Reviewerly/exHarmony huggingface dataset:
Ecological validity of virtual reality simulations in workstation health and safety assessment

The last decade saw a rapid rise of interest in Virtual Reality (VR) technologies, driven by more mature hardware and software tools. Within the ongoing digitalization of industry, VR technologies see uses in workstation design, operator training and tele-operation. This article focuses on how VR can contribute to workstation design including health and safety assessment. VR allows the inclusion of the operator in the workstation design process, permitting evaluation of the design in a safe, interactive and immersive virtual environment. This systematic literature review aims to qualify the ecological validity of VR tools and identify the current obstacles to safe and successful workstation design transfer. A standard systematic literature review procedure is used, on a wide selection of experimental research articles studying the validity of VR, within or outside of industrial contexts. We aggregate results from fundamental research on VR ecological validity regarding user perceptions, movement, cognition and stress. These results are discussed with respect to their influence on workstation OSH assessment in VR. Furthermore, we identify current technological factors and upcoming developments that mediate the validity of VR assessments.
Decentralized governance and artificial intelligence policy with blockchain-based voting in federated learning

Fruit losses in the supply chain owing to improper handling and a lack of proper control are common in the industry. As losses are caused by the inefficiency of the export method, selecting the appropriate export method is a possible solution. Several organizations employ only a single strategy, which is mainly based on a first-in-first-out approach. Such a policy is easy to manage but inefficient. Given that the batch of fruits may become overripe during transportation, frontline operators do not have the authority or immediate support to change the fruit dispatching strategy. Thus, this study aims to develop a dynamic strategy simulator to determine the sequence of delivery based on forecasting information projected from probabilistic data to reduce the amount of fruit loss.The proposed method to accomplish asynchronous federated learning (FL) is based on blockchain technology and a serially interacting smart contract. In this method, each party in the chain updates its model parameters and uses a voting system to reach a consensus. This study employs blockchain technology with smart contracts to serially enable asynchronous FL, with each party in the chain updating its parameter model. A smart contract combines a global model with a voting system to reach a common consensus. Its artificial intelligence (AI) and Internet of Things engine further strengthen the support for implementing the Long Short-Term Memory (LSTM) forecasting model. Based on AI technology, a system was constructed using FL in a decentralized governance AI policy on a blockchain network platform.With mangoes being selected as the category of fruit in the study, the system improves the cost-effectiveness of the fruit (mango) supply chain. In the proposed approach, the simulation outcomes show fewer mangoes lost (0.035%) and operational costs reduced.The proposed method shows improved cost-effectiveness in the fruit supply chain through the use of AI technology and blockchain. To evaluate the effectiveness of the proposed method, an Indonesian mango supply chain business case study has been selected. The results of the Indonesian mango supply chain case study indicate the effectiveness of the proposed approach in reducing fruit loss and operational costs.
Argument-based human–AI collaboration for supporting behavior change to improve health

This article presents an empirical requirement elicitation study for an argumentation-based digital companion for supporting behavior change, whose ultimate goal is the promotion and facilitation of healthy behavior. The study was conducted with non-expert users as well as with health experts and was in part supported by the development of prototypes. It focuses on human-centric aspects, in particular user motivations, as well as on expectations and perceptions regarding the role and interaction behavior of a digital companion. Based on the results of the study, a framework for person tailoring the agent's roles and behaviors, and argumentation schemes are proposed. The results indicate that the extent to which a digital companion argumentatively challenges or supports a user's attitudes and chosen behavior and how assertive and provocative the companion is may have a substantial and individualized effect on user acceptance, as well as on the effects of interacting with the digital companion. More broadly, the results shed some initial light on the perception of users and domain experts of "soft," meta-level aspects of argumentative dialogue, indicating potential for future research.
Procedures for Analyzing Multidimensional Mixture Data

The multidimensional mixture data structure exists in many test (or inventory) conditions. Heterogeneity also relatively exists in populations. Still, some researchers are interested in deciding to which subpopulation a participant belongs according to the participant’s factor pattern. Thus, in this study, we proposed three analysis procedures based on the factor mixture model to analyze data in the multidimensional mixture context. Simulations were manipulated with different levels of factor numbers, factor correlations, numbers of latent classes, and class separation. Issues with regard to model selection were discussed at first. The results showed that in the two-class situations the procedures of “factor structure first then class number” (Procedure 1) and “factor structure and class number considered simultaneously” (Procedure 3) performed better than the “class number first then factor structure” (Procedure 2) and yielded precise parameter estimation and classification accuracy. It would be appropriate to choose Procedures 1 and 3 when strong measurement invariance is assumed while using an information criterion, but Procedure 1 saved more time than Procedure 3. In the three-class situations, the performance of all three procedures was limited. Implementations and suggestions have been addressed in this research.
A study on the clusterability of latent representations in image pipelines

Latent representations are a necessary component of cognitive artificial intelligence (AI) systems. Here, we investigate the performance of various sequential clustering algorithms on latent representations generated by autoencoder and convolutional neural network (CNN) models. We also introduce a new algorithm, called Collage, which brings views and concepts into sequential clustering to bridge the gap with cognitive AI. The algorithm is designed to reduce memory requirements, numbers of operations (which translate into hardware clock cycles) and thus improve energy, speed and area performance of an accelerator for running said algorithm. Results show that plain autoencoders produce latent representations which have large inter-cluster overlaps. CNNs are shown to solve this problem, however introduce their own problems in the context of generalized cognitive pipelines.
Recruitment Strategies for a Changing Research Landscape

In the wake of a changing research landscape, through an increase of digital and online studies and narratives surrounding generalisability, it is important for researchers to adapt their recruitment strategies. This article presents some hints and tips for traditional and non-traditional methods of recruitment and weighs up the pros and cons of these methods, drawing on previous research. Each study is unique in its recruitment needs, and so this roundup draws on strategies to serve a number of different project types.
Semantic Tagging for the Urdu Language: Annotated Corpus and Multi-Target Classification Methods

Extracting and analysing meaning-related information from natural language data has attracted the attention of researchers in various fields, such as natural language processing, corpus linguistics, information retrieval, and data science. An important aspect of such automatic information extraction and analysis is the annotation of language data using semantic tagging tools. Different semantic tagging tools have been designed to carry out various levels of semantic analysis, for instance, named entity recognition and disambiguation, sentiment analysis, word sense disambiguation, content analysis, and semantic role labelling. Common to all of these tasks, in the supervised setting, is the requirement for a manually semantically annotated corpus, which acts as a knowledge base from which to train and test potential word and phrase-level sense annotations. Many benchmark corpora have been developed for various semantic tagging tasks, but most are for English and other European languages. There is a dearth of semantically annotated corpora for the Urdu language, which is widely spoken and used around the world. To fill this gap, this study presents a large benchmark corpus and methods for the semantic tagging task for the Urdu language. The proposed corpus contains 8,000 tokens in the following domains or genres: news, social media, Wikipedia, and historical text (each domain having 2K tokens). The corpus has been manually annotated with 21 major semantic fields and 232 sub-fields with the USAS (UCREL Semantic Analysis System) semantic taxonomy which provides a comprehensive set of semantic fields for coarse-grained annotation. Each word in our proposed corpus has been annotated with at least one and up to nine semantic field tags to provide a detailed semantic analysis of the language data, which allowed us to treat the problem of semantic tagging as a supervised multi-target classification task. To demonstrate how our proposed corpus can be used for the development and evaluation of Urdu semantic tagging methods, we extracted local, topical and semantic features from the proposed corpus and applied seven different supervised multi-target classifiers to them. Results show an accuracy of 94% on our proposed corpus which is free and publicly available to download.
A statistical methodology for classifying earthquake detections and for earthquake parameter estimation in smartphone-based earthquake early warning systems

Smartphone-based earthquake early warning systems (EEWSs) are emerging as a complementary solution to classic EEWSs based on expensive scientific-grade instruments. Smartphone-based systems, however, are characterized by a highly dynamic network geometry and by noisy measurements. Thus, there is a need to control the probability of false alarms and the probability of missed detection. This study proposes a statistical methodology to address this challenge and to jointly estimate in near real-time earthquake parameters like epicenter and depth. The methodology is based on a parametric statistical model, on hypothesis testing and on Monte Carlo simulation. The methodology is tested using data obtained from the Earthquake Network (EQN), a citizen science initiative that implements a global smartphone-based EEWS. It is discovered that, when the probability to miss an earthquake is fixed at 1%, the probability of false alarm is 0.8%, proving that EQN is a robust smartphone-based EEW system.
A Multi-Dimensional Covert Transaction Recognition Scheme for Blockchain

Covert communication was widely studied in recent years in terms of keeping the communication of entities on the Internet secret from the point of view of information security. Due to the anonymity of accounts and the publicness of the ledger, blockchain is a natural and ideal channel for helping users establish covert communication channels. Senders can embed secret messages into certain fields in transactions, and receivers can extract those messages from the transactions without attracting the attention of other users. However, to the best of our knowledge, most existing works have aimed at designing blockchain-based covert communication schemes. Few studies concentrated on the recognition of transactions used for covert communication. In this paper, we first analyze convolutional neural network (CNN)-based and attention-based covert transaction recognition schemes, and we explore the deep relationship between the appropriate extraction of features and the embedded fields of covert transactions. We further propose a multi-dimensional covert transaction recognition (M-CTR) scheme. It can simultaneously support both one-dimensional and two-dimensional feature extraction to recognize covert transactions. The experimental results show that the precision and recall of the M-CTR in recognizing covert transactions outperformed those of existing covert communication schemes.
Multi-Class Facial Emotion Recognition Using Hybrid Dense Squeeze Network

Automatic facial expression recognition (FER) is utilized in various applications like psychoanalysis, intelligent driving, robot manufacturing, etc. Numerous researchers have been looking for better techniques to improve the accuracy of FER. In fact, FER under laboratory conditions has almost achieved top accuracy. Besides, label deviations or errors caused by annotators’ subjectivity also make the FER task much tougher. Thus, more and more researchers begin to find new ways to handle with the FER problems. In this work, a new deep learning (DL) model called dense squeeze network with improved red deer optimization (DenseSNet_IRDO) is proposed for the recognition of facial emotions. The steps used for FER are pre-processing, fused deep feature extraction-selection and classification. Initially, the facial images are pre-processed using improved trilateral filter (ITF) for improving the quality of images. Next, the fusion of feature extraction and selection is performed using the DenseSNet. Here the extraction of deep features is done with the dense network and the relevant features are selected with the squeeze network. Finally, the last layer of squeeze network performs the classification of various facial emotions. Here, the loss in the classification is optimized using IRDO. This DenseSNet_IRDO architecture is more robust and avoids overfitting that occurs while training the small dataset. The datasets used in this work are CK[Formula: see text], JAFEE and FERFIN. The proposed FER classification using datasets CK[Formula: see text], JAFEE and FERFIN with DenseSNet_IRDO model achieved the accuracy of 99.91%, 99.90% and 99.89%, respectively. Thus, the proposed DenseSNet_IRDO classifier model obtained higher accuracy in the detection of FER than other methods.
Incorporating Transfer Learning in CNN Architecture

Machine learning (ML) is a data intensive process. For training of ML algorithms huge datasets are required.There are times when enough data is not available due to multitude of reasons. This could be due to lack ofavailability of annotated data in a particular domain or paucity of time in data collection process resulting innon-availability of enough data. Many a times data collection is very expensive and in few domains data collectionis very difficult. In such cases, if methods can be designed to reuse the knowledge gained in one domain havingenough training data, to some other related domain having less training data, then problems associated with lackof data can be overcome. Transfer Learning (TL) is one such method. TL improves the performance of the targetdomain through knowledge transfer from some different but related source domain. This knowledge transfer canbe in form of feature extraction, domain adaptation, rule extraction for advice and so on. TL also works withvarious kinds of ML tasks related to supervised, unsupervised and reinforcement learning. The ConvolutionalNeural Networks are well suited for the TL approach. The general features learned on a base network (source)are shifted to the target network. The target network then uses its own data and learns new features specific toits requirement.
An Approach for generating best possible questions from the given text using Natural Language Processing

A crucial ability for every person is the capacity to ask pertinent questions. By automating the process of question formation, an automatic question generator is able to decrease the time and effort needed for manual question creation. Along with benefitting educational institutions like schools and colleges, automated question generation can be used in chatbots and for automated tutoring systems. Question Generation is an area in NLP that is still under research for greater accuracy. Research work has been done in many languages too. The goal of an automatic question generator is to generate syntactically and semantically correct questions, valid according to the given input. The Bidirectional Encoder Representations from Transformers (BERT) model is one of the pre-trained models adopted to implement the same. Additionally, we used Python packages, including NLTK, Spacy, and PKE. To test our findings, we evaluated the validity and relevance of generated questions using human-level cognition and evaluation. We were successful in creating inquiries that adequately reflected several of the peculiarities of English so that a person might comprehend them.
A Development and Analysis of Color Image New Blind Watermarking Based on DWT-SVD Swapping and 3-Dimensional Cryptography Technique

Several acceptable watermarking methods are designed to minimize this problem depending on the desired applications. A watermarking system that is stable and healthy at the same time, however, is hard to achieve. This paper provides information about the standard method for watermarking by using different methodologies including an arrangement of Singular Value Decomposition (SVD) and Discrete wavelet transforms (DWT) technique, ensuring a fully blind and robust watermarking technique. New swapping techniques and a nonlinear three-dimensional Chaos-based simple technique of Cryptography are implemented to improve the protection of the proposed method. Three-dimensional chaos is the first time used technique for permutated positions and value transformation techniques. In addition, this work compares the efficiency and safety analysis provided for the various fractal cover combinations by some metrics, which are deliberated in the result section. The proposed project will also be compared with some metrics such as Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index (SSIM). The outcomes show that image degradation is much less in the dimension of the hidden image file than in the cover image file, which is double in size. Therefore, the proposed method saves the bandwidth and storage requirements.
Variable selection using the EM and CEM algorithms in mixtures of linear mixed models

In order to deal with data of great complexity, the need to develop new methods for variable selection has emerged. Among the new methods, methods based on penalty functions have received great attention. These methods can be used in complex data problems since they shrink a subset of coefficient estimates to zero, thus removing the associated variables from the model, which allows the identification of the subset of the most relevant explanatory variables and, consequently, drastically reduces the computational burden. In this work we analyse the problem of variable selection in mixtures of linear mixed models. In order to do it, we compare the performance of a penalized likelihood approach for variable selection via the Expectation-Maximization and the Classification Expectation-Maximization algorithms through a simulation study and a real-world application.
Authentication of Counterfeit Hundred Ringgit Malaysian Banknotes Using Fuzzy Graph Method

A banknote is a currency issued by a country, and it was first introduced in the 16th century. The counterfeiting of banknotes by cunning criminals became a great challenge with the current advanced technology. Forensic scientists are using chemical methods, such as Fourier transform infrared (FTIR) spectroscopy for differentiating genuine and counterfeit banknotes. However, the FTIR spectra of banknotes may require further pattern recognition analysis due to their high similarities. In this paper, a fuzzy graph-based algorithm for authentication of the FTIR spectrum, namely chemometrics fuzzy autocatalytic set (c-FACS), is used to discriminate between genuine and counterfeit hundred Ringgit Malaysian (RM100) banknotes. The results show that the genuine and counterfeit RM100 banknotes have slightly distinct patterns when analyzed using c-FACS. In addition, the results are compared with RM50 banknotes, and the results reveal that the nodes or dominant axis varies between the two banknotes. To verify the reliability of the results, the results obtained via c-FACS are compared with principal component analysis (PCA). The c-FACS showed better performances as compared to PCA in terms of time consumption and observation. Thus, the c-FACS has the ability to assist forensic investigations involving banknote counterfeiting crimes.
Statistical Adaptation Loss Improved SMALL Sample Ship Detection Method Based on an Attention Mechanism and Data Enhancement

Synthetic aperture radar (SAR) imagery is a promising data source for ocean activity detection. Ship target detection based on SAR images is widely used in maritime trade and the military. SAR image data are rare, and the amount of public data is small. For applications of SAR image ship target detection, a model with low data dependence, fast iteration and low training cost is needed. In this paper, the balanced positive and negative data enhancement method was used. Through statistical analysis of the training dataset, similar sea areas in the training set are filled with detection targets with comfortable size features. Increasing the proportion of positive samples in the data helps to improve the model detection effect. The regional attention preadaptation mechanism based on statistical analysis was implemented to extract information, and the scale-adaptive loss was combined to improve the detection accuracy of the model. Using the same data, our model exhibited better performance. When using 30% of the data, our model was stable in terms of accuracy and average precision (AP) and maintained detection results similar to the training results achieved using 100% of the dataset.
Analysis of interaction between miner decision making and user action for incentive mechanism of bitcoin blockchain

In Bitcoin blockchain, miner nodes are likely to choose transactions with high fee to be included in a block. This makes transactions with high fee being processed fast, affecting the amount of transaction fee that users want to pay. The reward for a winning miner consists of transaction fee and newly issued coins, and hence the amount of newly issued coins also affects the miner decision to participate in the mining competition. In addition, mining reward also affects the total hash computing power, which plays an important role of Bitcoin security for reducing the success probability of security attack by a malicious miner. In this paper, we develop a mathematical model for analyzing the interaction between miner decision making and user actions in terms of transaction fees, transaction-confirmation time, and security. We analyze the transaction-inclusion process with queueing theory, while decision making processes of miners and users are analyzed in the context of Nash equilibrium. The numerical examples show how the mining costs and newly issued coins affect miner decision making.