Reviewerly/exHarmony huggingface dataset:
N-Gram-Based Machine Learning Approach for Bot or Human Detection from Text Messages

Social bots are computer programs created for automating general human activities like the generation of messages. The rise of bots in social network platforms has led to malicious activities such as content pollution like spammers or malware dissemination of misinformation. Most of the researchers focused on detecting bot accounts in social media platforms to avoid the damages done to the opinions of users. In this work, n-gram based approach is proposed for a bot or human detection. The content-based features of character n-grams and word n-grams are used. The character and word n-grams are successfully proved in various authorship analysis tasks to improve accuracy. A huge number of n-grams is identified after applying different pre-processing techniques. The high dimensionality of features is reduced by using a feature selection technique of the Relevant Discrimination Criterion. The text is represented as vectors by using a reduced set of features. Different term weight measures are used in the experiment to compute the weight of n-grams features in the document vector representation. Two classification algorithms, Support Vector Machine, and Random Forest are used to train the model using document vectors. The proposed approach was applied to the dataset provided in PAN 2019 competition bot detection task. The Random Forest classifier obtained the best accuracy of 0.9456 for bot/human detection.
The Adoption of Artificial Intelligence in the South African E-Commerce Space: A Systematic Review

Globally, Artificial Intelligence (AI) in e-commerce has been widely accepted. A developing country like South Africa is expected to adopt AI especially in its e-commerce spaces. Notwithstanding the evidence that e-commerce is growing rapidly, South Africa's use of e-commerce is still growing with potential to grow further. This current study makes use of systematic review using snowballing approach. A total of 107 papers were searched and discovered for this study. These are papers related to e-commerce and AI fields. Iterations were applied to eliminate papers that were not relevant to this study. This was performed by using backward and forward snowballing. The paper also reviews the status of AI adoption in e-commerce and its application to the business landscape in South African context as well as the effectiveness on online shopping. The results of this review exercise show that South Africa has adopted Ai in -ecommerce over the past decades. However, it was further discovered that South Africa's e-commerce space is confronted with complex challenges and factors including the role played by the Internet of Things (IoT), Big Data analytics, ethical issues including privacy matters confronting e-commerce spaces. The role of government in the process is also notable as a factor and game changer in response to legislation and policy direction. The key outcome of the study shows that there is a need for a development of a framework for e-commerce in the context of South Africa.
Editable and Verifiable Anonymous Authentication Incorporating Blockchain in the Internet of Energy

With the continuous development of the Internet of Energy, the access of a large number of distributed renewable energy has caused difficulties in energy management. The traditional energy management mode cannot meet the needs of existing energy management and trading. Therefore, this paper proposes an integrated energy management platform based on blockchain, which allows multiple decentralized energy management systems to conduct unified identity authentication. At the same time, an editable and verifiable anonymous authentication (EVAA) scheme integrating blockchain in the Internet of Energy is designed, which combines the idea of chameleon hash (CH) and the algorithm of elliptic curve encryption (ECC) to realize the dual identity authentication of user information. Finally, the generated certificate is stored in the chain through blockchain consensus. The scheme is mainly constructed using multiple secure cryptographic algorithms, which combine the advantages of blockchain for information authentication. Thus, it can accomplish user cross-system authentication and transaction, and achieve more secure editable anonymous identity authentication through the algorithm performance test, compared with the existing similar schemes under the unified test standard. This paper not only ensures the operation efficiency, but also reflects the obvious advantage of realizing higher intensity security performance.
Using experimental evidence to improve delegated enforcement

Digital content today is governed by online providers like Facebook or YouTube. Increasingly, these providers are expected to enforce the law by removing illegal content, such as copyright infringement or hate speech. Typically, once they are notified of its existence, they have to assess it and, if infringing, remove it. Otherwise, they face liability. This system of content moderation is a form of delegation of the state’s tasks to private parties. In literature, it is empirically established that some schemes of delegated enforcement can trigger substantial false positives, mostly due to over-compliance by providers and under-assertion of rights by affected content creators. This results in a phenomenon known as over-blocking: collateral removal of lawful content. We conduct a laboratory experiment to test a possible solution to this issue, as proposed by Husovec (2016). Our results show that an external dispute resolution mechanism subject to a particular fee structure can significantly reduce over-compliance by providers and improve the accuracy of their decisions, largely thanks to the content creators taking initiative. It does so by re-calibrating the typical asymmetry of incentives under the delegated enforcement schemes. The principles behind the solution have the potential to improve also other schemes of delegated enforcement where providers have weak incentives to properly execute delegated tasks in the public interest.
Deep Learning for Predicting Pediatric Crohn's Disease Using Histopathological Imaging

The current gold standard for Crohn's disease diagnosis involves the examination of biopsied tissue by a trained physician. However, endoscopic images and histological features are only evident when the appropriate biopsy site is chosen and the image is of high quality [1]. Thus, to prevent delayed diagnoses or reclassifications over time, additional tools to reinforce pathologists' diagnoses are necessary. Recent studies have showcased successful applications of deep learning for developing whole-slide classifications of digital histology images. In this study, we developed a patch-level image classification model for prediction of Crohn's disease using a convolutional neural network. This study obtained data from two different hospitals: INOVA and Cincinnati Children's Hospital Medical Center (CCHMC). When trained and validated on the same data set, our INOVA and CCHMC models achieved validation accuracies of 84.6 % and 93.9 %, respectively. However, the models performed poorly when trained on data from one site and tested on data from the other site. To investigate this issue, we built an additional patch-level model that was able to predict hospital source of the biopsy with 99 % accuracy. These results suggest the presence of site-specific artifacts which are detectable by machine learning models. We reduced the effects of these artifacts using color-normalization, image cropping, and other transformations, lowering site-predictive accuracy to 74%. Therefore, we suggest further works investigate reasons for inter-site biopsy differences such that site-generalizable, histopathological deep learning models can be developed.
Analysis of Agricultural Products Supply Chain Traceability System Based on Internet of Things and Blockchain

The quality and safety of agricultural products cannot be separated from rural revitalization. This is also related to the interests and development of participating in the agricultural supply chain. The traditional unitary agricultural product quality supervision system meets the growing market demand and requirements. This paper constructs the traceability and blockchain of agricultural products supply chain, as well as the trusted computing model of Internet of things nodes based on blockchain. The results show that the trusted computing model of IOT nodes based on blockchain can effectively identify and remove malicious nodes, ensure the effectiveness, tamperability, security, and reliability of agricultural product process data, greatly reduce the transaction delay, and provide a guarantee.
“Track-track: Let's follow the cat!” Reflecting on children's biometric data processing through a micro puppet show

Track-track: Let's follow the cat! is a micro puppet show for children where the spectator's biometric data animates elements of the scenography. While seeing the show inside a box through a peephole, the spectator activates sensors that take the data to make the stars shine, blow the wind, and move the waves. Taking the format of the Brasilian Lambe Lambe theater – a peep box – this project reflects on children's new digital environment and the concerns around biometric data processing and the notion of privacy. This interactive puppet show explores how children can recognize themselves as a data source and perceive their own digital data as a resource.
Engineering Blockchain Enabling Win A Fortune Game among Novice through eAOM

Blockchain application development has received much attention nowadays. As development is complex and challenging, a systematic approach is needed to improve the product, services, and process quality. Despite the introduction of techniques, there are still inadequate models for demonstrating the blockchain's internal architecture. Hence, there is a gap when developing the blockchain application, a gap in the modelling environment of a blockchain development application. This paper introduces a new insight into blockchain application development through extended Agent-Oriented Modelling (eAOM). eAOM is a methodology for complex socio-technical system development, and we believe that it can reduce the complexity of implementing the blockchain application. In this paper, the eAOM is used to model a blockchain-based “win a fortune” system, which includes smart contract development. It showcases the feasibility of adopting eAOM to model a blockchain enabling application. A usability survey among the novices has further validated the usability and benefits of eAOM in the blockchain enabling application development.
Predictive Distillation Method of Anchor-Free Object Detection Model for Continual Learning

Continual learning (CL) is becoming increasingly important, not only for storage space because of the ever-increasing amount of data being generated, but also for associated copyright problems. In this study, we propose ground truth’ (GT’), which is a combination of ground truth (GT) and a prediction of the teacher model that distills the prediction results of the previously trained model, called the teacher model, by applying the knowledge distillation (KD) technique to an anchor-free object detection model. Among all the objects predicted by the teacher model, an object for which the prediction score is higher than the threshold value is distilled into the current trained model, called the student model. To avoid interference with new class learning, the IoU is obtained between every object of the GT and the predicted objects. Through the continual learning scenario, even if the reuse of past data is limited, if new data are sufficient, the proposed model minimizes catastrophic forgetting problems and enables learning for newly added classes. The proposed model was learned in PascalVOC 2007 + 2012 and tested in PascalVOC2007, with better results of 9.6% p mAP and 13.7% p F1i shown in the scenario 19 + 1. The result in scenario 15 + 5 showed better results than the compared algorithm, with 1.6% p mAP and 0.9% p F1i. The scenario 10 + 10 also outperformed the other alternatives, with 0.9% p mAP and 0.6% p F1i.
Mapping Child Care Access in King County

In this submission, I discuss my participatory design research on mapping and data visualization of childcare access in King County of Washington in the United States. Through a co-design process with a childcare business coalition, I investigate the affordances of a data-driven website for advocacy and social change. As grassroots coalitions begin to build economic and operational capacity from self-organizing, they break down silos within a fragmented childcare ecosystem. This proposed dissertation work aims to provide a means for an effective collaboration between public-sector and academic HCI partners in the iterative development of an online tool for structural changes to childcare access in King County.
CGAR: Critic Guided Action Redistribution in Reinforcement Leaning

Training a game-playing reinforcement learning agent requires multiple interactions with the environment. Ignorant random exploration may cause a waste of time and resources. It's essential to alleviate such waste. As discussed in this paper, under the settings of the off-policy actor critic algorithms, we demonstrate that the critic can bring more expected discounted rewards than or at least equal to the actor. Thus, the Q value predicted by the critic is a better signal to redistribute the action originally sampled from the policy distribution predicted by the actor. This paper introduces the novel Critic Guided Action Redistribution (CGAR) algorithm and tests it on the OpenAI MuJoCo tasks. The experimental results demonstrate that our method improves the sample efficiency and achieves state-of-the-art performance. Our code can be found at https://github.com/tairanhuang/CGAR.
Topic modeling approaches to counter online grooming

This paper describes a topic model-based approach for analyzing a large-scale dataset of chat messages exchanged between the users of LiveMe, a major social live streaming platform, in the context of broadcasts involving sexually explicit content. The analysis reveals the characteristics of predatory behavior targeting minors and its criminal dimension in an accessible and explainable manner.
Let the Chatbot Speak! Freedom of Expression and Synthetic Media.

ML-generated media (speech, image, etc.) can be used for many purposes such as robojournalism, therapeutic chatbots or the synthetic resuscitation of a dead actor or a deceased loved one. Can ML be a source of speech that is protected by the right to freedom of expression in Article 10 of the European Convention on Human Rights (ECHR)? I first discuss if ML-generated (or: "synthetic") media fall within the protective scope of freedom of expression (Article 10(1) ECHR). After concluding that this is the case, I look at specific complexities raised by ML-generated content in terms of limitations to freedom of expression (Article 10(2) ECHR). The first set of potential limitations that I explore are those following from copyright, data protection, privacy and confidentiality law. Some types of synthetic media could potentially circumvent these limitations. Second, I study how new types of content generated by ML can create normative grey areas where the boundaries of constitutionally protected and unprotected speech are not always easy to draw. In this context, I discuss two types of ML-generated content: virtual child pornography and fake news/disinformation. Third, I argue that the nuances of Article 10 ECHR are not easily captured in an automated filter and I discuss the potential implications of the arms race between automated filters and ML-generated content. In this context I discuss the newly adopted Digital Services Act (DSA) [1] and the recent (26 April 2022) judgement [2] of the ECJ (European Court of Justice) on automatic filtering of copyright-infringing content under Article 17 of the Directive on Copyright in the Digital Single Market (DSM Directive).
Optimization Algorithms for Scalable Stream Batch Clustering with k Estimation

The increasing volume and velocity of the continuously generated data (data stream) challenge machine learning algorithms, which must evolve to fit real-world problems. The data stream clustering algorithms face issues such as the rapidly increasing volume of the data, the variety of the number of clusters, and their shapes. The present work aims to improve the accuracy of sequential clustering batches of data streams for scenarios in which clusters evolve dynamically and continuously, automatically estimating their number. In order to achieve this goal, three evolutionary algorithms are presented, along with three novel algorithms designed to deal with clusters of normal distribution based on goodness-of-fit tests in the context of scalable batch stream clustering with automatic estimation of the number of clusters. All of them are developed on top of MapReduce, Discretized-Stream models, and the most recent MPC frameworks to provide scalability, reliability, resilience, and flexibility. The proposed algorithms are experimentally compared with state-of-the-art methods and present the best results for accuracy for normally distributed data sets, reaching their goal.
(Retracted) Swimming pool safety detection device based on computer vision

Computer vision (CV) technology refers to the process of computer simulation of human vision and has the ability to perceive the environment and technology of the human visual function. In reality, CV is essentially the study of visual perception, a discipline that studies how to make computers see as humans do. Machine vision mainly uses computers to simulate human visual functions, extract information from images of objective things, process and understand them, and finally use them for actual detection, measurement, and control. With the frequent occurrence of safety accidents, especially in swimming, it is necessary to make adequate preparations to reduce the occurrence of accidents. Swimming is affected by many external factors. While completing swimming technical movements, trainees must overcome the stimulation of water on the body and the interference of breathing, which can lead to accidents. We aim to study the safety detection device of swimming pools based on CV and use the current popular CV technology to monitor the safety problems of swimming pools. We study the target motion trajectory detection system based on CV and use Zhang’s plane calibration method to locate the camera. This research is based on how the computer back-end server realizes abnormal behavior detection, which greatly saves hardware costs. The experimental data shows that water quality is most important issue in swimming safety, accounting for 29% of the population, which indicates that water quality is the focus of users’ choosing swimming as an activity. Of those surveyed, 19 people could not deal with the risks when swimming, accounting for 24% of the total group surveyed, which indicates that people’s swimming safety coping ability needs to be improved.
Face Identification and Verification Under Computational and Security Constraints

Face verification methods have undergone significant changes through the last decade. Rapid increasing of hardware computing power allows engineers to create neural networks to solve face verification problems. Classical computer vision face verification such as Viola-Jones Algorithm or Haar cascades Algorithm were forced out by deep learning Siamese Networks approaches. Nowadays we are faced with the challenge of finding a balance between accuracy and performance. Many light-weighted “mobile” models have good computational performance but lower accuracy in unconstrained data. In this paper, we provide some ideas of modifying the MobileFaceNet approach to increase accuracy without falling the evaluation performance.
Air combat target maneuver trajectory prediction based on robust regularized Volterra series and adaptive ensemble online transfer learning

Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver decision-making. However, how to use a large amount of trajectory data generated by air combat confrontation training to achieve real-time and accurate prediction of target maneuver trajectory is an urgent problem to be solved. To solve this problem, in this paper, a hybrid algorithm based on transfer learning, online learning, ensemble learning, regularization technology, target maneuvering segmentation point recognition algorithm, and Volterra series, abbreviated as AERTrOS-Volterra is proposed. Firstly, the model makes full use of a large number of trajectory sample data generated by air combat confrontation training, and constructs a Tr-Volterra algorithm framework suitable for air combat target maneuver trajectory prediction, which realizes the extraction of effective information from the historical trajectory data. Secondly, in order to improve the real-time online prediction accuracy and robustness of the prediction model in complex electromagnetic environments, on the basis of the Tr-Volterra algorithm framework, a robust regularized online Sequential Volterra prediction model is proposed by integrating online learning method, regularization technology and inverse weighting calculation method based on the priori error. Finally, inspired by the preferable performance of models ensemble, ensemble learning scheme is also incorporated into our proposed algorithm, which adaptively updates the ensemble prediction model according to the performance of the model on real-time samples and the recognition results of target maneuvering segmentation points, including the adaptation of model weights; adaptation of parameters; and dynamic inclusion and removal of models. Compared with many existing time series prediction methods, the newly proposed target maneuver trajectory prediction algorithm can fully mine the prior knowledge contained in the historical data to assist the current prediction. The rationality and effectiveness of the proposed algorithm are verified by simulation on three sets of chaotic time series data sets and a set of real target maneuver trajectory data sets.
NOTE: Unavoidable Service to Unnoticeable Risks: A Study on How Healthcare Record Management Opens the Doors of Unnoticeable Vulnerabilities for Rohingya Refugees

Secure management of healthcare records in dynamic contexts requires an understanding of the overall infrastructure of record flows and poses more challenges for vulnerable environments such as amongst the Rohingya refugees in Bangladesh. Understanding the overall infrastructure of how health clinics are providing medical treatments and how they are collecting and storing patient records is crucial as any changes or mismanagement in these records enables misuse or deliberate misinterpretations of medical data on various levels amongst individuals and Rohingya communities. Through an extensive field study in the Rohingya refugee camps in Bangladesh, we explored the management of healthcare records in different organizations. Over the course of our fieldwork, we interviewed 22 medical service providers from nine healthcare organizations connected to the Rohingya camps. Based on our findings, we design an abstract record management model and analyze it using a data provenance approach to identify the limitations of the existing record management. Our study shows vulnerabilities in ID management and security practices in healthcare record management. We further illustrate potential exploitation of these vulnerabilities through political, financial, and social lenses. To the best of our knowledge, this study is the first to discuss vulnerabilities in Rohingya refugees’ medical record management from political, social and economic views.