proposed new approach is applied to some irregular domains with noisy test functions in 2D data sets in a comparative study. We finally investigate the effect of prior on the predictability of GPR models based on the real dataset. The derived results suggest the proposed method leads to more sparsity and well-conditioned kernel matrices in all cases.
Financial Data Analysis and Application Based on Big Data Mining Technology

We provide a brief overview of the connotation and characteristics of data mining technology in the era of big data, analyze the feasibility of data mining technology in business management from the economic and technical perspectives, and propose specific application suggestions according to the content and requirements of business management. This paper describes in detail the principles and steps of using the weighted plain Bayesian algorithm and the decision tree algorithm to analyze students' performance; firstly, we need to obtain the plain Bayesian analysis model of college students' learning literacy in physical education and the C4.5 graduation literacy analysis model, and then use certain rules to combine the weighted plain Bayesian algorithm and the decision tree algorithm to obtain the WNB-C4.5 college students' learning literacy analysis model. In addition, in the prediction of financial risks, the classification scheme can be used in the judgment of violation of regulations, but the most used classification scheme is the decision tree. Experiments show that the effectiveness of this scheme in data mining for financial companies is increased by 2% compared to the benchmark method.
Topic-word-constrained sentence generation with variational autoencoder

We propose a topic-word-constrained sentence-generation model with a variational autoencoder and convolutional neural network. It can generate sentences conditioned on a given topic distribution and a certain word. Unlike the vanilla variational autoencoder that assumes a standard Gaussian prior for the latent code, our model specifies the prior for the topic latent code as multiple Gaussian distributions, where each Gaussian distribution corresponds to a topic vector parameterized by a convolutional neural topic model. For word constraints, the decoder in the variational autoencoder generates sentences backward and forward starting from a given word. The topic latent space is arranged by the similarity of topic vectors, and the topic latent code restricts the sentence latent code through a loss term, through which expanded semantically meaningful latent spaces can be learned and provide topic guidance while generating sentences. Experimental results show that our model can generate coherent and diverse sentences related to given topics and words, while also avoiding the Kullback–Leibler divergence collapse problem. Moreover, it outperforms alternative approaches in terms of sentence reconstruction, latent space property and the quality, diversity, and topic controllability of generated sentences.
DeDigi: A Privacy-by-Design Platform for Image Forensics

With the explosion of multimedia has come the rise of advanced image processing and free editing tools, allowing individuals to readily change how an image or video appears in front of other eyes. This act is malicious and may cause disruption in the community; therefore, to resolve these issues, there has been many existing tools that use Digital Image Forensics (DIF) techniques. Even so, based on our MUUP evaluation system, these tools can lack guidance, difficult to utilize and can have privacy issues. Following that, we then investigate existing algorithms from various DIF techniques, and incorporate this knowledge into our DIF web tool - DeDigi. For DeDigi's design, based on our Design Science system, each design iteration will take into account user feedback on how the tool's design is perceived from their perspective. The final outcome of our "privacy-by-design" tool is five approaches from various categories of the focused field, a score of 3.92/5 for DeDigi (beta)'s user experience, and new user interfaces for training purpose.