Reviewerly/exHarmony huggingface dataset:
Is Seeing Still Believing? Leveraging Deepfake Technology for Livestock Farming

Deepfake technologies are known for the creation of forged celebrity pornography, face and voice swaps, and other fake media content. Despite the negative connotations the technology bears, the underlying machine learning algorithms have a huge potential that could be applied to not just digital media, but also to medicine, biology, affective science, and agriculture, just to name a few. Due to the ability to generate big datasets based on real data distributions, deepfake could also be used to positively impact non-human animals such as livestock. Generated data using Generative Adversarial Networks, one of the algorithms that deepfake is based on, could be used to train models to accurately identify and monitor animal health and emotions. Through data augmentation, using digital twins, and maybe even displaying digital conspecifics (digital avatars or metaverse) where social interactions are enhanced, deepfake technologies have the potential to increase animal health, emotionality, sociality, animal-human and animal-computer interactions and thereby productivity, and sustainability of the farming industry. The interactive 3D avatars and the digital twins of farm animals enabled by deepfake technology offers a timely and essential way in the digital transformation toward exploring the subtle nuances of animal behavior and cognition in enhancing farm animal welfare. Without offering conclusive remarks, the presented mini review is exploratory in nature due to the nascent stages of the deepfake technology.
Uninformed Bayesian quantum thermometry

We study the Bayesian approach to thermometry with no prior knowledge about the expected temperature scale, through the example of energy measurements on fully or partially thermalized qubit probes. We show that the most common Bayesian estimators, namely the mean and the median, lead to high-temperature divergences when used for uninformed thermometry. To circumvent this and achieve better overall accuracy, we propose two new estimators based on an optimization of relative deviations. Their global temperature-averaged behavior matches a modified van Trees bound, which complements the Cram\'er-Rao bound for smaller probe numbers and unrestricted temperature ranges. Furthermore, we show that, using partially thermalized probes, one can increase the range of temperatures to which the thermometer is sensitive at the cost of the local accuracy.
A Novel Elderly Tracking System Using Machine Learning to Classify Signals from Mobile and Wearable Sensors

A health or activity monitoring system is the most promising approach to assisting the elderly in their daily lives. The increase in the elderly population has increased the demand for health services so that the existing monitoring system is no longer able to meet the needs of sufficient care for the elderly. This paper proposes the development of an elderly tracking system using the integration of multiple technologies combined with machine learning to obtain a new elderly tracking system that covers aspects of activity tracking, geolocation, and personal information in an indoor and an outdoor environment. It also includes information and results from the collaboration of local agencies during the planning and development of the system. The results from testing devices and systems in a case study show that the k-nearest neighbor (k-NN) model with k = 5 was the most effective in classifying the nine activities of the elderly, with 96.40% accuracy. The developed system can monitor the elderly in real-time and can provide alerts. Furthermore, the system can display information of the elderly in a spatial format, and the elderly can use a messaging device to request help in an emergency. Our system supports elderly care with data collection, tracking and monitoring, and notification, as well as by providing supporting information to agencies relevant in elderly care.
ForkDec: Accurate Detection for Selfish Mining Attacks

Incentive mechanism is the key to the success of the Bitcoin system as a permissionless blockchain. It encourages participants to contribute their computing resources to ensure the correctness and consistency of user transaction records. Selfish mining attacks, however, prove that Bitcoin’s incentive mechanism is not incentive-compatible, which is contrary to traditional views. Selfish mining attacks may cause the loss of mining power, especially those of honest participants, which brings great security challenges to the Bitcoin system. Although there are a series of studies against selfish mining behaviors, these works have certain limitations: either the existing protocol needs to be modified or the detection effect for attacks is not satisfactory. We propose the ForkDec, a high-accuracy system for selfish mining detection based on the fully connected neural network, for the purpose of effectively deterring selfish attackers. The neural network contains a total of 100 neurons (10 hidden layers and 10 neurons per layer), learned on a training set containing about 200,000 fork samples. The data set, used to train the model, is generated by a Bitcoin mining simulator that we preconstructed. We also applied ForkDec to the test set to evaluate the attack detection and achieved a detection accuracy of 99.03%. The evaluation experiment demonstrates that ForkDec has certain application value and excellent research prospects.
Playing by the Rules? An Investigation of the Relationship Between Social Norms and Adolescents’ Hate Speech Perpetration in Schools

Hate speech is often discussed and investigated as an online phenomenon, while hate speech among adolescents in the real world (e.g., in schools) has rarely been researched. Consequently, not much is known about potential interpersonal correlates and theoretical frameworks that might help us better understand why adolescents engage in hate speech in offline environments. To add to the literature, this study investigates hate speech perpetration among young people by utilizing the Social Cognitive Theory; more specifically, the association between witnessing and perpetrating hate speech in schools, and whether this relation is weakened or strengthened by anti-hate speech injunctive norms and deviant peer pressure. The sample consists of 1719 young people (45.1% boys, 53.6% girls, 1.3% gender diverse) between 11 and 18 years old ( M age = 13.96; SD = .98) from 22 schools in Switzerland. Self-report questionnaires were administered to measure hate speech involvement and adolescents’ perception of social norms (i.e., anti-hate speech injunctive norms and deviant peer pressure). Multilevel regression analyses revealed that witnessing and perpetrating hate speech were positively associated. Moreover, injunctive anti-hate speech norms were negatively related and deviant peer pressure positively related to hate speech perpetration. Finally, increasing levels of injunctive anti-hate speech norms weakened the relation between witnessing and perpetrating hate speech, whereas higher levels of deviant peer pressure strengthened this association. The findings demonstrate that the Social Cognitive Theory is a useful framework for understanding hate speech perpetration in schools. The results also highlight the importance of taking into account social norms and interpersonal relationships (e.g., within the class) when designing anti-hate speech prevention programs and not focusing solely on intrapersonal factors.
Research on Video Quality Diagnosis Technology Based on Artificial Intelligence and Internet of Things

The remote video diagnosis system based on the Internet of Things is based on the Internet of Things and integrates advanced intelligent technology. To better promote a harmonious society, constructing a video surveillance system is accelerating in our country. Many enterprises and government agencies have invested much money to build video surveillance systems. The quality of video images is an important index to evaluate the video surveillance system. However, as the number of cameras continues to increase, the monitoring time continues to extend. In the face of many cameras, it is not realistic to rely on human eyes to diagnose video-solely quality. Besides, due to human eyes’ subjectivity, there will be some deviation in diagnosis through human eyes, and these factors bring new challenges to system maintenance. Therefore, relying on artificial intelligence technology and digital image processing technology, the intelligent diagnosis system of monitoring video quality is born using the computer’s efficient mathematical operation ability. Based on artificial intelligence, this paper focuses on studying video quality diagnosis technology and establishes a video quality diagnosis system for video definition detection and noise detection. This article takes the artificial intelligence algorithm in the diagnosis of video quality effect. Compared with the improved algorithm, the improved video quality diagnosis algorithm has excellent improvement and can well finish video quality inspection work. The accuracy of the improved definition evaluation function for the definition detection of surveillance video and noise detection is as high as 95.56%.
Case Study of a Cyber-Physical Attack Affecting Port and Ship Operational Safety

As the maritime sector embraces more technology to increase efficiency, lower carbon emissions, and adapt to meet modern challenges, cyber and cyberphysical safety become a more significant issue.However, unfortunately, much of past research view cyber-security issues in transportation as primarily information technology problems.This paper designs and uses a case study to illustrate how cyber-security and physical safety should be viewed together, cyber and physical (i.e.cyber-physical), when considering ship-to-ship and ship-to-shore interactions.While there is some scenario designing, this case study is built with real port data and ship systems to demonstrate a real-world cyber-attack on a ship.It shows plausible physical effects that affect the safety of those involved.This case study is also made realistic with a novel hybrid cyber range and hardware testbed environment, designed to examine the different effects a ship-based cyber-attack could potentially have on a port.This informs several solutions, technical and social, that could enhance cyberphysical safety in marine transportation.
Deep Learning Based Techniques for Sentiment Analysis: A Survey

The automated representation of human language using a number of techniques is called Natural Language Processing (NLP). Improvements to NLP applications are important and can be done using a variety of methods, including graphs, deep neural networks, and word embedding. Sentiment classification, which attempts to automatically classify opinionated text as positive, negative, or neutral, is a fundamental activity of sentiment analysis. Sentiment analysis methods focused on deep learning over the past five years are analyzed in this review
An automatic selection of optimal recurrent neural network architecture for processes dynamics modelling purposes

A problem related to the development of algorithms designed to find the structure of artificial neural network used for behavioural (black-box) modelling of selected dynamic processes has been addressed in this paper. The research has included four original proposals of algorithms dedicated to neural network architecture search. Algorithms have been based on well-known optimisation techniques such as evolutionary algorithms and gradient descent methods. In the presented research an artificial neural network of recurrent type has been used, whose architecture has been selected in an optimised way based on the above-mentioned algorithms. The optimality has been understood as achieving a trade-off between the size of the neural network and its accuracy in capturing the response of the mathematical model under which it has been learnt. During the optimisation, original specialised evolutionary operators have been proposed. The research involved an extended validation study based on data generated from a mathematical model of the fast processes occurring in a pressurised water nuclear reactor.
Modular Grammatical Evolution for the Generation of Artificial Neural Networks

This article presents a novel method, called Modular Grammatical Evolution (MGE), toward validating the hypothesis that restricting the solution space of NeuroEvolution to modular and simple neural networks enables the efficient generation of smaller and more structured neural networks while providing acceptable (and in some cases superior) accuracy on large data sets. MGE also enhances the state-of-the-art Grammatical Evolution (GE) methods in two directions. First, MGE's representation is modular in that each individual has a set of genes, and each gene is mapped to a neuron by grammatical rules. Second, the proposed representation mitigates two important drawbacks of GE, namely the low scalability and weak locality of representation, toward generating modular and multilayer networks with a high number of neurons. We define and evaluate five different forms of structures with and without modularity using MGE and find single-layer modules with no coupling more productive. Our experiments demonstrate that modularity helps in finding better neural networks faster. We have validated the proposed method using ten well-known classification benchmarks with different sizes, feature counts, and output class counts. Our experimental results indicate that MGE provides superior accuracy with respect to existing NeuroEvolution methods and returns classifiers that are significantly simpler than other machine learning generated classifiers. Finally, we empirically demonstrate that MGE outperforms other GE methods in terms of locality and scalability properties.
Gender Digital Divide and Education in Latin America: A Literature Review

Gender equity in education is one of the main targets for social justice and sustainable development. This literature review, from a gender approach, was conducted to understand how the gender digital divide (GDD) in information and communication technologies (ICT) and education are related in Latin American countries. A total of 28 articles have been analyzed as a satisfactory sample of the scientific literature to examine how this relation is explored and its influence, to acknowledge political stakeholders, as well as provide information and proposals to address the digital gender divide in education research in this region. The results show the need to develop research from the pedagogical and gender perspectives in Latin America, since they are not represented within an obvious problem.
Indicators of reproducibility and suitability for assessing the quality of production services

&lt;p&gt;A technique is proposed for estimating the probability of the possible appearance of defective products or the inconsistency of the production service on the basis of indexes of suitability and reproducibility of the production process.&lt;/p&gt;&lt;p&gt;The index of reproduction is recommended to be calculated on the basis of the standard deviation, which can be established by the average span of the control map and the limits of the tolerance field. At the same time, the production process must necessarily be in a state of statistical controllability. The suitability index cannot be calculated on the basis of the control card characteristics, but it is evaluated solely on the basis of the total of the standard deviation, which is calculated by the Bessel formula and the tolerance limits. The production process does not need to be statistically controllable. Conclusions on the suitability or insufficiency of the products (production services) are based on the value of the indexes of reproduction and suitability, which can take values less than or greater than one. On the basis of these indexes of obtained values, the probability of production appearance or characteristics of the production process, that does not match the established requirements, is established.&lt;/p&gt;
The Performance of Electrocoagulation Process in Removing Organic and Nitrogenous Compounds from Landfill Leachate in a Three-Compartment Reactor

In this study, the effectiveness of the electrocoagulation (EC) process was evaluated based on the reduction of organic and nitrogenous contaminants in landfill leachate.A three-compartment electrochemical reactor as pre-treatment of stabilized landfill leachate was carried out ahead of biological treatment.The removal efficiencies of COD, BOD, ammonia, and nitrate were analyzed at pH 4, 6, and 8 with the current densities of 20.83 and 29.17 mA•cm -2 .At pH 4, the highest removal of COD and NH 4 + was obtained, i.e., in the range of 72-81% and 43-59%, respectively.The ratio of BOD 5 /COD was increased after EC, from initially 0.11 to 0.32 at pH 4. In addition, EC effectively removed humic substances in the leachate by targeting a large amount of high molecular weight humic substances, with around 10 3 kDa.However, the higher removal efficiency observed at higher current density leads to higher specific energy consumption.At a current density of 29.17 mA•cm -2 , the specific energy consumption obtained in EC was around 10-17 Wh•g -1 COD and 99-148 Wh•g -1 NH 4 + .This could be decreased up to 50% at an applied current density of 20.83 mA•cm -2 with slightly lower efficiencies.
Designing Android learning media to improve problem-solving skills of ratio

This study aims to determine the process of developing android-based learning media on comparative material to improve mathematical problem-solving skills and find validation through android-based learning media to improve mathematical problem-solving abilities. This research was conducted through research and development, where this research refers to 4D development, which consists of four steps: define, design, develop, and disseminate. This research was carried out to develop measures due to restrictions on the activities of emergency communities affected by the COVID-19 pandemic; this is one of the limitations of this research. Researchers use documents such as curriculum to collect data at the define and design stages, while a validation sheet is used for the development stage. The data analysis technique used descriptive qualitative at the define and design stage, and the development stage uses quantitative descriptive analysis. The main finding in this research is the prototype of android learning media on ratio. The results showed that V Aiken obtained more than 0.750, so this media prototype can be used for the following research stage.
A novel pseudo-random number generator for IoT based on a coupled map lattice system using the generalised symmetric map

Abstract Pseudo-random number generators (PRNGs) are one of the building blocks of cryptographic methods and therefore, new and improved PRNGs are continuously developed. In this study, a novel method to generate pseudo-random sequences using coupled map lattices is presented. Chaotic maps only show their chaotic behaviour for a specified range of control parameters, what can restrict their application in cryptography. In this work, generalised symmetric maps with adaptive control parameter are presented. This novel idea allows the user to choose any symmetric chaotic map, while ensuring that the output is a stream of independent and random sequences. Furthermore, to increase the complexity of the generated sequences, a lattice-based structure where every local map is linked to its neighbouring node via coupling factor has been used. The dynamic behaviour and randomness of the proposed system has been studied using Kolmogorov–Sinai entropy, bifurcation diagrams and the NIST statistical suite for randomness. Experimental results show that the proposed PRNG provides a large key space, generates pseudo-random sequences and is computationally suitable for IoT devices.
A2Log: Attentive Augmented Log Anomaly Detection

Anomaly detection becomes increasingly important for the dependability and serviceability of IT services.As log lines record events during the execution of IT services, they are a primary source for diagnostics.Thereby, unsupervised methods provide a significant benefit since not all anomalies can be known at training time.Existing unsupervised methods need anomaly examples to obtain a suitable decision boundary required for the anomaly detection task.This requirement poses practical limitations.Therefore, we develop A2Log, which is an unsupervised anomaly detection method consisting of two steps: Anomaly scoring and anomaly decision.First, we utilize a self-attention neural network to perform the scoring for each log message.Second, we set the decision boundary based on data augmentation of the available normal training data.The method is evaluated on three publicly available datasets and one industry dataset.We show that our approach outperforms existing methods.Furthermore, we utilize available anomaly examples to set optimal decision boundaries to acquire strong baselines.We show that our approach, which determines decision boundaries without utilizing anomaly examples, can reach scores of the strong baselines.
An Examination on Autoencoder Designs for Anomaly Detection in Video Surveillance

Current anomaly detection methods for video surveillance find anomalies effectively enough; however, it comes at a high computational cost and specific hardware resources demanding.In counterpart, other video analysis tasks such as video action recognition now employ techniques that reduce the need for higher computational cost.Some of those techniques can be helpful for video anomaly detection.Therefore, this paper explores the effectiveness of the potential concepts of distillation and joint spatiotemporal training, adapted to two novel convolutional autoencoder architectures for anomaly detection in video surveillance.Our experimental results show the feasibility of reducing the computational resources requirements with smaller architectures (only 6K trainable parameters), competing and outperforming current methods in challenging benchmarks.
Classification of Image and Text Data Using Deep Learning-Based LSTM Model

The advent of social networking and the internet has resulted in a huge shift in how consumers express their loyalty and where firms acquire a reputation. Customers and businesses frequently leave comments, and entrepreneurs do the same. These write-ups may be useful to those with the ability to analyse them. However, analysing textual content without the use of computers and the associated tools is time-consuming and difficult. The goal of Sentiment Analysis (SA) is to discover client feedback, points of view, or complaints that describe the product in a more negative or optimistic light. You can expect this to be a result based on this data if you merely read and assess feedback or examine ratings. There was a time when only the use of standard techniques, such as linear regression and Support Vector Machines (SVM), was effective for the task of automatically discovering knowledge from written explanations, but the older approaches have now been mostly replaced by deep neural networks, and deep learning has gotten the job done. Convolution and compressing RNNs are useful for tasks like machine translation, caption creation, and language modelling, however they suffer from gradient disappearance or explosion issues with large words. This research uses a deep learning RNN for movie review sentiment prediction that is quite comparable to Long Short-Term Memory networks. A LSTM model was well suited for modelling long sequential data. Generally, sentence vectorization approaches are used to overcome the inconsistency of sentence form. We made an attempt to look into the effect of hyper parameters like dropout of layers, activation functions and we also tested the model with different neural network settings and showed results that have been presented in the various ways to take the data into account. IMDB is the official movie database which serves as the basis for all of the experimental studies in the proposed model.